Proceedings of the International Conference on Innovative trends in Electronics Communication and Applications (ICIECA 14) Association of Scientists, Developers and Faculties Proceedings of the First International Conference on Innovative trends in Electronics Communication and Applications – ICIECA 2014 held at IITM Research Park, India, Asia between 19th December, 2014 and 20th December 2014.
Association of Scientists, Developers and Faculties www.asdf.res.in
ISBN : 978-81-929742-1-7
International Conference on Innovative trends in Electronics Communication and Applications 2014
ICIECA 2014
International Conference on Innovative trends in Electronics Communication and Applications 2014
Volume 1 By ASDF, Chennai, India Financially Sponsored By Association of Scientists, Developers and Faculties, India
Innovation and Electronics
19 – 20, December 2014 Indian Institute of Technology Madras – Research Park Chennai, South India, Asia
Editor-in-Chief Kokula Krishna Hari K Editors: Anbuoli Parthasarathy, S Purushothaman, Saikishore Elangovan
Published by Association of Scientists, Developers and Faculties Address: RMZ Millennia Business Park, Campus 4B, Phase II, 6th Floor, No. 143, Dr. MGR Salai, Kandanchavady, Perungudi, Chennai – 600 096, India.
Email:
[email protected] || www.asdf.org.in
International Conference on Innovative trends in Electronics Communication and Applications 2014 (ICIECA 2014) VOLUME 1 Editor-in-Chief: Kokula Krishna Hari K Editors: Anbuoli Parthasarathy, S Purushothaman, Saikishore Elangovan
Copyright © 2014 ICIECA 2014 Organizers. All rights Reserved
This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the ICIECA 2014 Organizers or the Publisher.
Disclaimer: No responsibility is assumed by the ICIECA 2014 Organizers/Publisher for any injury and/ or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products or ideas contained in the material herein. Contents, used in the papers and how it is submitted and approved by the contributors after changes in the formatting. Whilst every attempt made to ensure that all aspects of the paper are uniform in style, the ICIECA 2014 Organizers, Publisher or the Editor(s) will not be responsible whatsoever for the accuracy, correctness or representation of any statements or documents presented in the papers.
ISBN-13: 978-81-929742-1-7 ISBN-10: 81-929742-1-9
PREFACE
Welcome to the International Conference on Innovative trends in Electronics Communication and Applications – ICIECA 2014 in Indian Institute of Technology – Madras Research Park, Chennai, India, Asia on 19 – 20 December, 2014. If this is your first time to Chennai, you need to look on more objects which you could never forget in your lifetime. There is much to see and experience. This Conference brought in forward to endeavour a greater height in the Electronics Communication and its implied applications. The present generation runs with a greater speed for communication; with many simplified protocols like Mobile 2G, 3G, 4G and grew 5G communication for voice and data stuffs. Also for the data link, it has grown from the basic terms like Dial-Up to Edge, Wi-Fi, Wi-Max and other advancements. The innovation in the current trends of the communication systems is growing rapidly without any pause in its development. The human kind is also flexible enough in adapting to the expressive developments which implies the status of the individuals socially as well as economically. Just imagining about the era of 1960’s and 2014; what a changeover in all the terms. For a message to be communicated, we have to spend a minimum of 15 days through Postal and transition. Now, it’s just 15 seconds and the people love it. We invite you to join us in this inspiring conversation. Finally, I thank my family, friends, students and colleagues for their constant encouragement and support for making this type of conference.
-- Kokula Krishna Hari K Editor-in-Chief www.kokulakrishnaharik.in
TECHNICAL REVIEWERS •
A S N Chakravarthy, JNTUK University College of Engineering, Vizianagaram, India
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Abdelghani Bellaachia, George Washington University, United States
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Abdeslam Jakimi, My Ismail University
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Abhishek Shukla, R. D. Engineering College, India
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Adethya Sudarsanan, Cognizant Technology Solutions, India
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Ahmed Salem, Old Dominion University, United States
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Ainuddin, University of Malaya, Malaysia
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Aissa Manallah, Institute of Optics and Precision Mechanics, Algeria
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Ajay Chakravarthy, University of Southampton, United Kingdom
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Alejandro Peña-Ayala, WOLNM - IPN, Mexico
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Ali Khalfallah, Sfax High Institute of Electronics and Communication, Tunisia
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Amin Daneshmand Malayeri, Dutch Academy of Management, Iran
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Amir Hajjam El Hassani, University Of Technology Belfort-Montbeliard, France
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Amit Chauhan, Babaria Institute of Technology, Vadora, India
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Angelina Geetha, B S Abdur Rahman University, Chennai
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Anirban Mitra, MITS Rayagada, India
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Annie Ibrahim, Telecommunication Software and Systems Group, Ireland
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Aramudhan M, PKIET, Karaikal, India
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Arivazhagan S, Mepco Schlenk Engineering College, India
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Arokiasamy A, Principal, ARJ College of Engineering and Technology, India
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Aroop Mukherjee, Professor, Universiti Putra Malaysia, Malaysia
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Arul Lawrence Selvakumar A, Director, India
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Aruna Deoskar, ATSS College of Business Studies and Computer Applications, Pune, India
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Asha Ambhaikar, Rungta College of Engineering and Technology, Bhilai
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Ashish Chaurasia, Gyan Ganga Institute of Technology & Sciences, Jabalpur, India
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Ashish Rastogi, Higher college of Technology - MUSCAT, Oman
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Ashok Kumar, PSG College of Technology, India
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Ashutosh Kumar Dubey, Trinity Institute of Technology & Research, India
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Assem Moussa, Egypt Airlines, Egypt
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Aswatha Mattur, K S School of Engineering and Management, India
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Ata Elahi, Southern Connecticut State University, United States of America
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Avadhani P S, Andhra University, India
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B K Murthy, Department of Information and Technology - GoI, India
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B Veera Jyothi, Chaitanya Bharathi Institute of Technology, India
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Balachandran A, Amrita University, India
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Balasubramanie Palanisamy, Kongu Engineering College, India
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Belur V. Dasarathy, Editor-in-Chief, Information Fusion
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Berk Canberk, Istanbul Technical University, Turkey
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Bhavana Gupta, All Saints College of Technology, India
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Bing Shi, University of Southampton, UK
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Bouhadada Thahar, Badji Mokhtar Universtiy of Annaba, Algeria
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Carlos Silva Cardenas, Pontificia Universidad Católica del Perú, Peru
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Célia Martinie, University Paul Sabatier - Toulouse III, France
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Chamin Morikawa, Motion Portrait Inc., Japan
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ChandraMohan P, Director, Professional Group of Institutions, India
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Chandrasekaran M, Government College of Engineering, Salem, India
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Chandrasekaran S, Kumaraguru College of Technology, Coimbatore, India
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Chaudhari A L, University of Pune, India
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Ching-Hsien Hsu, Chung Hua University, Taiwan
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Chitra Krishnamoorthy, St Josephs College of Engineering and Technology, India
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Chokri BEN AMAR, National Engineering School of Sfax, University of Sfax, Tunisia
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Chokri Ben Amar, University of Sfax, Tunisia
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Christian Esteve Rothenberg, CPqD (Telecom Research Center), Brazil
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Christos Chrysoulas, Technological Educational Institute of Patras, Greece
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Chun-Chieh Huang, Minghsin University of Science and Technology, Taiwan
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Constantin Filote, Stefan cel Mare University of Suceava, Romania
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Cristian-Gyozo Haba, Gheorghe Asachi Technical University of Iasi, Romania
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Darshan M Golla, Andhra University, India
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Deepak Rawat, Amrapali Group Of Institute, India
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Derkaoui Orkia, Algeria
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Dev Audsin, France Telecom R & D / Orange, United Kingdom
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Dianne Murray, British Computer Society, Great Britain
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Dimitrios Efstathiou, Technological Educational Institute of Central Macedonia, Serres, Greece
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Dinesh Kumar Saini, Sohar University Oman
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Dominique Archambault, Université Paris 8, France
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Dumitru Popescu, University Politehnica of Bucarest, Romania
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Ela Kumar, Dean, Gautam Buddha University, India
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Elvinia Riccobene, University of Milan, Italy
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EPPIES BABURAJ, SUN COLLEGE OF ENGINEERING AND TECHNOLOGY, India
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Essa, Tikrit University, Iraq
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Fazidah Othman, University of Malaya, Malaysia
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Fulvio Frati, University of Milan, Italy
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G Jeyakumar, Amrita School of Engineering, India
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Ganesan G, Adikavi Nannaya University, India
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Ganesh Neelakanta Iyer, Progress Software Development, India
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Gayathri Jayapal, Bharathidasan University, India
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Geetharamani R, Associate Professor, Department of Information Science and Technology, Anna University, Chennai, India
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Gemikonakli O, Middlesex University, UK
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Ghassemlooy Z, Associate Dean, Northumbria University, UK
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Gregorio Martinez Perez, Professor, University of Murcia, Spain
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Gunatharan Barani, Anna University Regional Centre, Coimbatore, India
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Gurudatt Kulkarni, Marathwada Mitra Mandal's Polytechnic, Pune, India
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Hamid Abdulla, University of Malaya, Malaysia
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Hanumantha Reddy T, Rao Bahadur Y Mahabaleswarappa Engineerng College, Bellary, India
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Hari Mohan Pandey, Middle East College, Muscat, Oman
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Helge Langseth, Professor, Norwegian University of Science and Technology, Norway
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Hussein Abdulmuttalib, Dubai Municipality, Dubai
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Indra Gandhi Raman, GKM Group of Educational Institutions, India
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Inese Barbare, Univeristy of Latvia, Latvia
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Ing. David Luigi FUSCHI, Bridging Consulting Ltd, United Kingdom
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Ion Tutanescu, University of Pitesti, Romania
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J Karthikeyan, Velammal College of Engineering and Technology, India
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Jaime Lloret, Universidad Politecnica de Valencia, Spain
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Jeya Mala D, Thiagarajar College of Engineering, India
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Jia Uddin JU, University of Ulsan, South Korea
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Jinjun Chen, Professor, University of Technology Sydney, Australia
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Joel Rodrigues, Professor, Instituto de Telecomunicações, University of Beira Interior, Portugal
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John Sanjeev Kumar A, Thiagarajar College of Engineering, India
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Joseph M, Mother Terasa College of Engineering & Technology, India
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K Maran, Director, Sairam Institute of Management Studies, India
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K N Rao, Andhra University, India
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Kachwala T, NMIMS University, India
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Kannan Balasubramanian, Mepco Schlenk Engineering College, India
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Kannan N, Jayaram College of Engineering and Technology, Trichy, India
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Kasturi Dewi Varathan, University of Malaya, Malaysia
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Kavita Singh, University of Delhi, India
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Kiran Kumari Patil, Reva Institute of Technology and Management, Bangalore, India
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Krishnamachar Sreenivasan, Indian Institute of Technology Ropar, India
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Kumar D, Periyar Maniammai University, Thanjavur, India
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Lain-Chyr Hwang, I-Shou University, Taiwan
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Lajos Hanzo, Chair of Telecommunications, University of Southampton, UK
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Loganathan D, Professor, Pondicherry Engineering College, India
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Longbing Cao, University of Technology, Sydney
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Lugmayr Artur, Texas State University, United States
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M HariHaraSudhan, Pondicherry University, India
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M Thanga Mani, Kongu Engineering College, India
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M. Ayaz Ahmad, University of Tabuk, Saudi Arabia
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M. C. Schraefel
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Maaruf Ali, University of Hail, KSA
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Maheswaran R, Mepco Schlenk Engineering College, India
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Manju Lata Agarwal, University of Delhi, India
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Manuela Aparicio, Professor, ISCTE-IUL, Lisboa, Portugal
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Marcin Paprzycki, Professor, Systems Research Institute of the Polish Academy of Sciences, Poland
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Mazliza Othman, University of Malaya, Malaysia
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Mehdi Asadi, Islamic Azad University, Iran
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Michael Joseph, St.Joseph's College of Engineering and Technology, India
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Mohamed Ali Kaafar MAK, National ICT Australia, Inria France, Australia
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Mohamed Moussaoui, ENSA Tangier, Abdelmalek Essaadi University, Morocco
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Mohammad M Banat, Jordan University of Science and Technology
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Mohammad Siam, Isra University (IU), Jordan
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Mohsen Tabejamaat, Islamic Azad University, Iran
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Moni S, National Informatics Centre - GoI, India
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Mónica Aguilar Igartua, Universitat Politècnica de Catalunya, Spain
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Mostafa Uddin, Old Dominion University, United States
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Muhammad Javed, Wayne State University, Detroit, Michigan, USA
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Mukesh D. Patil, Indian Institute of Technology, Mumbai, India
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Muthu Ramachandran, Leeds Metropolitan University, UK
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Nagarajan S K, Annamalai University, India
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Nallusamy R, Nandha College of Technology, India
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Nayan Jobanputra, Saurashtra University, India
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Neelanarayanan Venkataraman, VIT University, Chennai, India
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Nilanjan Chattopadhyay, S P Jain Institute of Management & Research, Mumbai, India
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Niloy Ganguly, IIT-KG, India
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Noreen Imran, Auckland University of Technology, New Zealand
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Nornazlita Hussin, University of Malaya, Malaysia
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P Chandrasekar, Dean, Professional Group of Institutions, India
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Panchanatham N, Annamalai University, India
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Parvatha Varthini B, St Joseph’s College of Engineering, India
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Parveen Begam, MAM College of Engineering and Technology, Trichy
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Pascal Hitzler, Wright State University, Dayton, US
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Pascal LORENZ, Director, University of Haute Alsace, France
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Peter Neema-Abooki, Dean, East African School of Higher Education Studies and Development (EASHESD), Uganda
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Pijush Kanti Bhattacharjee, Assam University, Assam, India
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Ponnammal Natarajan, Rajalakshmi Engineering College, Chennai, India
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Poorna Balakrishnan, Principal, SSS Jain College for Women, India
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Prabu Dorairaj, NetApp Inc., India
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Pradeep Tomar, Professor, Gautam Buddha University, India
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Pradip Kumar Bala, IIT, Roorkee
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Prasanna N, TMG College, India
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Prem Shankar Goel, Chairman - RAE, DRDO-GoI, India
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Priyesh Kanungo, Patel Group of Institutions, India
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R K Nair, Former CEO, TechnoPark, India
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R M Suresh, Principal, Jerusalem Engineering College, India
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R Mahalakshmi, Dean, GKM Group of Educational Institutions
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Radha S, SSN College of Engineering, Chennai, India
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Radhakrishnan V, Mookamibigai College of Engineering, India
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Rahim KACIMI, University of Toulouse, France
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Rahul Tyagi, Lucideus Tech Private Limited, India
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Raja K, Alpha College of Engineering, India
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Rakesh Kumar Mishra, Feroze Gandhi Institute of Engineering and Technology, India
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Ram Shanmugam, Texas State University, United States
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Ramkumar Jaganathan, VLB Janakiammal College of Arts and Science, India
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Rao D H, Jain College of Engineering, India
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Ravichandran C G, Excel Engineering College, India
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Ravikant Swami, Arni University, India
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Raviraja S, University of Malaya, Malaysia
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Reza Gharoie Ahangar, Azad University, Iran
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Rishad A Shafik, University of Southampton, UK
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Ritwik M, Amrita Vishwa Vidyapeetham, India
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Rudra P. Pradhan, IIT-KGP, India
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Russell Beale, Director, HCI Research Centre, University of Birmingham
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Ryma Abassi, Higher Institute of Communication Studies, Tunisia
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S A V Satya Murty, Director, Indira Gandhi Centre for Atomic Research, India
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S Albert Alexander, Kongu Engineering College, India
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S Geetha, Thiagarajar College of Engineering, India
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S Khan, Kohat University of Science and Technology, Pakistan
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S Krishnakumar, DRDO, India
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S Purushothaman, ISRO, Bangalore, India
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Sahaaya Arul Mary S A, Jayaram College of Engineering & Technology, India
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Samarjeet Borah, Sikkim Manipal Institute of Technology, India
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Sana Ullah, King Saud University, Saudi Arabia
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Sanjay Chaudhary, DA-IICT, India
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Sanjay K Jain, University of Delhi, India
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Sanjeevikumar Padmanaban, NIT Karaikal, India
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Saraju P. Mohanty, Professor, University of North Texas, United States
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Satheesh Kumar KG, Dean & Chairperson, Asian School of Business, Trivandrum, India
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Sathiyabhama Balasubramaniam, Sona College of Technology, India
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Satyadhyan Chickerur, Professor B V Bhoomaraddi College of Engineering and Technology, India
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Saurabh Dutta, Dr B C Roy Engineering College, Durgapur, India
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SEDDIK Hassene, ENSIT, Tunisia
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Senthil Arasu B, National Institute of Technology - Trichy, India
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Shanmugam A, SNS College of Technology, Coimbatore, India
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Sharon Pande, NMIMS University, India
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Sheila Anand, Rajalakshmi Engineering College, Chennai, India
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Shenbagaraj R, Mepco Schlenk Engineering College, India
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Shilpa Bhalerao, FCA Acropolis Institute of Technology and Research
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Shivaji Sinha, J.S.S. Academy of Technical Education, Noida
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Singaravel G, K. S. R. College of Engineering, India
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Sivakumar V J, National Institute of Technology - Trichy, India
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Sivasothy SHANMUGALINGAM, Institut Mines-Télécom, Télécom SudParis, France
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Sivasubramanian A, St Josephs College of Engineering and Technology, India
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Sreenivasa Reddy E, Acharya Nagarjuna University, India
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Sri Devi Ravana, University of Malaya, Malaysia
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Srinivasan A, MNM Jain Engineering College, Chennai
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Srinivasan K S , Turbomachinery Institute of Technology and Sciences, India
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Stefanos Gritzalis, University of the Aegean, Greece
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Stelvio Cimato, University of Milan, Italy
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Subash Chandra Bose J, Professional Group of Institutions, India
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Subramanian K, Director, IGNOU, India
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Sudakar P, M Kumarasamy College of Engineering, India
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Sundaram RMD, WIPRO Technologies, United States of America
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Sundeep Dhawan, National Physics Laboratory, GoI, India
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Sunil Chowdary, AMITY University, India
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Suresh G R, Easwari Engineering College, Chennai, India
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T Nalini, Bharath University, India
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Tulika Pandey, Department of Information and Technology - GoI, India
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U S Raghupathy, Kongu Engineering College, India
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Ubaid Abbasi, COMSATS Institute of Technology, Pakistan
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Uma N Dulhare, MJ College of Engineering and Technology, India
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Venkataramani Y, Director, Saranathan College of Engineering, India
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Verma R S, Joint Director, Department of Information and Technology - GoI, India
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Vicente Lucena, Federal University of Amazonas, Brazil
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Vijayalakshmi K, Mepco Schlenk Engineering College, India
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Vijayalakshmi S, Vellore Institute of Technology, India
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Vijyendra Niranjan, Price Waterhouse Coopers India Private Limited
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Vikrant Bhateja, Professor, SRM GPC, Lucknow, India
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Ville Luotonen, Senior Researcher, Hermia Limited, Spain
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Vimala Balakrishnan, University of Malaya, Malaysia
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Vishnuprasad Nagadevara, Indian Institute of Management - Bangalore, India
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Wang Wei, University of Nottingham, Malaysia
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Wei Wei, Xi'an University of Technology, China
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Yulei Wu, Chinese Academy of Sciences, China
Table of Contents Volume Month
01 December
Issue Year
01 2014
International Conference on Innovative trends in Electronics Communication and Applications
Titles & Authors
Employing second generation Mobile Technology in process plant for Temperature and Pressure process
Pages
pp1-pp4
by AR.Ramaiah
Innovative Bikes
by P.Boobathi raj
pp5-pp10
Single Tree Search Sphere Decoding Algorithm for Mimo Communication System
by P. Prakash and M. Kannan
pp11-pp16
EEE-Based Brain Controlled Vehicles Using Reachability Analysis by R.Parthiban
An Improved Restoration Tool Based on Blind Image DE convolution with Curve let Transform
pp17-pp20
pp21-pp27
by Kanwar Preet Singh and H.K.V.
Design and Implementation of a PLC-Based Real-Time Monitoring and Control System for a Hybrid Renewable Energy System
pp28-pp39
by G. Madhan and S. Muruganand
Noice To Electrical Energy Conversion Using Piezoelectric Devices
by T.LakshmiNarayanan, E.Dinesh, K.Dhinesh Kumar, R.Gokulakrishnan and D.Lakshmi
pp40-pp42
Safety systems in Bikes
by R.Rajiv, V.Gurupranav and P.Amarnath balaji
pp43-pp47
High Data Aggregation in Wireless Sensor Networks Using RENDEZVOUS-DRINA
by G.Rekha,A.S and Ajeetha Kumari
pp48-pp55
Three Phase Grid Interfaced Renewable Energy Source Using Active Power Filter
by R.Mahendran and T.Ranjith kumar
pp56-pp68
Wireless Trafficdensity Control USING Sensor
by Kirubasankar, Dillibabu, and B.V.SANTHOSH KRISHNAN
A Study on Big Data and its Traffic Management – Separation Based on Varieties of Data
pp69-pp70
pp71-pp77
by Naveen Kumar, S.Vijayabaskaran and S.Sankari Image Processing in Secure Manner using VLSI
by R.Vanmathi, M.Prabavathi and S.JOSEPH
pp79-pp87
Planar Inverted Multiband Slotted Patch Antenna for RFID Application
by Srithar.A, .E.D.Kanmani Ruby, G.Deepa and P.Yuvasri
pp88-pp94
Dynamic Voltage and Frequency Scheduling for Mobile Devices
by Bharani.M
pp95-pp98
The Conduct of Public Diplomacy in Malaysian Media
by Shafezah Abdul Wahab, Siti Najah Raihan Sakrani, Noor Soraya Mohd Jamal
Maritime Border Alert by Location Monitoring Using GPS for Protection of Fishermen
pp99-pp106
pp107-pp113
by Shree Krishna Priya J
Safety Chip for Women
by K.Aarthi and S.Abenaya
Performance Evaluation of Variable Bitrate Data Hiding Techniques on GSM AMR coder
pp114-pp117
pp118-pp123
by G.Indumathi and R.Anusuya An Overview of the Mobiles that is going to be used in Next Generation
by G. Nandhini, Kiruthika and J, K. Sivasakthi
pp124-pp129
A New Hybrid Cascaded H- Bridge Inverter for Photovoltaic-Wind Energy System
by S.Selvakumar and P.Kulanthaivel
Caudnal Nucleus Performance for Arrangement Changing Condition: Using Image Preprocessing
pp130-pp140
pp141-pp143
by Tamilarasi.K Wireless Breaking System
by D.Sangeetha Nandhini
pp144-pp147
Improving Efficiency of Solar Panels Using Music
by J.Poorinima and K.Sathyabama
pp148-pp152
Renewable energy resources (wind energy)
by S.Janani, M.Vinitha, S.Roseseetha
pp153-pp162
Power Production in Satellites by utilizing Solar Energy using Ultra capacitors
by A.TAMIL ILAKKIYA and J.SUGANYA
Efficient Design of FFT Module Using Dual Edge Triggered Flip Flop and Clock Gating
pp163-pp168
pp169-pp176
by R.Preyadharan and A.Tamilselvan Implementation of Bone Strain Measurement System using FPGA
by M. Siva sankari and K. Rathinakumar
pp177-pp184
Economical Antenna Reception Design for Software Defined Radio using RTL-SDR
by Govarthanam, K S, Abirami M and Kaushik J
pp185-pp190
Activities of Stakeholders in Development Phases a Review
by C.SenthilMurugan and S. Prakasam
pp191-pp196
Ratbots Using Embedded System
by Tharani.V and Malini.V
pp197-pp202
A Study of Wireless Electricity-An Emerging Technology
by A.Thiyagarajan, T.V.Sankaralingam and P.Ramanidharan
pp203-pp210
Year 2038 Problem: Y2K38
by S.N.Rawoof Ahamed and V.Saran Raj
pp211-pp215
Heart Surgery by Robot “A NEW KID ON THE BLOCK”
by A. Sumithra, M.Salome Helen Dharini and S.Gayathri
pp216-pp221
Routing Protocols of Wireless Sensor Network
by Raja Meena S and Poornima Talwai
pp222-pp235
Modeling of Magnetically Coupled Embedded Z Source Inverter
by V. Vidhya and Jeyasree Tagore
Design and Implementation of Ultrasonic Transducers Using HV Class-F Power Amplifier
pp236-pp241
pp242-pp246
by Jeyasree Tagore and V.Vidhya Design and analysis VLSI Architectures using am bipolar MISFETs
pp247-pp252
by Anjali Ashok K.A, Saravanan.P and Praveen Kumar.T Surveillance Patrol Robot for People Tracking in Indoor Environments
by Neerparaj Rai, Shakti Dhar and Rupam Kakati
pp253-pp261
An Efficient Key Generation Using Cellular Automata for Symmetric Cryptography
pp262-pp268
by K.Raghuram and VVSVSRamachandram
Automation of Rationshop Using RFID
by Vaishally.K
pp269-pp274
Sleuthing Wormhole Attacks Using Hidden Markov Model in Wireless Networks
by Sunitha M and Priyadharshini P
Secret Data Hiding in Encrypted Compressed Video Bit streams for Privacy Info Protection
pp275-pp280
pp281-pp285
by Pradeep Rajagopalan
Design of Analog Front-End for 128 Multi-Channel EEG Signal Acquisition in 90nm Technology
pp286-pp291 by Gurajala Srinivas Praharshith,Sarvani Kunapareddy, Spurthi Nagulapally, Pavani. Real Time Monitoring and Alert System of Human Health Using Bio Sensors, GSM and GPS
pp292-pp298
by Amirthaganesh.S
A Comparative Analysis of Wide Band Antenna with Reduced Radar Cross Section
by Srisuji. T and Nandagopal. C
pp299-pp305
Brain Tumor and Brain Abnormality Detection Using Image Processing
by G.V.Sandhiya and S.Lohitha
pp306-pp314
A Novel Selection Combining Technique for Multi relay Cooperative Communication System Using Decode and Forward Protocol
pp315-pp320
by K.Kavitha and A.Kumaresan
Designing & Implementation of Mobile Operated Toy Car by DTMF
by V.Bhuvaneswari and A.Sharmela
pp321-pp328
Design of an Integrating Meter for Magnetic Flux Measurement
by Aparna V and Ashok kumar V
pp329-pp336
Gesture Controlled Robot Using Image Processing
by ARVIND A and UTHIRA MOHAN
pp337-pp342
Development of Wireless Inductive Modem Charger
by V.prasanth, B.Ajay and B.Prasanna
pp343-pp347
Speed control of DC Motor by using Fuzzy Logic
by P.ARUN RAJA
Device & Voice Control System Based On Gesture Recognition Using 3d Mems Accelerometers
pp348-pp351
pp352-pp357
by R.Mukunthan, B.Mohamed Ibrahim Gani,G.Palani Kumar and P.Sivakumar Wireless Power Transmission
by M.Hare Sudhan
pp358-pp360
E-Crashcorder Next Generation Vehicular Black Box Device integrated with Airbag Control System that records Audio and Visual Footage of the Crash Scenario in addition to Vital Vehicle Parameters
pp361-pp366
by Kavitha S, Jayandran and S Navas Power Production in Satellites by Utilizing Solar Energy using Ultra capacitors
by A.TAMIL ILAKKIY and J.SUGANYA
Neuromorphic VLSI Implementation of Analog Inner Hair Cell and Auditory Nerve IC Using Non-Invasive Technique
pp367-pp373
pp374-pp382
by Saranya S A and J Raja Multimodel Authentication System Using Artificial Neural Network by R.Sherline jesie
pp383-pp390
A Public Safety Offline Application of Smartphone’s and Android OS
by M.Palanisamy
A Novel Method of Violated Constraint Prediction with Modified Spatial Analysis Based Fu zzy Sorting by K.Nithiya
pp391-pp394
pp395-pp401
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Employing second generation mobile technology in process plant for Temperature and Pressure process AR.Ramaiah Electronics and Instrumentation Engineering SRM’s Easwari Engineering college Chennai,India
Abstract—“Employing second generation mobile technology in process plant for Temperature and Pressure process” is designed to control instruments or high end appliances in process plant through mobile phones. Temperature sensor, pressure sensor and heater coil are connected to the PIC microcontroller using suitable signal conditioning circuits. A program is written to compare the set point values and measured values. If the measured value crosses the set point value, the PIC microcontroller sends an alter SMS to the process engineer’s mobile phone. The GSM module is used for transferring the alert message to the GSM network. An FBUS data cable is used to interface GSM module with PIC microcontroller.
I. INTRODUCTION The major problem faced by the industry is monitoring instruments in process plant throughout the process by an engineer. Our project is going to solve this problem by using GSM technology. The project is designed to control instruments in process plant by using mobile phone; this uses sensors, signal conditioning circuits, PIC Microcontroller, GSM network with module, FBUS data cable. GSM (Global System for Mobile communication) is a global network which allows international roaming capability, digital cellular technology used for transmitting mobile voice and data services at the speed of 9.6kbps and it operates in the bands of 850MHz to 1.9GHz. Terrestrial GSM network covers more than 90% of the world’s population. GSM satellite roaming has also extends service access to areas where terrestrial coverage is not available. “GSM based Control System” implements the emerging applications of the GSM technology. Using GSM networks, a control system has been proposed that will act as an embedded system which can monitor and control appliances and other devices locally using built-in input and output peripherals. Remotely the system allows the user to effectively monitor and control the house/office appliances and equipments via the mobile phone set by sending commands in the form of SMS messages and receiving the appliances status. The main concept behind the project is receiving the sent SMS and processing it further as required to perform several operations. The type of the operation to be performed depends on the nature of the SMS sent. The principle in which the project is based is fairly simple. First, the sent SMS is stored and polled from the receiver mobile station and then the required control signal is generated and sent to the intermediate hardware that we have designed according to the command received in form of the sent message. The messages are sent from the mobile set that contain commands in written form which are then processed accordingly to perform the required task. A microcontroller based system has been proposed for our project. There are several terminologies that are used extensively throughout this project report. GSM (Global System for Mobile Communications): It is a cellular communication standard.
Keywords – GSM, SMS, MODEM, CONTROL.
II. INSTRUMENTATION PHYSICAL SETUP 2.1 LM35 Temperature sensor The LM35 is an integrated circuit sensor that can be used to measure temperature with an electrical output proportional to the temperature (in oC).The general equation used to convert output voltage to temperature is:
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Temperature ( in oC) =V out *(100 oC/V) • • • • •
Integrated Circuit More accurate Sensor circuitry is sealed Not subjected to oxidation Higher output voltage
2.2 KP200 Pressure sensor Infineon Technologies KP200 is a fully monolithically integrated pressure sensor for the detection of different pressure. In this application, the pressure sensor is interfaced with PIC microcontroller trough suitable signal conditioning circuit. KP200 provides a signal pulse which is proportional to the pressure change in the process. The height of the signal pulse is independent of the ambient pressure, but dependent on the relative pressure change.
Ambient pressure range
53.6 to 110 kpa
Absolute pressure range
50.9 to 126.5 kpa
Supply voltage
4.5 to 11 V
Operation temperature
-40° to 85° C
2.3 PIC Microcontroller PIC is a family of modified Harvard architecture microcontrollers made by Microchip Technology, derived from the PIC1650 originally developed by General Instrument's Microelectronics Division. The name PIC initially referred to “Peripheral Interface Controller”.PICs are popular with both industrial developers and hobbyists alike due to their low cost, wide availability, large user base, extensive collection of application notes, availability of low cost or free development tools, and serial programming (and re-programming with flash memory) capability • • • • • •
Dual In-line Package Reduction Instruction Set Computing architecture Built in oscillator with selectable speeds Inexpensive microcontrollers Wide range of interfaces including USB, Ethernet Reduced complexity in Assembly programming.
III. COMMUNICATION BETWEEN INSTRUMENTS AND GSM MODULE: 3.1 FBUS RS232 FBUS (Fast BUS) is an ANSI/IEEE data bus oriented towards mobile phones. The standard specifies a way for various pieces of electronics hardware to communicate, typically with one piece acting as master (sending a request) and another acting as slave (returning an answer). It’s a bidirectional full-duplex serial type bus running at 1,15,200 bits/sec. It has three pins as follows:
First pin for data transmit Second pin for data receive Third pin for ground
3.2 GSM Network with module GSM is a second generation of mobile technology. GSM module has a modem which accepts a SIM card and operates over a subscription to a mobile operator, just like a mobile phone (For mobile operator, a GSM modem looks just like a mobile phone). Thus modem Generate, transmit and Decode data from a cellular network, this establishing communication between the cellular network and the microcontroller.
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AT commands are used to control modem. AT is an abbreviation for Attention. Wireless modems need AT commands to interact with microcontroller. This covers a way to dial a particular GSM mobile number as well as sends a SMS to it using AT commands with the help of Microcontroller. IV. BLOCK DIAGRAM
Fig.I Block diagram V. WORKING GSM module is used to establish communication between a computer and a system. Global System for Mobile communication (GSM) is an architecture used for mobile communication in most of the countries. Global Packet Radio Service (GPRS) is an extension of GSM that enables higher data transmission rate. GSM module consists of a GSM modem assembled together with power supply circuit and communication interfaces (like RS-232, USB, etc) for computer. Wireless MODEMs are the MODEM devices that generate, transmit or decode data from a cellular network, for establishing communication between the cellular network and the computer. These are manufactured for specific cellular network or specific cellular data standard technology (GPS/SIM). Wireless MODEMs like other MODEM devices use serial communication to interface with and need Hayes compatible AT commands for communication with the computer (any microprocessor or microcontroller system). GSM MODEM is a class of wireless MODEM devices that are designed for communication of a computer with the GSM network. It requires a SIM (Subscriber Identity Module) card just like mobile phones to activate communication with the network. Also they have IMEI(International Mobile Equipment Identity) number similar to mobile phones for their identification. A GSM MODEM can perform the following operations: 1. 2.
Receive, send or delete SMS messages in a SIM. Read, add, search phonebook entries of the SIM. 3.
Make, Receive, or reject a voice call.
The MODEM needs AT commands, for interacting with processor or controller, which are communicated through serial communication. These commands are sent by the controller/processor. The MODEM sends back a result after it receives a command. Different AT commands supported by the MODEM can be sent by the processor/controller/computer to interact with the GSM cellular network.
TABLE I. AT-Command set overview Command
ATE0
Description Check if serial interface and GSM modem is working. Turn echo off, less traffic on serial line.
AT+CNMI
Display of new incoming SMS.
AT+CPMS
Selection of SMS memory.
AT
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AT+CMGS
SMS string format, how they are compressed. Read new message from a given memory location. Send message to a given recipient.
AT+CMGD
Delete message.
AT+CMGF AT+CMGR
4
System is controlled by SMS commands: • • • • • • • • •
Start Stop Status SET Value RESET Value Default Value SET Time RESET Time Default Time VI. ADVANTAGES
• • • • •
Cost Effective as it uses ICs. Simply set up/ construction. Closed loop control system with negative feedback is not required, which also reduces complicated system arrangement. Wireless communication between various equipments/devices. Easy implementation.
APPLICATION
Can be employed in thermal power stations, in boiler drum operations to control Temperature. This set up can be used to control Pressure process also by replacing heater coil by I to P transmitter and replacing temperature sensor by pressure sensor. Both the process can also be controlled at the same time by small enhancement in the set up. Can be employed in process plants having higher end appliances. Can be employed in industries where physical monitoring of process plant is much complex. REFERENCES
[1] B. Woodward, R. S. H. Istepanian, and C.I. Richards, Design of a telemedicine system using a mobile telephone, IEEE Trans. on Information Technology in Biomedicine, vol.5, no. 1, pp. 13–15, March. 2001. [2] Jinwook C., Sooyoung Y., Heekyong P., and Jonghoon C, MobileMed: A PDA-based mobile clinical information system, IEEE Trans. On Information Technology in Biomedicine, vol. 10, no.3, July 2006. [3] Peersman, G., Cvetkovic, S., The Global System for mobile Communications Short Message Service, IEEE Personal Communications, , June 2000 [4] Daldal Nihat, GSM Based Security and Control System, M.Sc. Term Project, Gazi University,Ankara,2003. [5] Md.Asdaque Hussain and Kyung Sup Kwak, Positioning in Wireless Body Area Network using GSM, IEEE trans. on International Journal of Digital Content Technology and its Applications Vol 3, Number 3, September 2009.
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Innovative Bikes P.BOOBATHI RAJ VSB ENGINEERING COLLEGE KARUR Abstract- Nowadays people use to travel all the way with the help of automobile (i.e. four wheelers and two wheelers). The counting of two wheelers had been increased since 2000’s. Even though the cost of fuels increased the pupil never try to reduce the usage of motor bikes. To overcome this problem many organizations developed electric bikes in the existence but due to some electrical problems the usage of electric bikes had gone out of existence. When we see this problem in environmental point of view, the motor bikes generates more amounts of carbon monoxide and carbon dioxide gasses which causes air pollution. In this paper we are going introduce some technologies to make the electrical bikes more efficient and reliable. The technology used here is to charge the batteries of electric bikes during the running time, i.e. charge consumed during the process of running is converted to charge the batteries of electric bikes.
KEY EQUIPMENTS USED:
ELECTRICAL MOTOR AUTO TRANSFORMER MICOPROCESSOR
I.INTRODUCTION In this project we are going to see how does the battery of an electrical bike is charged during the running time. In this the motor which is used for riding is coupled with another motor which is a generator. In this the mechanical energy in the running motor is converted into the electrical energy in the generator with the principle of electromagnetic induction. Then the electrical energy is fed back into the battery to charge the battery. A microprocessor is connected between the generator motor and auto-transformer. Here microprocessor is used to organize the flow of current from generator motor to auto-transformer. EXISTING SYSTEM The electric bikes which are running around the cities are pollution less but battery backup is little bit worsened, which is an important part in the electrical bikes. In India electrical problems are being a big issue so it is very difficult to charge the battery in the day to day life. Hence it needs a technique to enhance the battery backup. PROPOSED SYSTEM We propose the system in which the battery of the electrical bike is charged during the running time. In this the charge used for the purpose of running the vehicle is converted and is again transferred to the battery or charging it. SYSTEM-BLOCK DIAGRAM The basic components of the system are Running motor Generator motor Microprocessor Auto-transformer Battery
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1]. RUNNING MOTOR: It is a normal electrical motor which is used for running of the electrical bikes. As we know motor converts electrical energy into mechanical energy. A DC motor relies on the fact that like magnet poles repel and unlike magnetic poles attract each other. A coil of wire with a current running through it generates an electromagnetic field aligned with the center of the coil. By switching the current on or off in a coil its magnetic field can be switched on or off or by switching the direction of the current in the coil the direction of the generated magnetic field can be switched 180°. A simple DC motor typically has a stationary set of magnets in the stator and an armature with a series of two or more windings of wire wrapped in insulated stack slots around iron pole pieces (called stack teeth) with the ends of the wires terminating on a commutator. The armature includes the mounting bearings that keep it in the center of the motor and the power shaft of the motor and the commutator connections. The winding in the armature continues to loop all the way around the armature and uses either single or parallel conductors (wires), and can circle several times around the stack teeth. The total amount of current sent to the coil, the coil's size and what it's wrapped around dictate the strength of the electromagnetic field created. The sequence of turning a particular coil on or off dictates what direction the effective electromagnetic fields are pointed. By turning on and off coils in sequence a rotating magnetic field can be created. These rotating magnetic fields interact with the magnetic fields of the magnets (permanent or electromagnets) in the stationary part of the motor (stator) to create a force on the armature which causes it to rotate. In some DC motor designs the stator fields use electromagnets to create their magnetic fields which allow greater control over the motor. 2].GENERATOR: It works on the principle of faradays law. In electricity generation, a generator is a device that converts mechanical energy to electrical energy for use in an external circuit. The source of mechanical energy may vary widely from a hand crank to an internal combustion engine. Generators provide nearly all of the power for electric power grid.
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The reverse conversion of electrical energy into mechanical energy is done by an electric motor, and motors and generators have many similarities. Many motors can be mechanically driven to generate electricity and frequently make acceptable generators PRINCIPLE: FARADAYS LAW OF ELECTRO- MAGNETIC INDUCTION: Faradays law of electromagnetic induction states that when a coil of wire or conductor is rotated in between the
magnetic field an emf is induced in the conductor.
The EMF generated by Faraday's law of induction due to relative movement of a circuit and a magnetic field is the phenomenon underlying electrical generator. When a permanent magnet is moved relative to a conductor, or vice versa, an electromotive force is created. If the wire is connected through an electrical load, current will flow, and thus electrical energy is generated, converting the mechanical energy of motion to electrical energy. 3].MICROPROCESSOR: Microprocessor used here is 8085 which is programed to work as a switch between the generator and autotransformer. Microprocessor is semiconductor device which is used to perform arthematic and logic unit. It transfers voltage from generator to auto-trasformer. The varying voltge generated by the generator is sensed and it is connected to the coresponding voltage level in auto-transformer. 4].AUTO-TRANSFORMER: An autotransformer (sometimes called autostep down transformer) is an electrical transformer with only one winding. The "auto" (Greek for "self") prefix refers to the single coil acting on itself and not to any kind of automatic mechanism. In an autotransformer, portions of the same winding act as both the primary and secondary sides of the transformer. The winding has at least threetaps where electrical connections are made. Autotransformers have the advantages of often being smaller, lighter, and cheaper than typical dual-winding transformers, but the disadvantage of not providing electrical isolation. Other advantages of autotransformers include lower leakage reactance, lower losses, lower excitation current, and increased KVA rating.] Autotransformers are often used to step up or step down voltages in the 110-115-120 V range and voltages in the 220230-240 volt range—for example. providing 110 V or 120 V (with taps) from 230 V input, allowing equipment designed for 100 or 120 volts to be used with a 230 volt supply (as in using US electrical equipment with higher European voltages).
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5].BATTERY: Battery is a device which consists of one or more electro chemical cells that converts stored chemical energy into an electrical energy. It stores the electrical energy for future uses. It is a main backup source of the electrical bikes for running. It is a rechargeable battery in which, when the charge of the battery gets lowered the battery can be recharged. Batteries play a main role in all electronic equipment’s. WORKING: As the shaft of running motor and the shaft of generator was coupled, the rotation of the running motor makes the generator to rotate. When electric bikes are started running the running motor takes the required power from the battery for running as the motor was coupled with the generator the generator’s shaft was rotated. Due to the effect of faraday’s principle in generator a huge amount of varying voltage is generated. Then the varying voltage is send into the microprocessor where it works as the switch and it connects it to the equivalent summing voltage in the autotransformer to get the ideal voltage to charge the battery. Then the regulated voltage from auto-transformer is send to the battery for charging. WORKING AT MICROPROCESSOR: In this the varying voltage is connected to the ports of the microprocessor . the microprocessor identifies the input voltage and it acts as the switch to connect to the particular voltage level in inverted auto-transformer for regulation of the voltage.
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WORKING CYCLE: The working process involves running motor , generator , microprocessor , auto-transformer and a battery. As the working process begins in the running motor and ends with the running motor , this process is termed as cyclic process.
ADVANTAGES:
It is free from fuels. It is free from pollution.
APPLICATIONS:
It can be used in two wheeler transportation. This idea can be implemented for any other rotaing devices such as fan and etc.
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CONCLUSION: As it does not need any kind of external power supply to charge the battery, it will play an important role in the world where the current scarcity is high. REFERENCE: [1]. Herman, Stephen. Industrial Motor Control. 6th ed. Delmar, Cengage [2]. William H. Yeadon, Alan W. Yeadon. Handbook of small electric motors. McGraw-Hill Professional, 2001. [3]. Blalock, Thomas J., "Alternating Current Electrification, 1886". IEEE History Center, IEEE Milestone. (ed. first practical demonstration of a dc generator - ac transformer system.) [4]. Yoshihide Hase, "10: Theory of generators", Handbook of Power System Engineering, John Wiley & Sons, 2007 [5]. Terrell Croft and Wilford Summers (ed), American Electricians' Handbook, Eleventh Edition, McGraw Hill, New York (1987) . [6]. Donald G. Fink and H. Wayne Beaty, Standard Handbook for Electrical Engineers, Eleventh Edition,McGraw-Hill, New York, 1978. [7]. Microsoft Macro Assembler 5.0 Reference Manual. Microsoft Corporation. 1987. "Timings and encodings in this manual are used with permission of Intel and come from the following publications: Intel Corporation. iAPX 86, 88, 186 and 188 User's Manual, Programmer's Reference, Santa Clara, Calif. 1986." (Similarly for iAPX 286, 80386, 80387.)
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Single Tree Search Sphere Decoding Algorithm for Mimo Communication System P. Prakash and M. Kannan Research Scholar, Department of Electronics, Anna University Chennai, Chennai 44, HOD, Department of Electronics, MIT Anna University, Chennai 44.
Abstract— Multiple Input Multiple Output (MIMO) systems use multiple antennas at both transmitter and receiver for higher Bandwidth efficiency. The implementation of MIMO detection becomes a difficult task as the computational complexity increases with the number of transmitting antenna and constellation size increases. The decoder for a 4*4 MIMO system with 16-QAM modulation and spatial multiplexing is implemented using Mat lab. Difficult part is MIMO detection; ML decoding cannot be implemented directly because increase the complexity exponentially if size of constellation and number of transmit antenna increases. Sphere decoding reduce the complexity of decoding with some improvement with the decoding rate and BER.In my project sphere decoding combined with single tree search and ML decoding greatly improve the decoding Rate and BER.In sphere decoding selecting the sphere radius is very important. Sphere decoding algorithm implemented in the tree search complexity of algorithm is more reduced. In tree search Tree pruning strategies are used to reduce the complexity of the tree search based algorithms. The basic idea is to reduce the number of tree nodes visited to achieve a ML result. The decision where to visit a node or prune it is based on its Partial Euclidean Distance(PED).Depending upon tree pruning strategy the algorithm achieve optimal BER.
Index Terms— Lattice point search, Multiple Input Multiple Output (MIMO) detection, Quadrature Amplitude Modulation, Sphere decoding, partial Euclidean distance(PED),Log Likelihood Ratio(LLR). I.
INTRODUCTION
With continuous need of higher communication rate within a fixed frequency spectrum, Multiple Input Multiple Output technology provide a potential solution for saving The frequency spectrum. Every new wireless mmunication protocol like WiMAX, WI-Fi, LTE etc. is employing MIMO technology to satisfy ever increasing demand of high data rate. In MIMO systems multiple antennas are employed on both transmitter and receiver side. Spatial multiplexing method is applied to increase the data rate. In spatial multiplexing higher rate input data stream is sub-divided into lower rate sub data streams. These data streams are then propagated from multiple antennas. It has a potential of increasing the capacity N times, where N is the minimum of the total number of transmit and receive antennas. On the receiver side received signal at each antenna corresponds to a combination of multiple data streams from all the transmit antennas. In spatial multiplexing the input data stream with high data rate is split into multiple smaller data rate bit streams. These separate data streams are then further modulated in which a set of bits are assigned a fixed codeword in the symbol constellation. The advantages of MIMO systems are more. A typical MIMO system can be used to increase link reliability and Quality of Service (QoS) by using spatial Diversity Gain methods. Using this method the input stream is space time coded and then transmitted which gives more robustness but data rate is lower. MIMO system can also be used in multiplexing gain mode where each transmit antenna sends out different bit streams hence throughput of the MIMO system is increased. Fig 1 shows system model of MIMO communication system.
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12
RELATED WORK
Decoding part is heart of the MIMO communication system. In MIMO decoding several detectors are available they are(i)Zero forcing(ZF)(ii)MMSE(Minimum Mean square error)(iii)Successive interference cancellation(SIC) detector(iv)Sphere decoding with Maximum Likelihood(ML) .Zero forcing and MMSE Detector coming under the family of reduced complexity detector is linear detector.Eventhough they reduce the computational complexity, their drawback is significant performance degradation.SIC detector are prone to error propagation. All the drawback is overcome by sphere decoding using ML.Sphere decoding have the ability to reach ML solution at lower complexity than exhaustive search, by finding the result within a sphere centered at receive signal. A. Sphere decoding algorithm Considering a MIMO system with M transmit antenna and N receive antennas, the received signal is given by the equation X = Hs +v (1) Where ‘X’ is a received signal vector and ‘v’ is an additive white Gaussian noise vector (AWGN). ‘H’ is the N*M channel matrix that can be assumed as known from perfect channel estimation. In each element of the transmit vector, ‘s’ is a constellation point and the channel matrix ‘H’ generates a lattice for the selected modulation scheme. Where size of the signal are X=[x1,x2,x3…..xNr] , S=[s1,s2,s3….sMt] and v=[v1,v2,v3…vNr] . 1. ML decoding algorithm In ML decoding algorithm is to find the minimum distance between the received point (X) and the examining lattice point (Hs) that Estimated s= arg min ||X- Hs ||2 (2) S ε ΩMt Where‘s’ is the decoded vector. The entries of‘s’ are chosen from a complex constellation ‘Ω’. The set of all possible transmitted vector symbols is denoted by ‘ΩMt. Here one drawback is exhaustive search. If Mt=2 and Ω=16(Number of constellation points) then search space= ΩMt=162=256 MIMO symbol, that is to find one symbol using ML decoding has to search 256 symbol and find the one symbol out of 256 MIMO symbols having less distance that symbol is correspond to required symbol. If Mt=4 and Ω=16(Number of constellation points) then search space= ΩMt=164=65,536,that is to find one symbol out of 65536 MIMO symbol ,ML decoding has to compare 65,536 symbol and find the one symbol having less distance that is expected correct symbol. So practically not possible, the searching time is more definitely the decoding rate is reduced. Thus, the ML-based sphere decoding system can be summarized as follows, Input: The input is the channel lattice generation matrix H and the received signal vector X. Output: A 1*M vector such that ‘sH’ is a lattice point that is the closest to ‘X’. 2. Sphere decoding In sphere decoding Estimated s= arg min ||x- Hs ||2 1), then go to State A; If ( xk < Lk and k=1), then go to State B; If ( xk >= Lk), then go to State C. 3) State A: Expand the search into (k-1) sublayer, find the parameters used to upgrade xk and Lk and, and go to State D. 4) State B: Compute dnew If dnew < dbest, record the currently best distance and the best lattice point, set k=N, and go to State D. If dnew > = dbest, then go to FSM. 5) State C: If k=N, stop the algorithm. Otherwise, move the search one layer up k=k+1 and go to FSM. 6) State D: Upgrade xk and Lk and that involve square root computations, and then go to FSM. III. PROPOSED WORK In botany,a tree is a plant with elongated stem, supporting branches and leaves. In data structure tree can be defined as collection of nodes (starts with root node), where each node is a data structure consist of values. In computer science, a search tree is a data structure to find required values from the set In order for a tree to work as a search tree, the key of each node should be greater than keys in subtree of the left and less than keys of subtree in the right. The advantages of search tree is searching time is less if tree is balanced, balanced in tree mean number leaves in both sides are equal. A.SINGLE TREE SEARCH SPHERE DECODING In Decoding Tree associated with Decoding sphere the number of level in the tree is equal to Number of transmitting antenna in MIMO system. Each node in the tree will have as many children nodes as constellation size. In each level branches accumulated PED (partial Euclidean distance) higher than the sphere radius that path is discarded that reduce searching point very less compare with the sphere decoding. Hence performance of MIMO communication system is improved very much if Sphere decoding applied with the Tree search.
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1. SINGLE TREE SEARCH ALGORITHEM The basic idea behind iterative tree-search algorithms lie in the conversion of the original ML decoding problem into a fully optimal ML decoding problem. ML detection is now changed into an equivalent tree search problem in which distances between received vector X and the received symbols Hs can be split into partial Euclidean distances (PEDs) di(s(i)) which depend only on s(i) and which is a non-reducing non-negative function when proceeding from a parent node to its child node. Based on these PEDs, tree-search algorithms objective is finding the leaf node that is associated with the smallest di(s (1)) which is expected from the ML solution. To create a tree structure first QR factorization is applied on H matrix which converts it into a product of upper triangular matrix R and a unitary matrix Q. WKT X=Hs+n By applying QR factorization H=QR then X=QRs+n Multiply both the sides by QH then QHX=QH QRs+QHn Where QH.Q=I , then QHX=Rs+QHn Assume QHX=y , Y=Rs+QHn (6) 2. Tree search using Sphere Decoding Sphere decoding algorithm can be implemented by tree search algorithm. Key idea here is to reduce the number of lattice symbol vectors to be considered for the ML solution. Search includes only those symbol vectors S that lie inside an sphere of radius D. ||x- Hs ||2 tm_year+1900,
tm_ptr->tm_mon+1,
tm_ptr-
printf("Time: %02d:%02d:%02d\n", tm_ptr->tm_hour, tm_ptr->tm_min, tm_ptr->tm_sec); } exit (0); }
Output: Tue
Jan
19
03:14:01
2038
Tue
Jan
19
03:14:02
2038
Tue
Jan
19
03:14:03
2038
Tue
Jan
19
03:14:04
2038
Tue
Jan
19
03:14:05
2038
Tue
Jan
19
03:14:06
2038
Tue
Jan
19
03:14:07
2038
Fri
Dec
13
20:45:52
1901
Fri
Dec
13
20:45:52
1901
Fri
Dec
13
20:45:52
1901
Output of the program shows the date and time changes after 03:14:07 UTC, Tuesday, January 19, 2038. The above program shows what will happen in 2038. All the time related functions, programs will give
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erroneous result. All the variable quantities are studied and measured with respect to time as it is considered to be the independent variable in most computer applications so it becomes important for this time_t variable to return correct and exact value. Some times and their time_t representation are, Table 1. Exact time_t representations of Date and Time in signed 32-bits data type. Date & Time 1-Jan-1970,12:00:00 AM GMT 1-Jan-1970,12:00:01 AM GMT 1-Jan-1970,12:01:00 AM GMT 1-Jan-1970,01:00:00 AM GMT 2-Jan-1970,12:00:00 AM GMT 3-Jan-1970,12:00:00 AM GMT 1-Feb -1970,12:00:00 AM GMT 1-Jan-1971,12:00:00 AM GMT 1-Jan-2038,12:00:00 AM GMT 19-Jan-2038 03:14:07 AM GMT
Representation 0 1 60 3600 86400 172800 2678400 31536000 2145916800 2147483647
IV.Y2K38 SOLUTIONS 1. Change the definition of time_t- This would result in code compatibility problems as there are certain applications which are dependent on the signed 32 bit representation of time. For example if time_t is changed to an unsigned 32 bit integer then, the applications that retrieve, store or manipulate the dates prior to 1970 will not be able to do so correctly. Though doing so will delay this problem to 2106. 2. Shift from 32 bit systems to 64 bit systems- Shifting from 32 bit systems to 64 bit systems gives us a new wrap around date which is over 20 times greater than the estimated age of universe i.e. about 292 billion years from now. 3. NetBSD’s-Startingsolutionwithits6thmajor release, NetBSD is using a 64 bit time_t for both 32 bit and 64 bit architectures which supports 32 bit time_t in applications 4. Possible solution for Linux that can be implemented. 5.
Re-define the time_t structure as 64-bit, This is not a solution as the binary compatibility of the
software would break here. Programmes depending on the binary representation of time would be in trouble. So we cannot even think of this one. 6.
Change time_t from 32-bit signed to 32-bit unsigned, This seems to be good at first look, but this
would just delay(post-pone) the judgement day to the year 2106 as it will give some more scope by adding another usable bit. You will be in the same trouble by then. So this is a feasible solution but not a practical one.
7.
Shift from 32-bit systems to 64-bit systems, Most 64-bit architectures use 64 bit storage to represent
time_t. The new wrap-around date with this new (signed) 64 bit representation will not come before 290 billion years. It is positively predicted that by the year 2038 all 32-bit systems will be phased out and all systems will be 64-bit
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CONCLUSIONS The Y2K problem kept us engrossed and worried in the year 1999. However, the Y2K38 problem is not as obvious. This problem arises out of programs not allocating enough bits to internal time representation, which results from computing dates moving into the year 2038 and beyond in 32-bit operating systems. Even today, any date calculations forecasted beyond Tuesday, Jan 19, 03:14:07, 2038, will be erroneous. Most of the Banks, Financial Institutions and Insurance Companies, which have servers running on Unix or Unix like OS, will need to react now, as many product and services offered by them are for longer duration such as Loans, Bonds and Polices, etc. Modifications in the source code of the current applications, as given here and/or switching to 64-bit computing is required to solve the problem Y2K38 being a serious problem needs to be solved properly and within time. But, a solution that can be applied Universally is not yet there. Most of the solutions are either for particular software, system or for delaying this problem.
ACKNOWLEDGMENT We would like to express our sincere thanks to Mr. Arul (HOD, Information Technology, D.C.E., Chennai) and Mrs.Geetha Rani (Faculty, Information Technology, D.C.E, Chennai) who were abundantly helpful and supported us throughout this research work. REFERENCES [1] http://en.wikipedia.org/wiki/Year_2000_problem [2]
http://www.2038bug.com/pivotal_gmtime_r.c.html
[3] http://www.rdg.opengroup.org/public/tech/base/year2000.html "The Open Group Base Specifications Issue 6 IEEE Std 1003.1, 2004 Edition (definition of epoch)". IEEE and the Open Group. The Open Group. 2004. Retrieved 7 March 2008. [5] Sun Microsystems. "Java API documentation: System.currentTimeMillis". Retrieved 7 May 2007.
[4]
[6]
http://en.wikipedia.org/wiki/Year_2038_problem
[7]
Millennium bug hits retailers, from BBC News, 29 December 1999.
[8]
http://www.crn.com.au/News/163864,bank-of-queensland-hit-by- y201k-glitch.aspx
[9]
http://www.informationweek.com/desktop/25-years-from-today-aid/1108280?
time-for-bugs/d/d-
[10] http://stablecross.com/files/End_Of_Time.html [11] http://web.archive.org/web/20060408161959/http://unununium.org/articles/uuutime [12] https://code.google.com/p/android/issues/detail?id=16899 [13] http://books.google.es/books?id=vENckcdtCwC&pg=PA49&dq=292,277,026,596&cd=1#v=onepage&q=29 2%2C277%2C026%2C596&f=false [14] http://substitute.livejournal.com/1430908.html [15] “The Case for Windowing: Technique by Raymond B. Howard, Year/2000 Journal, Mar/Apr 1998.
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HEART SURGERY BY ROBOT “A NEW KID ON THE BLOCK” Dr. A. Sumithra M.Tech., Ph.D. Ms.M.Salome Helen Dharini, Ms.S.Gayathri B.Tech.Information Technology (Final Year) Department of Information Technology Velammal College of Engineering and Technology Madurai, India
ABSTRACT:Nobody can deny the fact that Robots play a vital role in the life of human beings. Robots play many roles in the medical field also. Robotics is nothing but the brain on a chip.Robots can be defined as follows: "An automatic device that performs functions normally ascribed to humans or a machine in the form of a human." Robots can do surgery in the absence of surgeons through voice activation and remote controls. It performs surgery through very small incisions, greatly reducing the risk to patients. It also reduces complexity while handling the patients. This paper analyses the part of Robots in the area of heart surgery and brings this “NEW KID ON THE BLOCK TO LIGHT”.
I.
INTRODUCTION
"An automatic device that performs functions normally ascribed to humans or a machine in the form of a human." A robot is a virtual or mechanical artificial agent. In practice, it is usually an electro-mechanical system which, by its appearance or movements, conveys a sense that it has intent or agency of its own. The word robot can refer to both physical robots and virtual software agents, but the latter are usually referred to as bots. There is no consensus on which machines qualify as robots, but there is general agreement among experts and the public that robots tend to do some or all of the following: move around, operate a mechanical limb, sense and manipulate their environment, and exhibit intelligent behavior especially behavior which mimics humans or other animals The first digitally operated and programmable robot, the Unimate, was installed in 1961 to lift hot pieces of metal from a die casting machine and stack them. Today, commercial and industrial robots are in widespread use performing jobs more cheaply or with greater accuracy and reliability than humans. They are also employed for jobs which are too dirty, dangerous or dull to be suitable for humans. Robots are widely used in manufacturing, assembly and packing, transport, earth and space exploration, surgery, weaponry, laboratory research, and mass production of consumer and industrial goods.
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CHARACTERISTICS OF ROBOT: 1. 2. 3. 4. 5. 6. 7. 8.
It is artificially created. It can sense its environment, and manipulate or interact with things in it. It has some ability to make choices based on the environment, often using automatic control or a preprogrammed sequence. It is programmable. It moves with one or more axes of rotation or translation. It makes dexterous coordinated movements. It moves without direct human intervention. It appears to have intent or agency
BENEFITS OF ROBOTS: Robots offer specific benefits to workers, industries and countries. If introduced correctly, industrial robots can improve the quality of life by freeing workers from dirty, boring, dangerous and heavy labor. it is true that robots can cause unemployment by replacing human workers but robots also create jobs: robot technicians, salesmen, engineers, programmers and supervisors. The benefits of robots to industry include improved management control and productivity and consistently high quality products. Industrial robots can work tirelessly night and day on an assembly line without an loss in performance. Consequently, they can greatly reduce the costs of manufactured goods. As a result of these industrial benefits, countries that effectively use robots in their industries will have an economic advantage on world market. SURGERY: This paper deals with only about Heart Surgery. ROBOT-ASSISTEDHEART SURGERY – AN INTRODUCTION: Minimally invasive robot-assisted heart surgery (cardiac surgery) is a procedure that allows heart surgery to be performed through tiny incisions in the patients chest. Traditional open surgery requires that surgeons make incisions large enough to expose and provide access to the area that is being operated on. In minimally invasive surgery, in contrast, the instruments used for the surgery are inserted through incisions no larger than a dime (18mm or .7 inch), reducing the opportunity for bacterial infection, decreasing post-operative pain, and allowing for faster recovery.
LIMITATIONS OF ORDINARY SURGERY: Invasive surgery is performed with the surgeon directly manipulating the surgical instruments. Range of motion at the operation site is limited. At the same time, the endoscope, a small flexible tube with a lighted optical system that is inserted through the tiny incision to view the surgery site, provides only a 2-dimensional image, and surgeons must learn to visualize the procedure by looking at a screen. All of the surgeon movements are counterintuitive as though he or she were operating while looking in a mirror. This way of operating is fine for procedures such as knee repair or gallbladder removal that do not call for the complex microsurgical movements needed for heart surgery. The instruments that are used for minimally invasive heart surgery are also longer than
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instruments used for other types of surgery; therefore, the effect of hand tremors is magnified, making delicate manipulations difficult. ADVANTAGES OF ROBOTS IN SURGERY: To overcome these limitations with minimally invasive surgery, medical robots were developed. 1. 2. 3. 4. 5. 6. 7. 8. 9.
In robot-assisted surgery, instead of directly moving the instruments the surgeon uses a computer console to manipulate the instruments attached to multiple robot arms. The computer translates the surgeon movements, which are then carried out on the patient by the robot. The console is located in the same operating room as the patient, but is physically separated from the operative workspace. The surgeon does not need to be in the immediate location of the patient while the operation is being performed, it can be possible for specialists to perform remote surgery on patients from many miles away. Robots can perform heart surgery without a human surgeon In robot-assisted surgery, robots do not actually replace the surgeon, but rather enhance their ability to perform delicate, precise microsurgical movements. The controls to operate the robots are provided by a human through voice activation and remote control; In the United States, each robotic surgical system must receive approval from the FDA (Food and Drug Administration) for each surgical procedure during which a surgeon plans to use it. For example, clearance for use in gallbladder surgery does not allow a surgeon to use the same robotic system to, say, remove a tumor.
Commercially available robotic surgical systems have similar setups which include a control console with joystick-like hand controls and a 3-D viewer, at which the surgeon sits, and table-mounted robotic arms. One of the robotic arms holds the endoscope and the other two or three arms carry the surgical instruments. Heart Surgery Types Performed with robotic surgical systems. It is estimated that 70 to 90 hospitals in the United States now use minimally invasive surgical robots for heart surgery, and this number is expected to double by mid-2006. THREE SURGERY TYPES 1. 2. 3. 4.
Atrial septal defect repair the repair of a hole between the two upper chambers of the heart, Mitral valve repair the repair of the valve that prevents blood from regurgitating back into the upper heart chambers during contractions of the heart, Coronary artery bypass rerouting of blood supply by bypassing blocked arteries that provide blood to the heart. As surgeons experience and robotic technology develop, it is expected that robot-assisted procedures will be applied to additional types of heart surgery.
DA VINCI SURGICAL SYSTEM
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During robotic heart surgery, surgeons use a computer-enhanced tool called the da Vinci Surgical System to perform complicated procedures through small incisions. FEATURES: Prostatectomies, cardiac valve repair and gynecologic surgical procedures 1.It has four robotic arms, three of them are for tools that hold objects, act as a scalpel, scissors, bovie, or unipolar or dipolar electrocautery instruments. 2.The fourth arm is for a camera with two lenses that gives the surgeon full stereoscopic vision from the console. 3.The surgeon sits comfortably and looks through two eye holes at a 3-D image of the procedure, meanwhile maneuvering the arms with two foot pedals and two hand controllers.
The "'Da Vinci surgical system'" is minimally invasive, meaning the incisions used for surgery are very small. The arms also take away the tension and stress inflicted upon the incisions in laparoscopic surgery. The patient is therefore more apt to recover quickly and leave the hospital, reducing the average cost of keeping a patient in the hospital (after having surgery with the Da Vinci) by 33%. The Da Vinci allows the surgeon to be more precise in his surgery, and operate with significantly less shake than in a normal unassisted procedure. The surgeon can operate around delicate blood vessels and operate on some of the surgically difficult tumors that could not be removed before. The visualization of the Da Vinci has many advantages, giving the surgeon control of the camera and giving the surgeon a 3-D image upon which to operate on. The Da Vinci even stimulates pressure to the surgeon's controllers when he presses against something with one of his instruments. The Da Vinci elicits positive publicity towards the hospital where it's stationed; oftentimes effecting a patient's decision over the hospital they go to for medical care in general.
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ADVANTAGES: 1. 2. 3. 4.
The incisions are very small and, consequently, patient recovery is quick Reduces the number of staff needed during surgery, nursing care required after surgery, and, therefore, the overall cost of hospital stays. Compared with other minimally invasive surgery approaches, robot-assisted surgery gives the surgeon better control over the surgical instruments and a better view of the surgical site. The surgical robot can continuously be used by rotating surgery teams
THE POTENTIAL BENEFITS OF THE DA VINCI PROSTATECTOMY ARE: Effective Cancer Control Improved and Early Return of Sexual Function Improved and Early Return of Continence Improved Results Over Traditional Treatments Minimally Invasive Surgery:
DA VINCI VS. OPEN SURGERY & LAPAROSCOPY The following table looks at patient outcomes following surgery for prostate cancer (radical prostatectomy), and compares "best in class" data from three types of surgery. As you can see, da Vinci Prostatectomy (dVP) shows measurable advantages as compared to both conventional open surgery (open), performed through large incisions, as well as conventional minimally invasive laparoscopic (lap) surgery. IMPLEMENTATION: The Da Vinci system is implemented in foreign countries. In India it is implemented in only few places due to the cost effect. Future implementation is done to make this system more effective and research is still carried on to make this system implemented in all the countries.
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CONCLUSION Robotics is an absolutely fascinating field that interests most people. A robot can go where humans cannot. In fact, robots were created to help humans, especially in high risk or dangerous situations. Robots are filling an increasingly important role of enhancing patient safety in the hurried pace of clinics and hospitals where attention to details and where reliability are essential. In recent years, robots are moving closer to patient care, compared with their previous role as providing services in the infrastructure of medicine. The most surprising use of robots, perhaps, is their application in the actual performance of laparoscopic surgical procedures. the physical stress to perform surgical operations is reduced, so surgical robots are appreciated by surgeons who work more safely and proficiently! In future ordinary surgery will be replaced by Robotic surgery which enhances the effectiveness of the SURGERY REFERENCES [1]. [2]. [3]. [4]. [5]. [6]. [7].
www.montefiore.org www.davincisurgery.com www.roboticsurgery.com www.roboticsurgery.info www.ctsutc.edu www.india123.com www.biomed.brown.edu
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Routing Protocols of Wireless Sensor Network Raja Meena S and Prof. Poornima Talwai, Department of Electronics, Ramrao Adik Institute of Technology Nerul, Navi Mumbai Abstract--Many advances have been made in sensor technologies which are as varied as the applications and many more are in progress. It has been reasonable to design and develop small size sensor nodes of low cost and low power. Wireless sensor networks (WSNs) are large networks made of a numerous number of sensor nodes with sensing, computation, and wireless communication capabilities. The reasons for using wireless network are cost effectiveness of network deployment and its applicability to environments where wiring is not possible or it is preferable solution compared with wired networks. The software tool Network Simulator (Version 2), widely known as NS-2, is described and used for the evaluation and comparison of selected Flat Routing Protocols of wireless networks on the basis of certain metrics with different network sizes under four different scenarios. Index Terms--NS2, Wireless Sensor Network, Routing Protocols, Simulation, Flat Routing, Hierarchical Routing, Locationbased Routing, throughput, delay.
I. INTRODUCTION
W
ith the recent technological advances in wireless communications, processor, memory, low power, highly
integrated digital electronics, and micro electro mechanical systems (MEMS), it becomes possible to significantly develop small size, low power, and low cost multifunctional sensor nodes. These nodes are capable of wireless communications, sensing and computation. So, it is clear that wireless sensor network is the result of the combination of sensor techniques, embedded techniques, distributed information processing and communication mechanisms. A wireless sensor network (WSN) is a network that is made of hundreds or thousands of these sensor nodes which are densely deployed in an unattended environment with the capabilities of sensing, wireless communications and computations (i.e., collecting and disseminating environmental data). Many different routing, power management and data dissemination protocols have been designed for Wireless Sensor Networks (WSNs), dependent on both the architecture of Wireless Sensor Network (WSN) and the applications that WSN is intended to support. These protocols support the practical existence of WSNs and efficiently make them an integral part of our lives in the real world. These protocols are different from conventional ones; in essence they need to support various unique requirements and constraints to make wireless sensor networks practically useful and operating. The requirements and constraints are introduced by factors such as: memory, small-size, low-power consumption, fault-tolerance, low-latency, scalability, adaptivity, and robustness. In this dissertation, different routing protocols of WSN are presented. When designing wireless networks and/or studying their behaviors under various conditions, software simulation tools are often used. The software tool Network Simulator (Version 2), widely known as NS-2, is described and used for the evaluation of Flat Routing WSN protocols and their performances are compared on the basis of Throughput, Delay and Packet loss with different network sizes under four different scenarios. II. WIRELESS SENSOR NETWORK (WSN) A wireless sensor network is an active research area with numerous workshops and conferences arranged each year. A Wireless Sensor Network (WSN) is a set of hundreds or thousands of micro sensor nodes that have capabilities of sensing, establishing wireless communication between each other and doing computational and processing operations [1]. Sensor networks have a wide variety of applications and systems with vastly varying requirements and characteristics. The sensor networks can be used in Military environment, Disaster management, Habitat monitoring, Medical and health care, Industrial fields, Home networks, detecting chemical, biological, radiological, nuclear, and explosive material etc. Deployment of a sensor network in these applications can be in random fashion
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(e.g., dropped from an airplane) or can be planted manually (e.g., fire alarm sensors in a facility). For example, in a disaster management application, a large number of sensors can be dropped from a helicopter. Networking these sensors can assist rescue operations by locating survivors, identifying risky areas, and making the rescue team more aware of the overall situation in the disaster area. 2.1 Communication Architecture for Wireless Sensor Network We mentioned above that a wireless sensor network (WSN) is a network made of a numerous number of sensor nodes with sensing, wireless communications and computation capabilities. These sensor nodes are scattered in an unattended environment (i.e., sensor field) situated far from the user. Figure 1 represents the communication architecture for WSN [2]. The main entities that build up the architecture are: 1 The Sensor nodes that form the sensor network. Their main objectives are making discrete, local measurement about phenomenon surrounding these sensors, forming a wireless network by communicating over a wireless medium, and collect data and route data back to the user via sink (Base Station). 2 The Sink (Base Station) communicates with the user via internet or satellite communication. It is located near the sensor field or well-equipped nodes of the sensor network. Collected data from the sensor field routed back to the sink by a multi-hop infrastructure less architecture. 3 Phenomenon which is an entity of interest to the user to collect measurements about. This phenomenon is sensed and analyzed by the sensor nodes. 4 The User who is interested in obtaining information about specific phenomenon to measure/monitor its behavior.
Figure 1: Sensor nodes scattered in a sensor field and the Components of a single sensor node [2] 2.2 Network Characteristics As compared to the traditional wireless communication networks such as Mobile Ad hoc NETwork (MANET) and Cellular systems, wireless sensor networks have the following unique characteristics and constraints [3]. Dense sensor node deployment: Sensor nodes are usually densely deployed and can be several orders of magnitude higher than that in a MANET. Battery-powered sensor nodes: Sensor nodes are usually powered by battery and are deployed in a harsh environment where it is very difficult to change or recharge the batteries. Sensors nodes are having highly limited energy, computation, and storage capabilities. Self-configurable: Sensor nodes are usually randomly deployed and autonomously configure themselves into a communication network. Unreliable sensor nodes: Sensor nodes are prone to physical damages or failures due to its deployment in harsh or hostile environment. Data redundancy: In most sensor network applications, sensor nodes are densely deployed in a region of interest and collaborate to accomplish a common sensing task. Thus, the data sensed by multiple sensor nodes typically have a certain level of correlation or redundancy.
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Application specific: A sensor network is usually designed and deployed for a specific application. The design requirements of a sensor network change with its application. Many-to-one traffic pattern: In most sensor network applications, the data sensed by sensor nodes flow from multiple source sensor nodes to a particular sink, exhibiting a many-to-one traffic pattern. Frequent topology change: Network topology changes frequently due to the node failures, damage, addition, energy depletion, or channel fading. QoS support: In sensor networks, different applications may have different Quality- of-Service (QoS) requirements in terms of delivery latency and packet loss. 2.3 Need for routing protocol Routing in sensor networks is very challenging due to several characteristics [2] that distinguish them from contemporary communication and wireless ad-hoc networks. First of all, it is not possible to build a global addressing scheme for the deployment of sheer number of sensor nodes. Therefore, classical IP-based protocols cannot be applied to sensor networks. Second, in contrary to typical communication networks almost all applications of sensor networks require the flow of sensed data from multiple regions (sources) to a particular sink. Third, generated data traffic has significant redundancy in it since multiple sensors may generate same data within the vicinity of a phenomenon. Such redundancy needs to be exploited by the routing protocols to improve energy and band width utilization. Fourth, sensor nodes are tightly constrained in terms of transmission power, on-board energy, processing capacity and storage and thus require careful resource management. Due to such differences, many new algorithms have been proposed for the problem of routing data in sensor networks. These routing mechanisms have considered the characteristics of sensor nodes along with the application and architecture requirements. The design challenges in sensor networks involve the following main aspects. Limited energy capacity: Since sensor nodes are battery powered, they have limited energy capacity. Energy poses a big challenge for network designers in hostile environments, for example, a battlefield, where it is impossible to access the sensors and recharge their batteries. Thus, routing protocols designed for sensors should be as energy efficient as possible to extend their lifetime, and hence prolong the network lifetime while guaranteeing good performance overall. Limited hardware resources: In addition to limited energy capacity, sensor nodes have also limited processing and storage capacities, and thus can only perform limited computational functionalities. These hardware constraints present many challenges in software development and network protocol design for sensor networks. Sensor locations: Another challenge that faces the design of routing protocols is to manage the locations of the sensors. Most of the proposed protocols assume that the sensors either are equipped with Global Positioning System (GPS) receivers or use some localization technique to learn about their locations. Massive and random node deployment: Sensor node deployment in WSNs is application dependent and can be either manual or random which finally affects the performance of the routing protocol. In most applications, sensor nodes can be scattered randomly in an intended area or dropped massively over an inaccessible or hostile region. Network characteristics and unreliable environment: A sensor network usually operates in a dynamic and unreliable environment. The topology of a network, which is defined by the sensors and the communication links between the sensors, changes frequently due to sensor addition, deletion, node failures, damages, or energy depletion. Also, the sensor nodes are linked by a wireless medium, which is noisy, error prone, and time varying. Therefore, routing paths should consider network topology dynamics due to limited energy and sensor mobility as well as increasing the size of the network to maintain specific application requirements in terms of coverage and connectivity. Diverse sensing application requirements: Sensor networks have a wide range of diverse applications. No network protocol can meet the requirements of all applications. Therefore, the routing protocols should guarantee data delivery and its accuracy so that the sink can gather the required knowledge about the physical phenomenon on time. Scalability: Since the numbers of sensor nodes in sensor networks are in the order of tens, hundreds, or thousands, network protocols designed for sensor networks should be scalable to different network sizes. Reliability: Network protocols designed for sensor networks must provide error control and correction mechanisms to ensure reliable data delivery over noisy, error-prone, and time-varying wireless channels. Channel utilization: Since sensor networks have limited bandwidth resources, communication protocols designed for sensor networks should efficiently make use of the bandwidth to improve channel utilization.
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Fault tolerance: Sensor nodes are prone to failures due to harsh deployment environments and unattended operations. Thus, sensor nodes should be fault tolerant and have the abilities of self-testing, self-calibrating, selfrepairing and self-recovering. III. CLASSIFICATION OF ROUTING PROTOCOLS There are different ways by which we can classify the routing protocols [3]. According to network structure, these routing protocols can be classified as flat, hierarchical and location-based protocols. In flat-based routing, all nodes are assigned the same roles or functionalities. In hierarchical-based routing, nodes will play different roles or functionalities, aiming at routing techniques clustering the nodes with different roles so that the heads of the cluster can do some data aggregation in order to save power. In location based routing, sensor nodes' positions are exploited to route the data to specific regions other than the whole network.In flat-based routing, all nodes are assigned the same roles or functionalities. In hierarchical-based routing, nodes will play different roles or functionalities, aiming at routing techniques clustering the nodes with different roles so that the heads of the cluster can do some data aggregation in order to save power, while in location based routing, sensor nodes' positions are exploited to route the data to specific regions other than the whole network. Typical flat routing algorithm includes Flooding algorithm, Gossiping, Directed Diffusion (DD), Sequential Assignment Routing (SAR), Sensor Protocols for Information via Negotiation (SPIN), Cougar, etc. Hierarchical routing protocols mainly include Low Energy Adaptive Clustering Hierarchy (LEACH), Threshold Sensitive Energy Efficient Sensor Network Protocol (TEEN), Power-Efficient GAthering in Sensor Information Systems (PEGASIS), etc. Location-based protocols includes Geographic Adaptive Fidelity (GAF), Geographic and Energy Aware Routing Protocol (GEAR), etc IV. FLAT ROUTING The first category of routing protocols is the multihop flat routing protocols. In flat networks, each node typically plays the same role and sensor nodes collaborate together to perform the sensing task. Due to the large number of such nodes, it is not feasible to assign a global identifier to each node. This consideration has led to data centric routing, where the BS sends queries to certain regions and waits for data from the sensors located in the selected regions. Since data is being requested through queries, attribute-based naming is necessary to specify the properties of data. SPIN [2] [3] is the first data centric protocol, which considers data negotiation between nodes in order to eliminate redundant data and save energy. Later, Directed Diffusion [5] [6] has been developed and has become a breakthrough in data-centric routing. Then, many other protocols have been proposed either based on Directed Diffusion or following a similar concept. In this section, description of these protocols in detail and their key ideas are given. A. Sensor Protocols for Information via Negotiation (SPIN) Sensor Protocols for Information via Negotiations (SPIN) [3] is a family of adaptive protocols for WSNs. Their design goal is to avoid the drawbacks of flooding protocols mentioned above by utilizing data negotiation and resource adaptive algorithms. SPIN is designed based on two basic ideas: 1 to operate efficiently and to conserve energy by sending metadata (i.e., sending data about sensor data instead of sending the whole data that sensor nodes already have or need to obtain) 2 nodes in a network must be aware of changes in their own energy resources and adapt to these changes to extend the operating lifetime of the system. SPIN has three types of messages as shown in Fig.2. namely, ADV, REQ, and DATA. ADV: when a node has data to send, it advertises via broadcasting this message containing meta-data (i.e., descriptor) to all nodes in the network. REQ: an interested node sends this message when it wishes to receive some data. DATA: Data message contains the actual sensor data along with meta-data header. SPIN is based on data-centric routing where the sensor nodes send ADV message via broadcasting for the data they have and wait for REQ messages from interested sinks or nodes. The semantics of SPIN's meta-data format is
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application dependent and not supported by SPIN. In another words, SPIN uses application specific meta-data to name the sensed data.
Figure 2: Data Transmission in SPIN [3]
Advantages 1 Solving the problems associated with classic flooding protocols, and 2 Topological changes are localized. Disadvantages 1 Scalability, SPIN is not scalable, 2 If the sink is interested in too many events, this could make the sensor nodes around it deplete their energy and 3 SPIN's data advertisement technique cannot guarantee the delivery of data if the interested nodes are far away from the source node and the nodes in between are not interested in that data. The SPIN family of protocols includes many protocols. The main two protocols are called SPIN-1 and SPIN-2, which incorporate negotiation before transmitting data in order to ensure that only useful information will be transferred. Also, each node has its own resource manager which keeps track of resource consumption, and is polled by the nodes before data transmission. The SPIN-1 protocol is a 3-stage protocol, as described above. An extension to SPIN-1 is SPIN-2, which incorporates threshold-based resource awareness mechanism in addition to negotiation. When energy in the nodes is abundant, SPIN-2 communicates using the 3-stage protocol of SPIN-1. However, when the energy in a node starts approaching a low energy threshold, it reduces its participation in the protocol, i.e., it participates only when it believes that it can complete all the other stages of the protocol without going below the low-energy threshold. B. Directed Diffusion Directed diffusion is another data dissemination and aggregation protocol. It is a data-centric and application aware routing protocol for WSNs. It aims at naming all data generated by sensor nodes by attribute-value pairs [4]. In order to construct the route between the sink (inquirer) and the sensors that interest to the sink's request, there are four stages [5]; 1. Interest propagation, 2. Gradient setup, 3. Reinforcement and 4. Data delivery. Below is a detailed description for each stage: Interest propagation: When a sink detects an event, it initiates the interest messages and floods them to all nodes in the network. These messages are exploratory messages indicating the nodes with matching data for the specific task. During this stage, the sink periodically broadcasts the interest message. Once the interest message is received, each sensor node saves it in an interest cache. After that, the nodes flood this message to the other nodes until the node that is interested in this interest message is reached as shown in Figure 3(a). Gradient setup: Based on local rules, different techniques are used in gradient setup. For example, the nodes with highest remaining energy could be chosen when setting up the gradient. During the interest propagation through the network, the gradients from source back to sink will be setup. A node becomes a source node if its observation matches the interest message and sends its data through the gradient path back to the sink as shown in Figure 3(b).
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Reinforcement: During the gradient setup phase, many paths have formed from the source to the sink. This means the source can send the data to the sink through multiple routes. However, as shown in Figure 3(c), the sink reinforces one specific path by resending the same interest through the specified path, which is chosen based on many rules, like the best link quality, number of packets received from a neighbor or lowest delay. Along this path, each node just forwards the reinforcement to its next hop. Finally, during this phase, the sink could select multiple paths in order to provide multi-path delivery. Data delivery: After the reinforcement phase, as shown in Figure 3(d), the route between the source and the sink has been constructed and the data is ready for transmission. Directed diffusion assists in saving sensors' energy by selecting good paths by caching and processing data innetwork since each node has the ability for performing data aggregation and caching. On the other hand; Directed diffusion has its limitations such as: implementing data aggregation requires deployment of synchronization techniques which is not realizable in WSNs. Also, the overhead in data aggregation involves recording information. These two drawbacks may contribute to the cost of sensor node, which is not desired. In addition, the naming schemes used in Directed Diffusion are application dependent and each time should be defined a priori.
Figure 3: Operation of the directed diffusion protocol [5] Advantages 1. It is designed to retrieve data aggregates from a single node. 2. Data is named by attributed-value pairs. 3. It works well in multipurpose wireless sensor net-works and in sensor networks that query. 4. Unlike other routing algorithms, in Directed Diffusion more than one sink can make queries and receive data at the same time; hence, simultaneous queries could be handled inside a single network. 5. The interests/queries are issued by the sink not by the sources, and only when there is a request. Moreover, all communication is neighbor-to-neighbor, which removes the need for addressing and permits each node to aggregate data. As a result, both points contribute to reduce energy consumption. 6. It provides application-dependent routes based on the interests of the user. 7. It requires neither a global node addressing mechanism nor a global network topology. Moreover, the routes are formed only when there is an interest. As a result, it achieves energy efficiency. Disadvantages 1. It is generally based on a flat topology. Hence, scalability and congestion (especially in the nodes that are near to the sink) problems exist. 2. An overhead problem occurs at the sensors during the matching process for data and queries. 3. In Directed Diffusion, the initial interest contains a low data rate. However, an important overhead is caused during flooding operation of interest propagation phase.
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4. Due to the flooding required to propagate the interest on each node, it is not optimized for energy efficiency and need high amounts of memory to store interest gradients and received messages. 5. It mostly selects the shortest path between the source and the destination, which leads to quick death of nodes on that path. Directed diffusion differs from SPIN in two aspects. First, directed diffusion issues on demand data queries as the BS send queries to the sensor nodes by flooding some tasks. In SPIN, however, sensors advertise the availability of data allowing interested nodes to query that data. Second, all communication in directed diffusion is neighbor-toneighbor with each node having the capability of performing data aggregation and caching. Unlike SPIN, there is no need to maintain global network topology in directed diffusion. However, directed diffusion may not be applied to applications (e.g., environmental monitoring) that require continuous data delivery to the BS. This is because the query- driven on demand data model may not help in this regard. Moreover, matching data to queries might require some extra overhead at the sensor nodes. C. Cougar A data-centric protocol that views the network as a huge distributed database system is proposed in [6]. The main idea is to use declarative queries in order to abstract query processing from the network layer functions such as selection of relevant sensors etc. and utilize in-network data aggregation to save energy. The abstraction is supported through a new query layer between the network and application layers. Cougar proposes architecture [3] for the sensor database system where sensor nodes select a leader node to perform aggregation and transmit the data to the gateway (sink). The architecture is depicted in Figure 4. The gateway is responsible for generating a query plan, which specifies the necessary information about the data flow and in-network computation for the incoming query and send it to the relevant nodes. The query plan also describes how to select a leader for the query. The architecture provides in-network computation ability for all the sensor nodes. Such ability ensures energy efficiency especially when the number of sensors generating and sending data to the leader is huge. Although Cougar provides a network-layer independent solution for querying the sensors, it has some drawbacks: First of all, introducing additional query layer on each sensor node will bring extra overhead to sensor nodes in terms of energy consumption and storage. Second, in network data computation from several nodes will require synchronization, i.e. a relaying node should wait every packet from each incoming source, before sending the data to the leader node. Third, the leader nodes should be dynamically maintained to prevent them from failure.
Figure 4: Query plan at a leader node [3]
V. SYSTEM IMPLEMENTATION Network Simulator 2 (NS-2) [7] is one of the most popular non-specific network simulators and supports a wide range of protocols in all layers. Following are the steps [8] for writing a script in NS-2. 1. Create a new simulator object. 2. Turn on tracing [Open your own trace files]. 3. Create network (physical layer). 4. Create link and queue (data-link layer). 5. Define routing protocol.
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6. Create transport connection (transport layer). 7. Create traffic (application layer). 8. Insert errors. Table 5.1 gives the input parameters that are used in our simulation scenario along with their range values.
Input Simulation Parameters used are as follows: Parameters Node Deployment Initial Energy Transmitting Power Receiving Power Network Area No of Nodes Range of each node Packet size Bandwidth Traffic Interval
Details Fixed/Random 20 joules 0.mW 0.2mW 300m X 300m 20-120 15 m radius 500/1000 bytes 3 Mbps 0.005 sec
PARAMETERS USED FOR PERFORMANCE EVALUATION In order to check the performance of protocols in terms of its effectiveness there are different metrics to be used. In this study, throughput, packet loss and End-to-End delay are used for the evaluation of protocols. The reasons behind the selection of these metrics are their importance in any data communication network. Furthermore, any protocol needs to be evaluated against these metrics to check its performance. In order to check the protocol effectiveness in finding routes towards destination, it is interesting to check how much packets it sends successfully. This metric used to measure the internal algorithms efficiency of routing protocol. The larger is routing overhead of a protocols (in packets/ bytes), larger will be the wastage of the resources (bandwidth). Thus throughput shows protocols successful deliveries for a time. This means the higher is throughput the better is protocol performance. Also lower is the delay, finer is the protocol performance. Throughput: Throughput is the rate of successfully delivered data per second to individual destinations during network simulation. Throughput is associated with the efficiency of the protocol. A low delay in the network translates into higher throughput. Delay is one of the factors effecting throughput, other factors are routing overhead, area and bandwidth. Throughput gives the fraction of the channel capacity used for useful transmission and is one of the dimensional parameters of the network. Packet Loss Packet loss is the failure of one or more transmitted packets to arrive at their destination. The effects of severe packet loss are 1. It produces errors 2. It can cause severe mutilation of received data or even complete absence of a received signal. The causes of packet loss include inadequate strength at the destination, excessive system noise or overburdened network nodes. End-to-End Delay The term end-to-end delay refers to the time taken by a packet to be transmitted across a network from source node to destination node that includes all possible delays caused during route discovery latency, queue in data packet transmission, retransmission delays, propagation and transfer times. The protocol which shows higher end-to-end delay means the performance of the protocol is not good due to network congestion. The lower value of end -to-end delay means the better performance of the protocol. RESULTS AND ANALYSIS
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In this project, four test scenarios are taken. In the first scenario, three Flat routing protocols are implemented with fixed nodes. Simulation results are evaluated and compared on the basis of throughput, delay and packet loss with different no of nodes. In the second scenario, Protocols are implemented with mobile nodes and the results are evaluated. In the third scenario, Protocols are simulated with different packet sizes. In the fourth scenario, simulation is done under the fixed deployment of nodes. Scenario I: Fixed Nodes The nodes in WSNs may be static or dynamic. Most of the routing protocols assume that the sensor nodes and the base stations are fixed i.e., they are static. In static wireless sensor networks (SWSNs), the sensor nodes are stationary or static; that is, the sensor nodes are deployed randomly, and after deployment their positions do not change. Network Throughput SPIN uses the shortest path algorithm. As the no of nodes increases, the node links to shortest path increases. SPIN operation will transport almost zero redundant data packet and decrease the operation of sending wasted data packets. In DD, with larger sensor nodes, each node transmits the same packet multiple times, once to each neighbor. Diffusion is less impacted by this because it performs in-network suppression of identical data. Finally with large sensor field, the event delivery ratio falls. This can be attributed to suppression. Nodes that links to the shortest path nodes and the gradient links use a lot of energy for transmitting and receiving packet. Thus, they generate overhead and reduce the life time of the nodes in the network. When this occurs, the topology and links for every node will change. The distance for transmitting and receiving packet will be a bit larger and will consume a lot of energy. In Cougar, Dynamic selection of aggregation points minimizes overall data movement. The nested query localizes data traffic near the triggered event rather than sending it to the sink, thus reducing network traffic and latency. Data aggregation reduces the no of transmissions. Figure 5.a shows the Throughput for Fixed Nodes of the three protocols. End to End Delay Delay value for SPIN is less because it uses the shortest path algorithm. As the no of nodes increases, the node links to shortest path increases. SPIN operation will transport almost zero redundant data packet and decrease the operation of sending wasted data packets. In DD, the Reinforcement rules find the low delay path. In-Network processing can reduce data traffic. The larger network has longer alternate paths. These alternate paths are pruned by negative reinforcement because they consistently deliver events with higher latency. In Cougar, in network data computation from several nodes will require synchronization, i.e. a relaying node should wait every packet from each incoming source, before sending the data to the leader node. The intermediate nodes suppress duplicate data by simply not propagating it. The intermediate nodes simply delay and aggregate data from multiple sensor nodes. Figure 5.b shows the Delay for Fixed Nodes of the three protocols. Packet Loss SPIN protocol has very less dead nodes. SPIN will start with advertise its interest, and then waiting for a request from any node before start transmitting data again. SPIN nodes negotiate with each other before transmitting data. Negotiation helps to ensure that only useful information will be transferred. Packet loss for SPIN remains constant even though the no of nodes increases. SPIN’s data advertisement technique cannot guarantee the delivery of data if the interested nodes are far away from the source node and the nodes in between are not interested in that data.
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In DD, One would expect that DD would expend energy to find alternate paths. But several reinforced paths- highquality paths are kept alive in normal operation. Thus DD does not need to do extra work. In smaller sensor fields, it can suppress duplicates. In larger sensor fields, less aggregation. In the absence of negative reinforcement, more paths are used and without suppression more copies of data are sent, resulting in subsequent delays. In Cougar, packet loss is less due to the query based approach which reduces the irrelevant data transfers and also the aggregate operator directly sends its data to the BS. Figure 5.c shows the Packet Loss for Fixed Nodes of the three protocols.
Figure 5.a Throughput for Fixed Nodes
Figure 5.b Delay for Fixed Nodes
Figure 5.c Packet loss for Fixed Nodes
ScenarioII: MobileNodes In mobile wireless sensor networks, the sensor nodes can move on their own, and after deployment, they can interact with the physical environment by controlling their own movement. Advances in robotics have made it possible to develop such mobile sensors which are autonomous and have the ability to sense, compute, and communicate like static sensors. The prime difference between static and mobile WSNs is that mobile nodes are able to reposition and organize themselves in the network, and after initial deployment, the nodes spread out to gather information. Mobile nodes can communicate with one another when they are within the range of each other, and only then they can exchange their information gathered by them. In this scenario, movement of node is performed with the speed of 15m/s after the interval of 0.5 sec.
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Due to the mobility of nodes, increased link failures in SPIN protocol results in reduced throughput and higher packet losses. The path has to be set up again from the beginning. DD has good latency properties and not delayed because of failure of links. The other paths stored in the cache can be used for further routing. In Cougar, the mobility nodes will be traced by its corresponding aggregate operator and hence not much affected by the mobility of nodes. Figure 6.a,6.b and 6.c shows the Throughput, Delay and Packet Loss for Mobile Nodes of the three protocols respectively.
Figure 6.a Throughput for Mobile Nodes
Figure 6.b Delay for Mobile Nodes
Figure 6.c Packet Loss for Mobile Nodes
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Scenario III: Different Packet Sizes It is a known fact in WSN that the data packet size could directly affect the reliability and the quality of the communication between the wireless nodes. Hence throughput performance is affected by the packet size. As the packet size increases, more bytes of data will be transmitted resulting in higher throughput. Here we have considered two different packet sizes of 500 bytes and 1000bytes and results are obtained showing higher throughput for higher value of packet size. Figure 7.a and 7.b shows the Throughput for Packet size of 500 and 1000 bytes respectively of the three protocols.
Figure 7.a Throughput for Packet size =500 bytes
Figure 7.b Throughput for Packet size =1000 bytes
Scenario IV: Fixed Deployment of Nodes There are two deployment strategies mentioned in the literature which are deterministic and random. In deterministic deployment, sensors are manually placed. The main deployment objectives of any sensor network are coverage, lifetime, and routing. In this scenario, node positions i.e, x and y co-ordinates are entered manually and kept as constant for all the protocols and the results are obtained. Under the same conditions, i.e., with the same position of nodes, Cougar and DD are showing better performance in terms of throughput, delay and Packet loss. Figure 8.a. 8.b and 8.c shows the Throughput, Delay and Packet loss respectively for Fixed Deployment of Nodes of the three protocols.
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Figure 8.a Throughput for Fixed Deployment of nodes
Figure8.b Delay for Fixed Deployment of nodes
Figure 8.c Packet Loss for Fixed Deployment of nodes
CONCLUSION The past few years have witnessed a lot of attention on routing for wireless sensor networks and introduced unique challenges compared to traditional data routing in wired networks. Routing in sensor networks is a new area of research. Since sensor networks are designed for specific applications, designing efficient routing protocols for sensor networks is very important. In this dissertation, a comprehensive survey of routing techniques in wireless sensor networks is given. Depending on the network structure, these protocols are categorized as Hierarchical, Flat and Location based. Flat Routing Protocols include SPIN, DD and Cougar. Since the sensor networks are application specific, it cannot be said that any particular protocol is better than other. We can compare these protocols with respect to some parameters only. For designing wireless networks and for studying their behavior under various conditions, software simulation tool, NS 2 is used. Performance evaluation and analysis of Flat Routing Protocols has been done with different network sizes under four scenarios with respect to parameters such as throughput, packet loss and end-to-end delay. Due to the aggregation and reinforcement rules, DD and Cougar are showing better performance than SPIN. Redundancy is reduced by means of suppression in case of DD and Cougar and by meta-data negotiation for SPIN. SPIN’s data advertisement technique cannot guarantee the delivery of data. SPIN protocol is inappropriate when there is a need for constant monitoring by the sensor network. Whereas Cougar provides the facility of constant monitoring. Thus DD and cougar are showing better overall performance than SPIN.
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REFERENCES [1] Shio Kumar Singh, MP Singh and D K Singh, "Routing Protocols in Wireless Sensor Networks - A Survey", International Journal of Computer Science and Engineering Survey (IJCSES) Vol.1, No.2, November 2010. [2] Yazeed Al-Obaisat and Robin Braun," On Wireless Sensor Networks: Architectures, Protocols, Applications, and Management", Institute of Information and Communication Technologies University of Technology, Sydney. [3] Jamal N. Al-Karaki and Ahmed E. Kamal,"Routing Techniques in Wireless Sensor Networks: A Survey", ICUBE initiative of Iowa State University, Ames. [4] C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, and F. Silva, "Directed Diffusion for Wireless Sensor Networking", IEEE/ACM Transactions on Networking, Feb. 2003. [5] Neha Singh, Prof.Rajeshwar Lal Dua and Vinita Mathur, "Wireless Sensor Networks: Architecture, Protocols, Simulator Tool", International Journal of Advanced Research in Computer Science and Software Engineering, May 2012. [6]Kemal Akkaya and Mohamed Younis, "A Survey on Routing Protocols for Wireless Sensor Networks ", University of Maryland, Baltimore. [7] Almargni Ezreik and Abdalla Gheryani," Design and Simulation of Wireless Network using NS-2", 2nd International Conference on Computer Science and Information Technology, Singapore, April 2012. [8] Kristoffer Clyde Magsino and H. Srikanth Kamath," Simulations of Routing Protocols of Wireless Sensor Networks", World Academy of Science, Engineering and Technology, 2009.
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Modeling of Magnetically Coupled Embedded Z Source Inverter V. Vidhya, Jeyasree Tagore ME. Applied Electronics, Sriram Engineering College, Perumalpattu, Chennai Tamilnadu, India. Abstract This Project deals with an Embedded Z source inverter to control the three phase induction motor. The Z source inverters are recent topological options for buck boost energy conversion with a number of possible voltage and current type circuitries. Embedded Z Source inverter is a single stage converter which performs both buck boost energy conversions using the LC impedance network. The proposed inverters provide high boost voltage inversion ability, a lower voltage stress across the active switching devices, a continuous input current and a reduced voltage stress on the capacitors. In addition, they can suppress the startup inrush current, which otherwise might destroy the devices. The microcontroller is used to generate PWM pulses and to control operation of Z Source inverter. The complete hardware is designed to drive the three phase induction motor. This paper presents the operating principles, analysis, and simulation results, and compares them to the conventional switched inductor Z source inverter. The desired three phase PWM signals are generated by using control circuit and detailed hardware results are presented. KEYWORD Embedded Z source, PWM, voltage boost, Z Source Inverter, Boost inversion ability, motor drives, buck boost.
1. INTRODUCTION Now-a-days, PV energy has attracted interest as a next generation energy source capable of solving the problems of global warming and energy exhaustion caused by increasing energy consumption. So, solar energy is considered as input source and this energy is given to the Z-Source. As we know solar module can give constant energy so to satisfy load conditions i.e. buck boost operations can be achieved by using Z Source inverter. As we know, we have voltage source inverter where voltage buck operation is achieved and in current source inverter, boost operation is achieved. So, to overcome these effects and to achieve buck boost operation in a single stage converter, a new technique is implemented i.e. z source inverter, by using Z source Voltage buck boost operation is achieved in single stage conversion. In recent years, a lot of work has been done in power quality improvement using variable generators and power electronic devices. Small-scale stand-alone wind energy systems are an important alternative source of electrical energy, finding applications in locations where conventional generation is not practical. Unfortunately, most of these systems do not capture power at every wind speed especially low wind speeds which are low in power but can be very common, but modern permanent magnet synchronous generator technology offers high efficiency power conversion from mechanical into electrical power. Moreover, it allows for special machine design with very low speed e.g. in gearless wind and hydro application and at very high speed for micro-gas turbines, which is of interest for several regenerative or co-generative power conversion technologies. A survey of already realized prototypes or in use PM generator systems is presented for that purpose. Impedance source inverter also referred as Z-Source Inverter is an advanced PWM inverter topology. Z Source Inverter is more advantageous over traditional inverters with high efficiency, improved power factor and THD, EMI immunity and so on. Nowadays PWM control method is mostly used in power converter applications. These PWM signals can be generated using analog circuit as well as digital circuit. PWM generation using analog circuit requires large number of discrete circuits such as triangular carrier wave generator circuit, sine wave generator circuit; comparator, adder circuits and phase shifters etc. Each of these circuits is formed by connecting many discrete components together such as transistors, resistors, capacitors, inductors, op-amps and so on. In addition analog method of three phase PWM generation requires accurately designed phase shifter circuits and other circuit. Also the response of analog circuit may get affected by environmental conditions, noise, changes in the voltages and currents in the circuit and so on. Thus analog method is critical and increases complexity and cost of the circuit. Digital method of PWM generation requires only microcontroller and its
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minimum configuration. With the advent in the technology now many microcontrollers has in built feature of PWM generation. While some special controller ICs are also available that are designed and fabricated for three phase PWM generation and control purpose. PWM generation digitally require only knowledge of internal architecture of controller and good programming skill. II .RELATED RESEARCH Some of the recent researches related to power quality improvement for wind energy conversion systems and solar PV using z source inverter are discussed Fang Zheng Peng et al. [1] have proposed control methods for the Z-source inverter and their relationships of voltage boost versus modulation index. A maximum boost control is presented to produce the maximum voltage boost (or voltage gain) under a given modulation index. The control method, relationships of voltage gain versus modulation index and voltage stress versus voltage gain are analyzed in detail and verified by simulation. Shahrokh FARHANGI [2] have presented the design procedure of the Z-source converter as a single phase PV grid connected transformer-less inverter has been presented in this paper. An optimal switching pattern for the modulation of the converter has been proposed, which reduces the switching loss and common mode EMI. In single phase application, the output power is not constant and leads to a low frequency ripple in the converter elements. An approximate analysis has been proposed, which considers this effect. The validity of the design method has been verified by simulation. III SYSTEM CONFIGURATION An impedance network abbreviated as Z-Source is couples the inverter main circuit and input power source. ZSource circuit consists of two capacitors and two inductors connected in such a way as to form second order filter, smoothens dc link voltage and current. Z-source inverter circuit provides both voltage buck and boost properties, which cannot be achieved with conventional voltage source and current source inverters. Three phase inverter circuit consists of six switches connected in three legs, converts input dc link voltage in to corresponding three phase ac voltage. Microcontroller and driver circuit is used to control on/off time of switching devices in a proper sequence in a particular time used in the main inverter circuit. Microcontroller PIC used to generate modified maximum constant boost PWM signal. These PWM signal is applied to the gate terminals of MOSFETs through gate. Z-Source is the X –Shaped structure consists of L1, L2, C1, and C2 in which we can obtain both buck-boost operations in single stage conversion. In this DC Source is placed at farleft in series with diode. So, by this, chopping is occurred in the source current which is caused by the commutation of diode D. So, in this condition to smoothen the source current an additional LC filter is required which would rise over all cost of the system and construction of the system by this additional LC filter is complex. So, to overcome the above drawbacks, a new technique is proposed i.e. embedded source inverter. In this EZ-Source inverter, source is placed in series with the inductor L1 andL2 and chopping current i.e. Obtained in the previous section is filtered. EZ-Source inverter, smoothens the source current without any additional LC filter but the gain of the Z-Source and Ez Source is same but only source filtering is achieved without any additional LC filter. The voltage stress experienced by C1 and C2 is lower than the original network but in this EZ-Source inverter requires two PV panels which are cost effective. So to reduce stress across the capacitor and its rating, to achieve lower harmonics, to obtain low switching losses and to make system compact in size, a new technique is implemented i.e. partially parallel EZ-Source inverter with reduced switches. The Z-source network makes the shoot-through zero state possible. This shoot-through zero state provides the unique buck-boost feature to the inverter. The Z-source inverter can be operated in three modes. Mode 1 In this mode, the inverter bridge is operating in one of the six traditional active vectors; the equivalent circuit. The inverter bridge acts as a current source viewed from the DC link. Both the inductors have an identical current value because of the circuit symmetry. This unique feature widens the line current conducting intervals, thus reducing harmonic current.
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Non-shoot-Through (Sx≠Sx‗, x = A, B, or C; D = ON) VL=Vdc /2− VCVi= 2VC Vd=VD= 0 Idc=IL+IC
Ii= IL− IC
Idc≠0 Mode III:
Shoot-Through(Sx= Sx‗ON, x = A, B, or C; D = OFF) VL=VC +V dc /2 Vi= 0 Vd= VD= The−2VCinverter bridge is operating in one of the seven shoot-through states. The equivalent circuit of the inverter bridge in this mode is as shown. This mode separates the dc link from the ac line. This shoot-through mode is to be used in every switching cycle during the traditional zero vector period generated by the PWM control. Depending on how much a voltage boost is needed, the shoot-through interval (T0) or its duty cycle (T0/T) is determined. It can be seen that the shoot-through interval is only a fraction of the switching cycle. IV TYPES OF EZ-SOURCE In this embedded Z-Source inverter, we have A) Shunt embedded Z-Source inverter B) Parallel embedded Z-Source inverter A) Shunt embedded z-source inverter: In general we have jumping currents which flow in the input DC source which will induce the power Interruption in the input. By this jumping currents, the complexity to control maximum power and designing of system increases. To overcome the traditional network, Shunt EZ-Source is proposed, Shunt EZ-Source consist two types 1. Partially shunt EZ-Source 2. Fully shunt EZ-source
Partially shunt embedded Z (PSEZ)-Source inverter: In this PSEZ-Source inverter, VSI operates as current source inverter during shoot-through conditions. A switch
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SW is used for removing the unwanted conditions of the diode during voltage boost operations and it should be turned off during traditional CSI
In this, Vdc and inductor L are used for inverter boost operation and this voltage boost is equal to traditional CSI. Alternatively for buck operation open circuit tate should be connected in the switching sequence referring to SX and SX’ (X=A, meanwhile SW turns ON toset the effective dc-link voltage. The voltage-buck (currentboost) ratio equals to the traditional current type Z-Source inverter. Fully shunt embedded Z (FSEZ)- Source inverter: In FSEZ-Source inverter,an equal voltage dc source is placed instead of a capacitor C 1 in the shunt leg. Voltage boost and buck capability are same in both FSEZ and PSEZ.The current ratio of L 1 and L 2 are reduced due toequal DC-link currents of L1 and L 2. But huge circulating currents between inductor and source are occurred and reduce output voltage amplitude due to unequal input of two dc voltage sources are considered. So to avoid this, small rated capacitor is placed in series with one dc source to share the voltage difference. This current type SEZSource inverter is not an ideal choice for substituting traditional Z-Source inverter but easily smoothen source current.
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V.SIMULATION RESULTS For PWM generation microcontroller is used. The control circuit diagram is shown in the following in fig, drawn using Proteus software. The six PWM signals are send at port P2 pins P2.0 through P2.5. The control circuit simulation is performed using Proteus software. Five switches are provided for special purposes, to interrupt microcontroller, increment, decrement shoot-through time and input voltage values and to change the mode of operation two types of edge aligned PWM waveforms are generated using microcontroller. This circuit includes LCD interface at port0 of microcontroller, five push button switches and one led interfaced to port3 pins, gate driver circuit not shown interfaced to PWM output port2 pins and microcontroller minimum circuit. LCD display is used to display starting message regarding project title, welcome message and provides user interface. It also displays theoretical values of output voltage for given input voltage. For this two modes of operation are provided: one is traditional mode of operation and other is boost mode. In traditional mode of operation traditional PWM is generated, while in boost mode of operation some part of traditional zero state is converted into shoot-through state.
The E Z-source inverter can be operated in both boosts and buck operations depending on values of ‘M’. If M is greater than 0.5 it acts as boost inverter, if M is less than 0.5 then it acts as buck inverter. Here a 3-phase RLC parallel load is connected to ZSI.
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The 3-phase output voltage across load is shown CONCLUSION The partially parallel embedded Z-Source inverter with reduced switches reduces number of PV panels and wind power; Capacitor rating smoothens source current and also reduces total harmonic distortion. PPEZSource inverter makes system compact which reduces cost and switching losses, lower voltage stresses and line harmonics. The two main objectives for WECSs are extracting maximum power from wind and feeding the grid with high-quality electricity. Compared to conventional WECS with simple boost converter based Z-Source inverter method, the voltage profile is improved 35% by using maximum constant boost with third harmonic injection based Z-source inverter method. REFERENCE [1]Miao Zhu, Member, IEEE, Kun Yu, Student Member, IEEE, and Fang Lin Luo, Senior Member, IEEE,” Switched Inductor ZSource Inverter.” IEEE Trans. Power Electron, VOL. 25, No. 8, August 2010. [2]Miao Zhu, Member, IEEE, Kun Yu, Student Member, IEEE, and Fang Lin Luo, Senior Member, IEEE,” Switched Inductor Z-Source Inverter.” IEEE Trans. Power Electron, VOL.25, No. 8, August 2010. [3]S.Kamalakkannan, S.Deve Gowda, “Digital simulation of renewableEnergy Source Controlled EZ-Source inverter system”, 2012 IEEE. [4] J. Li, J. Liu, and Z. Liu, “Loss Oriented Evaluation and Comparison of Z-Source Inverters Using Different Pulse Width Modulation Strategies”, in 2009 IEEE Applied Power Electronics Conference and Exposition. [5] F.Z. Peng, M. Shen, and Z. Qiang, “Maximum Boost Control of the Z-Source Inverter”, IEEE Transactions on Power Electronics, vol. 20no.4, pp. 833-838, July 2004. [6] P.C. Loh, D.M. Vilathgamuwa, Y.S. Lai, G.T. Chua, and Y. Li, “Pulse-Width Modulation of Z-Source Inverter”, in Conf. Rec. 2004 IEEE Industry Applications Conferences, pp. 148-155 [7] FZ. Peng, "Z-source inverter", IEEE Transactions on Industry Applications, vol. 39, pp. 504-510, Mar-Apr 2003. [8] J. B. Liu, J. G. Hu, and L. Y Xu, "A modified space vector PWM for z-source inverter - Modeling and design," ICEMS 2005, in Proceedings of the Eighth International Conference on Electrical Machines and Systems, Vols 1-3, pp. 1242-1247, 200
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Design and Implementation of Ultrasonic Transducers Using HV Class-F Power Amplifier Jeyasree Tagore,V.Vidhya, M. E.Applied Electronics, Sriram Engineering College, Perumalpattu, Chennai, Tamilnadu, India Abstract This paper provides an integrated, high efficiency, high voltage, class-F Power Amplifier (PA) for increasing the efficiency of Ultrasonic Transducer. It also provides harmonic termination technique for high efficiency class F PA’s. The effect of different output harmonic terminations on the Power- Added Efficiency (PAE) of the PA has also been analyzed. Theoretically, high efficiency can be attained in class- F operation by maximally flattening the current and voltage waveforms at the drain of active device.
Keywords: Ultrasound transmitter, Class-F PA, Power Added Efficiency, Harmonic Termination Network. I.
INTRODUCTION
THE ULTRASOUND transmitter, which generates high voltage (HV) signals to excite the transducers, is one of the most critical components in the entire medical ultrasound imaging system. Most of today’s commercial medical ultrasound machines use HV digital pulsers (unipolar or bipolar) as the transmitters. These digital pulsers, in spites of being simple, usually contain high harmonic components with a second-order harmonic distortion (HD2) between −30 and −40 dB. Real time ultra-sound imaging systems have been available for more than thirty years. Nonetheless, considerable advancement in the function of ultra-sound systems and output display is presently underway. Integration and advanced electronics play a key role in ultrasound imaging. The advancement in deep sub-micron CMOS technology is easily achievable for digital signal and low-frequency signal processing. However, in order to reach the final goal of System-on-a-Chip (SoC) solution, the final piece of puzzle is still missing – the RF front end. In fact, being the most power hungry component of the RF front end is the RF power amplifier (PA). It is one of the most critical building blocks in low power SoC integration. II.
DPD TECHNIQUE
CPU
BASIC BLOCK DIAGRAM
TRANSMITTER
OBJECT
RECEIVER
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POWER AMPLIFIER
APPLICATION
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Different PA classes can be divided into two major groups: linear and non-linear PAs. Class A, AB, B and C PA are some of the well-known linear PAs, which are distinguished primarily by their bias condition. Linear PAs have the advantage of high linearity that is important for variable envelope modulation schemes (e.g. p/4QPSK). However, linear amplifiers suffer from poor maximum power efficiency which limits their applications in low power devices. In practice, an efficiency of only below 20% can be achieved in those systems. In contrast, non-linear PAs (also known as switched mode PAs) can achieve better efficiency. As suggested by its name, non-linear PAs have poor linearity performance. Nevertheless, it is still acceptable for constant envelope modulation schemes (e.g. FSK). To overcome the problem of linearity to adapt to variable envelope systems, many linearization techniques have been proposed for non-linear amplifiers. Therefore, due their high efficiency and the development of linearization techniques, non-linear PAs have received more attention over linear topologies in mobile communication in the last decade. Class E and F are the most common classes of non-linear PAs. In comparison, Class E PA requires fast switching driver signal that is not required for Class-F PA. Moreover, because of relatively large switch stresses to active devices, Class E amplifiers do not scale gracefully with the trend toward lower-power technology with lower breakdown voltage. For these reasons, Class-F PA has drawn more attention for its easier implementation and better integration with sub-micron CMOS technology. This project proposes a clear cut idea of increasing the efficiency and resolution of ultrasound images by the use of Class F RF Power Amplifier. III.
HARMONIC TERMINATION OF CLASS F POWER AMPLIFIER
Several design methods have been proposed to enable multi-band operation of various circuits such as impedance transformers low-noise amplifiers (LNAs), mixers, and PAs. The design of multi-band PAs is challenging as stringent requirements on the efficiency, output power, and linearity have to be satisfied. In modern transmitters, harmonic-tuned PAs (e.g., class-F) are preferred to their linear counterparts to achieve higher efficiencies. The harmonic termination network (HTN) should provide open-circuit impedance in the drain of the transistors at odd-order harmonics and the short-circuit impedance at the even-order harmonics for class-F operation. Most of the proposed HTNs for class-F PAs are limited to terminating up to three harmonics. Three general approaches have been developed for multi-band operation of PAs [9]. Several PA units can be connected in parallel and optimized for each band, while the band selection is performed using switches or diplexers. Another approach is based on using reconfigurable components, e.g., varactors, in the matching networks. The third approach is based on employing multi-band impedance matching networks. This approach enables concurrent operation of the PA at multiple frequency bands and also avoids the use of switches or reconfigurable elements with control voltages. Achieving multi-band operation for class-F PAs is more challenging than linear PAs as the HTN should provide proper impedance terminations for fundamental frequencies as well as their harmonics at several distinct frequency bands. The HTNs reported for concurrent multi-band class-F PAs are mostly limited to two frequency bands and provide terminations for up to three harmonic. A method for terminating higher number of harmonics is shown in Fig. 1(a). The drain and gate bias lines are embedded in the HTNs. This obviates the need for the RF choke that commonly is realized using a large inductor or a quarter-wavelength transmission line (TL). Thus, the bias lines are reused by the HTNs and the chip area is reduced. The HTNs ideally provide open-circuit impedance at the frequency of operation, ωo, while the matching networks transform the load/source resistance into the optimum load/source impedance of the transistor ( Z L,opt and Z S,opt ) that maximizes the output power or efficiency. At the harmonics of ωo, the matching networks exhibit open circuit, while the HTNs are designed to provide open circuit at the odd-order harmonics and short circuit at the even-order harmonics. The open-circuit condition of impedance matching networks at harmonics of can be achieved by using multiple parallel LC networks inserted in series with each other, each resonating at one of the harmonics. However, this method requires a large number of elements that their loss, especially at the output matching network, degrades efficiency of the PA. Another method is to
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adopt a series LC network resonating at ωo. This network exhibits short circuit impedance at frequency of operation, while at the nth harmonic of ωo it provides reactive impedance of (n-1/n) ωoL . The inductance should be chosen large enough that this impedance can approximately provide an open-circuit condition, i.e., it should be much larger than other impedance levels in the circuit. It should be noted that this impedance increases with the order of harmonic, indicating that it is enough to provide the open-circuit condition at the second harmonic. This series LC network can also be reused in the matching network for impedance transformation. In practice, the impedance matching network at harmonics of introduces large impedance in parallel with parasitic capacitance of the transistor that should also be absorbed into the HTN. The effects of this impedance can be accounted for using circuit simulations. The equivalent circuit of the HTN is shown in Fig. 1(b), where C p denotes the output/input parasitic capacitance of the transistor. The network order is n+1, where n is an odd number.Depending on frequency of operation and the transistor physical layout and packaging, the parasitic inductances in series with the gate and drain terminals can also have sensible effect on the PA performance. The output/input series parasitic inductance of the transistor can be accounted for by L n .
Fig. 1. (a) Proposed harmonic termination technique for class-F PAs (b) Equivalent HTN. IV.DIGITAL PREDISTORTION TECHNIQUE To reduce the harmonic distortions from the transmitter, a DPD linearization technique is designed and implemented. The DPD system is composed of a digital-to-analog converter (DAC), an analog-to-digital converter, and a field programmable gate array (FPGA) where the digital components for the DPD algorithm, the delay synchronization unit, and the lookup table memory are implemented. The DPD linearization is divided into two stages: calibration and evaluation. At the beginning of the calibration stage t0, the DPD FPGA and the DAC send an ideal input sinusoidal calibration signal u(t) to the input, where the output of the amplifier o(t) can be expressed as a Taylor expansion in terms of u(t), i.e., o(t) = A1 u(t) + A2 u(t)2 + A3 u(t)3 +… where A1, A2, and A3 are the first-, second-, and third order gains of the amplifier. As discussed in [15], the output signal of the amplifier is attenuated, fed back into the DPD FPGA, delay adjusted, and then subtracted into the ideal input signal to equalize the input containing the inverse response of the power amplifier nonlinearities. During the evaluation stage, the equalized amplifier output signal becomes oeq(t + tD) =A1 eq(ωt)+A2 eq(2ωt)+A3 eq(3ωt)+… =A1 u(ωt) + residue, where tD is the amplifier group delay, eq(ωt) is the equalized input signal with ω being the angular frequency, and residue is the remaining harmonic components above the fourth order.
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V .IMPLEMENTATION AND EXPERIMENTAL RESULTS
The class-F amplifiers with the nonlinear capacitor are investigated. To validate the voltage waveform shaping by the nonlinear output capacitor and the highly efficient operation of the saturated amplifier, we designed and implemented the amplifier at 2.655 GHz using the Cree GaN HEMT CGH40010 packaged device containing a CGH60015 bare die. Since the commercial device model includes the package effects such as the bonding wires, package leads, and parasitic, the simulation is carried out using the bare-chip model to explore the inherent operation of the saturated amplifier. In addition, the saturated amplifier is compared with the class-F amplifier using the bare-chip model. For the implementation, the packaged device containing the bare chip is employed for simulation and to build the amplifier. Fig.3 shows the simulated efficiencies and power gains of the class-F and saturated amplifiers using the barechip model. As expected, the saturated amplifier delivers the improved gain and efficiency characteristic compared to the class-F amplifier. However, the gain compression is not that fast. The efficiency curves for the two PAs are also similar to the previous simulation result. Fig. 4 shows the second harmonic load–pull contours and time-domain voltage and current waveforms of the saturated amplifier using the bare-chip model. During the simulation, the fundamental and third harmonic loads are set to and, respectively. Due to the harmonic generation of the nonlinear output capacitor, the high efficiency is maintained across the wide second harmonic impedance region. Moreover, even if the input power is low, the half sinusoidal voltage waveform is generated, proving the harmonic generation by the nonlinear output capacitor.
Fig.3. Simulated efficiencies and power gains of the class-F and saturated amplifiers using a real device.
Fig. 4. Simulated and measured output power, drain efficiency, PAE and power gain.
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CONCLUSION Table I. Comparison between Class AB and Class F Power Amplifier. Thus an integrated HV power amplifier for medical ultrasound transmitters to be used in advanced ultrasonic imaging modes such as THI for enhanced imaging quality is designed. A current feedback technique, a DPD technique, and a dynamic biasing current modulation technique are used to improve the power amplifier’s bandwidth, output signal linearity, and power efficiency. The HV power amplifier is fabricated using a 1-μm HV process. Measurement results indicate that the linear power amplifier is capable of driving a load of a 100Ω resistor in parallel with a 300-Pf capacitor with a signal voltage swing up to 180 Vpp and an HD2 as low as −50 dB. Measurement results also show that the amplifier achieves a maximum slew rate of 4 V/ns and a power efficiency of 60%.
REFERENCE 1. An Integrated High-Voltage Low-Distortion Current-Feedback Linear Power Amplifier for Ultrasound Transmitters Using Digital Pre-distortion and Dynamic Current Biasing Techniques Zheng Gao, Ping Gui, Senior Member, IEEE, and Rick Jordanger, IEEE Transactions on circuits and systems—ii: Express briefs, vol. 61, no. 6, June 2014. 2. Analysis and Design of a High Voltage Integrated Class-B Amplifier for Ultra-Sound Transducers, IEEE Transaction on Circuits and system, July, 2014. 3. A Harmonic Termination Technique for Single- and Multi-Band High-Efficiency Class-F MMIC Power Amplifiers, IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, VOL. 62, NO. 5, MAY 2014. 4. Characterization of Class-F Power Amplifier With Wide Amplitude and Phase Bandwidth for Outphasing Architecture, IEEE MICROWAVE AND WIRELESS COMPONENTS LETTERS, VOL. 24, NO. 3, MARCH 2014. 5. A Class F-1/F 24-to-31GHz Power Amplifier with 40.7% Peak PAE, 15dBm OP1dB, and 50mW Psat in 0.13μm SiGe BiCMOS, 2014 IEEE International Solid-State Circuits Conference, 978-1-47990920-9/14/$31.00 c2014. 6. Digitally Modified Filter-less Receiver for 2D Digital Pre-distortion Of Concurrent Dual-Band Power Amplifiers, 978·1-4799-3869-8/14/$31.00 ®2014. 7. Behaviors of class F and class F¯¹ Amplifiers, IEEE Transactions on Microwave Theory and Techniques, June 2012. 8. Design of a Class F Power Amplifiers, PIERSONLINE, VOL.6, NO.2, 2010. 9. High efficiency Class F Power Amplifier Design, 0- 7803- 8246- 1/ 04/ $20.00 2004, IEEE.
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Amplifier
Pout (dBm)
Gain (dB)
Efficiency (%)
Class AB
33.89
17.89
40.09
Class F
34.66
17.86
65.17(appro x)
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Design and analysis VLSI Architectures using am bipolar MISFETs Anjali Ashok K.A, Saravanan.P, Praveen Kumar.T Department of ECE, V.S.B. Engineering College, Karur, Tamilnadu. Abstract-Organic and inorganic thin film based transistor plays a major role in future generation electronic devices which uses micro, nano and picoelectronics circuits for inverters, oscillators, DRAM and backplane circuits in the display of most of the electronic devices with reduced power consumption. We analyzed based on self-assembled mono layer organic dielectric material Trichloro (Octadecyl) Silane (TOS) and a high- κ inorganic dielectric material Zirconium dioxide (ZrO2).Then we fabricated this Metal-Insulator-Semiconductor Field Effect Transistor (MISFET) with the configuration of Silver (Ag) as the metal at the top of the device as Source and Drain, pursued by the stack of TOS and ZrO2 as the Insulator (dielectric layer) which was previously coated on the ITO which act as the Semiconductor (Back Gate). Finally the IV characterization study illustrates the terrific result, which describes the am bipolarity operation, a future revolution in the unipolar n-channel and p-channel transistor.
I.
INTRODUCTION
Emergence of the metal-oxide-semiconductor (MOS) system over past 50 years witness the SiO2 gate oxide has been acting as the vital enabling material in scaling silicon MOS technology. As the gate oxide leakage is increasing with decreasing SiO2 thickness as well as SiO2 has reached atomic layer limitation level rejecting further reduction which makes repeated SiO2 gate oxide scaling is becoming extremely difficult. Since Moore’s law extends scaling and device performance into the 21st century, for high-performance and low-power CMOS applications in the 45 nm nodes and beyond requires high-κ gate dielectrics and metal gate electrodes. Nevertheless, although the dielectric capacitance and strength of the gate dielectric are incredibly important properties the surface characteristics [1] of the gate dielectric can also plays a vital role. In addition to facilitating standard MOSFET, the high-κ dielectric/metal gate combination is also important for enabling future high-performance and low gate leakage emerging thin film MOSFET built upon non-silicon high-mobility materials e.g. Ge, carbon nanotubes, and ITO substrates [13]. II.
DESIGN METHODOLOGY
High dielectric-constant (k) gate dielectrics are characterized by a relatively rough surface morphology upon deposition in a vacuum chamber. The rough surfaces result in an inferior channel/dielectric interface along with poor Crystalline growth of the ODTS channel and thus OTFTs fabricated on such dielectric surfaces usually exhibit undesirable device characteristics with low current ON-OFF ratio. The rare earth oxides (ZrO2), Er2O3, Pr2O3, ZrO2 etc., are reported with very high dielectric constant and low leakage current reliable for gate dielectrics in microelectronics [9, 12]. ODTS film is deposited on ZrO2surface using two step deposition method. Deposited ODTS film is found poly crystalline in nature. ODTS organic semiconductors have become one of the most promising materials for future thin, light, and flexible display applications. The performance of the OTFTs can be improved by proper selection of Gate dielectric material and metal electrodes [11] (metals which can give well. In the normal OTFT’s silicon is used but it consumes more power almost up to 20v so for reducing the power we are going for highκ dielectric materials like ZrO2 which can reduce the voltage below 5v [4, 7].So the researchers all over the world are researching on various high dielectric constant insulators Al2O3 [7], H fLaO [4,5],H fSiOx [6], Pr6O11 [11], La2O3 [3] etc.
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EXPERIMENTAL DETAILS
This present work focuses on preparation of high quality Zirconium oxide (ZrO2) thin films, a high-κ dielectric material by a modified sol-gel technique. The OTFT MIMFET transistor includes three layers such as the substrate layer or the metal layer, the oxide layer and the semiconductor layer.
The block diagram of experiment procedure is presented in fig.1. The experimental procedure for sol-gel Technique is to add Zirconium (IV) Prop oxide (2mL) in proper proportion with Iso Propyl Alcohol (9mL). Acetylacetone in Iso Propyl Alcohol was used as a gelatine agent. The prepared Zirconia is coated onto a glass plate and found that there was poor adhesion after firing (100oC for 5h). Hence a coating of Poly ethylene terephthalate (PET)was used before ZrO2 film formation on a glass plate, which results in better adhesive and uniform distribution. Since it is unable to peel off, the ZrO2 film is coated on the flexible ITO /PET substrate. Then the commercially available ODTS is diluted by using Iso Propyl Alcohol and distilled water and its molecules are broken down to Nano size and are coated over the ZrO2/PET layer. The terminals are soldered using the silver paste. Thus, the thin m MIMFET is fabricated using inorganic highκ dielectric ZrO2as the high-κ dielectrics and ODTS as the organic layer [1] on a flexible PET substrate as a self-assembled monolayer (SAM).
Figure 2. Structure of transistor.
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Figure 3. Experimental arrangement of Layers.
Figure 4. Structure of the DRAM. Ultimately, the fabricated thin film OTFT MIMFET is connected with a conventional capacitor to form a DRAM cell and it is analyzed. The structure of DRAM cell with thin film OTFT is shown in the fig.4. IV.
RESULTS AND DISCUSSION
The fundamental principle of any FET is when a gate voltage is applied, as drain bias increases the current conduction between source and drain occurs. Figure 5 explores IV characteristics between the gate voltage (Vg) and the drain current (Id) at constant drain bias voltage (Vd) ranges from 0.05V to 2V.
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Figure 5. IV Characteristics of the stacked TFT.
Figure 6. IV Characteristics. The IV Characteristics gives following device nature. When positive voltage is applied the current conduction is from source to drain with the negative drain current. This describes the am bipolar nature of the carriers [3, 13]. When the device is active, it operates in the linear region between 0.3V to 1V and after that it starts to saturate. The threshold voltage is incredibly degraded to 0.3V. Therefore switching speed of the device is faster contrast to the traditionally used Si transistors.
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SEM IMAGE
The SEM images of the prepared ODTS and Zirconia Nanoparticle are shown below.
Figure 6. SEM image of ODTS. The fig.7 displays SEM image of Zirconia in nano scale
Figure 7. SEM image of Nano Zirconia. CONCLUSION The low voltage OTFTs are fabricated utilizing two step deposition on ZrO2dielectric. Since OTFTs possess low threshold voltage and sub threshold swing, they can be applied in portable devices. Numerous research works on low threshold voltage OTFTs have been presented but in those works complicated fabrication techniques sometimes more than one fabrication technique and insulating layers are employed. On the contrary, in this work we use traditional fabrication technique which is widely applied in commercial fabrications of TFTs, nowadays. Using this method, fabrication of low cost OTFT will become achievable so that the traditional Si-TFTs can be replaced.
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REFERENCES [1] Nalini B, Nirmal D, Cyril Robinson Azariah J, “Fabrication and Characteristics Of Flexible Thin Film Depletion Mode Field Effect Transistor (FET) using High-κ Dielectric Nano Zirconia”,IJETED, 2(3),295-299, March 2013. [2] C.T. Pan et al., “Design and fabrication of flexible piezo-microgenerator by depositing ZnO thin films on PET substrates”, Sensors and Actuators, vol. A159, 2010, pp. 96–104. [3] R. Sarma and D. Saikia, K. Konwar and B. Baishya , Indian J.Phys, 84 (5), pp.547, 2010. [4] M.F. Chang, P.T. Lee, S.P. Mcalister, and albert Chin, IEEE Electron Device Lett, 30 (2), pp.133, 2009. [5] M.F. Chang, P.T. Lee, S.P. Mcalister, and albert Chin, IEEE Electron Device Lett, 29(3), pp.215, 2008. [6] Hu Yan, TsubasaKagata, Susumu Arima, Hiroshi Sato, and HidenoriOkuzaki, Phys stat sol (a), 205 (12), pp.2970, 2008. [7] Xiao-Hong Zhang, Benoit Domercq et al., “Organic Electronics 8”, pp.718, 2007. [8] R. Garg, D. Misra and P.K. Swain , J. Electrochem Soc., 153(2), F29, 2006. [9] Markku Leskela, KAgpo Kukli and Mikko Ritala, Journal of alloys and Compounds, 418, 27, 2006. [10] M. Bhaskaran, P.K. Swain and D. Misra, Electrochemical and Solid-State Letters, 7 (6), F38, 2004. [11] B.C. Shekar, Jiyeon Lee and Shi-Woo Rhee, Korean J OfChe., Eng, vol. 21(1), pp.267, 2004. [12] Markku Leskela and Mikko Ritala, Journal of Solid State Chemistry 171, pp.170, 2003. [13] Marwa A. Hassan Al-Janabi," Construction and characterization of MIS Heterojunction devices", MSc (Al-Mustansiriya University), pp.31, 2009.
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Surveillance Patrol Robot for People Tracking in Indoor Environments Neerparaj Rai, Shakti Dhar, Rupam Kakati Abstract-There is a great challenge that a mobile robot reliably and continuously tracks a specific person in indoor environments. In this paper, a novel method is presented, which can effectively recognize and reliably track a target person based on mobile robot vision. Such a robot is equipped with a camera which senses a moving object and starts tracking the object. The on-board camera develops a computer vision system for detection of the object/target to control and guide the movement of mobile robot. In order to effectively track the specific person, upper body color clothes region is proposed for extracting the pattern features. The system applies Centre-Of-Mass based computation, filtering and color segmentation algorithm to locate the target and the position of the robot.Artificial Neural Network (ANN) is introduced for controlling the robot to follow the person with voice aided instructions from the robot. Experimental results validate the robustness and the reliability of this approach. Keywords: Mobile Robot, People tracking, Color segmentation, Filtering.
I. INTRODUCTION The origins of robot manipulators date back to the 1940s, when Walters invented “Machina” the first robotic manipulator. Then, Devol invented the first industrial robot, “Unimate” in the late 1950s. Since then, industrial robot arms have proliferated and in today’s automated industries, robot arms are well employed in various tasks on the factory floor. The current technological revolution in robotics and automation has trans- formed the concept by accomplishing industrial tasks in a safer, optimized and much more efficient manner. A service robot, which is designed to serve people in special domains or help them in their daily life, must be able to detect and continuously track its users. The detection and reliable tracking of people in real time is a difficult problem in dynamic indoor environments, where lighting conditions are uncontrolled, multi- ple people wearing the similar color clothes are moving about, and temporary occlusions can occur at any time. The problem would become more intricate, if the robot need reliably track a recognized person in these complex environments. In order to enable a robot to automatically track the specific person, it is necessary to develop a technique that allows it robustly to recognize and track the person. Recently, much work on detecting and tracking people with mobile robots has focused on visual methods [1–3]. There are also recent approaches that make use of the laser range finders to detect and track people [4,5]. A few approaches have attempted to use a fusion of multi-cues, such as combining visual and range information from laser and sonar sensors [6] and using additional microphones for sound source localization [7]. Most of these systems are not aimed at tracking a specific person. In this paper, a novel-tracking method is presented, which can continuously track a specific person based on monocular vision mounted on the mobile robot. There are many tracking methods. A common approach is to employ predictive filtering and use the statistics of object’s color and location in the distance computation while updating the object model by constant weights. When the measurement noises are assumed to be Gaussian, the optimal solution is provided by the Kalman filter [9]. When the process is non-linear, the EKF and the UKF were developed. The most general class of filters is represented by particle filters, which are based on Monte Carlo integration methods. Blake and Isard [10] introduced the particle filter to vision tracking. Although it provides tractable solutions to non-linear and nonGaussian systems, it relies on important sampling and, as result, require the design of proposal distributions that can approximate the posterior distribution reasonably well. In general, it is hard to design such proposal. To solve this problem, Merve et al. [11] had developed the unscented particle filter (UPF), which utilized the UKF to generate proposal distributions. In the context of visual-based surveillance applications, there are many conditions for which deciding a prior placement of vision sensor puts limits on system performance. We refer, for example, to those cases in which an alarm situation can occur with the same probability in any area of the monitored environment or to those situations that require the tracking of mobile objects in wide areas (e.g., a vehicle moving on a road or a people moving in a building). In these situations, especially in the context of indoor environments, the employment of mobile robots equipped with specific visual sensors for surveillance purposes can become an important issue. The integration of a mobile robot in a visual-based surveillance system can allow the coverage of all types of
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environments, can extend the perceiving capabilities of the system (e.g., acquire images of higher quality by reducing the distance from the camera to the target). II. PLATFORM DESCRIPTION Figure 1 shows the experimental setup of Person Tracking Robot (PTR). The overall control system one Webcam and Laptop mounted on a robot with gear motors. The surveillance robot also includes one microcontroller and its necessary external circuitry for its control operation. Each motor control is done by sending a PWM (pulse width modulation) signal, a series of repeating pulses of variable width. The proposed system is a 2D-Image based vision system which is equipped with an intelligent image analysis and object detection algorithm that was developed in MATLAB®. The corresponding inputs to PC are the position error I ex in horizontal axis with respect to the object image centre i.e., (I x ,I y ) and the rectangular area of the object and also the distance of the object from PTR. The details can be found in Section 3 and the output is Δθ for corresponding gear motors. The algorithms, including image-processing algorithm for Webcam, and the routines for receiving the image information and sending the reference command, are implemented in the Laptop.
Figure 1. Image of the complete setup used in this paper
The proposed framework combines control and image-processing to perform desired operations. In order to find the specific person and reliably track him, the robot needs to first find the target. PTR follows a circular path for finding the target. In a typical application, once a user's command is encountered, the workspace is scanned and corresponding images are captured. These images are processed to identify target object and their coordinates. The acquired coordinates are then passed to the Laptop (Matlab Program) to compute the necessary movement required to reach the target position. III. PROPOSED METHODOLOGY The proposed system has been build for performing two main activities: vision and control. The vision process aims to identify all objects moving in the scene and to verify which one must be considered the most important to monitor. To achieve this objective, a tracking module is involved. The motion detection module aims to solve the problem of the detection of moving objects in the scene. This problem has been addressed by applying an image differencing technique after the alignment of two consecutive frames I(x, t) and I(x, t + 1). The threshold output of this process is a binary image I(x, y) representing the pixels belonging to moving objects. Once the objects have been identified, a tracking module is applied to maintain track of their movements, by maintaining the objects inside the field of view of the camera. 3.1. Image acquisition This intermediate block was focused on describing and applying standard signal processing techniques in images [20]. These techniques cover image enhancement algorithms such as noise reduction, filtering, contrast adaptation, etc., and also image analysis procedures applied after segmentation and morphological filtering, such as size, position, orientation, distance and average color estimate. The Matlab environment has enormous library of functions (or toolbox) dedicated to “Image Acquisition”. This toolbox enables simple, fast and powerful access to image grabbers or cameras connected to the computer either with the USB or FireWire busses.
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However, this toolbox is an optional purchase and may not be available in a standard computer laboratory facility equipped with Matlab. The image acquisition toolbox can be used to access image acquisition devices and store a sequence of images for offline analysis. The destination of the images acquired can be set as logging to disk or as logging to memory. Fig.2 shows the image acquired by PTR along with the target detection by rectangular box.
Figure 2. Image captured by Webcamera
The logging to disk mode has a ‘preview’function that forces the connection of the camera and shows a preview of the last image acquired by the active camera. The most interesting feature is that this live image can be retrieved from the Matlab environment as an image matrix with a simple snapshot function. Fig. 3 shows an illustrative script with the basic toolbox functions required for image acquisition. The first step is the creation of the input video object link (for continuous image acquisition) with one camera connected to the computer with the function videoinput that requires as parameters the adaptor’s name, the number of the camera and the image format requested (which must be one of the formats offered by the camera). The format specified is the correct codification to obtain RGB color images with 24-bit depth and resolution of 640×480 pixels. % input video object creation % videoinput(ADAPTOR,DEVICE,FORMAT); object = videoinput(‘winvideo’,1,’RGB24_320x240’); % activate preview (opens an auxiliary window) preview(object); % starting the image acquisition loop while condition % get an RGB image matrix image = getsnapshot(object); % image processing and robot control end % delete the video object when finishing delete(object); Figure 3. Sample Matlab script to acquire live images with the “Image Acquisition” toolbox.
3.2. Conversion of Color Space Most digital images use the RGB color space. However, individual R, G, and B components may have unstable variations under changing illumination conditions. On the other hand, it is easier to use the Y UV color space (where Y represents the luminance component, and U, V are chrominance components) for the segmentation of desired features. Vadakkepat et al. [22] verified that the UV color space for the face tracking problem is more effective and robust than that of the RGB. Hence, the color base of RGB is first transformed into that of Y UV with U and V ∈[0, 255] as Y U V
=
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0.299 0.587 0.114 −0.169 −0.331 −0.500 0.5 −0.419 −0.018
R G B
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3.3. Image Segmentation Since the value of Y is strongly dependent on luminance, it will not be considered in this paper. The values of U and V are set to different ranges as U ∈[Umin, Umax] and V ∈[Vmin, Vmax] for the identification of different objects, e.g., object (red color of the upper body) in Fig 3. Hence, the ranges [Umin, Umax] and [Vmin, Vmax] for the image features i.e., red color, are assigned as [0, 110] and [0, 120], respectively. The process of image segmentation is more robust in distinguishing the object on the ground with non uniform illumination, and strong reflection. 3.4. Binary The type of image processing followed is real-time processing, which has fast image processing time and is immune to the varying of the size irrespective of how far the object is from the camera. Color filtering technique is used to extract the current position of the gripper and the static position of the object. The object of interest colors to be considered is red for the current position of the gripper and light green color for the object while other colors are discarded. The binary of the image is to choose a suitable threshold value Tb for U and V. The corresponding relation is
P (Ix, Iy) = �
1 if f(Ix, Iy) ≥ Tb 0 if f(Ix, Iy) < 𝑇𝑇𝑇𝑇
(2)
where f(Ix, Iy) denotes the values of U and V on the image plane (Ix, Iy), P(Ix, Iy) = 1 stands for the white pixels, and P(Ix, Iy) = 0 stands for the black pixels. The purpose of the binary is to reduce the storage amount as well as the computation load. The value of Tb is less sensitive to lighting conditions because the Y component is not considered for the binary operation. In Fig.5 the red color object are segmented from the background for further processing. 3.5. Filtering A median filtering is used to remove noises produced by additional factors such as lighting intensities and the presence of unwanted particles. Median filtering is similar to using an averaging filter, in that each output pixel is set to an average of the pixel values in the neighbourhood of the corresponding input pixel. However, with median filtering, the value of an output pixel is determined by the median of the neighbourhood pixels, rather than the mean. The median is much less sensitive than the mean to extreme values (called outliers). Median filtering is therefore better able to remove these outliers without reducing the sharpness of the image. Then a Gaussian filter is used to further smoothen the image but will preserve edges better than the more basic mean filter. The resulting object is shown in Fig. 6 forWebcam. By weighting a pixels contribution to the final pixel value this filter can better preserve edges than the mean filter which specifies equal weights to all pixels within the filter window. For a 1-D Gaussian filter the single filter values are defined as
G(x) =�
1
√2π
.e
x2 22
(3)
Figure 4. Binary segmented image for Webcam
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Figure 5. Noise reduced and filtered image for Webcam
3.6. Image Representation and Description The centre and coordinate of the image features are considered to represent the pose of the PTR on the image plane coordinate. For the digital image, their 2-D centred moments are defined as follows [21]–[23]:
(I 1ix , I 1iy ) = ∑(Ix,Iy)ϵΩi ∑(Ix, Iy) /N (I 1ox , I 1oy ) = ∑(Ix,Iy)ϵΩo ∑(Ix, Iy)/N
(4) (5)
The centres of the whole image frame and image object i.e. (I 1ix , I 1iy ) and (I 1ox , I 1oy ) in fig 7 can be calculated by (4) and (5) respectively for the desired image features Ωi and Ωo. The error between centres of the whole image frame and image object in horizontal axis is determined as:
(I 1ex ) = (│ I 1ix – I 1ox │)
(6)
Figure 6. Image representing gripper and object centre coordinates for Webcam.
IV. NEURAL NETWORKS IN AREA CALCULATION Multi-layer perceptrons are one of many different types of existing neural networks. They comprise a number of neurons connected together to form a network. The “strengths” or “weights” of the links between the neurons is where the functionality of the network resides. Its basic structure is shown in Fig. 7.
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Figure 7. Structure of a multi-layer perceptron.
The idea behind neural networks stems from studies of the structure and function of the human brain. Neural networks are useful to model the behaviours of real-world phenomena. Being able to model the behaviours of certain phenomena, a neural network is able to subsequently classify the different aspects of those behaviours, recognise what is going on at the moment, diagnose whether this is correct or faulty, predict what it will do next, and if necessary respond to what it will do next. Feed-forward networks [17] often have one or more hidden layers of sigmoid neurons followed by an output layer of linear neurons. Multiple layers of neurons with nonlinear transfer functions allow the network to learn nonlinear and linear relationships between input and output vectors. Table 1 shows the training and testing data collected in this project. The inputs of the network are the areas from the selected frames at different distances. TABLE I: TRAINING AND TESTING DATA Sl.No 1. 2. 3. 4. 5. 6. 7. 8 9. 10.
Area (in pixel square) 52198 25162 12365 9682 6788 5053 4123 2881 1305 789
Distance (in metres) 2 3 4 5 6 7 8 9 10 11
One of the common problems when using Multilayer Perceptrons is how to choose the number of neurons in the hidden layer. There are many suggestions on how to choose the number of hidden neurons in Multilayer Perceptrons. For example, the minimum number of neurons, h, can be:
h≥
p−1 n+2
(5)
where p is the number of training examples and n is the number of inputs of the network [19]. The ANN model used to approximate the distance between the PTR and object comprises of one input and one output neuron along with four hidden neurons. The fitting function obtained after the training of the model in shown in Fig. 8.
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Figure 8. Fitting function for the trained ANN model.
V. EXPERIMENT RESULTS The proposed method has been tested on sequences acquired in an indoor environment. Several experiments have been executed following a strategy that involves an increasing complexity for the tests. The robot is equipped with a vision sensor and an on board PC. The tests have been performed on a laptop equipped with an Intel processor and 1 GB of RAM. In this section, we verify the feasibility of the object tracking. We designed a testing environment to verify the feasibility of object tracking as shown in Fig. 9. During the experiment, a target object starts moving from a starting point in the upper left corner. The target object moves according to a predefined route (dotted line in Fig. 9) with speed 20 cm/s and stops walking when it arrives at the starting point again. PTR starts to follow the target object until the distance between itself and the target object becomes longer than 1m. The traces of SSPR are represented by black circles in Fig. 10. We observed that PTR tried to follow the target object and kept the distance between itself and the target object within 1m. As long as the distance was less than 1m, SSPR stopped moving. Furthermore, SSPR kept correcting its direction according to the measured distance of ultrasonic sensors. As a result, PTR made the right turns at each corner. This test result verified the feasibility of object tracking by using vision sensor on a mobile robot. .
Figure 9. Testing environment for object tracking.
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Figure 10. Experiment result of object tracking.
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CONCLUSION We have proposed a practical design of PTR for home environment. PTR can trace a moving object actively and establish audio/video streams between itself and the target. The PTR has been specifically designed to help a operator in monitoring wide indoor areas. For such purposes, it is able to move around a specific indoor environment (e.g., a building) and to track moving people. The selection of the target object to be tracked can be decided by the remote operator or autonomously by the PTR itself in the case that a suspicious behavior has been detected (e.g., a person entering a forbidden area, etc.). Several experiments [3-5] on indoor sequences have demonstrated that the proposed PTR performs a robust detection of the motion inside the monitored scene. Then, to achieve a good identification of the mobile objects and to track them with enough accuracy, the ASV maintains the objects inside the field of view. Finally, the system shows a good object-detection rate for further person identification. When tracking a moving object, the robot may not keep the moving object in its viewing range due to a sudden change of motion direction. In this case, the robot follows a circular path in search of the moving object so that the mobile robot can detect the moving object as quickly as possible
Although the results appear promising, the constraints imposed still limit the exploitation of such a vehicle as a fully autonomous surveillance system. In particular, a single vision sensor cannot provide the complete scene of the area. Thus, the use of a multiple camera network could be useful to improve the understanding of the monitored scene. Moreover, there is a limit to the maximum speed of the vehicle. Indeed, when the speed remarkably increases, the parallax error cannot be addressed by the proposed method. This problem can be solved using additional information on the scene depth that could be acquired by range sensors mounted on board. Hence, future works will certainly give the robustness required to efficiently adopt the proposed PTR for the surveillance of indoor environments. REFERENCES [1]SeoKH, ShinJH,KimW,LeeJJ.Real-timeobjecttrackingandsegmentation using adaptive color snake model. International Journal of Control, Automation, and Systems 2006; 4(2):236–46. [2]SongKT, ChenWJ, Face recognition and tracking for human-robot interaction. In: IEEE international conference on systems, man and cybernetics, The Hague, Netherlands: Vol.3.2004.p.2877–82. [3] KwonH, YoonY, ParkJB, KakAC. Person tracking with a mobile robot using two uncalibrated independently moving cameras.In: IEEE international conference on robotics and automation. Barcelona, Spain:2005.p.2877– 83. [4] FodA, HowardA, aMataricMJ.Laser based people tracking. In:proc of the IEEE international conference on robotics & automation (ICRA).Vol.3. Washington, DC, UnitedStates: 2002.p.3024–9. [5] MontemerloM, ThunS, WhittakerW. Conditional particle filters for simultaneous mobile robot localization and people-tracking. In: Proceedings of the IEEE international conference on robotics & automation (ICRA). Washington, DC, USA:Vol.1.2002.P.695–701. [6] ScheutzM, McRavenJ, CsereyG.Fast, reliable, adaptive, bimodal people tracking for indoor environments. In: Proceedings of the 2004 IEEE/RSJ international conference on intelligent robots and systems (IROS’04).Vol.2. Sendai, Japan: 2004.p.1347–52. [7] FritschJ, KleinehagenbrockM, LangS, FinkGA, SagererG. Audio visual person tracking with a mobile robot. In: GroenF, editor. Proceedings of the international conference on intelligent autonomous systems. Amsterdam: IOS Press; 2004.p.898–906. [8] Chen CY. Obstacle avoidance design for a humanoid intelligent robot with ultrasonic sensors. Journal of Vibration and Control 2011;17(12):1798–804. [9] Boykov Y, Huttenlocher D. Adaptive bayesian recognition in tracking rigid objects. In: Proceedings of IEEE conference on computer vision and pattern recognition. Vol. 2. Hilton Head, SC: 2000. p. 697–704. [10] Isard M, Blake A. Visual tracking by stochastic propagation of conditional density. In: Proceedings of the fourth European conference computer vision. Cambridge, UK: 1996. p. 343–56. [11] Merwe R, Doucet A, Freitas N, Wan E. The unscented particle filter, Technical Report CUED/FINFENG/TR 380, Cambridge University Engineering Depart- ment, 2000.
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[12] Song KT, Chang CC. Ultrasonic sensor data fusion for environment recogni- tion. In: Proceedings of international conference on intelligent robots and systems 1, 1993. p. 384–90. [13] T. Kanade, R. Collins, A. Lipton, P. Burt, and L. Wixson, “Advances in cooperative multisensor video surveillance,” in Proc. DARPA Image Understanding Workshop, Monterey, CA, Nov. 20–23, 1998, pp. 3–24. [14] G. L. Foresti, “Object recognition and tracking for remote video surveillance,” IEEE Trans. Circuits Syst. Video Technol., vol. 9, no. 7, pp. 1045–1062, Oct. 1999. [15] Z. Zhu, G. Xu, B. Yang, D. Shi, and X. Lin, “VISATRAM: A realtime vision system for automatic traffic monitoring,” Image Vis. Comput., vol. 18, no. 10, pp. 781–794, Jul. 2000. [16] S. Dockstader and M. Tekalp, “Multiple camera tracking of interacting and occluded human motion,” Proc. IEEE, vol. 89, no. 10, pp. 1441–1455,Oct. 2001. [17] S. Haykin, Neural Networks, A Comprehensive Foundation. Prentice Hall, New Jersey, 1999. [18] Zhen, B., Wu, X. and Chi, H., “On the Importance of Components of the MFCC in Speech and Speaker Recognition”, Center for Information Science, Peking University, China, 2001. [19] N. K. Kasabov, Foundations of Neural Network, Fuzzy Systems, and Knowledge Engineering. The MIT Press Cambridge, London, 1996. [20] C. Solomon and T. Breckon.: Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab.,New York, NY, USA: Wiley, 2010. [21] E. D. Davies, Machine Vision—Theory, Algorithms, Practicalities, 3rd ed.Amsterdam, The Netherlands: Elsevier, 2005. [22] P. Vadakkepat, P. Lim, L. C. De Silva, L. Jing, and L. L. Ling, “Multimodal approach to human-face detection and tracking,” IEEE Trans. Ind. Electron., vol. 55, no. 3, pp. 1385–1393, Mar. 2008. [23] T. Komuro and M. Ishikawa, “A moment-based 3D object tracking algorithm for high-speed vision,” in Proc. Int. Conf. Robot.
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An Efficient Key Generation Using Cellular Automata for Symmetric Cryptography K.Raghuram, VVSVSRamachandram Assoct.Prof,ECE Dept,Pragati Engg.College,Surampalem,VVSVSRamachandram,M.Tech Student ,PragatiEngg.College,Surampalem. Abstract- the security of a block cipher mainly depends on the key strength .In order to get the randomness in the key, we have used the concept of cellular automata. In this algorithm we have mainly concentrated on key generation module we have 8 different rules for different rounds .In previous algorithm the message and key size was 128bit,but in this project we have increased the message and key bit size to 256bit and performed 16 Rounds of operation. This increases the security. We have increased complexity by implementing different modules such as bit-permutation, inverse bit-permutation NRCA, RCA. Keywords-RCA. NRCA, bit permutation, RBP.
I. INTRODUCTION Cryptography is the art and science of keeping messages secure. the basic objective of network security and cryptographic algorithms is to communicate securely over an insecure medium. The security to the data is provided by the cryptographic algorithms. The word cryptography has come from a Greek word, which means secret writing. The message to be sent through an unreliable medium is known as plaintext, which is encrypted before sending over the medium. The encrypted message is known as cipher text, which is received at the other end of the medium and decrypted to get back the original plaintext message. Cryptographic algorithm is a mathematical function used for encryption and decryption. Cryptographic algorithms are broadly classified into three types:- i.e. symmetric algorithm, asymmetric algorithm and authentication. As per the symmetric algorithm, same key is used for encryption and decryption. In asymmetric algorithm, different keys are used for encryption and decryption. Authentication algorithms mean that the receiver should be sure about sender’s identity. The cellular automata (CA) have been used since the forties of last century. It was used in many physical applications. The applications of Cellular Automata extended to fields such as biological models, image processing, language recognition, simulation, computer architecture, cryptography etc. The Cellular Automata is also one of the modern methods used to generate binary pseudo–random a Sequences using registers. The concept of CA was initiated by J. Von Neumann and Stan Ulam in the early 1940's. He devised a CA in which each cell has a state space of 29 states, and showed that the devised CA can execute any computable operation. He studied the 1 dimensional rules of Cellular Automata. However, due to its complexity, in the 1970, the mathematician John Conway proposed his now famous game of life which received widespread interest among researchers. His research was based on 2D Cellular Automata rules. Stephen Wolfram studied in much detail and showed that a family of simple one-dimensional cellular automata rules (now famous Wolfram rules) and are capable of emulating complex behavior . The main concern of this paper is secret key systems. In such systems the encryption key and the decryption key are same (symmetric key). The encryption process is based on generation of pseudorandom bit sequences, and CAs can be effectively used for this purpose Cellular Automata (CA) is an organized lattice of cells and each cell have finite number of states, such as "TRUE" (T) or "FALSE" (F). The lattice dimensions can be of any finite value. Each cell within a collection of cells is called as hood. It is particular cell. To start with at time t=0, a state is assigned to the cells. The new states of the cell depend on its own previous state and states of its neighborhood. The new states are assigned based on some predefined rule using mathematical calculations. Cellular Automata has following inherent Properties • Parallelism means that the individual cell updates are performed independently of each other. That is, we
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think of all of the updates being done at once Homogeneity means that each cell is updated according to the same rules.
In this paper a new algorithm for encryption and decryption is introduced. Here, we have mostly concentrated on securing the key, by using the concept of cellular automata. In cellular automata, we have different rules based on each rule we generate the random key for each round. By using these keys, we are performing encryption and decryption operation. In encryption, we have xor module, bit permutation, non reversible cellular automata. In decryption, we have xor module, reverse bit permutation, non reversible cellular automata. The structure of the paper is as follows: Section 1 describes introduction, Section 2 describes key generation, Section 3 describes encryption and Section 4 describes decryption, Section 5 describes simulation waveforms, Section 6 describes conclusion, Section 7 describes references. II. KEY GENERATION The input key is 256 bit. It is divided into 2 blocks of 128 bit. Each block is given to RCA module. First block is further divided into sixteen 8 bit blocks then given to rule 30 then we xor k1 with k16 to get the k16 of next block. Likewise k15 is xored with old k16 to get new k15 and so on. After completion of this operation we generate the random key for second round. As for first round the input key itself serves as the round key. In a similar manner, we apply rule 45,rule 86,rule 90,rule 105,rule 150,rule 165,rule 218,rule 30,rule 45, rule 86,rule 90,rule 105,rule 150,rule 165 for the further rounds. The same procedure follows for the second block as well.
Fig: 1
Fig: 2
III. ENCRYPTION In encryption, we have three blocks i.e. xor block, bit permutation, NRCA. The input to encryption is 256 bit plain text. This is divided into two 128 bit blocks. For the first block, we are passing 128 bit plaintext and 128 bit key, then the plaintext and key is further divided into sixteen 8 bit blocks. All these blocks of plaintext and key are given as input to xor block. Then we get sixteen 8 bit blocks as output from xor block. This output is clubbed such that we get 4 blocks of 32 bit. This serves as input to bit permutation block. In bit permutation the bits are arranged in different order based on the formula (9*I mod 31) +1. The permuted output from bit permutation block is divided into sixteen 8 bit blocks and given to NRCA block along with input key. One of the input to the NRCA is the 8 bit input key. Each bit of this 8 bit input key is applied with one particular rule. Then the above output along with bit permutation output is xored. The same operation follows for 2nd block also. These operations are performed for 15 more rounds.
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Fig:3
XORmodule
Fig: 4 BITPERMUTATION
Fig: 5
Fig:6
NRCA
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Fig:7
Fig:8
IV. DECRYPTION In decryption, we have three blocks i.e. xor block, Reverse bit permutation, NRCA. The input to decryption is 256 bit cipher text. It is divided into two 128 bit blocks. For the first block, we are passing 128 bit cipher text and 128 bit key, then the cipher text and key is further divided into sixteen 8 bit blocks. All these blocks of cipher text and key are given as input to NRCA block. Then we get sixteen 8 bit blocks as output from xor block. This output is clubbed such that we get 4 blocks of 32 bit. This serves as input to reverse bit permutation block. In reverse bit permutation the bits are re organized into the original position. The output from Reverse bit permutation block is divided into sixteen 8 bit blocks and given to xor block along with input key. The same operation follows for 2nd block also. These operations are performed for 15 more rounds. NRCA
Fig:9
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Fig:10
REVERSEBITPERMUTATION
Fig:11
Fig:12
XORmodule
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Fig:14 V. SIMULATION WAVEFORMS Encryption
Decryption
CONCULSION In this paper, the 256 bit cellular automata encryption and decryption is designed and synthesized using vhdl codes in Xilinx ise 13.2i REFERENCES [1] B.Schneier, “Applied Cryptography,” Wiley,New York, 1996. [2] S Tripathy and S Nandi,LCASE: “Lightweight Cellular Lightweight Cellular Automata-based Symmetric-key Encryption,” International Journal of Network Security, Vol.8, , Mar. No.2 2009. [3] Palash Sarkar, “A Brief History of Cellular automata,” Journal of ACM Computing Surveys (CSUR), Volume 32 Issue 1, March 2000. [4] S. Wolfram, “Cryptography with Cellular Automata,” Crypto ‘85, LNCS 218, pp. 429- 432, Springer-Verlag, 1986. [5] S. Wolfram, “Random sequence generation by cellular automata,” Advances in Applied Maths, vol. 7, No. 2, pp. 123-169, 1986. [7] Franciszek Seredynski, Pascal Bouvry, and Albert Y. Zomaya. “Cellular automata Computations and secret key cryptography,” Parallel Computing Journal, 30(5-6):753–766, 2004. F. Standaert, G. Piret, G. Rouvroy, J. Quisquater, and J. Legat, “ICEBERG : An involutional cipher efficient for block encryption in reconfigurable hard- ware,” FSE ‘04, LNCS 3017, pp. 279-299, Springer- Verlag, 2004. [8] N. Sklavos, N. A. Moldovyan, and O. Koufopavlou, “High speed networking: Design and implementation of two new DDP-based ciphers, Mobile Networks and Applications-MONET,” Vol. 25, No. 1-2, pp. 219-231, Springer-Verlag, 2005. [9] N. A. Moldovyan, P. A. Moldovyan, and D.H. Sum- merville, “On software implementation of fast DDPbased ciphers,” International Journal of Network Security, Vol. 4, No. 1, pp. 81-89, 2007. [10]T. Toffoli and N. Margolus, “Invertible cellular automata: A review,” Physica D, vol. 45, pp. 229-253, (reprinted with correction as of Oct. 2001). [11] Dr A.Kumaravel and Oinam Nickson Meetei “An application of Non-uniform cellular automata for efficient cryptography ,proceedings of 2013 IEEE conference & communication technologies(ICT)2013
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Authors K Raghuram received B.Tech ECE degree from ECE Branch from Aditya Engineering College affiliated to Jawaharlal Nehru Technological University, Kakinada in 2006 and received M.Tech VLSI degree from Bharat University in 2009 at Chennai. Presently , he is working as associate Professor in Pragati Engineering College, surampalem, A.P.
V V S V S Ramachandram received B.Tech ECE degree from ECE Branch from Jawaharlal Nehru Technological University college of engineering, Hyderabad in 1995 and received M.E(EST) degree from Vinayaka Missions University in 2009 at Tamilnadu. Presently, he is M.Tech VLSI-SD student in Pragati Engineering College, surampalem, A.P
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AUTOMATION OF RATIONSHOP USING RFID VAISHALLY.K Vel Tech, Avadi-Chennai-62. Abstract— Rationing distribution is one of the widely controversial issue that involves corruption and illegal smuggling of goods. One reason of this to happen is because every job in the ration shop involves manual work and there is no specific technology involved in automating the job. These irregularities or illegal activities are for example - wrong entries in stock register of shop containing wrong stock information of the products that is supplied to the public, sometimes there are chance of distribution of low quality/graded products than the actual products provided by the Government for supplying to the public, also the information regarding the actual available stock quantity in a ration shop that is provided by the Government to the public. In this paper we propose the concept of replacing manual work/job in distributing the commodities to the public by automated system which can be installed at the ration shop with ease. Here RFID card reader is used and the person has to show the RF card to the card reader which is provided to the user. This prompted us to interface RFID reader to the microcontroller (PIC 16F877A) and PC via RS232 to develop such a system. Using such a system, Government would have all required control/monitoring over the transactions at ration shop. In our project we designed the hardware for three commodities namely Sugar, Rice and Kerosene as other products can be provided as packets to the users. These three commodities are stored in reservoir tanks and they are measured and supplied to the user as and when required. In our project there is also option to block the card if the user has lost his card. The user can block the card with the help of the worker by displaying any proof of the user that he is belonging to the same family. The user after blocking the card, can also be able to apply for a new card. The user can also able to change their personal details like changing address, adding or removing the members of the family by showing birth certificate or death certificate to the worker. Hence it is possible to prevent the corruption and irregularities at ration shop. Keywords — RFID Tags, RFID Reader.
I. INTRODUCTION Our paper focuses on design and implementation of Automation of Ration shop. Due to manual measurements in the conventional system, the user cannot able to get the accurate quantity of the material. There is also a chance for the illegal usage of our products in the conventional system. To avoid these limitations, we go for Automation of Ration Shop System. In this system, RF tags will be provided to all the users in which the information about the users like photo of the head of the family, address, number of persons in the family, names and ages of the family members. Only one worker is enough in this system for paying money to the commodities. The user have to bring the RF card near the RFID reader. Each RFID will be having unique ID number. The user have to remember this ID for blocking the card incase if they missed their card. The reader reads the ID of the card and it displays on the PC. There will be three options on the PC. They are PURCHASE, BLOCK, DETAIL CHANGING. Then the user have to select the option with the help of the worker. Based on the number of persons in the family the commodities will be allocated. The user has to enter the required amount of goods and they have to pay to the worker. Then the user should go near the vending machine and should place their bag. The worker will enter OK button on the PC so that the entered amount of the commodity will be flowing from the vending machine. The database will be maintained on each ration shop so that if the user changes their address which does not include that ration shop then that RFID number will get stored in the respective area ration shop’s database. II. RFID TAGS RFID tag is a small device which stores and sends data to RFID reader. They are categorized in two typesactive tag and passive tag. Active tags are those which contain an internal battery and do not require power from the reader. Typically active tags have a longer distance range than passive tags. Passive tags are smaller
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and lighter in size than the active tags. They do not contain an internal battery and thus depend on RFID reader for operating power and certainly have a low range limited up to few meters.
Fig 1: A RECTANGULAR PASSIVE RFID TAG
Fig 2: REMOVING THE BACKSIDE LAYER OF THE TAG DISCLOSES THE ABOVE LOOK. In this paper, passive tags are used. A passive tag consists of a microchip surrounded by a printed antenna and some form of encapsulation, plastic laminates with adhesive that can be attached to a product or a small glass vial for implantation. The tag reader powers and communicates with passive tags. The tag’s antenna conducts the process of energy capture and ID transfer. A tag’s chip typically holds data to identify an individual product, the product model and manufacturer. Fig 2 shows the look of the passive tag when the backside layer of the tag is removed. The copper coil is also known as the antenna in fig 2. III. RFID READERS Fig 3: DATA TRANSMISSION BETWEEN RFID CARD AND RFID READER Radio Frequency Identification Devices (RFID) is a data communication method for remotely storing and retrieving data between a Reader and a Card / Tag as shown in fig 3. The communication distance range from a couple inches to many meters. In fig 4, the RFID Reader additional board is used to read identification cards (RFID Cards) using radio waves. This additional board features a receiver/transmitter module with antenna and a 2x5 male connector that enables connection with development systems. The operation of the RFID Reader board is based on amplitude modulation of radio waves and electromagnetic induction. The RFID card is not provided with the RFID Reader, but you can buy it separately. The presence of the power supply is indicated by a LED marked POWER. When the RFID Reader is turned on, a 125 kHz voltage is supplied on its antenna. As a result, the antenna starts emitting an electromagnetic field necessary for reading the RFID identification card. As passive RFID card doesn’t have its own power supply, it features a coil where the voltage is automatically induced by approaching the card to the RFID Reader’s antenna. This voltage is necessary for the chip featured on the RFID card to work. The memory chip on the RFID card contains a unique identification code. This code is sent by the card when it
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is placed close to the RFID Reader’s antenna. The code is received via this antenna. Then, it is sent to the microcontroller for further processing.
Fig 4: RFID READER
Fig 5: Operation of RFID CARD READER IV. GENERAL BLOCK DIAGRAM OF THE SYSTEM
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Fig 6: BLOCK DIAGRAM Fig 6 shows the general block diagram of the proposed system. The reader passes a large alternating current through a reading coil, results in an alternating magnetic field. A tag that incorporates a smaller coil in this field, an alternating voltage will appear across it. This voltage is rectified and coupled to a capacitor, a reservoir of charge accumulates, which is used to power the tag chip. The tag use near field coupling send data back to the reader using load modulation (the data). Then this data is passed to the microcontroller (PIC 16F877A) and gives output in digital form which is sent to the PC via RS232. And the PC makes the given data to produce the details of the card holder and further processing of good happens through the DC motor and the solenoid valve. DC motor is used to draw rice and sugar materials which controls the flow of them using a DC driver and the flow of oil is controlled by the solenoid valve which opens and close as and when required which is operated with the help of relay. 4.1POWER SUPPLY As we all know any invention of latest technology cannot be activated without the source of power. All the electronic components starting from diode to Intel IC’s only work with a DC supply ranging from _+5v to _+12. We are utilizing for the same, the cheapest and commonly available energy source of 230v-50Hz and stepping down, rectifying, filtering and regulating the voltage. The power supply output is given to micro controller and other circuit also; the design of the power supply is mainly because of the micro controller, the micro controller work in Dc source with a voltage of +5v. As we are getting the line voltage VL has 230v in ac source, so it is not possible. This power supply designs an output of +5v DC to activate the micro controller. 4.2 MICRO CONTROLLER The micro controller, which we are using here, is PIC 16F877A. It consists of 5 ports, ADC, CLK& MCLR. These are inbuilt with in 40 pins. The micro controller accepts and gives the o/p in digital form.
Fig 7: PIN DIAGRAM OF PIC16F877A 4.3. RELAY A relay is an electrical switch that opens and closes under the control of another electrical circuit. In the original form, the switch is operated by an electromagnet to open or close one or many sets of contacts. Because a relay is able to control an output circuit of higher power than the input circuit, it can be considered, in a broad sense, to be a form of an electrical amplifier. Mechanical relays are devices that can turn on or turn off the power
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supplied to another device, like a switch. However, instead of having a person flip the switch, mechanical relays switch when provided with a small amount of power. This allows high-power circuits to be controlled by lowpower devices. Mechanical relays use an electromagnetic coil to open or close the circuit. When current runs through the input and energizes the coil, it creates a small magnetic field which either pulls the arm of the switch away from the other contact of the switch, or pushes it down to close the switch depending on the how the switch is made. A relay also serves as an isolator, because the control (input) and load (output) ends of the relay are not electrically connected. This allows you to protect the device you're using to control the relay from power surges in your application. Relays are designed to be controlled by a particular voltage applied to the coil. Since mechanical relays are nothing more than a controllable switch, they support both AC and DC loads. A relay has an electromagnet, called a coil, and a lightweight switch inside it. When you energize the coil, a piece of the switch is attracted by the coil's magnetic field, which switches the switch on or off. 4.4 DC MOTOR In any electric motor, operation is based on simple electromagnetism. A current-carrying conductor generates a magnetic field; when this is then placed in an external magnetic field, it will experience a force proportional to the current in the conductor, and to the strength of the external magnetic field. The internal configuration of a DC motor is designed to harness the magnetic interaction between a current-carrying conductor and an external magnetic field to generate rotational motion. This DC or direct current motor works on the principal, when a current carrying conductor is placed in a magnetic field, it experiences a torque and has a tendency to move. This is known as motoring action. If the direction of electric current in the wire is reversed, the direction of rotation also reverses. When magnetic field and electric field interact they produce a mechanical force.
4.5. RS 232 In telecommunications, RS-232 (Recommended standard 232) is a standard for serial binary data signals connecting between a DTE (Data terminal equipment) and a DCE (Data Circuit-terminating equipment). The most common communication interface for short distance is RS-232. RS-232 defines a serial communication for one device to one computer communication port, with speeds upto 19,200 baud. Typically 7 or 8 bits (on/off) signal are transmitted to represent a character or digit. The 9 pin connector is used. The pin details is given in fig 8.
Fig 8: RS-232 9 PIN CONNECTOR
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CONCLUSION The cost of the proposed system is found to be very less when compared to the existing ones. This system helps to avoid unnecessary losses to the public. Since we use a security system it is used as a good database management where a persons information cannot be stolen at any cost. Even the illiterate people can make use of this proposed system with ease. With this system the user can easily change their personal details with more security. In case of missing their card, the user can easily block their card so that misuse of the cards can be easily prevented. With the help of this system, the government can have a accurate database about the amount of commodities used by the public. So the irregularities at the ration shop can be prevented.
REFERENCES [1] http://www.hbeonlabs.com/synopsis/RFID%20Based% 20Road%20Toll%20Tax.doc [2] http://www.gojohnedwards.com/RFIDMontreal.pdfb [3]“Vehicle Tracking and Ticketing System (VTTS) RFID”http://www.slideshare.net/computercriminals/complete-rfidproject-document-i-presentation [4] Sunrom Technologies, Datasheet - RFID Reader, 30-Dec-2011 (Available http://www.sunrom.com/files/1206-datasheet.pdf) [5] S.Lahiri, RFID sourcebook, USA: IBM press, (2006).
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Sleuthing Wormhole Attacks Using Hidden Markov Model in Wireless Networks Sunitha M, Priyadharshini P, CSE Department, GRT Institute of Engineering and Technology, Tiruttani Abstract---- Wireless networks are vulnerable to many attacks such as attacks like eavesdropping, man-in-the-middle, etc. Therefore, security assailability should be identified and safeguard against wireless networks.We bring out the wormhole attack, a severe attack in wireless networks that is particularly challenging to protect against. The wormhole attack is very powerful, and preventing the attack has proven to be very difficult. In the wormhole attack, an attacker captures packet at one location in the network, tunnels them to another location, and retransmits them there into the network. To struggle against wormhole attacks, we propose an anomaly-based detection system by using strategically distributed monitoring stubs (MSs).The MSs, by sniffing the traffic, extract features for detecting these attacks and construct normal usage behavior profiles. We have used a model called, Hidden Markov Model (HMM), to compute behavioral distance in order to compare the normal usage behavioral profiles to detect intrusions. The monitoring stubs produces sound alarm on the sender side when data gets attacked. Keywords – Wormhole attack, Mobile Ad hoc Network, Security, Intrusion detection. I.
INTRODUCTION
Network security consists of the provisions and policies adopted by a network administrator to prevent and monitor unauthorized access, misuse, modification, or denial of a computer network and network-accessible resources. Network security involves the authorization of access to data in a network, which is controlled by the network administrator. Users choose or are assigned an ID and password or other authenticating information that allows them access to information and programs within their authority. Network security covers a variety of computer networks, both public and private, that are used in everyday jobs conducting transactions and communications among businesses, government agencies and individuals. Networks can be private, such as within a company, and others which might be open to public access. Network security is involved in organizations, enterprises, and other types of institutions. Network security starts with authenticating the user, commonly with a username and a password.Once authenticated, a firewall enforces access policies such as what services are allowed to be accessed by the network users. Though effective to prevent unauthorized access, this component may fail to check potentially harmful content such as computer worms or Trojans being transmitted over the network. Anti-virus software or an intrusion prevention system (IPS) help detect and inhibit the action of such malware. An anomaly-based intrusion detection system may also monitor the network and traffic for unexpected (i.e. suspicious) content or behavior and other anomalies to protect resources, e.g. from denial of service attacks. Suppose a strange man is standing in front of your house. He looks around, studying the surroundings, and then goes to the front door and starts turning the knob. The door is locked. He moves to a nearby window and gently tries to open it. It, too, is locked. It seems your house is secure. So why install an alarm.
II.
TYPES OF INTRUSION DETECTION SYSTEM
2.1. Network Intrusion Detection System (NIDS) Network Intrusion Detection System (NIDS) is an intrusion detection system that attempts to discover unauthorized access to a computer network by analyzing traffic on the network for signs of malicious activity. Network intrusion detection systems gain access to network traffic by connecting to a network hub, network switch configured for port mirroring, or network tap. 2.2 Host-Based Intrusion Detection System (HIDS) A host-based intrusion detection system (HIDS) is an intrusion detection system that monitors and analyzes the internals of a computing system as well as the network packets on its network interfaces.
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2.3. Stack-Based Intrusion Detection System (SIDS) This type of system consists of an evolution to the HIDS systems. The packets are examined as they go through the TCP/IP stack and, therefore, it is not necessary for them to work with the network interface in promiscuous mode. III. RELATED WORK The Internet and computer networks are exposed to an increasing number of security threats. With new types of attacks appearing continually, developing flexible and adaptive security oriented approaches is a severe challenge. Garcia-Teodoro et al [1]. Describes the most well-known anomaly-based intrusion detection techniques, available platforms, systems under development and research projects in the area are presented. The authors discusses the foundations of the main A-NIDS technologies, together with their general operational architecture, and provides a classification for them according to the type of processing related to the ‘‘behavioural’’ model for the target system. Another valuable aspect of studying this paper is that it describes, in a concise way, the main features of several currently available IDS systems/ platforms. Finally, the most significant open issues regarding A-NIDS are identified, among which that of assessment is given particular emphasis. The information presented in this paper constitutes an important starting point for addressing R&D in the field of IDS. Some work has been done to detect wormhole attacks. Most of them based on the fact that transmission time between two wormhole nodes or between two fake neighbors is much longer than that between two real neighbors which are close together. Because two wormhole nodes (or two fake neighbors) are far from each other and packets sent between two wormhole nodes may be go through several intermediate nodes so it takes a longer time to transmit a packet between two wormhole nodes (or two fake neighbors) than between two real neighbors which are close together. By detecting this difference, we can identify wormhole attacks. Yih-chun hu et al [2]. Introduced the notion of a packet leash as a general mechanism for detecting and thus defending against wormhole attacks. A leash is any information that is added to a packet designed to restrict the packet’s maximum allowed transmission distance. We distinguish between geographicalleashes and temporal leashes. A geographical leash ensures that the recipient of the packet is within a certain distance from the sender. A temporal leash ensures that the packet has an upper bound on its lifetime, which restricts the maximum travel distance, since the packet can travel at most at the speed of light. Either type of leash can prevent the wormhole attack, because it allows the receiver of a packet to detect if the packet traveled further than the leash allows. The authors presented the design and operation of protocol which is named TIK. This TIK protocol is used to implement the temporal leashes. TIK stands for TESLA with Instant Key disclosure, and is an extension of the TESLA broadcastauthentication protocol. The author then described several stages of the TIK protocol. Finally the author evaluate the performance of the TIK protocol and compare the geographical and temporal leashes which shows geographic leashes are less efficient than temporal leashes.In order to avoid using special hardware, Jane Zhen and Sampalli Srinivas introduced a mechanism called Round Trip Time (RTT) to detect wormhole between two nodes. In this paper the authors classified the wormhole attacks into two categories namely hidden and exposed attacks. A node, say A, calculates the RTT with another node, say B, by sending a message to node B requiring an immediate reply from B. The RTT between A and B is the time between A’s sending the request message and receiving the reply message from B. In this mechanism each node (called N) will calculate the RTT between N and all N’s neighbors. Because the RTT between two fake neighbors is higher than that between two real neighbors so by comparing these RTTs between A and A’s neighbors, node A can identify which neighbors are fake neighbors and which neighbors are real neighbors. This mechanism do not require any special hardware and easy to implement but it cannot detect exposed attacks because no fake neighbor is created in exposed attacks. In order to overcome the above problem Hon Sun Chiu and King-Shan Lui [4] proposed another mechanism called DelPHI (Delay Per Hop Indicator), which is able to detect both hidden and exposed wormhole attacks. In this mechanism, the authors tried to find every available disjoint path between a sender and a receiver. Then, they calculate delay time & length of each path, computing Delay Per Hop value (average delay time per hop along each path). Delay per Hop values of paths are used to identify wormhole: the path containing wormhole link will have greater Delay Per Hop value. This mechanism can detect both kind of wormhole but they cannot pinpoint the wormhole location. Moreover, because lengths of paths are changed by every node (including wormhole nodes) so wormhole nodes could change the path length in a certain way to make them unable to be detected.
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There are several other approaches which do not use transmission time to detect wormhole. Levente et al [5]. two statistical approaches to detect wormhole attack in Wireless Ad Hoc Networks namely Neighbor Number Test and All Distance Test. The Neighbor Number Test bases on a simple assumption that a wormhole will increase the number of neighbors of the nodes (fake neighbors) in its radius. The base station will get neighborhood information from all sensor nodes, computes the hypothetical distribution of the number of neighbors and uses statistical test to decide if there is a wormhole or not. An All Distance Test detects wormhole by computing the distribution of the length of the shortest paths between all pairs of nodes. In these two algorithms, most of the workload is done in the base station to save sensor nodes’ resources. However, one of the major drawbacks is that they cannot pinpoint the location of wormhole which is necessary for a successful defense. This is corresponding to the observation values generated by the states of hidden Markov models. So complicate network attacks can be described by hidden Markov models. Each attack step is corresponding to a state of hidden Markov models. The transition of attack steps is corresponding to the transition between the states of hidden Markov models. IV. SYSTEM ARCHITECTURE
Client User
Acknowledgement
Server
Start Server User Registration
Sound Alert
User Login
Administrator Ready to receive data
Select Server IP
Select Data to send
Monitoring stub
Receive the data
Behavioral Distance (Hidden Markov Model)
Reports
Send Data Attacks
No
The data Detection and Trace back
Yes
Capture Server IP Action to be performed Wormhole Attacker
The process starts with the user or client authentications. The admin will have permission to view the entire processes done by the user. The user can only view the authenticated page after getting registered to the approach. User can view their personal information and the data which sent by him. In the server module have the static and secure login to enter and receive the data starts the server to. The network has divided by workgroups. This will help us to get the connected and the active systems in the network. After getting login to
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our process, this will get the connected systems and shows to the users. The user can select the system to deliver their data by file transfer. The disconnected and the shutdown systems are to transfer the data and the file to be transferred. The selected file will be encrypted for secured transfer. When the data received by the desired path of destination, the key automatically enabled and decrypted. When the user starts the process, the monitoring stub will initiate automatically to find behavioral distance and the evolutionary distances. In our process, we have to monitor the client data, which are sent to the receiver with a certain path. After the intruder affects the current data, there is no use of reports. So here, we trace back the path of every data information.Tracing the path of the data from one end to another end. when the data information path getting differ from the desired paths. V.
METHODOLOGY
Wireless networks are vulnerable to many attacks such as attacks like eavesdropping, man-in-the-middle, etc. Therefore, security assail ability in wireless networks should be identified and safeguard against. In this paper, we bring out the wormhole attack, a severe attack in wireless networks that is particularly challenging to protect against. The wormhole attack is very powerful, and preventing the attack has proven to be very difficult. In the wormhole attack, an attacker records packet at one location in the network, tunnels them to another location, and retransmits them there into the network. The wormhole attack can form a serious threat in wireless networks without some mechanism to protect against the wormhole attack, would be unable to find routes longer than one or two hops, severely interrupting communication. Wormhole attacks, we propose an anomaly-based detection system by using strategically distributed monitoring stubs (MSs).The MSs, by sniffing the traffic, extract features for detecting these attacks and construct normal usage behavior profiles. Upon detecting suspicious activities due to the deviations from these normal profiles, the MSs notify to struggle against the victim servers, which may then take necessary actions. In addition to detecting attacks, the MSs can also trace back the originating network of the attack. In this paper we have applied a model called Hidden Markov Model (HMM), to compute the behavioral distance in order to compare the normal usage behavioral profiles to detect intrusions. The monitoring stubs produces sound alarm on the sender side when data gets attacked. The unbridled growth of the Internet and the network-based applications has contributed to enormous security leaks. Even the cryptographic protocols, which are used to provide secure communication, are often targeted by diverse attacks. Intrusion detection systems (IDSs) are often employed to monitor network traffic and host activities that may lead to unauthorized accesses and attacks against vulnerable services. Most of the conventional misuse-based and anomaly-based IDSs are ineffective against attacks targeted at encrypted protocols since they heavily rely on inspecting the payload contents. Fight against wormhole attacks on encrypted protocols; we propose an anomaly-based detection system by using strategically distributed monitoring stubs (MSs). We have introduced one type of attack called wormhole attacks against cryptographic protocols. The MSs, by sniffing the encrypted traffic, extract features for detecting these attacks and construct normal usage behavior profiles. Upon detecting suspicious activities due to the deviations from these normal profiles, the MSs notify the victim servers, which may then take necessary actions. In addition to detecting attacks, the MSs can also trace back the originating network of the attack. Cryptographic protocols rely upon encryption to provide secure communication between involved parties. Secure Socket Layer (SSL) and its successor Transport Layer Security (TLS) are extensively used to provide authentication and encryption in order to transmit sensitive data. The purpose of all these encrypted protocols is to resist malicious intrusions and eavesdropping. The number of attacks against encrypted protocols has increased significantly in recent times. With the evolution of high-speed Internet and processing power, it is only natural to assume that more sophisticated attacks will emerge and pose serious threats to encrypted protocols. In a distributed detection mechanism that is able to detect the anomalous events as early as possible, especially before significant damage is inflicted on the victim by the attacker. The coordination of distinct agents monitoring the network flows at different points requires an appropriated architecture that must be developed. We address these issues in our project effectively and attempt to design adequate solutions to these problems. We propose a model called Hidden Markov Model which is not limited to constructing a defensive mechanism to discover wormhole attacks; we devise an aggressive countermeasure that not only detects a potential threat, but also investigates the root of the threat by attempting to trace back the attacker’s network or sub network.
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Monitoring stub will helps to improve the efficiency of the trace back mechanism and also identifying the paths. Behavioral distance used to find out the hacking of information before the data corrupted or updated by the intruder. Behavioral distance is calculated by using Hidden Markov Model. Fighting against attacks on encrypted protocols in the wireless network environment. The proposed detection scheme manages to avoid false alarms when the flash crowd occurred. We present an approach based on a novel Hidden Markov Model (HMM) for computing behavioral distance, and present the design, implementation, and evaluation of a novel architecture using HMM-based behavioral distance to detect attacks. An HMM models a doubly stochastic process; there is an underlying stochastic process that is not observable (it is “hidden”) but that influences another that produces a sequence of observable symbols. When applied to our problem of computing behavioral distance, the observed symbols are process behaviors, and the hidden states correspond to aggregate tasks performed by the processes (e.g., read from a file). An interesting and important observation is that since these hidden tasks should be the same, it should be possible to reliably correlate the simultaneous observable behaviors of the two processes when no attack is occurring, and to notice an increased behavioral distance when wormhole attack succeeds on one of them. CONCLUSION AND FUTURE ENHANCEMENT The client knows the data loss after it reached the intruder level. Client may trace back it but it is inefficient, because it takes more time to trace. The receiver checks the count of the packets and received packets if it is differed then they know that data may hacked. The existing system is less flexible, less secure and also less compatible. Monitoring stub will helps to improve the efficiency of the trace back mechanism. Behavioral distance used to find out the hacking of information before the data corrupted or updated by the intruder. The proposed detection scheme manages to avoid false alarms. It is used to find out the hacking of information before the data is hacked. The further extensions of our work may also facilitate fighting against wormhole attacks on encrypted protocols in the mobile ad hoc environment. REFERENCES [1] Garcia-Teodoro; J. Diaz-Verdejo; G. Macia-Fernandez; E. Vazquez., “Anomaly-based network intrusion detection: Techniques, systems and challenges(2009)” Computers& SecurityVolume 28, Issues 1–2, February– March 2009, Pages 18–28,Elsevier. [2] Y. Hu, A. Perrig, and D. Johnson: “Packet leashes: a defense against wormhole attacks in Wireless Ad Hoc Networks”. In Proceedings of the IEEE Conference on Computer Communications (Infocom), 2003. [3] J. Zhen and S. Srinivas. “Preventing replay attacks for secure routing in ad hoc networks”. Proc. of 2nd Ad Hoc Networks & Wireless (ADHOCNOW' 03), pp. 140--150, 2003. [4] Hon Sun Chiu King-Shan Lui, “DelPHI: Wormhole Detection Mechanism for Ad Hoc Wireless Networks”, International Symposium on Wireless Pervasive Computing ISWPC 2006. [5] Levente Buttyán, László Dóra, István Vajda: “Statistical Wormhole Detection in Sensor Networks”. Second European Workshop on Security and Privacy in Ad Hoc and Sensor Networks (ESAS 2005) Visegrád, Hungary, July 13-14, 2005: 128-141 [6] Lijun Qian, Ning Song, and Xiangfang Li. “Detecting and locating wormhole attacks in Wireless Ad Hoc Networks through statistical analysis of multi-path”. IEEE Wireless Communications and Networking Conference - WCNC 2005. [7] Phuong Van Tran; Le Xuan Hung; Young-Koo Lee; Sungyoung Lee; and Heejo Lee, “TTM: An Efficient Mechanism to Detect Wormhole Attacks in Wireless Ad-hoc Networks(2007)” AsiaPacific Service Computing Conference, The 2nd IEEE,pp 172-178 on 11-14 december 2007. [8] Shi Zhicai, Xia Y ongxiang, “A Novel Hidden Markov Model for Detecting Complicate Network Attacks” Wireless Communications, Networking and Information Security (WCNIS), 2010 IEEE International Conference on 25-27 june 2010.
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[9] Subramanian Neelakantan , Shrisha Rao, “A Threat-Aware Anomaly-Based Intrusion-Detection Approach for Obtaining Network-Specific Useful Alarms” ICDCN 2009, LNCS 5408, pp. 175–180, 2009. @ SpringerVerlag Berlin Heidelberg 2009. [10] Rutvij H. Jhaveri, Ashish D. Patel, Jatin D. Parmar, Bhavin I. Shah, “ MANET Routing Protocols and Wormhole Attack against AODV”, IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.4, April 2010. [11] V. Jyothsna, V. V. Rama Prasad, K. Munivara Prasad,“A Review of Anomaly based Intrusion Detection Systems”, International Journal of Computer Applications (0975 – 8887) Volume 28– No.7, August 2011. [13] Zaw Tun, Aung Htein Maw, “Wormhole Attack Detection in Wireless Sensor Networks”, World Academy of Science, Engineering and Technology 46 2008. [12] Zaw Tun and Aung Htein Maw, “Wormhole Attack Detection in Wireless Sensor Networks”, World Academy of Science, Engineering and Technology 46 2008. [13] I. Khalil, S. Bagchi, and N.B. Shroff. “LITEWORP: A Lightweight Countermeasure for the Wormhole Attack in Multihop Wireless Networks”. In proceeding of International Conference on DependableSystems and Networks (DSN 2005), Yokohama, Japan. [14] I. Khalil, S. Bagchi,N.B. Shroff., “LITEWORP: A Lightweight Countermeasure for the Wormhole Attack in Multihop Wireless Networks”. In proceeding of International Conference on DependableSystems and Networks (DSN 2005), Yokohama, Japan. [15] Saurabh Gupta, Subrat Kar, S Dharmaraja,“WHOP: Wormhole Attack Detection Protocol using Hound Packet”, pp (226-231) Innovations in Information Technology (IIT), 2011. [16] Ming-Yang Su. “Warp: A wormhole-avoidance routing protocol by anamoly detection in mobile ad hoc networks”,Computer Security, vol.29, March 2010. [17] Sung-Bae Cho, Hyuk-Jang Park, “Efficient anomaly detection by modeling privilege flows using hidden Markov model,” Computers& Security, vo1.22, Jan. 2003, pp. 45-55. [18] German Florez-Larrahondo, Susan M. Bridges, Rayford Vaughn, “Efficient modeling of discrete events for anomaly detection using hidden Markov models,” Proc. of the 8th International Conference on Information Security, Sep. 2005, pp. 506-514. [19] Andr'e Ames, Fredrik Valeur, Giovanni Vigna, and Richard A. Kemmerer, “Using hidden Markov models to evaluate the risks of intrusions, ”Proc. of the Recent Advances in Intrusion Detection (RAID 2006) Symp., Sep. 2006, pp. 1 45-164. [20] Zhang Song-hong, Wang Ya-di, Han Ju-hong, “Approach to forecasting multi-step attack based on HMM,” Computer Engineering, vo1.34, pp (131 -133) March 2008.
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Secret Data Hiding in Encrypted Compressed Video Bit streams for Privacy Info Protection Pradeep Rajagopalan Department of Electrical and Electronic Engineering Easwari Engineering College, Anna University, Chennai, Tamilnadu, India Abstract - This paper presents the encryption of compressed video bit streams and hiding privacy information to protect videos during transmission or cloud storage. Digital video sometimes needs to be stored and processed in an encrypted format to maintain security and privacy. Here, data hiding directly in the encrypted version of H.264/AVC video stream is approached, which includes the following three parts. By analyzing the property of H.264/AVC codec, the code words of intra prediction modes, the code words of motion vector differences, and the code words of residual coefficients are encrypted with stream ciphers. Then, a data hider may embed additional data in the encrypted domain by using wrapping technique, without knowing the original video content. The paper results shows that used methods provides better performance inerms of computation efficiency, high data security and video quality after decryption. The parameters such as RMSE, PSNR, CC are evaluated to measure its efficiency. Index terms – H.264 Compression, chaos encryption, Bit wrapping based data hiding. I.
INTRODUCTION
It is widely used in medical and military imagery for secret data communication. The system uses the h.264 video encoding techniques for low bandwidth video transferring progress. In existing, pixel difference expansion based RDH is the spatial domain process to conceal secret text messages within a cover image. The data hiding involves histogram adjustment to reduce overflow and underflow error and adjacent pixels are subtracted to determine the differences values. Then difference will be either incremented or decremented based on message bits. This technique produces the spatial distortion leads to degrade an image quality and it is less compatible and complex one. This will be overcome by the method of least significant bit replacement algorithm. In Vacating room after encryption, the secret messages are concealed into encrypted domain by replacement of some pixel intensities. This spatial domain technique distorts an image quality wherever the secret message bits were hidden. With the consideration of these problems, the system proposes the reserve room approach with lifting wavelet transformation for preserving an image quality and improve the security of transmission. The technique lifting wavelet decomposes an image into frequency subbands which contains approximation and detailed coefficients. The system will reserve the coefficients from detailed components which have texture, edges and region boundary. It is insensible region for human visual system. In addition with this approach, chaos crypto system, adaptive least significant bit replacement will be used for image encryption and message embedding. Data recovery is the reverse process of the encryption and embedding to get lossless extracted image and messages. The simulated result shows performance of the used methodologies interms of metrics evaluation such as mean square error, peak signal to noise ratio and correlation coefficients. II.
PROPOSED MODELS
In this section, a novel scheme of data hiding in the encrypted version of H.264/AVC videos is presented, which includes three parts, i.e., H.264/AVC video encryption, data embedding and data extraction. The content owner encrypts the original H.264/AVC video stream using standard stream ciphers with encryption keys to produce an encrypted video stream. Then, the data-hider (e.g., a cloud server) can embed the additional data into the encrypted video stream by using bit wrapping method, without knowing the original video content. At the receiver end, the hidden data extraction can be accomplished either in encrypted or in decrypted version. After the compression process the encoded bit streams are going to encrypted using chaos encryption method. An H.264/AVC video encryption scheme with good performance including security, efficiency, and format compliance is proposed. By analyzing the property of H.264/AVC codec, three sensitive parts (i.e., IPMs, MVDs, and residual coefficients) are encrypted with stream ciphers. The proposed encryption algorithm is
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performed not during H.264/AVC encoding but in the H.264/AVC compressed domain. In this case, the bit stream will be modified directly.
Secret Data Encryption It is process of scrambling original information into unknown form using either symmetric or asymmetric key standard. Here it is one of the advanced encryption standard called chaos crypto system used. It encrypts the original image pixel values with encryption key value generated from chaotic sequence with threshold function by bitxor operation. Secret Text
Chaotic sequence
Bitxor
Encrypted Text
Logistic mapping
Here logistic map is used for generation of chaotic map sequence. It is very useful to transmit the secret image through unsecure channel securely which prevents data hacking. The chaotic systems are defined on a complex or real number space called as boundary continuous space. The chaotic sequence will be defined by, Cn+1= U* Cn*(1-Cn) and encrypted pixel form defined by E = bitxor(P, Cn+1) Video Frames Encryption The compressed bit streams are encrypted using bitxor operation. Then the encrypted text was hidden in the encrypted compressed bit streams. The below diagram shows the video frames and compressed bit frames.
Input frames
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Encrypted Frames Bit Wrapping The process of bit wrapping method is to hide the encrypted secret data into the encrypted bit stream in the form of compression. The objective of steganography is a method of embedding additional information into the digital contents that is undetectable to listeners. We are investigating its embedding, detecting, and coding techniques. The idea behind the LSB algorithm is to insert the bits of the hidden message into the least significant bits of the pixels. As the application domain of embedding data in digital multimedia sources becomes broaden, several terms are used by various groups of researchers, including steganography, digital watermarking, and data hiding. The most frequently used steganography method is the technique of LSB substitution. In a gray-level image, every pixel consists of 8 bits. One pixel can hence display 28=256 variations. The weighting configuration of an 8-bit number is illustrated. The basic concept of LSB substitution is to embed the confidential data at the right most bits (bits with the smallest weighting) so that the embedding procedure does not affect the original pixel value greatly. The mathematical representation for LSB method is: x represents the i th pixel value of the stego-image, i x represents that of the original cover-image, and i m represents the decimal value of the i th block in confidential data. The number of LSBs to be substituted is denoted as k. The extraction process is to copy the k-rightmost bits directly. Mathematically the extracted message is represented as: Hence, a simple permutation of the extracted i m gives us the original confidential data. This method is easy and straightforward. However, when the capacity is greatly increased, the image quality decreases a lot and hence a suspected stego-image results. Furthermore, the confidential data might be easily stolen by simply extracting the k-rightmost bits directly. A 8-bit gray scale image matrix consisting m × n pixels and a secret message consisting of k bits. The first bit of message is embedded into the LSB of the first pixel and the second bit of message is embedded into the second pixel and so on. The resultant Stego-image which holds the secret message is also a 8-bit gray scale image and difference between the cover image and the Stego-image is not visually perceptible. The quality of the image, however degrades with the increase in number of LSBs. This hiding process will introduce the error between input and output image and it is determined by mean square error and Peak signal to noise ratio determines the image quality.
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Process Flow: Embedding
Input Video
H.264 Encoding Bitxor Secret Data Coordinate Extraction
Encryption
Data Concealment Decoding
Stego Video Performance Analysis
Extraction
Encrypted Stego Video
Data Extraction Text Decryption
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Experimental Results The performance of used methodology will be evaluated with different amount of characters on natural images. Here the metrics such as Mean square Error, PSNR and Correlation measured. Correlation = 0.8963 and PSNR = 43.78db. CONCLUSION The paper presented that protection of Video quality and hidden data during transmission based on approach of H.264 encoding and chaotic crypto system with bit wrapping based data concealment. Here, h.264 encoding method is used for compress the video frames effectively and chaos encryption was used as to protect image contents. This system was generated the stego video with less error under maximum data hiding capacity. It was better compatible approach and flexibility with better efficiency rather than prior methods REFERENCES [1] W. J. Lu, A. Varna, and M. Wu, “Secure video processing: Problems and challenges,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Prague, Czech Republic, May 2011, pp. 5856–5859. [2] B. Zhao, W. D. Kou, and H. Li, “Effective watermarking scheme in the encrypted domain for buyer-seller watermarking protocol,” Inf. Sci., vol. 180, no. 23, pp. 4672–4684, 2010. [3] W. Puech, M. Chaumont, and O. Strauss, “A reversible data hiding method for encrypted images,” Proc. SPIE, vol. 6819, pp. 68191E-1–68191E-9, Jan. 2008 [4] X. P. Zhang, “Reversible data hiding in encrypted image,” IEEE Signal Process. Lett., vol. 18, no. 4, pp. 255–258, Apr. 2011. [5] W. Hong, T. S. Chen, and H. Y. Wu, “An improved reversible data hiding in encrypted images using side match,” IEEE Signal Process. Lett., vol. 19, no. 4, pp. 199–202, Apr. 2012. [6] X. P. Zhang, “Separable reversible data hiding in encrypted image,” IEEE Trans. Inf. Forensics Security, vol. 7, no. 2, pp. 826–832, Apr. 2012 [7] K. D. Ma, W. M. Zhang, X. F. Zhao, N. Yu, and F. Li, “Reversible data hiding in encrypted images by reserving room before encryption,” IEEE Trans. Inf. Forensics Security, vol. 8, no. 3, pp. 553–562, Mar. 2013.
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Design of Analog Front-End for 128 MultiChannel EEG Signal Acquisition in 90nm Technology Gurajala Srinivas Praharshith, Sarvani Kunapareddy, Spurthi Nagulapally, Pavani. Abstract - This paper presents the design and implementation of a fully differential analog front-end which pre-processes the acquired Electro Encephalogram (EEG) signals before digitization. It consists of an analog multiplexer and a filter which can efficiently condition real-time EEG signals. The circuit has been designed so as to be compatible with a 128 multi-channel data acquisition system and hence, a 128 multi-channel analog multiplexer has been designed which aids in selecting the signal to be processed. Further, EEG signals are known to exist in the frequency range of 0-5 KHz and amplitude range of 100nv to 10mv. Hence, an eighth order low-pass filter with a bandwidth of 5 KHz, a stop band attenuation of -105dB, and a pass-band gain of 35.56dB has been implemented using cascaded bi-quads. The entire filter possesses fully-differential architecture because of its ability to effectively suppress power-line interference and on-chip noise. Chebyshev implementation of Ackerberg-Mosseberg bi-quad helped in conserving the chip area and aided in effective noise suppression. The schematics of the above mentioned circuits have been simulated in the 90nm technology.
I. INTRODUCTION The electrical signals generated due to the cumulative action potentials of the neurons, have, over the years provided an insight into the functioning of the brain. These signals, acquired through strategically placed electrodes either invasively or non-invasively, comprises of the EEG [4-7]. It is challenging to acquire invasive EEG signals and thus, noninvasively acquired EEG signals are more popular [4-7]. But, these signals have to be thoroughly pre-processed through strict filtering before they can be sampled, digitized and stored on-chip. A block diagram of the pre-processing system and the components adjoining it, is shown in Fig.1. There are 128 electrodes placed spatially on the scalp to allow analysis of signals from different parts of brain. So, a 128 channel input multiplexer is designed to facilitate selection of any one of the 128 channels as the input to the filter. This filter’s output is then fed to a sigma-delta ADC which gives out a 24-bit quantized signal. These signals can be processed and are used
Fig.1. Block Diagram of the System to diagnose several brain disorders and diseases.
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As most of the neural activity occurs in the lower frequencies, it is aimed to design a low-pass filter with a cut off of 5 KHz [5]. The voltage range of the neural EEG signals is typically 100nV to 10mv [5]. The minimum voltage that needs to be resolved is 100nv, which when digitized needs 24 bits to be represented. Each bit in the resolution results in a -6dB/octave roll-off, hence we need a -140 dB/decade roll-off. Since the input voltages of the ADC are in the range of 600mV rail to rail, it is required to implement the filter with a gain of 35.56dB or 60V/V. The number of electrodes to be used is 128. Hence, it conditions to have a 128 multi-channel multiplexer. The filter design is done in a bottom-up process. Initially, single stage operational amplifier (Op-Amp) and two stage Op-Amp are designed using telescopic Op-Amp with gain boosted first stage and a common source second stage. Ackerberg-Mossberg bi-quad uses ideal Op-Amp’s cascaded in inverting and non-inverting configurations with a feedback using appropriate resistors and capacitors, providing required gain. In order to approximate the behaviour of designed Op-Amp’s to the behaviour of ideal Op-Amp’s, a tolerance of 1% in the transfer function is allowed. MATLAB simulations have been done and a gain of 40dB for single stage Op-Amp and a gain of 60dB for two stage Op-Amp are fixed as ideal Op-Amp’s. A cascade of four Ackerberg-Mossberg bi-quads is used to get the desired low-pass filter. A decoder and a chain of transmission gates are used to design the analog multiplexer which facilitates the user to select the channel on which pre-processing has to be done. II. DESIGN FLOW 2.1 Current Source Design Current sources which supply current in the orders of milli-amperes consume huge on-die area. By using transistors working in saturation region and adjusting their sizes, any ratio of the supply current can be mirrored and steered from a single current source to the entire circuit. Using this concept, a current source of micro-amperes is employed through which hundreds of micro-amperes of required current is mirrored into each of the Op-Amps in the circuit. We are using a 100µA current source in our design. 2.2. Amplifier Design Before starting the design of the amplifier, certain specifications are available at our disposal. These include VDD, Tail current ISS, Maximum Slew rate, Unity Gain Bandwidth (UGBW), Load Capacitance CL, Maximum power to be drawn and output swing. For the set of transistors to work as an amplifier, each of them has to work in the saturation region. Hence the condition needed to be satisfied, in case of a NMOS is: Vds > Vdsat and PMOS is: Vsd >| Vdsat | (1) Initially, we assume certain Vdsat for each of the transistors. Further, we allocate a margin of twice the sum of the Vdsat of all the transistors and also allocate the amount of output swing. This margin voltage will be helpful in cases when there is a shifting of common-mode of the amplifier due to certain design approximations [2]. Hence, we can now formulate: Vdsatin + Vdsatload = VDSAT Margin = 2 VDSAT
(2) (3)
Hence, VDSAT +Margin + Output Swing = VDD (4) To make sure that all the transistors in the branch are in saturation, we allocate a certain Vds, just like how we allocated Vdsat in the previous step, to each of the transistors. The condition to be satisfied here is, Vdsin + Vdsload = VDD (5) In analog design, the finger width of each of the transistor is kept constant. This is to reduce gate resistance. Further, if we use a single finger instead of serially connected fingers, then the time for the current to travel from one end of
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the finger to the other end is more. It is possible to calculate the trans-conductance of the input transistors, if the load capacitance and the UGBW are known. The relationship is as follows: UGBW = gmin / (2 *π *CL) (6) Further, with the given maximum power, Pmax, it is possible to calculate the maximum amount of current that we can draw into the amplifier as, Pmax = V DD * ISSmax (7) For design purposes, it is possible to choose a convenient ISS which is less than ISSmax. Choosing a value too low will prompt us to use larger MOSFET’s for the design, while choosing a value too large, say ISSmax, we will be burning a large amount of power. Hence there exists a trade-off between area and power and it the decision of the designer which paves the way for further design. With the help of the calculated gm, allocated Vdsat, allocated Vds and the current to be burnt we can calculate the W/L of the input transistors. It is to be made sure that the length, L of these transistors is kept to minimum. This is because, the entire switching activity happens at this transistor and for the circuit to be fast, we need the L to be minimum, which for a 90nm technology is given as 100nm. The L for the load transistors is usually higher, in-order to obtain higher Rout, which ultimately will reflect on the gain of the amplifier. It is needed for us to have a gain as high as 60dB and hence a telescopic single-stage amplifier, gain boosted using auxiliary NMOS and PMOS amplifiers has been designed. Implementing this in Cadence, a gain of 64.5dB has been obtained. 2.3 Common-Mode Feedback Design (CMFB) Different topologies of CMFB are available for usage, and basing on the requirements of the user, the designer can choose [2]. The sizing strategy that is to be followed is as follows: The sizes of the four input transistors are equal, and are given by: (W/L)in = 1/mirroring ratio *(W/L)1 (8) Where (W/L)1 is the size of the input transistors of the amplifier. While, the sizes of the load transistors are given by, (W/L)load = 1/mirroring ratio*2* (W/L)4 (9) Where (W/L)4 is the size of the load transistor in the amplifier. 2.4 Differential Amplifier Second-Stage Design The second-stage is usually a common-source amplifier. Also, if the first-stage is a PMOS based amplifier, then the second-stage is an NMOS based amplifier. Now, the second-stage of the Op-Amp introduces a new set of poles, in addition to the poles introduced by the first stage [3]. So, in-order for the system to be stable and to ensure that these poles do not interfere, we shift the poles of the second-stage further away from those of the first-stage by at least5 times. This can be achieved by burning 5 times of the current being burnt in the first stage. The sizes can then be calculated from the design steps as mentioned in the first stage. To obtain high swing of 600mV rail to rail, two stage amplifier has been designed which gives a gain of 82.34 dB. 2.5. Frequency Compensation The phase margin to be achieved is 60 degrees, to ensure that the system is stable. Frequency compensation involves placing a resistance-capacitance pair in between the two stages of the Op-Amp. The values of the resistance and capacitance are varied using the parametric analysis of the Cadence tool and the phase margin is plotted. The values of resistance and capacitance for which the phase margin is 60 degrees, is taken as the compensation resistancecapacitance. 2.6 Bi-quad Design The Ackerberg-Mossberg bi-quad is found to operate at relatively high frequencies even for large Q values [1]. Therefore we used an Ackerberg-Mossberg bi-quad in our design. The number of poles required toachieve the given
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cut-off and roll-off is found to be eight. The transfer function of the filter is calculated using the cut-off frequency and the location of the poles. The transfer function is divided into four second-order transfer functions and each of the transfer functions is implemented using a different bi-quad. The capacitance and resistance values for each of the bi-quads are calculated separately according to the placement of the poles using the equation. ω0=1/R*C (10) where, ω0 is the frequency of the pole location [1]. In order to minimize the area of the system, small capacitances (in the order of pico farads) are used.A gain of 35.56 dB has to be achieved. This is done by distributing the gain among the four bi-quads according to the value of Q. The first three bi-quads require only single stage Op-Amps and hence the single stage Op-Amp which we have designed earlier can be used. Since the output swing is also a major concern in our design, the last bi-quad requires a single stage Op-Amps and a two stage Op-Amps. All the bi-quads are connected in series and the output of the last bi-quad is taken as the final output of the filter. 2.7 128 Input Multiplexer Initially the signal which is to be pre-processed i.e., filtered and amplified has to be selected. The selection is done by choosing one channel among the 128 channels available and the signal obtained through that channel is preprocessed. The signal inputs are directly given to a chain of transmission gates. These transmission gates are controlled by using a decoder. This whole system acts as a 128 multi-channel multiplexer which has the decoder inputs as the select lines. Thus the required channel on which the pre-processing has to be done is selected by manually controlling the decoder inputs. It is also possible to design a digital logic, based on the requirements of the user which can then perform automatic selection of the input. III. SIMULATION RESULTS The simulation results of our system are shown here. Fig.2 is the Cadence implementation of the differential Ackerberg-Mossberg bi-quad with two Op-Amp connected in inverting and non-inverting configurations. Fig.3 is the overall filter design as a cascade of four bi-quads. Fig.4 and 5 are the gain and phase plots of the overall filter and of each bi-quad respectively. Fig.6 is the transient response at each node of the filter when a 5mV rail to rail sinusoid of frequency 2.5 KHz is given as input.
Fig.2. Bi-quad Design
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Fig.3. Overall Filter (Low-Pass) –Cascade of four Bi-quads
Fig.4. Gain Plot – Overall Filter and individual Bi-quads
Fig.5. Phase Plot –Overall Filter and individual Bi-quads
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Fig.6. Transient Response at each node of the Filter
CONCLUSION The eighth order low-pass filter which we have designed has a cut off of 4.89 KHz which is approximately equal to 5 KHz and a roll off of -140.56dB at 50 KHz. The Chebyshev implementation of Ackerberg-Mosseberg bi-quad provided better results when compared to Tow-Thomas Butterworth implementation and Sallen key bi-quad. The output common-mode of the designed operational amplifier is 600mV and the output swings obtained were from 300mV to 900mV which gives a rail to rail of 600mV as required. Unity Gain bandwidth of this Op-Amp is 700MHz. The overall filter has a gain of 35.56dB by obtained cascading four bi-quads each with a gain of 20.383mdB, 5.976dB, 13.995dB and 15.572dB respectively. The swings of the bi-quads are 7mV, 18mv, 91mV and 583mV respectively for 2.5 KHz input frequency. The area occupied by the system is approximately 0.4mm2 and the power consumed is nearly 30mW.
REFERENCES [1] Schaumann, Rolf, and Mac E. Van Valkenburg. Design ofanalog filters. Oxford University Press, 2009. [2] Razavi, Behzad. Design of analog CMOS integrated circuits. Tata McGraw-Hill Education, 2002. [3] Gray, Paul R., and Robert G. Meyer. Analysis and design of analog integrated circuits. John Wiley and Sons, Inc., 1990. [4] Farshchi, Shahin, Istvan Mody, and Jack W. Judy. “A TinyOS-based wireless neural interface.” Engineering in Medicine and Biology Society, 2004. IEMBS’04. 26th Annual International Conference of the IEEE. Vol. 2.IEEE, 2004. [5] Shahrokhi, Farzaneh, et al. “The 128-channel fully differential digital integrated neural recording and stimulation interface.” Biomedical Circuits and Systems, IEEE Transactions on 4.3 (2010): 149-161. [6] Gosselin, Benoit. “Recent advances in neural recording microsystems.” Sensors 11.5 (2011): 4572-4597. [7] Hirata, Masayuki, et al. “A fully-implantable wireless system for human brain-machine interfaces using brain surface electrodes: W-HERBS.” IEICE transactions on communications 94.9 (2011): 2448-2453.
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REAL TIME MONITORING AND ALERT SYSTEM OF HUMAN HEALTH USING BIO SENSORS, GSM and GPS Amirthaganesh.S UG Student, Electronics and Communication Engineering, Sri Manakula Vinayagar Engineering College, Puducherry India
Abstract- This proposed system aims on treatment to patients who suffer from sudden illness like heart attack and no one is there along with them to save them. Previous works have cited only monitoring of the human health using only biosensor and providing them information of their health periodically. This system goes a step ahead of it. Bio-Sensors are used to monitor any abnormal change in the health of a person. If it senses any change, through a GSM interactive module it sends a message to a nearby hospital or health Centre .With the help of GPS we can locate the location of the victim. Index Terms- blood flow rate, CC2500, troponin, shortest path, NFC.
I .INTRODUCTION Technology development can be considered as day to day inflation. Even though there are advancements in technology, the major role played by technology is in the field of medicine. Today’s world is prone to various types of diseases which can be cured with the available equipment in an accurate manner. Deadly diseases like cancer too has a solution like it can be treated by radiation method. When the person is diagnosed with any disease the victim gets admitted in a hospital and treated. But nowadays, people suffer from sudden heart attack and other dreadful diseases. The most painful thing is that they are prone to these when they are alone. This proposed system may give a solution to this problem. One of the finest inventions by human among various sensors is Bio-Sensors. A BioSensor is a type of sensor which monitors any abnormal change in our body such as change in heart beat rate etc. When it analyzes any abnormal change it alerts the GSM module. The GSM module sends the information. The message is sent to a nearby hospital or Health Centre. The additional feature about this system is that a GPS module is included here to know the exact location where the victim is. II. DESIGN OF THE SYSTEM The proposed system is basically an embedded system. Hence a microcontroller is used here to take the necessary actions
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2.1. BIO-SENSORS Bio-Sensors are analytical devices which converts a biological response to an electrical signal. Here the biological response is nothing but any abnormal change in the heart beat rate of human body. Researchers say that a person before getting a severe cardiac attack, the flow rate of blood decreases cardiac tissue deadens, cells release natural chemicals, such as troponins. Implanted biosensors as shown in figure 1 may be used effectively to sense the release of troponin. Another type of biosensor shown in figure 2 is used to measure only the blood rate flowing. It is also evident that measuring the flow of blood rate is enough to indicate the cardiac attack. But by using implanted biosensor better results are obtained. The working model of biosensor is shown in figure 3. The electrical signal retrieved will be sent for further processing.
Figure 1: Bio-Sensor
Figure 2: A Biosensor attached to the Human Body
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Figure 3: Working of Bio-Sensor
2.2 GSM MODULE The GSM Module used here is the familiar SimCom SIM900 module. Since most of the devices for communication use this module. This will be more compatible for our project. The module is shown in figure 4.
Figure 4: GSM Module
2.3. GPS MODULE GPS also known as “Global Positioning System” is used to locate the exact destination of an object. It tracks the latitude and longitude to find the body. It is a satellite based tracking device. The reason for using this device is that it can be helpful for zeroing the location of the victim and to send the information about the location along with data to a hospital or Health Centre. The main function of the GPS in this project is that it sends the distance of various hospitals from the victim’s current location. The GPS module is also interfaced with the microcontroller. For reliability we choose GPS Module-Parallax PMB-648 as shown in figure 5. [5].
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Figure 5: GPS Module- Parallax PMB-648
2.4. MICROCONTROLLER The whole system of this project is controlled by the microcontroller. The microcontroller is the heart of the system. The GSM module is activated only when the microcontroller gets the electrical output from the bio-sensor. It coordinates the work performed by the various modules present in the system. The Micro-Controller performs the operation of selecting the near-by hospital from the information provided by the GPS module. Here the compatible microcontroller system for our project is Arduino Mega 2560[4].
Figure 6: Arduino Mega 2560 Micro Controller Board
2.5. RF TRANSMITTER AND RECIEVER It doesn’t looks pleasing if there is a wired connection from the biosensor to the module. Hence to avoid that an RF transceiver which works on RF frequency 2.4GHz is used. To satisfy this requirement RF transceiver CC2500 is used here as shown in figure 7.
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Figure 7: RF transceiver CC2500
III. IMPLEMENATATION OF THE SYSTEM A Bio-Sensor is attached to the body of a person. This bio sensor monitors the health of the human being. Furthermore the sensor is connected to the system which has microcontroller. The microcontroller operates the GSM module and GPS module. A separate power source is required to operate these modules. As per the definition, bio-sensor is nothing but a transducer. When there is any change in the blood circulation, it affects the inner part of the bio-sensor, thus it produces an electrical output. The electrical output is a trigger for the microcontroller to activate the GSM module and GPS module. The electrical output is sent to a RF receiver in the module through a RF transmitter connected to the output of the biosensor. The program is fed to the microcontroller such that the location tracked by the GPS module is also sent as a message to the station. The working of the system is explained as follows: •
The Bio-Sensor senses for any antibody enzymes, proteins and microorganisms. When a person suffers from sudden cardiac attack or any other respiratory problem, the bio-sensor senses the abnormal change. This produces an electrical output from the bio-sensor.
•
The generated electrical signal is given to the microcontroller. The microcontroller is fed with a program such that various attacks such as cardiac attack or respiratory problems are assigned with a value and a message is generated within it according to the electrical signal
. •
The generated message is transmitted wirelessly through a GSM module. The GSM module sends the information to a nearby hospital or health Centre from the data from the GPS.
•
To know the exact location of the victim, the GPS module provides the exact location and also the distance of the available hospitals or health cares from the victim’s location .That message is also sent to the
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microcontroller. The microcontroller decides the shortest path and in the program fed to it a timer is set and if the force doesn’t arrive, it sends the message to the next shortest path. The microcontroller sends the command to the GSM module to send the message.
Figure 8: Block diagram for Working of the system
CONCLUSION Sudden cardiac attacks are unpredictable. Even though after these kinds of attacks there are chances of surviving if immediate primary treatment is given. This can be accomplished by this project. The cost of this proposed system is not much more than a life costs. The patient without any hesitation can carry along with him this module because; it serves as a guardian when they are alone.
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FUTURE WORKS This system can be extended by developing an android app by using the latest Near Field Communication (NFC) technology which is more compatible for smart phones. Hence, a separate module is not required for this entire process to happen. ACKNOWLEDGMENT I would like to thank Dr. V.S.K Venkatachalapathy, Director, Sri Manakula Vinyagar Engineering College. I would like to express my sincere gratitude to my Head of the department Mr. P.Raja. I am also thankful to my guide Mr. R .Imtiaz for his constant support. REFERENCES [1]. Sarvesh B.Rothe, Prof. V.G.Girhepunje, “Design and Implementation of Real Time Wireless Biomedical System Based on ZigBee-GSM interactive module”, International Journal of Engineering Research and Applications (IJERA). [2]. Jose´ I. Reyes De Corcuera, Ralph P. Cavalieri, “Biosensors”, Washington State University, Pullman, Washington, U.S.A. [3]. SIMCOM900 (http://wm.sim.com/producten.aspx?id=1019 ) [4]. Arduino Mega 2560(http://arduino.cc/en/Main/arduinoBoardMega2560) [5]. GPS Module PMB-648 (http://learn.parallax.com/KickStart/28500 ) [6]. CC2500 (http://www.ti.com/product/cc2500 ) [7]. Near Field Communication (http://developer.android.com/guide/topics/connectivity/nfc/index.html )
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A Comparative Analysis of Wide Band Antenna with Reduced Radar Cross Section Srisuji. T (PG Scholar) and Nandagopal. C (AP/ ECE) M. Kumarasamy College of Engineering Karur, Tamilnadu India Abstract- An antenna with reduced radar cross section is the most applicable one for military and stealth technology to reduce the radar cross section of sensitive targets, thus makes invisible to the enemy radar. Radar cross section reduction plays a main role in the defense industry. In this paper, the operating frequency and RCS reduction are compared with different wide band antenna. A planar octagonal shaped UWB antenna reduces the radar cross section for the whole operational bandwidth and found best for radar applications with 10dB RCS reduction at the whole operational bandwidth, 25dB RCS reduction especially at low frequencies with good antenna performance. Index Terms— Ultra-wideband (UWB) antenna, monopole antenna, reduced radar cross section.
I. INTRODUCTION As per the federal communication commission (FCC), the operating frequency of ultra wide band has been declared as 3.1 to 10.6 GHz. Mostly UWB antennas are used in target sensing, location finding and tracking applications. The main advantages of ultra wide band antennas are higher bandwidth, low power requirements, minimum fading in multipath, low profile, easy fabrication, low complexity, low cost, high data rate wireless communication. A design of an antenna with low RCS is not easier. Reduction of RCS for a whole operational bandwidth is a challenging one. But designing an antenna with low RCS is mandatory in low observable platforms. Radar cross section is classified into two types: Monostatic RCS and Bistatic RCS. The Monostatic RCS is obtained, when the transmitter or receiver will be at same location. The Bistatic RCS is obtained, when the transmitter or receiver are not at the same location. Antenna is also a better scatter; scattering is mainly related to feed port. The scattering characteristics are controlled by feed terminations of the antenna. Scattering of an antenna are of structural mode, antenna mode scattering. When the feed port of an antenna is being terminated by matched load, then the scattering would be called as structural mode. If the antenna is terminated with other loads, then the part of the energy would be reflected back, called antenna mode scattering. For reducing the RCS, radar absorbing material also can be used. But it will make the antenna from wide band to narrow band [7]. This paper compares the different wide band antenna for reducing the radar cross section. Mostly, reducing the RCS at low frequency is a challenge. Reduction of Radar cross section depends on geometrical shape or radar absorbing material obtaining low RCS at high frequencies has been done. A novel Printed Circular Disc Monopole Antenna has been designed with circular patch [1], a novel ultra wide band antenna with reduced radar cross section is a system has been proposed with ring antenna [3], a novel low RCS mobius-band monopole antenna has been designed for reducing RCS [2] are compared with planar octagonal shaped ultra wide band antenna, where the reduction of RCS is obtained for the whole operational band [6]. This paper has been organized in the following sections. Section II describes the antenna design and performance of different ultra wide band antenna. Section III gives the analysis of the results of the different antennas. Section IV describes the conclusion of this comparative study.
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II. ANTENNA DESIGN AND PERFORMANCE 2.1 Printed Circular Disc Monopole Antenna (PCDMA) A circular disc monopole has r as a radius and microstrip feed line as 50Ω are printed on the same side of the FR4substrate whose thickness is 1.5 mm and relative permittivity is 4.7. Length and Width of the dielectric substrate is denoted as L and W respectively. To achieve 50 Ω impedance, the width of the microstrip feed line has fixed at W1= 2.6mm. The length is L1= 20mm covers the section of the microstrip feed line h denotes the height of the feed gap between the ground plane and the feed point shown in Fig. 1 The antenna performances are affected by two design parameters, the dimension of the disc and the width of the ground plane. The return loss will be 10dB from 2.78 to 9.78 GHz. The distribution of current will be at the edge of the disc. When diameter of the disc increases, the resonant frequency gets decreases. The current distribution of different frequencies has been given in Fig. 2. This figure illustrated in [1].
Figure 1. Structure of Printed circular Disc Monopole
(a) 3GHz
(b) 6GHz
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(C) 9 GHz Figure 2: Simulated current distribution at different frequencies
If the diameter of the disc increases it will lead to larger size, radar cross section will also be increased. 2.1 A Novel Ultra Wide Band Antenna with Reduced Radar Cross Section The novel wide band antenna contains the patch as circular ring, a ground plane; a microstrip feed line, a square shape substrate as a dielectric with thickness of 1.6mm, permittivity of 2.65 has been proposed in [3]. Two circular rings are available, among this two ring one of the rings are fed by 50Ω microstrip line would be printed at the top of the substrate, whereas the bottom of the substrate with the ground plane. To connect the top and bottom of the surfaces with the ground plane metallic via holes are constructed. The measured return loss is -10dB over the frequency range 1.99 to 10.8GHz, the structural mode scattering will not change, when the antenna is terminated with different loads, at the same time antenna mode scattering will change according to the load. Due to the small shape of dual circular ring radiator, the radar cross section has been reduced largely at high frequencies. But in low frequencies, the reduction of RCS is ineffective, because the geometry of the antenna is similar to the incident wavelength. At that time the structural mode scattering mentioned as the low frequency resonant scattering. Due to this reasons the structure of an antenna becomes inefficient to reduce the RCS. 2.2 A Novel Low RCS Mobius- Band Monopole Antenna This type of monopole antenna is also constructed in order to reduce the RCS antenna. The structure of the antenna is shown in fig. 3. This topological monopole antenna is a one- sided surface. One- sided surface is made from a rectangle, Keeping one end fixed, rotating the other end by 180o, connecting to the first end. The lower annuluses are presented at inner part. The upper annuluses are presented at upper part. The metallic holes are used to connect the upper and lower annuluses. The antenna is printed on the ground patch. The larger ring is printed on the top of the substrate and fed with 50Ω microstrip line, whereas the smaller ring is printed on the bottom of the substrate. The operating bandwidth of the antenna is 2.8- 11.0GHz.This concept is illustrated in [2].
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Figure 3. Structure of mobius- band monopole antenna The RCS reduction is obtained for the whole band. Reduction of RCS is about 7dB has given in [5]. 2.3 Planar Octagonal- Shaped UWB antenna with reduced Radar Cross section The reduction of RCS in the whole operational bandwidth is a challenging one. But it is obtained by a planar octagonal shaped UWB antenna. The operating bandwidth is 2.5- 18GHz. RCS reduction is done by subtracting the metal areas where the current distribution is small. The patches are of different types. They are in elliptical, circular, square shape, but among all of these shapes, octagonal patch antenna gives return loss less than -20dB at 5.513.5GHz [4]. The structure of the antenna is shown in fig. 4.
Figure 4 Octagonal shape UWB antenna The effectiveness of ground plane is very important to control the antenna bandwidth. Feeding plays an important role, which is used to control the scattering characteristics. If the antenna is terminated with the matched, then it would be called as scattering mode, if it is terminated with different load then the scattering would be called as antenna mode scattering. The behavior of radiation is fully depends upon the surface current distribution in the metallic areas. The smaller current distributed metallic areas are subtracted to reduce the RCS. The antenna is fed with the microstrip feed, according to the distribution of the current; an elliptical geometry will be subtracted from the layer of the ground. In this antenna, the current distribution is small in the middle part of the antenna. That circular part is eliminated from the patch. The RCS of the antenna is reduced about 10dB at 4.518GHz, especially at low frequencies the RCS reduction is about 25dB has been proposed in [6].
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III. RESULTS AND DISCUSSIONS Printed Circular Disc Monopole Antenna (PCDMA)
The results have been discussed for different types of ultra-wide band antenna. The fig. 5 shows the simulation result of the printed circular disc monopole antenna.
Figure. 5 Simulated return loss curves for different dimensions of the circular disc The figure indicates the relation between the frequency and return loss. The radius of the disc is increased to reduce the return loss. 3.2.A Novel Ultra Wide Band Antenna with Reduced Radar Cross Section
Figure. 6 Simulated curve for frequency versus RCS (dBsm) of different kinds of load
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The above figure shows the curve with frequency versus RCS 3.3. A Novel Low RCS Mobius- Band Monopole Antenna The following figure determines the RCS, of the Mobius- Band monopole antenna.
Figure. 7 Simulates RCS antenna 3.4. Planar Octagonal- Shaped UWB antenna with reduced Radar Cross section The following two diagrams shows the return loss, voltage standing wave ratio of the planar octagonal shaped ultra wide band antenna.
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TABLE I Comparative analysis of different wide band antenna S. No
Antenna
Operating Frequency
Reduction of RCS If the diameter increases reduction of RCS decreased
1
Printed Circular disc monopole antenna (PCDMA)
2
A Novel Ultra Wide Band Antenna with Reduced Radar Cross Section
1.99-10.8GHz
RCS is reduced at high frequencies, not at low frequencies
3
A Novel Low RCS Mobius- Band Monopole Antenna
2.8-11.0 GHz
RCS is reduced about 7dB
4
Planar Octagonal Shaped UWB antenna with reduced radar cross section
2.5- 18GHz
2.69-10.16 GHZ
10dB at low frequencies, 25dB at high frequencies
CONCLUSION In this paper, different types of wide band antenna are compared for reduction of RCS. In printed circular disc monopole antenna, if the diameter of the disc increases the RCS also increases. The diameter is increased to make low resonant frequencies. But the RCS is not reduced. In a novel ultra wide band antenna with reduced radar cross section, RCS is greatly reduced at high frequencies not at low frequencies. In a novel low mobius- band monopole antenna, the RCS is reduced about 7dB. Comparing the entire above antenna, a planar octagonal shaped UWB antenna obtains 10dB RCS reduction at the whole operational bandwidth, 25dB RCS reduction especially at low frequencies with good antenna performance. The metallic area of an antenna has been subtracted, where smaller current is distributed. The radius of 4mm is removed from the patch to obtain the above said RCS reduction. REFERENCES [1] Liang, J., C. Chiau, X. Chen, and C. Parini, Study of a printed circular disc monopole antenna for UWB systems," IEEE Trans. Antennas Propag., Vol. 53, No. 11, 3500{3504, 2005. [2] W. Jinag, S. X. Gong, Y. P. Li, T. Hong, X. Wang, and L. T. Jiang, “A novel low RCS Mobius band monopole antenna,” J. Electromagn.Waves Appl., vol. 23, no. 14–15, pp. 1887–1895, 2009 [3] T. Hong, S. -X. Gong, W. Jiang, Y.-X. Xu, and X. Wang, “A novel ultra-wideband antenna with reduced radar cross section,” Progr. Electromagn. Res., vol. PIER 96, pp. 300–308, 2009. [4] Cengizhan M. Dikmen, Sibel CIMEN, Gonca CAKIR. “An Octagonal Shaped Ultra Wide Band Antenna With Reduced RCS”, International Japan-Egypt Conference on Electronics, Communications and Computers. 2013 [5] Cengizhan M. Dikmen, Gonca CAKIR,” Double side Axe shaped UWB antenna with Reduced RCS”, Asia pacific Microwave Conference Proceedings, 2013 [6] Cengizhan M. Dikmen, Sibel Cimen and Gonca Cakir, “ Planar Octagonal- shaped UWB antenna with Reduced Radar Cross Section”, IEEE Transactions on Antenna and Propagation, Vol. 62, No. 6, June 2014. [7] Y. Li, H. Zhang, Y. Fu, and N. Yuan, “RCS reduction of ridged waveguide slot antenna array using EBG radar absorbing material,” IEEE Antennas Wireless Propag. Lett., vol. 7, pp. 473–476, 2008
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BRAIN TUMOR AND BRAIN ABNOMALITY DETECTION USING IMAGE PROCESSING G.V.Sandhiya, B.E-ECE-IV year, S.Lohitha,B.E-ECE-IV SRI VENKATESWARA COLLEGE OF ENGINEERING AND TECHNOLOGY-THIRUVALLUR. Abstract: Brain tumor analysis is done by doctors but its grading gives different conclusions which may vary from one doctor to another. So for the ease of doctors, a research was done which made the use of software with edge detection and segmentation methods, which gave the edge pattern and segment of brain and brain tumor itself. Medical image segmentation had been a vital point of research, as it inherited complex problems for the proper diagnosis of brain disorders. In this research, it provides a foundation of segmentation and edge detection, as the first step towards brain tumor grading and also introduces an inexpensive, user friendly general-purpose image processing tool and visualization program specifically designed in MATLAB to detect much of the brain disorders as early as possible. The application provides clinical and quantitative analysis of medical images. Minute structural difference of brain gradually results in major disorders such as schizophrenia, Epilepsy, inherited speech and language disorder, Alzheimer's dementia etc. Here the main focusing is given to diagnose the disease related to the brain and its psychic nature. Current segmentation approaches are reviewed the use of image segmentation in different imaging modalities is also described along with the difficulties encountered in each modality. Key words: Brain tumor, MRI images, image processing, Edge detection, segmentation, cerebral cortex, image registration ,Neuroinformatics. I. INTRODUCTION The human brain is the center of the human nervous system and is the most complex organ in any creature on earth. Any abnormality in brain leads to the total collapse of entire vital functions of the body.Brain tumor, which is one of the most common brain diseases, has affected and devastated many lives. According to International Agency for Research on Cancer (IARC) approximately, more than 126000 people are diagnosed for brain tumor per year around the world, with more than 97000 mortality rate [1]. Despite consistent efforts toovercome the problems of brain tumors, statistics still shows low survival rate of brain tumor patients. To combat this, recently, researchers are using multi-disciplinary approach involving knowledge in medicine, mathematics and computer science to better understand the disease and find more effective treatment methods. Magnetic resonance (MR) imaging and computer tomography (CT) scanning of the brain are the two most common tests undertaken to confirm the presence of brain tumor and to identify its location for selected specialist treatment options. Currently, there are different treatment options available for brain tumor. These options include surgery, radiation therapy, and chemotherapy. The choice for the treatment options depends on the size, type, and grade of the tumor. It also dependents on whether or not the tumor is putting pressure on vital parts of the brain. Whether the tumor has spread to other parts of the central nervous system (CNS) or body, and possible side effects on the patient concerning treatment preferences and overall health [2] are important considerations when deciding the treatment options. Accurate detection of the type of brain abnormality is highly essential for treatment planning in order to minimize diagnostic errors. The accuracy can be improved by using computer aided diagnosis (CAD) systems. The basic concept of CAD is to provide a computer output as a second opinion to assist radiologists’ image interpretation and to reduce image reading time. This improves the accuracy and consistency of radiological diagnosis. However,segmentation of the image of brain tumors is a very difficult task. In the first place, there are a large class of tumor types which have a variety of shapes and sizes [3]. Appearance of brain tumors at different locations in the brainwith different image intensities [2] is another factor that makes automated brain tumor image detection and segmentation difficult. This paper presents a review of the methods and techniques used during brain tumor detection through MRI image segmentation. The paper introduces a concept of simple user friendly GUI application to process an image of brain and analyze its morphological abnormalities II BRAIN TUMOR AND BRAIN ABNORMALITY Brain tumor is an abnormal growth of cells inside the skull. Normally the tumor will grow from the cells of the brain,blood vessels, nerves that emerge from the brain. There are two types of tumor which are- benign( noncancerous) and malignant (cancerous) tumors. The former is described as slow growing tumors that will exert ICIECA 2014
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potentially damaging pressure but it will not spread into surrounding brain tissue. However, the latter is described as rapid growing tumor and it is able to spread into surrounding brain. Tumors can damage the normal brain cells by producing inflammation, exerting pressure on parts of brain and increasing pressure within the skull.
Figure 1 shows the presence of tumor in the brain. Typical abnormality of brain is the shrinking of its cerebral cortex (as shown in Figure 2). The spaces in the folds of the brain (the sulci) are grossly enlarged. This type of abnormality leads to Alzheimer’s disease. Radiologists examine the patient physically by using Computed Tomography (CT scan) and Magnetic Resonance Imaging (MRI). MRI images showed the brain structures, tumor’s size and location of shrinking of brain abnormalities. From the MRI images the information such as tumors location and shrinking of brain provided radiologists, an easy way to diagnose the tumor, abnormality and plan the surgical approach for its removal.
Figure:2 abnormality of brain III MRI BRAIN IMAGING AND CHARACTERISTICS OF BRAIN TUMORS MRI’s use radiofrequency and magnetic field to result image’s human body without ionised radiations. Imaging plays a central role in the technique for automatic detection of some types of brain abnormalities, along with techniques for tumor segmentation in MRI sequences. They presented an automated and clinically-tested method for detection of brain abnormalities and tumor-edema segmentation using MRI sequences.. On MRI, they appear either hypo (darker than brain tissue) or iso tense (same intensity as brain tissue) on T1-weighted scans, or hyper intense (brighter than brain tissue) on T2-weighted MRI. They presented an automated and clinically-tested method for detection of brain abnormalities and tumor-edema segmentation using MRI sequences. Their method follows a Radiologist’s approach to the brain diagnosis using multiple MRI sequences instead of any prior models or training phases. Their procedure consists of the following steps: i. Pre-processing of the MRI sequences, T2, T1, and T1 post-contrast (enhanced) for size standardization, contrast equalization and division into active cells. ii. Identification of the T2 MRI sequence as normal or abnormal by exploiting the vertical symmetry of the brain. iii. Determination of the region of abnormality using its hyper-intense nature. iv. Separation of tumor from edema using the T1 and its post-contrast (enhanced) sequences.
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v. Estimation of the volume of tumor found and generation of an anatomical differential of the possible disorders. In medical, doctors don’t have method that can be used for brain tumor detection standardization which leads to varying conclusions between one doctor to another . Edge-based method is by far the most common method of detecting boundaries, discontinuities in an image and segmentation. The parts on which immediate changes in grey tones occur in the images are called edges. Edge detection techniques transform images to edge images benefiting from the changes of grey tones in the images. As a result of this transformation, edge based brain segmentation image is obtained without encountering any changes in physical qualities of the main image . This image processing consist of image enhancement using histogram equalization, edge detection and segmentation process to take patterns of brain tumors, so the process of making computer aided diagnosis for brain tumor grading will be easier. IV LITERATURE REVIEW The image segmentation is entailed with the division or separation of the image into regions of similar features. In this paper, we will discuss an illustrate a number of approaches and show improvements in segmentation performance that can be achieved by combining methods from distinct categories such as techniques in which edge detection s combined with thresholding. The definitive aim in image processing applications is to extract important attributes from the image data, from which a descriptive, interpretative, or understandable prospect can be obtained by the machine. Time consumption during the segmentation of brain tumor from magnetic resonance imaging is a crucial drawback. Thus, we have studied the foundations of brain segmentation and edge detection, by various techniques employed by researchers. The segmentation & edge detection approaches were studied under 5 categories. These are as follows- 1) Thresholding approaches, 2) Region growing approaches, 3) Genetic Algorithm approaches,4) Clustering approaches ,5) Neural network approaches. Several authors suggested various algorithms for segmentation. Thresholding based methods: In thresholding approach; image segmentation is based on gray level intensity value of pixels. A thresholding procedure attempts to determine an intensity value called the threshold, which separates the desired classes. The segmentation is then achieved by grouping all pixels with intensity greater than the threshold into one class, and all other pixels into another class. Region growing: Region growing connects neighboring points to make bigger region. The process of region growing is dictated by certain condition associated with the selection of a threshold value [16, 17, 18]. Seeded region growing starts with one or more seed points and then grows within the region to form a larger region satisfying some homogeneity constraint. The homogeneity of a region can be dependent upon any characteristic of the region in the image: texture, color or average intensity. Genetic Algorithm approaches: The split method begins with the entire image, and repeatedly splits each segment into quarters if the homogeneity criterion is not satisfied. These splits can sometimes divide portions of one object. The merge method joins adjacent segments of the same object. Spatial clustering: Image segmentation and image clustering are different. In image segmentation, the grouping of the image is carried out in spatial domain. In image clustering, grouping is performed in the measurement space. Overlapping regions can be the result of clustering. It is not possible to produce overlapping regions from segmentation. Clustering and spatial segmentation can be combined to form spatial clustering, which combine histogram techniques with spatial linkage techniques for better results. Neural networks based method: Neural network based segmentation methods use artificial neural network computational models consisting of processing elements (called neurons) and weighed connections between them. The weights (coefficients) are multipliers at the connections. Training is required to obtain the values of the coefficients. Several types of neural networks have been designed and TESTED RESULTS USING MATLAB Multilayer perceptron, backpropagation learning algorithm (MLP), Hopfield neural networks (HNN) and selforganizing maps (SOM) neural
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network are some of the algorithms used in segmentation process. A thorough treatment of neural networks can be found. The property of neural networks with respect to their ability to learn segmentation procedure through some form of learning process have attracted more researchers in image segmentation than other image processing techniques. Zhang presented the analysis and comparison of these evaluation methods are performed according to the classification and assessment criteria for methods and performance metrics proposed in that survey. The results reveal the advantages and limitation of these new methods, and provide additional understanding about the evaluation procedure. This review presents also some novel procedures for image generation under different conditions. Dzung L. Pham, Chenyang Xu, Jerry L. Prince proposed the basics that thresholding approaches segment scalar images by creating a binary partitioning of the image intensities. It attempts to determine an intensity value, called the threshold, which separates the desired classes. Segmentation is achieved by grouping all pixels with intensity greater than the threshold into one class, & all other pixels into another class. Determination of more than one threshold value is a process called multi thresholding.
V PROJECT GOALS A.Alzheimer- The Brain Disorder: Just like the rest of our bodies, our brains change as we age.Most of us notice some slowed thinking and occasional problems remembering certain things.However, serious memory loss, confusion and other major changes in the way our minds workare not a normal part of aging [6]. These may be the signs of brain cells failure [7].The brain has 100 billion nerve cells (neurons). Each nerve cell communicates with many others to form network. Nerve cell networks have special jobs. Some are involved in thinking, learning and remembering. Others help us see, hear and smell. Still others tell our muscles when to move.In Alzheimer’s disease, as in other types of dementia, increasing numbers of brain cells deteriorate and die [6].
5.1. Brain imaging methods: Functional imaging of electric brain activity requires specific models to transform the signals recorded at the surface of the human head into an image [8]. Two categories of model are available: 1) single-time-point and 2) spatio-temporal methods. Theinstantaneous methods rely only on few voltage differences measured at one sampling point. To create a spatial image from this limited information, they require strict assumptions that rarely conform to the underlying physiology. Spatio-temporal models create two kinds of images: first, a spatial image of discrete equivalent multiple dipoles or regional sources, and second, an image of source current waveforms that reflect the temporal dynamics of the brain activity in circumscribed areas. The accuracy of the spatial image is model dependent and limited, but it can be validated from the spatiotemporal data by the "regional source imaging" technique, introduced here. The source waveforms are linear
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combinations of the scalp waveforms, and thus, specific derivations which image local brain activities at a macroscopic level. 5.2 Functional MRI: Functional MRI or functional Magnetic Resonance Imaging (fMRI) is a type of specialized MRI scan. It measures the haemodynamic response related to neural activity in the brain or spinal cord of humans or other animals. It is one of the most recently developed forms of neuroimaging. Since the early 1990s, fMRI has come to dominate the brain mapping field due to its low invasiveness, lack of radiation exposure, and relatively wide availability. 5.3Positron Emission Tomography (PET): Positron emission tomography (PET) is a nuclear medicine imaging technique which produces a three-dimensional image or picture of functional processes in the body. The system detects pairs of gamma rays emitted indirectly by a positronemitting radionuclide (tracer), which is introduced into the body on a biologically active molecule. Images of tracer concentration in 3-dimensional space within the body are then reconstructed by computer analysis. In modern scanners, this reconstruction is often accomplished with the aid of a CT X-ray scan performed on the patient during the same session, in the same machine. If the biologically active molecule chosen for PET is FDG, an analogue of glucose, the concentrations of tracer imaged then give tissue metabolic activity, in terms of regional glucose uptake. Although use of this tracer results in the most common type of PET scan, other tracer Nmolecules are used in PET to image the tissue concentration of many other types of molecules of interest. 5.4 Image Registration: In this project, sets of data acquired by sampling the same scene or object at different times, or from different perspectives, will be in different coordinate systems. Image registration is the process of transforming the different sets of data into one coordinate system. Registration is necessary in order to be able to compare or integrate the data obtained from different measurements. Medical image registration (for data of the same patient taken at different points in time) often additionally involves elastic (also known as nonrigid) registration to cope with deformation of the subject (due to breathing, anatomical changes, and so forth). Nonrigid registration of medical images can also be used to register a patient's data to an anatomical atlas, such as the Talairach atlas for neuroimaging. Image registration is the process of overlaying two or more images of the same scene taken at different times, from different viewpoints, and/or by different sensors. It geometrically aligns two images—the reference and sensed images. The present differences between images are introduced due to different imaging conditions. 5.5. Image registration methodology: Image registration, as it was mentioned above, is widely used in remote sensing, medical imaging, computer vision etc. In general, its applications can be divided into four main groups according to the manner of the image acquisition: 1. Different viewpoints (multi-view analysis). Images of the same scene are acquired from different viewpoints. The aim is to gain larger a 2D view or a 3D representation of the scanned scene. Examples of applications: Remote sensing—mosaicing of images of the surveyed area. Computer vision—shape recovery (shape from stereo). 2. Different times (multi-temporal analysis). Images of the same scene are acquired at different times, often on regular basis, and possibly under different conditions. The aim is to find and evaluate changes in the scene which appeared between the consecutive image acquisitions.Examples of applications: Remote sensing— monitoring of global land usage, landscape planning. Computer vision— automatic change detection for security monitoring, motion tracking. Medical imaging— monitoring of the healing therapy, monitoring of the tumor evolution. 3. Images of the same scene are acquired by different sensors. The aim is to integrate the information obtained from different source streams to gain more complex and detailed scene representation. Examples of applications: Remote sensing—fusion of information from sensors with different characteristics like panchromatic images, offering better spatial resolution, color/multispectral images with better spectral resolution, or radar images independent of cloud cover and solar illumination. Medical imaging—combination of sensors recording the anatomical body structure like magnetic resonance image (MRI), ultrasound or CT with sensors monitoring functional and metabolic body activities like positron emission tomography (PET), single photon emission computed tomography (SPECT) or magnetic resonance spectroscopy (MRS). Results can be applied, for instance, in radiotherapy and nuclear medicine. 4. Scene to model registration. Images of a scene and a model of the scene are registered. The model can be a computer representation of the scene, for instance maps or digital elevation models (DEM) in GIS, another scene with similar content (another patient), ‘average’ specimen, etc. The aim is to localize the acquired image in the scene/model and/or to compare them. Examples of applications: Remote sensing—registration of aerial or
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satellite data into maps or other GIS layers. Computer vision—target template matching with real-time images, automatic quality inspection. Medical imaging— comparison of the patient’s image with digital anatomical atlases specimen classification. E.Defining Project Goals: Fundamental problems in the analysis of functional and structural imaging data include data transport, boundary identification (including manual tracing, edge detection, and tissue segmentation), volume estimation, three-dimensional reconstruction and display, surface and volume rendering, shape analysis, and image overlay. These problems require that research investigators have access to suitable methods of image analysis, implemented on a set of software programs, in order to conduct neuroimaging research. The registration algorithm described is a robust and flexible tool that can be used to address a variety of image registration problems. Registration strategies can be tailored to meet different needs by optimizing tradeoffs between speed and accuracy. The main emphasis is given to the color combination of some brain tissues. The spaces in the folds of the brain (the sulci) can be easily detected by their color combination. Color intensity of that area will be measured; based on that measurement results, hypothesis will be generated. Cavity in cerebral cortex region stained in PET image is shown in figure 4.
FIGURE 4: Cerebral Cortex with Color Stain (PET Image).
TESTED RESULTS USING MATLAB:
Figure 5: Simulated Image First Load
FIGURE 6: Image in Zoom
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FIGURE 7: Normal Area.
FIGURE 8: Abnormal Area.
FIGURE 9: Measure of Abnormality.
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CONCLUSION Relevance of these approaches is the direct medical application for segmentation and edge detection. We have reviewed the techniques of the MRI image enhancement in terms of tumor pixels detected. We have studied several digital image processing methods and discussed its requirements and properties in brain tumor detection.Due to high cost and requirement of professionalism much of medical imaging softwares are far from common man. Here the attempt was develop simple software in MATLAB to detect the structural abnormality of brain. The task almost fulfilled but it requires much perfection. The highlight of this software is simplicity, user friendliness and keen observation of the image at minute level This paper gives enhanced information about brain tumor detection and segmentation. The marked area is segmented and the assessment of this tool from the radiologist, whom the project is concerned with, is positive and this tool helps them in diagnosis, the treatment procedure and state of the tumor monitoring. FUTURE SCOPE Future research in the segmentation of medical images will lead towards improving the accuracy, exactness, and computational speed of segmentation approaches, as well as minimising the amount of manual interaction. These can be improved by incorporating discrete and continuous-based segmentation methods. Computational effectiveness will be crucial in real-time processing applications. Segmentation methods have proved their utility in research are as and are now emphasizing increased use for automated diagnosis and radiotherapy. These will be particularly important in applications such as computer integrated surgery, where envision of the anatomy is a significant component. In this research the measurement of cavity area is totally based on the color coding system. This measurement should be converted into some metric form. After that the abnormality can be categorized as mild, moderate and severe. REFERENCES [1] H. D. Cheng, Y. H. Chen, and X. H. Jiang, \Thresholding using two dimensional histogram and fuzzy entropy principle,. IEEE Trans. Image Processing, vol. 9, pp. 732-735, 2000. [2] M.S. Atkins and B.T. Mackiewich, 1 J.C. Bezdek. Fully Automatic segmentation of the brain in MRI. IEEE T. Med.Imag.,17:98.109. [3] L.O. Hall and L.P. Clarke. \Review of MR image segmentation techniques using pattern recognition". Med. Phys., 20:1033.1048, 1993. 1998 [4] T.N. Pappas. An adaptive clustering algorithm for image segmentation. IEEE T. Signal Process., 40:901 914,1992. [5] Fan, J., Han, M. and Wang, J. Single point iterative weighted fuzzy c-means clustering algorithm for remote sensing image segmentation. Pattern Recognition, 42(11), pp. 2527.2540., 2009. [6] Chen, J., Pan, D. and Maz, Z.,. Image-object detectable in multi scale analysis on high-resolution remotely sensed imagery. International Journal of Remote sensing, 30(14), pp. 3585.3602, 2009. [7] Chen, Z., Zhao, Z., Gong, P. and Zeng, B \A new process for the segmentation of high resolution remote sensing imagery.. International Journal of Remote Sensing, 27(22), pp. 4991-5001., (2006). [8] H. D. Cheng, Y. H. Chen, and X. H. Jiang, \Thresholding using two dimensional histogram and fuzzy entropy principle,. IEEE Trans. Image Processing, vol. 9, pp. 732-735, 2000. [9] M.S. Atkins and B.T. Mackiewich, 1 J.C. Bezdek. Fully Automatic segmentation of the brain in MRI. IEEE T. Med.Imag.,17:98.109. [10] L.O. Hall and L.P. Clarke. \Review of MR image segmentation techniques using pattern recognition". Med. Phys., 20:1033.1048, 1993. 1998 [11] T.N. Pappas. An adaptive clustering algorithm for image segmentation. IEEE T. Signal Process., 40:901 914,1992.
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[12] Fan, J., Han, M. and Wang, J. Single point iterative weighted fuzzy c-means clustering algorithm for remote sensing image segmentation. Pattern Recognition, 42(11), pp. 2527.2540., 2009. [13] Chen, J., Pan, D. and Maz, Z.,. Image-object detectable in multi scale analysis on high-resolution remotely sensed imagery. International Journal of Remote sensing, 30(14), pp. 3585.3602, 2009. [14] Chen, Z., Zhao, Z., Gong, P. and Zeng, B \A new process for the segmentation of high resolution remote sensing imagery.. International Journal of Remote Sensing, 27(22), pp. 4991-5001., (2006). [15] Marshall, Louise H., and Magoun, Horace Winchell (1998). “Discoveries in the Human Brain: Neuroscience Prehistory, Brain Structure, and Function, 1st ed.”, Humana Press: USA. ISBN-10: 0896034356 | ISBN-13: 978-0896034358.
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A Novel Selection Combining Technique for Multi relay Cooperative Communication System Using Decode and Forward Protocol K.Kavitha , A.Kumaresan Associate Professor , Assistant Professor Department of ECE, Kumaraguru College of Technology, Coimbatore, India Abstract— The wireless data communication system demands robust methodologies to combat the effect of fading to increase the throughput. The paper proposes a novel selection combining algorithm for multi relay cooperative system using decode and forward algorithm. Cooperative communication is one of the promising diversity techniques which have attracted the attention of the researchers currently. In the conventional cooperative communication system, the data is transmitted through ‘N’ number of relays to the destination. The signals received from all the relays are combined to decode the data at the destination.. In Literature, there are various combining schemes such as equal gain combining, maximum ratio combining scaled selection combining techniques. In the conventional selection combining, only one relay signal is selected based on its channel state information (CSI). In this, the source to relay CSI is not utilized. It has been proved that by utilizing full CSI the error performance could be improved of the selection combining algorithm. In the proposed system, instantaneous signal to noise ratio (SNR) is calculated at the relay stations and it is used to take the decision whether to decode the signal and forward (DF) the same to destination or to be dropped. Only the signals with SNR above the threshold from the received relay signals is considered for combining. The performance of the proposed system with maximum ratio combining and scaled selection combining has been analyzed. In this system the error performance is improved by using selected relay signal, whereas only one relay is used in the conventional selection combining system.
Index Terms—Decode and forward (DF), M-ary phase-shift keying (MPSK), Rayleigh fading, Threshold selection combining, Symbol error probability (SEP). I.INTRODUCTION
Diversity combining technique is one of the efficient methods formulated against Fading in wireless Communication. In this cooperative diversity, users in this network will share their antennas or resources to provide diversity. Most commonly using diversity technique is spatial diversity which involves multiple antennas which be used with enough distance separation between them. So that the receiver can receive multiple independently fading signal paths. By focusing on this problem we came across with one alternative which involves the distributed space diversity named as Cooperative Communication. At the base station , Spatial diversity is obtained by combining the same information bearing signals from the different physically separated mobile units[1].Several protocols are proposed in the literature for implementing user co-operation, from which the decode-and-forward (DF) protocol is used in the paper. In the DF protocol, the relays decode and forward the source’s data to the destination [2] .The impact of channels state information (CSI) of the source-to-relay link, and the relay-to-destination link on various relay selection cooperative schemes has been presented in [3]. In [4],an optimum threshold that minimizes the end-to-end bit error probability, has been analyzed for a singlerelay cooperative system. A two-relay distributed switch and stay combining has been proposed with amplify and forward and DF protocols and the outage error analysis has been presented in [5].In [6],the error performance of a single-relay cooperative diversity system with DF relaying has been studied, and this work consider that the relay forwards the source’s data only if the instantaneous signal-to-noise (SNR) is above a threshold. In[7],[8], the performance analysis of multi-relay cooperative diversity with DF protocol and without threshold relaying has been investigated. The outage performance of selection DF cooperative relay networks has been analyzed in [9] for Rayleigh fading and in[10] for Nagakami Fading. In [11],the M-ary PSK error
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performance of a single-relay cooperative diversity system with selection combining (that does not include the effect of the source-to-relay link) has been presented using a new paired error approach. In this paper, instantaneous signal to noise ratio (SNR) is calculated at the relay stations and it is used to take the decision whether to decode the signal and forward (DF) the same to destination or to be dropped. Only the signal with SNR above the threshold from the received relay signals is considered for combining. The performance of the proposed system versus Non cooperation signal and scaled selection combining has been analyzed. In this paper, we consider a cooperative diversity system which has a source, multiple relays and destination in a flat Rayleigh fading environment with statically independent links. The main drawback of the usual instantaneous SNR-based selection combining with DF protocol for selection between the source-to-destination and relay-to-destination links is that it does not incorporate the effect of the source-to-relay link, that is the interuser link which plays a important role in the error performance analysis. To overcome this problem, we present here a deterministic scaled factor, which incorporates the effect of source-to-relay fading . We derived the end-to-end symbol error probability (SEP) of this scheme for binary PSK (BPSK) in closed-form, we also give a method of optimizing the scale factor such that it minimizes the end-to-end SEP. II.SYSTEM MODEL In this paper, we consider a cooperative diversity system which has a source, multiple relays and a destination in a flat Rayleigh fading environment with statically independent links (Fig.1).
Fig.1.Multiple relaying path The binary information is modulated by M-ary PSK modulator and broadcasted to destination as well as to the relay over flat fading environment. That is carried out by 2 phases of operation in progress with this diversity scheme. In first phase, source will broadcast the BPSK signal to the destination and as well as to the relays in the fading environment. The complex baseband signal received at the corresponding destination and at the relays. The received signal is demodulated and detected using DF algorithm. Let the information carrying complex signal have energy 2E s to the constellation S, which is refers to symbol and it is given by, S m =√2𝐸𝐸 s exp� R
Where, j = √−1.
𝑗𝑗2𝜋𝜋(𝑚𝑚−1) M
�, m=1,2,3,…..M
The complex baseband received signal at the destination and at the relay at the first time slot is represented as, 𝑟𝑟𝑠𝑠𝑠𝑠 = ℎ𝑠𝑠𝑠𝑠 . 𝑆𝑆 + 𝑛𝑛𝑠𝑠𝑠𝑠 𝑟𝑟𝑠𝑠𝑠𝑠 = ℎ𝑠𝑠𝑠𝑠 . 𝑆𝑆 + 𝑛𝑛𝑠𝑠𝑠𝑠
Where h sd and h sr are the random complex fading gains of the source to destination (SD) link and source to relay (SR) link. Further, the additive white Gaussian noises of the source to destination and source relay links are n sd , n sr respectively. Whereas the fading gains are formed as symmetric Gaussian random variable with zero Mean and Variance Ωsd and Ω sr respectively. The noises are formed as zero mean complex Gaussian random variable with the variance 2N o.
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In second Phase, the relay regenerate the symbol ^𝑠𝑠 and transmit to the destination. The destination receives the relay signal as, 𝑟𝑟𝑟𝑟𝑟𝑟 = ℎ𝑟𝑟𝑟𝑟 . ^𝑠𝑠 + 𝑛𝑛𝑟𝑟𝑟𝑟 where hrd and nrd are fading gains and additive white Gaussian noise of the Relay Destination link respectively. We assume that the destination have knowledge about the channel state information (CSI) of all links including relay to destination and source to destination, Meanwhile relay has the CSI of SR link. Now we indicate the instantaneous SNRs of the SD, SR, and the RD links, respectively as
ϒsd =
𝐸𝐸s
𝑁𝑁o
2 �ℎ𝑠𝑠𝑠𝑠 � ,ϒsr =
𝐸𝐸s
𝑁𝑁o
2 �ℎ𝑠𝑠𝑠𝑠 � , ϒrd =
𝐸𝐸s
𝑁𝑁o
2 �ℎ𝑟𝑟𝑟𝑟 �
The corresponding average SNRs are given by,
ᴦ𝑠𝑠𝑠𝑠 = 𝐸𝐸[ϒ𝑠𝑠𝑠𝑠 ] =
ᴦ𝑠𝑠𝑠𝑠 = 𝐸𝐸[ϒ𝑠𝑠𝑠𝑠 ] =
ᴦ𝑟𝑟𝑟𝑟 = 𝐸𝐸[ϒ𝑟𝑟𝑟𝑟 ] =
ES Ω𝑠𝑠𝑠𝑠 NO
ES Ω𝑠𝑠𝑠𝑠 NO
ES Ω𝑟𝑟𝑟𝑟 NO
, ,
R
.
Where, ᴦth is known as assigned average SNR threshold value and E [.] is known as expectation operator. The destination detects received signal Š and finally detected symbol is obtained from the proposed decision rule is, R
Š partial to the following conditions,
max ∗ ∗ ⎧arg �𝑠𝑠 ∈ 𝑆𝑆𝑅𝑅𝑅𝑅(S hSD ϒSD )� ⎫ ⎪ ⎪ ⎪ ⎪ max ∗ ∗ )� Š = arg �𝑠𝑠 ∈ 𝑆𝑆𝑅𝑅𝑅𝑅(S hSR ϒSR ⎨ ⎬ max ⎪ ⎪arg � ⎪ 𝑅𝑅𝑅𝑅(S ∗ h∗RD ϒRD )�⎪ 𝑠𝑠 ∈ 𝑆𝑆 ⎩ ⎭
𝑖𝑖𝑖𝑖 ϒSD > ᴦth , �𝑖𝑖𝑖𝑖 ϒSD < ᴦth and ϒSR > ϒRD , 𝑖𝑖𝑖𝑖 ϒSD < ᴦth and ϒSD > ϒRD . In this system we compared our proposed performance with the conventional schemes. Which articulates different combining schemes nurtured together to novel multiple selection combining scheme.
In previous conventional selection combining and threshold selection combining schemes out of N signal random signal is took for analysis and decision purpose. But in this proposed combining scheme (Hybrid of threshold and selection combining scheme) we enhanced the performance by use of dual threshold level process. At the destination we formulate selection combining scheme for best performance from received signal Š.
III.PERFORMANCE ANALYSIS We represent the instantaneous SNR as, ϒ=
𝐸𝐸s
𝑁𝑁o
|ℎ |2 ,
In this section, we use the mathematical probability model for DF relaying proposed in [4] to derive a closedform expression for the outage probability of the SC system. The conditional error probability for MPSK is given by, 𝑃𝑃𝑒𝑒 (ϒ) = ICIECA 2014
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𝜋𝜋
∫0
𝜋𝜋(𝑀𝑀−1) 𝑀𝑀
exp �−
ϒsin2
π M
𝑠𝑠𝑠𝑠𝑠𝑠2 ∅
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A. SEP for Source-Destination link The probability of error in SD link is derived in the form under the condition ϒ𝑆𝑆𝑆𝑆 > ϒ 𝑇𝑇ℎ or ϒ𝑆𝑆𝑆𝑆 > ϒ𝑅𝑅𝑅𝑅 given by, 𝜋𝜋(𝑀𝑀−1) 𝑀𝑀
∞ 1
𝑃𝑃𝑒𝑒𝑒𝑒𝑒𝑒1 = ∫ᴦ
∫0
𝑡𝑡ℎ 𝜋𝜋
ϒsin2
𝑒𝑒𝑒𝑒𝑒𝑒 �−
π M
𝑠𝑠𝑠𝑠𝑠𝑠2 ∅
�×
�
1
ᴦ𝑆𝑆𝑆𝑆
� 𝑒𝑒𝑒𝑒𝑒𝑒 �−
𝑥𝑥
ᴦ𝑆𝑆𝑆𝑆
is
� 𝑑𝑑𝑑𝑑𝑑𝑑∅
Simplify error probability equation by applying integral over x, 𝑃𝑃𝑒𝑒𝑒𝑒𝑒𝑒1 =
𝜋𝜋(𝑀𝑀−1) 𝑀𝑀
1
∫0
𝜋𝜋ᴦ𝑆𝑆𝑆𝑆
1
π sin2 M 1 + � � 𝑠𝑠𝑠𝑠𝑠𝑠2 ∅ ᴦ𝑆𝑆𝑆𝑆
π M 2 𝑠𝑠𝑠𝑠𝑠𝑠 ∅
sin2
×
𝑒𝑒𝑥𝑥𝑥𝑥 �− �
+
1
ᴦ𝑆𝑆𝑆𝑆
�� ᴦ𝑡𝑡ℎ 𝑑𝑑∅
Probability of error in source to destination link ϒ𝑆𝑆𝑆𝑆 > ϒ𝑅𝑅𝑅𝑅 is obtained by applying integral over x and further substitution we get total symbol error probability of S-D link is given as, 𝑷𝑷𝒆𝒆𝒆𝒆𝒆𝒆 =
𝝆𝝆𝟒𝟒(ᴦ𝑺𝑺𝑺𝑺,ᴦ𝑹𝑹𝑹𝑹,∅𝟎𝟎 ) + 𝝆𝝆𝟑𝟑(ᴦ𝑺𝑺𝑺𝑺,ᴦ𝑹𝑹𝑹𝑹,∅𝟎𝟎 ,ᴦ𝒕𝒕𝒕𝒕)
𝟏𝟏
𝝆𝝆𝟐𝟐(ᴦ𝑺𝑺𝑺𝑺,∅𝟎𝟎 ) − 𝝆𝝆𝟏𝟏�ᴦ𝑺𝑺𝑺𝑺,∅𝟎𝟎, ᴦ𝒕𝒕𝒕𝒕� −
ᴦ𝑺𝑺𝑺𝑺
B. SEP for Source-Relay-Destination Link The probability of ϒ𝑠𝑠𝑠𝑠 < ϒ𝑟𝑟𝑟𝑟 is given by , ∞
ᴦ
error
∞
𝑃𝑃𝑒𝑒𝑒𝑒𝑒𝑒 = ∫𝑧𝑧 ∫0 𝑡𝑡ℎ ∫0 𝑝𝑝𝑒𝑒 (𝑦𝑦, 𝑧𝑧) ᴦ
1
𝑆𝑆𝑆𝑆
Lets define 𝑃𝑃𝑒𝑒𝑒𝑒𝑒𝑒1 , 𝑃𝑃𝑒𝑒𝑒𝑒𝑒𝑒2 , 𝑃𝑃𝑒𝑒𝑒𝑒𝑒𝑒3 𝑃𝑃𝑒𝑒𝑒𝑒𝑒𝑒1 = �
1
ᴦ𝑆𝑆𝑆𝑆
in
R-D
𝑒𝑒𝑒𝑒𝑒𝑒 �−
𝑥𝑥
ᴦ𝑆𝑆𝑆𝑆
(Relay-Destination)
�×
∞
� 𝜌𝜌2 (ᴦ𝑆𝑆𝑆𝑆 , ∅0 ) ∫𝑥𝑥 (1 (𝑦𝑦)) ×
∞
� 1
�
ᴦ𝑅𝑅𝑅𝑅
� 𝑒𝑒𝑒𝑒𝑒𝑒 �−
𝑃𝑃𝑒𝑒𝑒𝑒𝑒𝑒2 = � 𝑃𝑃𝑒𝑒 (𝑦𝑦) 0
1
ᴦ𝑅𝑅𝑅𝑅
∞
link,
� 𝑒𝑒𝑒𝑒𝑒𝑒 �− 𝑦𝑦
ᴦ𝑅𝑅𝑅𝑅
𝑦𝑦
�
under
1
ᴦ𝑅𝑅𝑅𝑅 ᴦ𝑆𝑆𝑆𝑆
𝑒𝑒𝑒𝑒𝑝𝑝 �−
the
𝑧𝑧
ᴦ𝑆𝑆𝑆𝑆
condition
of
� 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
� 𝑑𝑑𝑑𝑑
1 𝑧𝑧 𝑒𝑒𝑒𝑒𝑒𝑒 �− � 𝑑𝑑𝑑𝑑 ᴦ𝑆𝑆𝑆𝑆 ᴦ𝑆𝑆𝑆𝑆
𝑃𝑃𝑒𝑒𝑒𝑒𝑒𝑒3 = � 𝑃𝑃𝑙𝑙,𝑘𝑘 (𝑦𝑦) � 𝑃𝑃𝑘𝑘,𝑙𝑙 (𝑧𝑧) 0
1 𝑧𝑧 𝑒𝑒𝑒𝑒𝑒𝑒 �− � 𝑑𝑑𝑑𝑑 ᴦ𝑆𝑆𝑆𝑆 ᴦ𝑆𝑆𝑆𝑆
𝑃𝑃𝑙𝑙,𝑘𝑘 (𝑧𝑧) is probability of error in S-R link when Sk is transmitted in S-R link and it is detected as Sl at the relay
node. 𝑃𝑃𝑘𝑘.𝑙𝑙(𝑦𝑦) is probability of error in R-D link when Sl is transmitted from relay to destination and it is detected as Sk at the destination node. By solving above equations we get, 1
𝜋𝜋(𝑀𝑀−1) 𝑀𝑀
𝑃𝑃𝑒𝑒𝑒𝑒𝑒𝑒1 = �1 − 𝑃𝑃𝑒𝑒 (𝑦𝑦)� ∫0 𝜋𝜋
∞ 1
∫0
Now applying integral over y in as, By Substituting 𝑝𝑝𝑒𝑒 (𝑦𝑦) we get,
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ᴦ𝑆𝑆𝑆𝑆
×
sin2
𝑒𝑒𝑒𝑒𝑒𝑒 �− �
π M
𝑠𝑠𝑠𝑠𝑠𝑠2 ∅
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1
ᴦ𝑆𝑆𝑆𝑆
�� 𝑧𝑧𝑧𝑧zd∅
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𝑃𝑃𝑒𝑒𝑒𝑒𝑒𝑒1 = �
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1
ᴦ𝑆𝑆𝑆𝑆
� 𝜌𝜌2 (ᴦ𝑆𝑆𝑆𝑆 , ∅0 ) × � 1
ᴦ𝑅𝑅𝑅𝑅
1
ᴦ𝑅𝑅𝑅𝑅
�×
𝑒𝑒𝑒𝑒𝑒𝑒 �−
𝑥𝑥
ᴦ𝑅𝑅𝑅𝑅
𝜋𝜋(𝑀𝑀−1) 𝑀𝑀
� ∫0
�� 𝑥𝑥𝑥𝑥∅
1
π sin2 M 1 + � 𝑠𝑠𝑠𝑠𝑠𝑠2 ∅ ᴦ𝑅𝑅𝑅𝑅
�
𝑒𝑒𝑒𝑒𝑒𝑒 �− �
sin2
π M
𝑠𝑠𝑠𝑠𝑠𝑠2 ∅
319
+
By applying integral over x and by further substitution, we get total symbol error probability of R-D link is given as 𝑷𝑷𝒆𝒆𝒆𝒆𝒆𝒆 = ∑𝑴𝑴 𝒍𝒍=𝟏𝟏,𝒍𝒍≠𝒌𝒌
𝟏𝟏
𝟒𝟒𝟒𝟒ᴦ𝑺𝑺𝑺𝑺 ᴦ𝑺𝑺𝑺𝑺
[𝝆𝝆𝟐𝟐 (ᴦ𝑺𝑺𝑺𝑺 , ∅𝟏𝟏 ) −
𝝆𝝆𝟐𝟐 (ᴦ𝑺𝑺𝑺𝑺 , ∅𝟐𝟐 )] × (𝑷𝑷𝑷𝑷 − 𝑷𝑷𝑷𝑷)
Where, P1 and P2 is given as, 𝜋𝜋−∅3
𝑃𝑃1 = ∫0
𝜋𝜋−∅4
𝑃𝑃2 = ∫0
1
1
×
1
1
×
sin2 ∅4 1 sin2 ∅3 1 1 + + + � �� � ᴦ𝑅𝑅𝑅𝑅 ᴦ𝑅𝑅𝑅𝑅 ᴦ𝑆𝑆𝑆𝑆 𝑠𝑠𝑠𝑠𝑠𝑠2 ∅ 𝑠𝑠𝑠𝑠𝑠𝑠2 ∅
�
sin2 ∅4 1 sin2 ∅4 1 1 + + + �� � ᴦ𝑅𝑅𝑅𝑅 ᴦ𝑅𝑅𝑅𝑅 ᴦ𝑆𝑆𝑆𝑆 𝑠𝑠𝑠𝑠𝑠𝑠2 ∅ 𝑠𝑠𝑠𝑠𝑠𝑠2 ∅
sin2 ∅3
�1 − 𝑒𝑒𝑒𝑒𝑒𝑒 �− �
𝑠𝑠𝑠𝑠𝑠𝑠2 ∅
1 �−𝑒𝑒𝑒𝑒𝑒𝑒 �− �sin2 ∅4 +
+
𝑠𝑠𝑠𝑠𝑠𝑠2 ∅
1
1
ᴦ𝑅𝑅𝑅𝑅
ᴦ𝑅𝑅𝑅𝑅
+
+ 1
1
ᴦ𝑆𝑆𝑆𝑆
ᴦ𝑆𝑆𝑆𝑆
�� ᴦ𝑡𝑡ℎ � 𝑑𝑑∅
�� ᴦ𝑡𝑡ℎ � 𝑑𝑑∅
IV.NUMERICAL RESULTS In this section, we discuss about the error analysis using DF relaying protocol and Symbol error probability (SEP) measure is carried out . Plots are done for received signal SEP with varying average SNR the decision rule. Fig.2, shows that the results for Non cooperation, conventional Scaled selection combining and Threshold switching selection combining for multiple threshold combining schemes with the outage probability error measure for various instantaneous SNR values of BPSK. In this evaluated result we use switched threshold decision rule for error performance analysis, which proved as better combining scheme for multiple relay with the better error reduction. Fig 2, shows that the BER performance of proposed scheme is improved to the conventional selection combining. Schemes, In this proposed scheme, we fixed a threshold value at each relay station and at the destination station. Based upon the instantaneous SNR we took proper decision to forward the desired signal for proper filtering and combining at receiver node. The above plot shows that the multiple thresholds combining scheme provides better error performance than existing schemes with achieved gain of 15dB over other schemes from non-cooperative schemes.
Fig 2. BER performance for BPSK Signalling for multiple threshold scheme for multiple relays
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CONCLUSION In this paper, we proposed the threshold switching technique which incorporates the effect of source to relay link to reduce the probability of error. The end to end symbol error probability is derived for M-PSK signalling with DF relaying in flat Rayleigh fading. We have proved that the BER of proposed scheme is reduced than conventional non-cooperation and scaled selection combining schemes for multiple relays with multiple threshold combining scheme. ACKNOWLEDGEMENT The authors thank the management and principal of Kumara guru College of Technology, Coimbatore for providing excellent computing facilities and encouragement. REFERRENCE [1] A.Sendonaris, E.Erkip, and B.Aazhang, “User cooperation diversity – part I: system description,” IEEE Transactions on Communications, vol. 51, no. 11, pp. 1927–1938, November 2003. [2] J.N.Laneman, D.N.C.Tse, and G.W.Wornell, “Cooperative diversity in wireless networks: efficient protocols and outage behavior,” IEEE Transactions on Information Theory, vol. 50, no. 12, pp. 3062–3080, December 2004. [3] M.D.Selvaraj and R.K.Mallik, ”Error analysis of decode-and-forward protocol with selection combining,” IEEE Transactions on Wireless Communications, vol. 8, no.6, pp. 3086-3094, June 2009. [4] F. A. Onat, Y. Fan, H. Yanikomeroglu, and J. S. Thompson, Asymptotic BER analysis of threshold digital relaying schemes in cooperative wireless systems,”, IEEE Trans. Wireless Commun., vol. 7, no. 12, pp. 49384947, Dec. 2008. [5] M.D.Selvaraj and R.K.Mallik, Single-Relay Cooperative Diversity with Scaled Selection Combining, IEEE Transactions on Communications,vol. 59, no.3, pp. 3086-3094, March 2011. [6] F. A. Onat, A. Adinoyi, Y. Fan, H. Yanikomeroglu, J. Thompson, and I. Marsland, Threshold selection for SNR-based selective digital relaying in cooperative wireless networks, IEEE Trans. Wireless Commun., vol. 7, no. 11, pp. 42264237, Nov. 2008. [7] F. A. Onat, A. Adinoyi, Y. Fan, H. Yanikomeroglu, and J. S. Thompson, Optimum threshold for SNR-based selective digital relaying schemes in cooperative wireless networks, in Proc. IEEE Wireless Communications and Networking Conference (WCNC), 2007. [8] W. P. Siriwongpairat, T. Himsoon, W. Su, and K. J. R. Liu, Optimum threshold-selection relaying for decode-and-forward cooperation protocol,” in Conf. Rec. IEEE Wireless Commun. Netw. Conf., vol. 2, Apr. 2006, pp. 1015-1020. [9] T. Eng, N. Kong, and L. B. Milstein, Comparison of diversity combining techniques for Rayleigh-fading channels, IEEE Trans. Commun., vol. 44, no. 9, pp. 11171129, Sep. 1996. [10] C.C.Tan and N.C.Beaulieu, ”Infinite series representations of the bivariate rayleigh and Nakagami-m distributions,” IEEE Transactions on Communications,vol. 45, no. 10, pp. 1066-1073, October 1997. [11] R.K.Mallik and M.Z.Win, ”Analysis of hybrid selection/ maximalratio combining in correlated Nakagami fading,” IEEE Transactions on Comm.., vol. 50,no. 8, pp. 1372-1383, August 2002.[12] T. Eng, N. Kong, and L. B. Milstein, “Comparison of diversity combining techniques for Rayleigh fading channels,” IEEE Trans. Commun., vol. 44, pp. 1117–1129, Sept. 1996. [13] J. Lu, T. T. Tjhung, and C. C. Chai, “Error probability performance of L-branch diversity reception of MQAM in Rayleigh fading,” IEEE Trans. Commun., vol. 46, pp. 178–181, Feb. 1998. [14] M. Schwartz, W. R. Bennett, and S. Stein, Communication Systems and Techniques. New York: McGrawHill, 1966, ch. 10. [15] M. D. Yacoub, Foundations of Mobile Radio Engineering. Boca Raton, FL: CRC Press, 2003. [16] K.-T.Wu and S.-A. Tsaur, “Selection diversity for DD-SSMA communications on Nakagami fading channels,” IEEE Trans. Veh. Technol., vol. 43, pp. 428–438, Aug. 2007.
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Designing & Implementation of Mobile Operated Toy Car by DTMF V.Bhuvaneswari, A.Sharmela Dept. of Electronics and communication engineering, University college of Engineering, Arni Thiruvannamalai Dt. Abstract- This paper advocates the operation of a toy car that is controlled by a mobile phone that makes a call to the mobile phone attached to the car. In the course of a call, if any button is pressed, a tone corresponding to the button pressed is heard at the other end of the call. This tone is called DTMF (dual-tonemultiple- frequency).The car perceives this DTMF tone with the help of the phone stacked in the car. The received tone is processed by the (ATmega16) microcontroller with the help of DTMF decoder MT887o. The decoder decodes the DTMF tone into its equivalent binary digit and this binary number is sent to the microcontroller. The microcontroller is programmed to take a decision for any given input and outputs its decision to motor drivers in order to drive the motors in forward direction or backward direction or left and right direction. The mobile phone that makes a call to mobile phone stacked in the car act as a remote. For that reason this paper does not require the construction of receiver and transmitter units. Keywords: DTMF tone, DTMF decoder, DTMF technology. I. INTRODUCTION Dual-tone multi-frequency (DTMF) signaling is used for telecommunication signaling over analog telephone lines in the voice- frequency band between telephone handsets and other communications devices and the switching center [1]. The version of DTMF used for telephone tone dialing is known by the trademarked term Touch-Tone (cancelled March 13, 1984), and is standardized by ITU-T Recommendation Q.23. It is also known in the UK as MF4 [2]. Other multi-frequency systems are used for signaling internal to the telephone networks a method of in-band signaling. DTMF tones were also used by cable television broadcasters to indicate the start and stop times of local commercial insertion points during station breaks for the benefit of cable companies. Until better out-of-band signaling equipment was developed in the 1990s, fast, unacknowledged, and loud DTMF tone sequences could be heard during the commercial breaks of cable channels in the United States and elsewhere [3]. The conventional wireless controlled toy car user circuits have drawbacks of limited working range, limited frequency range and limited control. However, these limitations can be overcome by using the mobile phone technologies in this purpose. It provides the advantages to control, working range as large as the coverage area of the service provider, no interference with other controllers and up to twelve controls [4].
II. LITERATURE REVIEW This propeller-driven radio controlled boat, built by Nikola Tesla in 1898, is the original prototype of all modernday uninhabited aerial vehicles and precision guided weapons. In fact, it is among all remotely operated vehicles in air, land or sea [5]. Powered by lead-acid batteries and an electric drive motor, the vessel was designed to be maneuvered alongside a target using instructions received from a wireless remote-control transmitter. Once in position, a command would be sent to detonate an explosive charge contained within the boat’s forward compartment. The weapon’s guidance system incorporated a secure communications link between the pilot’s controller and the surface-running torpedo in an effort to assure that control could be maintained even in the presence of electronic counter measures [6]
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During World War II in the European Theatre the U.S. Air Force with three basic forms radio-control guided weapons. In each case, the weapon would be directed to its target by a crew member on a control plane. The first weapon was essentially a standard bomb fitted with steering controls. The next evolution involved the fitting of a bomb to a glider airframe, one version, the GB-4 having a TV camera to assist the controller with targeting. The third class of guided weapon was the remote controlled B-17. It’s known that Germany deployed a number of more advanced guided strike weapons that saw combat before either the V-1 or V-2. They were the radio-controlled Herschel’s Hs293A and Ruhrstahl’s SD1400X, known as “FritzX,’ both air-launched, primarily against ships at sea [5]. DTMF is the most common telecommunications signaling method used in Australia. DTMF stands for Dual Tone Multiple Frequency; it is used to send information through phone lines to and from local exchange. Dual Tone Multiple Frequency (DTMF) is also known as Touch-tone, Tone Dialing, VF Signaling and MF Dialing [7]. Each DTMF tone consists of two simultaneous tones (one from the high group and one from the low group), which are used to indicate which number or symbol that is press on the telephone's keypad. For example if number 5 is pressed in telephone's keypad, the tones that will hear are 1336 Hz and 770 Hz played simultaneously. Dual Tone Multiple Frequency is the basis of voice communications control. Modern telephone circuits use DTMF to dial numbers, configure telephone exchanges (switchboards) from remote locations, and program certain equipment and so on. Almost any mobile phone is capable of generating DTMF, providing a connection has already been established. This is for the use of phone banking; voicemail services and other DTMF controlled applications. DTMF was designed so that it is possible to use acoustic transfer. The DTMF tones can be sent from a standard speaker and be received using a standard microphone (providing it is connected to a decoding circuit of some type). DTMF tones are simply two frequencies played simultaneously by a standard home phone/fax or mobile phone. Each key on your telephone's keypad has a unique frequency assigned to it. When any key is pressed on your telephone's keypad the circuit plays the corresponding DTMF tone and sends it to your local exchange for processing. DTMF tones can be imitated by using a White Box or Tone Dialer. It is also possible to record DTMF tones using a tape recorder or computer microphone and then played into the mouthpiece of your telephone to dial numbers. However if there is a significant amount of background sound behind the recorded DTMF tones, the tones may not work properly and cause problems when trying to dial numbers. Below is a Dual Tone Multi Frequency (DTMF) map for a 4X4-matrix keypad, the map shows each unique frequency which is assigned to each key on a standard 4X4 telephone keypad. The frequencies are exactly the same for a 3X4 matrix keypad, without the keys A, B, C and D.
Fig-1: Dual Tone Multi Frequency (DTMF) map. However, this is not a standard keypad. This keypad has 4 more keys than a standard keypad (3X4-matrix). The keys A, B, C and D are not commonly used on standard home phone/fax, office phone or payphone. Each of the keys A, B, C and D are system tones/codes and are mainly used to configure telephone exchanges or to perform other special functions at an exchange. For example, the corresponding tone/code assigned to the A key is used on some networks to move through various carriers (this function is prohibited by most carriers).
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Filter is one of the very important devices of this DTMF technology. When DTMF was created individual and unique frequencies were chosen so that it would be quite easy to design frequency filters and so that the tones could easily pass through telephone lines (the maximum guaranteed bandwidth for a standard telephone line extends from around 300 Hz to 3.5 kHz). DTMF was not intended for data transfer; it was designed for control signals only. With a standard DTMF encoder/decoder, it is possible to signal at a rate of around 10 tones/signals per second. A standard DTMF tone should always be played for at least 50ms with a further 50ms space duration for maximum reliability. The contemporary mobile keypad is laid out in a 3x4 grid, although the original DTMF keypad had an additional column for four menu selector keys. When used to dial a telephone number, pressing a single key will produce a pitch consisting of two simultaneous pure tone sinusoidal frequencies. The row in which the key appears determines the frequency, and the column determines the high frequency. For example, pressing the key will result in a sound composed of both 697 Hz and 1209 Hz tone [8, 15]. The original keypads had levers inside, so each button activated two contacts. The multiple tones are the reason for calling the system multi frequency. These tones are then decoded by the switching center to determine which key was pressed.
Fig-2: A DTMF Mobile Keypad.
697 Hz 770Hz 852Hz 941Hz
1209 Hz 1 4 7 *
1336 Hz 2 5 8 0
1477 Hz 3 6 9 #
1633 Hz A B C D
Table 1: DTMF Keypad Frequencies (With Sound Clips). III. CIRCUIT DESIGN The important components of this car are DTMF decoder, microcontroller and motor driver. An MT8870 series DTMF decoder is used here. All types of the MT8870 series use digital counting techniques to detect and decode all the 16 DTMF tone pairs into a 4-bit code output. The built-in dial tone rejection circuit eliminates the need of prefiltering. When the input signal given at pin 2(IN-) in single-ended input configuration is recognized to be effective, the correct 4-bit decode signal of the DTMF tone is transferred to (pin11) through (pin14) outputs. The pin11 to pin14 of DTMF decoder are connected to the pins of microcontroller (pa0 to pa3).The ATmega16 is a low power, 8bit CMOS microcontroller based on the AVR enhanced RISC architecture. It provides the following features: 16kb of in-system programmable flash program memory with read-while-write capabilities, 512 bytes of EEPROM, 1kb SRAM, and 32 (I\O) lines. Outputs from port pins PD0 through PD3 and PD7 of the microcontroller are fed to the inputsIN1 through IN4 and enable pins (EN1 and EN2) of motor driver L293D IC, respectively to drive two geared dc motors. Switch S1 is used for manual reset. The microcontroller output is not sufficient to drive the dc motors, so Current drivers are required for motor rotation. The L293D is a quad, high- current, half-h driver designed to provide bidirectional drive currents of up to 600mA at voltages from 4.5V to 36V. It makes it easier to drive the dc motors. The L293D consists of four drivers. Pins IN1 through IN4 and OUT1 through OUT4 are the input and output pins respectively, of driver 1 through driver 4. Drivers 1 and 2, and driver 3 and 4 are enabled by enable pin 1(EN1) and pin 9 (EN2), respectively. When enable input EN1 (pin1) is high, drivers 1 and 2 are enabled and the outputs corresponding to their inputs are active. Similarly, enable input EN2 (pin9) enables drivers 3. The complete circuit diagram is illustrated in figure-3 below.
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In order to control the toy car, a call need to make to the cell phone attached to the toy car (through headphone) from any phone, which sends DTMF tunes on pressing the numeric buttons. The cell phone in the car kept in 'auto answer' mode. So after a ring, the cell phone accepts the call. Now particular button may press on the mobile phone for predefined desired action. The DTMF tones thus produced are received by the cell phone in the car. These tones are fed to the circuit by headset of the cell phone. The MT8870 decodes the received tone and sends the equivalent binary number to the microcontroller. According to the program in the microcontroller, the car starts moving. When the number key '2' (binary equivalent 00000010) is pressed on the mobile phone, the microcontroller outputs ‘10001001’binary equivalent. Port pins PD0, PD3 and PD7 are high. The high output at PD7 of the microcontroller drives the motor driver (L293D). Port pins PD0 and PD3 drive motors M1 and M2 in forward direction (as per table). Similarly, stop condition as per the condition. Details conditions are shown in the flow chart diagram. The primary objective of this project was to build a cell phone link between a transmitter and a dumb car and provide the capability to operate the car from a remote location. The purpose of using the cell phone was to make the operation possible from any remote location in the world where cell phone use is available. The product would include two interface systems. One interface would operate between the transmitter and a sending cell phone, and a second interface would operate between the receiving cell phone and the dumb car. The interface on the sending side would allow production and encoding of signals suitable for transmission via a cell phone. The interface on the receiving end would process the signals received by the cell phone and control the dumb car. The simple diagram below illustrates the concept of the project.
Fig-4: Pictorial representation of the complete system
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Fig-5: Practical Implementation of the system. In order to the work properly this system, certain specifications are made. They are: The system must have an interface between the transmitter, the sending phone and another interface between the receiver phone and the robot. The cell phones should be any common cell phone. However, a specific model can be chosen because of hands free set connection type. Very negligible delay compare to the operation of the off-the-shelf unit one. The system should be a low power device. Both mobile phones should have activated DTMF service for controlling the robot from a remote location. The working procedure for this model is very simple. At first, the robot needs to turn ON by giving power supply of 10v battery. Now dial a mobile number that is connected with robot at remote location. Then after ringing the remote mobile connected with robot, it will automatically connected by Auto-Answer option in mobile phone just like an internet connection established between two systems. It needs to be ensured that DTMF tones sending facility should be active between both mobiles. After connection establishment the keyboard need to use to operate the robot car in particular direction. The flow chart of the whole circuit is very helpful for the complete comprehending of the working principle of this model. For that purpose the flow chart is shown below. There are 3 main parts of this system. They are the decoder, microcontroller and the motor driver. The following paragraph explains about their working procedure in briefly. The MT-8870 DTMF DECODER IC is a full DTMF Receiver that integrates both band split filter and decoder functions into a single18-pin DIP or SOIC package. Manufactured using CMOS process technology, the M-8870 offers low power consumption (35mW max) and precise data handling. Its filter section uses switched capacitor technology for both the high and low group filters and for dial tone rejection. Its decoder uses digital counting techniques to detect and decode all 16 DTMF tone pairs into a 4-bit code. External component count is minimized by provision of an on-chip differential input amplifier, clock generator and latched tri-state interface bus. Minimal external components required include a low-cost 3.579545 MHz color burst crystal, a timing resistor, and a timing capacitor. The M-8870-02 provides a “powerdown” option which, when enabled, drops consumption to less than 0.5mW [10]. The M-8870-02 can also inhibit the decoding of fourth column digits. M-8870 operating functions include a band split filter that separates the high
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and low tones of the received pair and a digital decoder that verifies both the frequency and duration of the received tones before passing the resulting 4-bit code to the output bus [12]. The low and high group tones are separated by applying the dual-tone signal to the inputs of two 6th order switched capacitor band pass filters with bandwidths. That corresponds to the bands enclosing the low and high group tones. The filter also incorporates notches at 350 and 440 Hz, providing excellent dial tone rejection. Each filter output is followed by a single-order switched capacitor section that smooth the signals prior to limiting. Signal limiting is performed by high gain comparators provided with hysteresis to prevent detection of unwanted low-level signals and noise. The comparator outputs provide full-rail logic swings at the frequencies of the incoming tones. Decoder the M-8870 uses a digital counting technique to determine the frequencies of the limited tones and to verify that they correspond to standard DTMF frequencies [11]. A complex averaging algorithm is used to protect against tone simulation by extraneous signals (such as voice) while tolerating small frequency variations. The algorithm ensures an optimum combination of immunity to talk off and tolerance to interfering signals (Third tones) and noise. When the detector recognizes the simultaneous presence of two valid tones (known as signal condition), it raises the Early Steering flag (Est.) [12]. Any subsequent loss of signal condition will cause Est. to fall. As the microcontroller used in this system is a very popular one hence the details about this microcontroller is show explained here. However, information about the ATMEGA16 microcontroller can be found on [13,16]. The L293D IC (Motor Driver) Device is a monolithic integrated high voltage, high current four channel driver designed to accept standard DTL or TTL logic levels and drive inductive loads (such as relays solenoids, DC and stepping motors) and switching power transistors [14]. To simplify use as two bridges each pair of channels is equipped with an enable input. A separate supply input is provided for the logic, allowing operation at a lower voltage and internal clamp diodes are included. This device is suitable for use in switching application at frequencies up to 5 kHz. The L293D is assembled in a 16 lead plastic Package which has 4 center pins connected together and used for heat sinking. The L293DD is assembled in a 20 lead surface Mount which has 8 centre pins connected together and used for heat sinking. A circuit connection of the motor driver is shown.
Fig-7: Circuit Configuration of L293D. IV. APPLICATION It can also be used to make mobile bomb by making some modification. If we use the switching IC instead of the driver IC we can turn on and off any appliances connected to this toy car. This toy car can carry in their capacity. This can be fitted in an attractive form of a toy car for child pleaser. Adding a camera could highly increase its popularity.
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Password protected systems have used in many war conditions and so on. CONCLUSION In this project, the toy car is controlled by a mobile phone that makes a call to the mobile phone attached to the car. In the course of a call, if any button is pressed, a tone corresponding to the button pressed is heard at the other end of the call. This is a wireless controller toy car hence the limitation of wired is completely overcome by using latest technology of mobile phones. However, there are still lots of scopes to improve the stability and ability of this system. The mobile phone that makes a call to mobile phone stacked in the car act as a remote. Hence this project does not require the construction of receiver and transmitter units. It is undoubtedly true that, this model can be a very significant device in case of the information acquisition from the remote areas where direct interference of human being is quite impossible hence it would be a very crucial topic to do further research on it.
APPENDIX The programming codes for this model are done in the programming language and are given below void main(void) { unsigned int k, h; DDRC=0x00; DDRD=0XFF; while (1) { k =~PINC; h=k & 0x0F; switch (h) { case 0x02: //if I/P is 0x02 { //O/P 0x89 ie Forward PORTD2_bit = 1; PORTD3_bit = 0; PORTD7_bit = 1; break; } case 0x08: //if I/P is 0x08 { //O/P 0x86 ie Backward PORTD2_bit = 0; PORTD3_bit = 1; PORTD7_bit = 1; break; } case 0x04: { // Left turn PORTD0_bit = 1; PORTD1_bit = 0; PORTD7_bit = 1; break; } case 0x06:
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{ // Right turn PORTD0_bit = 0; PORTD1_bit = 1; PORTD7_bit = 1; break; } case 0x05: { PORTD=0x00; // Stop break; } } }} REFERENCES [1] Awab Fakih, Jovita Serrao, Cell Phone Operated Robotic Car. International Journal of Scientific & Engineering Research, ISSN 2229-5518. [2] L. Schenker, "Pushbutton Calling with a Two- Group Voice-Frequency Code", The Bell System Technical Journal, 39(1), 1960, 235–255, ISSN 0005-8580 [3] Siegmund M. Redl, Matthias K. Weber, Malcolm W. Oilphant, GSM and Personal Communications Handbook, Artech House Boston, London. [4] V. Subramanyam, Electric Drives (Mc-Graw Hill, 1996) [5] Information available at the following link, http://www.tfcbooks.com/special/missiles.htm [6] Leland Anderson, Nikola Tesla —Guided Weapons & Computer Technology’, Tesla Presents Series Part 3. [7] Analog Telephony Compliance Requirements Overview, Hermon Laboratories TI Ltd. 2009. [8] Hector of SCP, Dual Tone Multiple Frequency A guide to understanding and exploiting Australians most common telecommunications signaling method. December 10th 2003 [9] S. A. Nasar, I. Boldea, Electric Drives (CRC/Taylor and Francis, 2006) [10] M-8870 DTMF Receiver, Information www.datasheetcatalog.org/datasheet/clare/M-8870-01SMTR.pdf
available
at
the
following
link,
[11] Milove H. Kothari, Telephonic Control Of Electrical Equipment. Electrical Engineering From L.D. College Of Engineering. [12] “DTMF Tester”, Electronics For You’ Magazine, Online Edition (June 2010). [13] Introduction to the Atmel ATmega16 Microcontroller, San José State University Dept. of Mechanical and Aerospace Engineering, 6th.September, 2010. [14] J. Banuchandar, V. Kaliraj, P. Balasubramanian, S. Deepa, N. Thamilarasi, Automated Unmanned Railway Level Crossing System, International Journal of Modern Engineering Research (IJMER), Vol.2, Issue.1, Jan-Feb 2012 pp-458-463 ISSN: 2249-6645. [15] Edwin Wise, Robotics Demystified (Mc-Graw Hill, 2005) [16] K. J. Ayala, 8051 Microcontroller (Delmar Learning, Thomson, 3rd Edition)
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Design of an Integrating Meter for Magnetic Flux Measurement Aparna V Central Scientific Instruments Organisation Council of Scientific and Industrial Research Chennai, India
Ashok kumar V Metros Unit Larsen & Toubro Construction Chennai, India Abstract-This is primarily a tool designed for use in measurement and industrial systems. This flux meter can measure total flux, from which the magnetic field strength and flux density, and various other parameters can be determined. This meter can be immensely valuable for accurate speed control of machines at low speed, for measurement of persistent current in a superconducting magnetic ring and for measuring the stray magnetic field around the transformers. Standard flux indicators presently in use are only providing a qualitative idea of magnetic flux levels which may not be sufficient to provide the optimum conditions for carrying out various magnetic inspections. Thus this meter is designed to measure the magnetic flux density whose value can be too low or high and can also measure the flux density in any direction.
Keywords—Integrating Flux meter, Magnetic Flux meter, Stray flux, Persistent current, Magnetic flux density. I. INTRODUCTION Magnetic flux, is a measure of the magnetic field strength existing on a two dimensional surface, such as one side of a magnet. Usually, a cluster of vectors attached to a geometrical abstract surface is referred to as magnetic flux, wherein each vector will intersect a separate point on the abstract surface as in [1]. The definition of surface integral relies on splitting the surface into small surface elements. Each surface element is associated with vector dS, whose magnitude is equal to the area of the element, and whose direction is orthogonal to the element and pointing outwards.
Fig. 1 A Vector Field of Normal to a Surface
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More generally, the magnetic flux at any angle to a surface is defined by a scalar product of the magnetic field and the area element vector as in [2]. The direction of the magnetic field vector B is by definition from the south to the North Pole of a magnet. Outside of the magnet, the field lines will go from north to south. The magnetic flux through a surface S is defined as the integral of the magnetic field over the area of the surface, Φm = ∬S B. dS.
(1)
Where, Φ m is the magnetic flux B is the magnetic field S is the surface (area) Denotes dot product dS is an infinitesimal vector, whose direction is the surface normal, and whose magnitude is the area of a differential element of S.
Fig. 2 Representation of a Magnetic Field which points Away From a Magnet's North Pole And towards Its South Pole
Magnetic flux may also be defined as path integral of magnetic potential, Φm = ∮∑ A. dl.
(2)
Where, ∑ is a surface bounded by the closed contour A is the magnetic potential dl is an infinitesimal vector element of the contour ∑
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If the magnetic field is constant with a value ‘B’ and the surface is a planar surface of area ‘A’, then the above formula simplifies to, Φm = BA cos θ.
(3)
Where, θ is the angle between the surface normal to S and B. The magnetic flux is usually measured with a flux meter. The flux meter contains sensing or measuring coils and electronics that evaluates the change of voltage in the measuring coils to calculate the magnetic flux. This flux meter employs only the electronic circuits and Integrators are used in this magnetic measurement because of the physical relationship between magnetic flux and the coils of wire. Also, the usage of only electronic circuits facilitates very less drift and low cost of the instrument.
II. IMPORTANT FEATURES OF THIS FLUX METER The instrument’s low drift improves productivity with fewer adjustments. It has many features to enhance throughput like faster settling times. Further, it also recovers quickly from reading reset to starting a new reading cycle. Using this, both a positive and negative peak can be captured from the same pulse. The high resolution of this flux meter is reinforced by a low noise floor. A configurable filter helps to keep the readings quiet. It is quick to setup and easy to use. This flux meter protects the operator and surrounding area from electric burn or shock, excessive temperature, spread of fire from the instrument and mechanical hazards. Drift is the most noticeable and often the largest source of error in integrating flux meters. Drift is a slow change in reading when no change in flux exists. It is caused by any small error voltage at the integrator input. This flux meter has very low drift.
Magnetic inspection often calls for the use of a specified level of magnetic flux within steel components. Standard flux indicators only provide a qualitative idea of magnetic flux levels which may not be sufficient to guarantee the optimum conditions for carrying out magnetic inspection. If the magnetic flux level is too low, then the defects may be overlooked, if the flux is too high then spurious indications may occur in a conventional flux meter. This flux meter has been produced to measure the magnetic flux density just below the surface whose value can be too low or high. Most magnetic field meters and gauss meters only measure the level of magnetism outside components under inspection. Yet it is the value of magnetic flux density within components which determine whether or not Magnetic Particle Inspection can be successfully carried out. Hence this flux meter is designed to measure the flux density in any direction.
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The integrator reacts continuously to the changing input to give an accurate area measurement. It is inconvenient to use this relationship directly for DC measurements because the voltage disappears as soon as the flux stops changing. The voltage is also proportional to the rate of change in flux and not the total change in flux which is often the desired measurement.
With only slight modifications to the integrator, a flux meter can measure periodic AC fields. The integrator output voltage can be processed by a peak detector to find maximum flux or through an RMS converter to find the RMS flux value. The relationships hold true for non-sinusoidal AC fields also.
Keeping drifts or other temperature changes away from the coil lead contacts and resetting the integrator often before every critical measurement will help in minimizing the integrator drift.
III. COMPONENTS OF THE FLUX METER The major component of this flux meter is an integrator. The integrator is very flexible. It performs well in a variety of magnetic applications from a fast pulse to a slow ramp. This flux meter fits well into test and measurement operations from all manual to full automation.
Fig. 3 A Typical Integrator Circuit with No Compensator Resistor
All capacitors exhibit a characteristic that can be described as a tendency to rebound from any fast change. When capacitors are discharged to zero volts momentarily, a small voltage will rise a few seconds later across the capacitor. Likewise, a rapid charge of a capacitor to some voltage will be followed by a slight reduction of that potential occurring over several seconds as in [3]. This characteristic is usually referred to as Dielectric Absorption.
The effect of dielectric absorption in this flux meter is a slight reading change over several seconds after a larger reading change. This occurs predictably during reading changes from 0 to some level and more notably occurs when the reading is reset. A reset from a large, full scale reading will show a “creeping up” of the reading for several seconds after the reset as in [4]. The level of this effect is approximately 0.03% of the reading change. The effect is most noticeable in the first few seconds and stabilizes after 20-30 seconds. For the most accurate reset of larger measurements an initial reset should be followed by a second reset a few seconds later.
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Fig. 4 A Circuit Model for Explaining a Time-Delayed Voltage Build-Up by Parallel RC-Timers
Fast changing, high voltage, or very low voltage signals are integrated most accurately with its integrators. IV. MATLAB SIMULATION MODEL OF THE FLUX METER Figure below shows the model of the flux meter created in MATLAB.
Fig. 5 Simulation Model of the Integrating Flux Meter
Sensing coils are sensitive to AC magnetic fields. Hence this flux meter can measure periodic AC fields over a wide frequency range using simple sensing coils.
Fig. 6 A Typical Sensing Coil in Flux Meter
Sensitivity is the instantaneous voltage produced for a given rate of change in flux. The coil voltage is directly proportional to the number of turns, as well as the rate of change in flux. Total change in flux can be measured as the flux meter integrates the instantaneous voltage over the measurement interval. Here the coils should be designed so the instantaneous coil voltage does not exceed the rated input voltage of the integrator. Coil resistance is sometimes overlooked because it does not appear in ideal equations for a coil or integrator, but it can limit sensitivity. Wire also
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has resistance and with more turns resistance can become large. Coil resistance must be accounted for when it is a meaningful percentage of the input resistance of the integrator. Also, dc coil resistance must be added to the integrator input resistance to get an accurate volt second reading.
Fig. 7 Effect of Sensing Coil Resistance on Magnetic Flux Density
Coil voltage is related to the number of changing flux lines passing through the center of the coil. The flux measured is a true indication of the number of lines passing through. The angle of the flux lines passing through the coil does not matter but changing coil orientation relative to a magnet often changes the number of flux lines that pass through the coil as in [5]. Orient the coil perpendicular to the flux lines for the most repeatable measurements.
Flux measurement is a true indication of lines of flux passing through a coil as in [6]. Field uniformity does not affect flux measurement, but other magnetic measurements such as flux density assume uniform flux over the coil area.
The flux meter reads the average flux density, while measuring flux density in a non-uniform field. Hence, the coil's length to outer diameter ratio should be optimized to measure flux density at the center of the coil rather than the average flux density. All loops other than the sensing coil should be eliminated or reduced. Loops in lead wires also witness changing flux as in a coil. Their voltage is an error added or subtracted from the coil voltage. Twisted leads from the coil to the flux meter are needed to reduce loop area and minimize error voltage.
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V. POTENTIAL AREAS OF APPLICATION
5.1 Accurate Speed Control At Low Speeds In speed control, the V-F control facilitates higher proportion of losses and a lower efficiency of the machine. Also the machines exhibit unstable performance at lower speeds as given in [7]. Thus this flux meter can be used in more accurate speed controls at low speeds by the measurement of large ranges of dc and ac flux.
Fig. 8 Block Diagram Schematic of V/F Control of VSI Fed 3-Phase Induction Motor Drive
5.2 Measurement Of Persistent Current In a super conducting ring, when dc current of large magnitude is induced, the magnetic flux is trapped inside the ring and hence the current persists in the ring for a long time. This current acts to exclude the magnetic field of the magnet. This current effectively forms an electromagnet that repels the magnet. This flux meter would help in the measurement of this persistent current.
This measurement would incorporate in several applications, like in electric power transmission, transformers, highperformance smart grids, electric motors, power storage devices, magnetic levitation devices, superconducting magnetic refrigeration and fault current limiters, etc., thus leading to higher relative efficiency, smaller size and weight and relatively lower cost.
5.3 Measurement Of Stray Magnetic Field This meter is useful for measuring stray fields around transformers. It can also be effectively used for measuring stray fields around the poles of a rotating magnet as in [8].
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CONCLUSION The cost part is very much understood from the design itself because with the type of materials used for designing, the cost would be inherently lesser in comparison to its counterparts. Only few of the potential applications are discussed in the paper, a few more could be identified. This could possibly be an area of extension. REFERENCES [1]. Griffiths, David J. (1999)" Introduction to Electrodynamics" (3rd ed.) Prentice Hall ,pp. 222–225,255-258,266268,332,422,438 [2]. "With record magnetic fields to the http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=823621
21st
Century"
IEEE
Xplore
[3]. Tipler, Paul (2004) "Physics for Scientists and Engineers: Electricity, Magnetism, Light, and Elementary Modern Physics" (5th ed.) W. H. Freeman ISBN 0-7167-0810-8 OCLC 51095685 [4]. Jackson, John D. (1999) "Classical Electrodynamics" (3rd ed.) Wiley ISBN 0-471-30932-X OCLC 224523909 [5]. Durney, Carl H. and Johnson, Curtis C. (1969) "Introduction to modern electromagnetics" McGraw-Hill [6]. Rao, Nannapaneni N. (1994)" Elements of engineering electromagnetics"(4th ed.) Prentice Hall. [7]. McCulloch, Malcolm,"A2: Electrical Power and Machines", Rotating magnetic field. eng.ox.ac.uk [8]. Furlani, Edward P. (2001) "Permanent Magnet and Electromechanical Devices: Materials, Analysis and Applications
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Gesture Controlled Robot Using Image Processing ARVIND.A, UTHIRA MOHAN Department of Electronics and Communication Engineering, meenakshi sundararajan college of engineering Abstract: Recent gesture controlled robots operates with humans next to them. Whereas, our project deals with interface of robots through gesture controlled technique but far away from the user. This can be achieved through image processing technique. The command signals are generated from these gestures using image processing. These signals are then passed to the robot to navigate it in the specified direction. KEYWORDS: Halide, PIC18 Microcontroller, MRF24WG0MA/ MRF24WG0MB, L293D: Motor Driver I.INTRODUCTION Nowadays, automation has become a most desirable means of operating a device. The word which comes to our mind when we say “automation” is “ROBOTICS”. Robotics plays a vital role in our society. Many undergoing projects are about robotics since, they reduce man power. Some of the robotic projects are also used for even crucial tasks which may harm the life of mankind. In early days, for operation of robots a person should be physically present beside it for operation. Our paper is about to operate a bot which may not be near us and could be operated from a station. How this is made possible? This can be executed through a technique called IMAGE PROCESSING. That is we can operate the bot from a base station by means of hand gesture. The hand gesture is received by the robot and it operates accordingly.
II.TECHNOLOGIES USED • • • •
Halide PIC18 Microcontroller MRF24WG0MA/ MRF24WG0MB L293D: Motor Driver
III.DESIGN 3.1PIC18 MICROCONTROLLER Microcontroller is considered as the heart of our project. It sends various instructions to the whole system for desired action. Here we use PIC18 Microcontroller which can easily interface with the Wi-Fi module which we have chosen. The unique features of the micro-controller are: • • •
83 (16-bit wide) Up to 2 MB addressable program memory 4KB RAM (max)
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32 level hardware stack 1 (8-bit) file select register Integrated 8x8 hardware multiply Highest performance 8-bit architecture Low power consumption
Figure 1: PIC Micro-controller chip
Figure 2: PIC18 Architecture Block Diagram 3.2 .MRF24WG0MA/ MRF24WG0MB The MRF Module connects to hundreds of PICmicrocontrollers via a 4-wire SPI interface and interrupt and is an ideal solution for lower-power, low data-rate Wi-Fi® sensor networks, home automation, building automation and consumer applications. The combination of the module and a PIC MCU running the TCP/IP stack results in support for IEEE Standard 802.11 and IP services. This allows for the immediate implementation of a wireless web server.
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Figure 3: MRF24WG0MA 3.3. HALIDE Halide is a new programming language designed to make it easier to write high-performance image processing code on modern machines. Its current front end is embedded in C++. Compiler targets include x86/SSE, ARM v7/NEON, CUDA, Native Client, and OpenCL 3.4. L293D: MOTOR DRIVER It takes digital signal as an input fromthePIC Micro-controllerand gives digital output to the DC motors of the robot. Power supply to the circuit is given by rechargeable batteries. In this system some rechargeable mobile batteries are used as power supply each of 3.7V. To provide more voltage to drive the motors, 2-3 such batteries are connected in series.
Figure 4: L293D motordriver IV. IMPLEMENTATION The user is present at a station far from the location of the bot from which he operates the robot. The user uses a webcam through a PC or Laptop to perform this process. He waves his hand upwards, downwards, left or right. The robot navigates following the hand gesture. This is the basic operation of our system. The detailed working of our system is discussed below.
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The webcam captures the video stream of the hand gestures in real time environment. The hand gestures are recorded and sent as a generated signal through a Wi-Fi from the station to the Wi-Fi module present in the robot. Therefore, Wi-Fi acts as a channel for transmission of signals.
Two ways of recording the hand gestures are followed namely, a) Finger Count method b) Hand Palm technique
The efficiently used method is hand palm technique since; finger count method does not provide required depth in output. The Wi-Fi signal is received by the module in the system i.e, MRF24WG0MA. It works at standard IEEE range 802.11. It sends the received signal from the station to the micro-controller.
The program fed to the micro-controller is HALIDE. This is the most efficiently used programming languages for image processing.
The micro-controller sends the command to the whole system proportional to the signal received. The micro-controller performs image thresholdingand performs the operations. Later, the signal is converted to digital signal.
Finally the processed signal is sent as a command to the motor driver. The motor driver facilitates the navigation of the robot to happen. Unless, the gesture is changed by the user, the robot continues moves in the previous direction.
Figure 5: Input image
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Figure 6: Threshold image
nnnnnnnnnn
BASE STATION
WEBCAM
MRF24WG0MA
LOCAL WI-FI MODULE
PIC Microcontroller embedded with Halide Program
LOCAL WI-FI MODULE
L293D Motor Driver DC MOTORS
Figure 6: Block diagram for Working of the system
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DC MOTORS
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CONCLUSION The Gesture Controlled Robot System gives an alternative way of controlling robots. Gesture control being a more natural way of controlling devices makes control of robots more efficient and easy. This paper adds s special feature that this gesture controlled robot can be operated from a considerably far off place through hand gestures. Hence it reduces the chances of danger for human life in crucial areas. Application wise, this robot can be for defense purpose. REFERNCES [1]. MARK BECKER, EFTHIMIA KEFALEA, ERIC MA¨EL, CHRISTOPH VON DER MALSBURG∗∗, MIKE PAGEL, JOCHEN TRIESCH, JAN C. VORBR¨ UGGEN, ROLF P. W¨ URTZ, AND STEFAN ZADEL , Grip See: A Gesture-controlled Robot for Object Perception and Manipulation. [3]L293D Motor Driver http://luckylarry.co.uk/arduino-projects/control-a-dc-motor-with-arduino-and-l293d-chip/ [4]DC Motors www.globalspec.com/learnmore/motion_controls /motors/dc_motors [5]. Halide (http://halide-lang.org/) [6]. PIC18 PIC Microcontrollers (http://www.microchip.com/pagehandler/en-us/family/8bit/architecture/pic18.html) [7]. Embedded Wi-Fi(http://www.microchip.com/pagehandler/en-us/technology/wifi/products/home.html)
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DEVELOPMENT OF WIRELESS INDUCTIVE MODEM CHARGER V.prasanth Dept. of computer science and engineering , Velammal Institute of technology,Chennai, B.Ajay Dept. of computer science and engineering , Amrita Vishwa Vidyapeetham , Coimbatore, B.Prasanna Dept. of computer science and engineering , S.R.M. University,Chennai Abstract-The project’s objective was to create an inductively coupled wireless fidelity charger which inductively charges the devices connected to it and also helps in the transmission of data to devices that have inductive charging and Wi-Fi enabled. The device is power efficient as it can switch on and off depending if the device connected to iwi-Fi, also spelled Wi-Fi or Wi-Fi, is a local area wireless technology that allows an electronic device to exchange data or connect to the internet using 2.4 GHz UHF and 5 GHz SHF radio waves. Inductive charging is usually done with a charging station. Energy is sent through an inductive coupling to an electrical device, which can then use that energy to charge batteries or run the device. To prevent any data error during transmission the device would have data check and data error correction present. Key Words-Modem , Wi-fi, Inductive charging,coupling, linking , E.M.F. , Flux,Magnetic field I.INTRODUCTION We live in a world of technological advancement. New technologies emerge each and every day to make our life simpler. Despite all these, we still rely on the classical and conventional wire system to charge our everyday use low power devices such as mobile phones, digital camera etc. and even mid power devices such as laptops. The conventional wire system creates a mess when it comes to charging several devices simultaneously. It also takes up a lot of electric sockets and not to mention the fact that each device has its own design for the charging port. At this point a question might arise. ―What if a single device can be used to charge these devices simultaneously without the use of wires and not creating a mess in the process?‖ We gave it a thought and came up with an idea. The solution to all these dilemma lies with inductive coupling, a simple and effective way of transferring power wirelessly.Wireless Power Transmission (WPT) is the efficient transmission of electric power from one point to another trough vacuum or an atmosphere without the use of wire or any other substance. This can beused for applications where either an instantaneous amount or a continuous delivery of energy isneeded, but where conventional wires are unaffordable, inconvenient, expensive, hazardous, unwantedor impossible. The power can be transmitted using Inductive coupling for short range, ResonantInduction for mid range and Electromagnetic wave power transfer for high range. WPT is a technologythat can transport power to locations, which are otherwise not possible or impractical to reach.Charging low power devices and eventually mid power devices by means of inductive coupling could be the next big thing
II. EXISTING SYSTEM In the system present in India, There are currently modems that only transmit data and does not inductively charge devices connected to it. There are no proximity sensors present in modems that help us in detecting the location where the range of the Wi-Fi signals can be maximized. The modems present are not charged inductively but are charged through wire cables. The modems present today are capable of controlling certain devices but are not integrated with inductive charging and sensors. In the present world, the modems have no power efficient consumption techniques which help in reducing the power consumption.
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III. PROPOSED SYSTEM
3.1. Inductively Charged Modem Inductive charging (also known as "wireless charging") uses an electromagnetic field to transfer energy between two objects. This essentially means that there isn’t any physical contact between the charger and the device. This is usually done with a charging station. Mutual induction comes to our rescue here. Energy is sent through an inductive coupling to an electrical device, which can then use that energy to charge batteries or run the device. A Wi-Fi modem and router is usually plugged in and supplied a constant DC voltage source of roughly around 10 to 15 V. The consumption in the quiet state is in the range of a few hundred milli-amperes according to usage, the consumption at transmitting is up to a few Amperes. This is just for a single device. More devices implies greater consumption. Now since this modem is needs to be charged inductively, the idea of an inductive charger that’s already being used is implemented here with modifications. This inductive charger unit has an inductor coil, basically a copper wire wound over an iron core. The same unit is installed in a Wi-Fi modem, then when they are kept together, in contact. What actually happens is this, when there is a current through an inductor there is a magnetic flux linkage leading to magnetic field. This field if constant (if it’s not changing) is unable to induce an E.M.F or current in the linked coil. Hence, there is a changing magnetic field used by changing the current in the inducing coil to induce current in the nearby coil. So, the nearby coil is linked and a potential difference is created in the coil so, there is a flow of current if the circuit is closed. This current is used to charge the device’s battery.
Since the idea proposed above just eliminates the usage of wires in every aspect, it is obvious that a wireless router will be used. A wireless ADSL router serves the purpose perfectly as it has an in-built ADSL modem. Here, instead of plugging in the modem; we use inductive charging to provide voltage. It just means that a normal charger behaves as a charging station due to the addition of transmitter coil that uses high frequency AC current to create a magnetic field which extends to the receiver coil (kept at a specific distance) present in the device (modem in this case). The receiver coil converts the AC current to DC and is used to power the modem up as usually charged by using induction .inductive charging can be made possible by using an E.M.F field 3.2. Error control. Since Wireless transmission is considered to be an unreliable communication channel. We have decided to implement a data error checking and correction module in the prototype in order to make the digital data transmitted through the wireless modem proposed be efficient without any errors. The proposed prototype device would check if the data transmitted has an error or not using data error correction techniques. The data error checking and correction technique we have implemented in the prototype is cyclic redundancy check. A cyclic redundancy check (CRC) is a single-burst-error-detecting cyclic code and non-secure hash function designed to detect accidental changes to digital data in computer networks. It is not suitable for detecting maliciously introduced errors. It is characterized by specification of a so-called generator polynomial, which is used as the divisor in a polynomial long division over a finite field, taking the input data as the dividend, and where the remainder becomes the result. In U.S.A the most common CRC code is CRC-16. With CRC -16, 16 bits are used for block check sequence. For error correction we use Forward error Correction. Since Forward error correction is the only error – correction scheme that actually detects and corrects transmission errors when they are received without retransmission. In this bit redundant bits are added before the transmission of the data. When an error is detected, the redundant bits help in finding which bit is in error. FEC is generally used when acknowledgements are impossible. 3.3. Controlling devices using modem The project in discussion extensively uses a Wi-Fi modem setup so it only makes sense to go with the concept of a RF (radio frequency) remote control as Wi-Fi setups also transmit radio signals at varying frequencies. This ensures that similar bandwidths can be used to connect computers and also to operate television and radio sets by the same wi-fi modem. These remote controls are also easy to operate, cheap and very common.
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Instead of sending out light signals, an RF remote transmits radio waves that correspond to the binary command for the button you're pushing. A radio receiver on the controlled device (here, computers, television and radio sets) receives the signal and decodes it. The only problem with RF remotes is thesheer number of radio signals flying through the air at any given time. The greatest advantage to radio-frequency remotes is their range: They can transmit up to 100 feet from the receiver (the range for Bluetooth/Wi-Fi direct is shorter), and radio signals can go through walls. This benefit is why you'll now find IR/RF remotes for many long distance transmission components and devices. These remotes use RF-to-IR converters to extend the range of an infrared remote. Here, instead of using a remote, we’ll just use a modem to transmit radio waves to the receivers in various devices which can be differentiated by the frequency of the radio waves emitted by the transmitter (modem). In other words, the modem is to be used as a universal remote. We will first need to establish a connection between the modem and the device to be operated. This can be achieved by macro programming a power button, as well as a switch or series of buttons to select which device the remote is controlling at the moment. A typical selection includes TV, VCR, DVD, and CBL/SAT, along with other devices that sometimes include DVRs, audio equipment or home automation devices. This actually means that a remote will be programmed into a modem so as to utilize the functions of both devices from a single setup. That is, the final modem/remote byproduct that is obtained will basically be a modem with a set of buttons that can be operated like the buttons on a remote control. 3.4. Inductively charging connected devices Now that we’ve proposed the inductive charging method for the modem the next step is to use that for the connected devices. It’s the next step for inductive chargers too. The inductive chargers, “wireless” chargers are actually plugged to power supply points and the inductive chargers are then kept in contact with the devices, but now that magnetic fields can be made stronger so that their field of flux can cover a bigger area. If that can be achieved then these devices that are connected to the Wi-Fi modem can be inductively charged with the help of existing magnetic flux by coupling them inductively. The computing device and the charging unit contain inductor coils. Basically inductor coils are copper (usually) wires wrapped around a core (preferably iron core). When the portable computing device is in the proximity of wireless network, the proximity of the coils allows an electromagnetic field to be created. This electromagnetic field allows electricity to be passed from one coil (in the charging unit) to the other (in the phone) sue to the phenomenon of mutual induction. The induction coil in the computing device then uses the transferred electricity to charge the device battery. This additionally reduces time for connection, abolishes the messy wirings and at the same time will be very efficient in charging multiple devices simultaneously. These times our computing (portable) devices are sucks power while connected to Wi-Fi networks, or any other network. So if charging the devices may not have their rate over consumption rates at least can equalize hence can save battery drain to a greater extent without any actual conscious efforts, happens with just the act of connection to the Wi-Fi network. 3.5. Power saver modem Modems are all time switched on, this leads to lack of security and power loss. Security in the sense, the data packets that the modem sends in search of the computing devices (in an effort to connect or either to upload or download) can actually be received by other devices that are unauthorized, hence its leads to security threats. So, if the Wi-Fi network is connected to an authorized user or an administrator the other computing devices that are trying to connect can be noticed or will be notified. Hence, the best way is to having the modem on only when the admin (or authorized user. Thus, reducing the network’s vulnerability due to hacking) due to is connected. This can be done by manually switching the modem off or by doing it automatically. The working goes this way, actually when a computing device is connected it either uploads or downloads, does at least any one of these at an instant.
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Therefore, it means when there is no uploading or downloading it indirectly means no device is connected. This can be used to our advantage, the modem can automatically be switched off when not connected (or when there is no downloading or uploading process gong on). This saves power and data. The security and safety of the network is ensured. 3.6. Working principle Our wireless fidelity modules are split into many modules. The first module consists of the modem which inductively charges itself. The modem basically inductively charges itself with the help of a strong electromagnetic field. The E.M, F field is used in charging the modem and also inductively charges every other device which are inductive and Wi-Fi enabled. The inductively charging of the modem enables us to place the modem at anyplace in the room. Once the mobile – computed devices are connected to the modem, the device will be charged once the device is authorized by the administrator. This enables us to use the connected devices anywhere instead of using near a plug point. The second module consists of power efficiency. In the proposed prototype the modem will have a power efficient switch on /off feature .When there are no devices connected to the modem, the modem will switch off saving power. When the administrator receives a request from the device to connect to the Wi-Fi, the modem switches on (only if the administrator grants permission). In The third module, the modem once connected to a device (mobile –computed device or a I-R device), the modem will have access to use the device (switch on and off the T.V. using the Wi-Fi signals). The modem operates various devices by using wireless signals. The modem basically acts a remote control. The fourth module consists of Data error control. Since wireless communication is considered to be relatively unsafe to communicate / transmit data, we have employed data error control. The data error detection is the process of monitoring data transmission and determining when error has occurred. The data detection is done by cyclic redundancy checking. With CRC, the entire stream is considered as a long continues binary number. Cyclic block codes are often written as (n, k) cyclic codes where n= bit length of transmission and k= bit length of message. The length of block check code is given as BCC = n-k In CRC, modulo – 2 division is used, where the remainder is derived from the exclusive OR operation. In the receiver the data strean (including the CRC code) is divided by the same generating function P(x). If the remainder is 0, there is no error else an error is present. Mathematically, CRC can be expressed as G(x) = Q(x) + R(x) P(x) Where G(x) = message polynomial. P(x) = generator polynomial. Q(x) = quotient R(x) = remainder. The number of bits in the CRC code is equal to the highest exponent of the generating polynomial. The data error correction technique used is Forward Error correction. It is the only error –correction scheme that actually does not require retransmission. In FEC redundant bits are added in order to determine which bit is in error. In order to correct the bit, we have to complement it. The number of redundant bits required is much larger than the number of bits required to simply detect error occurs Forward error correction are used in devices where there is no acknowledgment. The most common used error correction code used is the hamming code. The hamming code will correct only single bit error and not burst errors. Hamming bits are inserted into a character at random locations. The combination of the data bits and the Hamming bits is called hamming code .The sender and the receiver must agree on the location of the hamming bits. The formulae in order to determine the number of hamming bits is given by 2n >= M+N+1 Where M =number of bits in each data character N= number of hamming bits.
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3.7. Advantages of the system •
Connected devices can be charged from anywhere in the room
•
No data error during transmission
•
The modem can be placed anywhere in the room to improve the range of the signals
CONCLUSION We conclude by saying that by implementing our project on a large scale, devices can be charged in an inductive manner which would be cost efficient and convenient. ACKNOWLEDGEMENT The Authors would like to thank the authors of the base paper for their permission in using their paper as a base. We would like to thank Dr. K. Kokula krishna hari R. (international secretary of ASDF) and the staff of Velammal Institute of technology,Chennai , Amritha Vishwa Vidyapeetham University, Coimbatore , SRM University ,Chennai for their guidance and help. We would also like to thank our families and friends for their help in making the project a success. REFERENCE
[1] Russell M Kerchner and George F Corcoran, ―Alternating-Current Circuits‖, pp. 273-324, 1960. [2] G. Grandi, M.K. Kazimierczuk, A. Massarini, ―Optimal Design of Single-Layer Solenoid Air-CoreInductors for High Frequency Applications‖, Circuit Systems, Vol. 1, pp . 358-361, 1997. [3] A. Kurs, A. Karalis, R. Moffatt, J. D. Joannopoulos, P. Fisher, M. Soijacic, ―Wireless Power Transfer via Strongly Coupled Magnetic Resonances‖, Massachusetts Institute of Technology, 2007 Science, Vol. 317. no. 5834, pp. 83— 86, 2007[4 ] Jacob Millman and Christos C. Halkias, ―Integrated Electronics: Analog and Digital Circuits and Systems‖, pp . 103-107, 2007[5] Muhammad H. Rashid , ―Power Electronics: Circuits, Devices, and Applications‖, pp.37-63, 2nd Edition, 2000. [4] Robert L. Boylestad and Louis Nashelsky,‖Electronic Devices and Circuit Theory‖,9th Edition,2006, pp. 79-82 [5]Documents http://en.wikipedia.org/wiki/Error_detection_and_correction#Implementation [6] ."Electronic Communication Systems , Fifth edition , Wayne Tomasi ,ISBN:978-81-3171953-4 p(848-857) [7] Documents from http://en.wikipedia.org/wiki/Wi-Fi [8] Documents from http://www.cnet.com/how-to/home-networking-explained-part-1-heres-theurl-for-you. [9]http://www.academia.edu/2329757/Wireless_Charger_for_low_power_devices_using_inducti ve_coupling [10] http://en.wikipedia.org/wiki?curid=10375
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Speed control of DC Motor by using Fuzzy Logic P.ARUN RAJA MTECH (CONTROL AND INSTRUMENTION ENGINEERING) DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING AMRITA UNIVERSITY, KOLLAM AbstractThis paper presents an introduction to fuzzy logic and its application in speed control of dc motor. Lotfi Zadeh, a professor at the university of California at Berkley who conceived the concept of fuzzy logic, and presented not as a control methodology ,but as a way of processing the data by allowing partial set membership rather than crisp set membership or non-membership.This approach to set theory was not applied to control system until the 70’s due to insufficient small computer capability prior to that time. Prof zadeh reasoned that people do not require precise, numerical information input, and yet they are capable of highly adaptive control. If feedback controllers could be programmed to accept noisy, imprecise input, they would be much more effective and perhaps easier to implement. Keywords: fuzzy logic controllers, dc motor control, set theory
I INTRODUCTION TO FUZZY LOGIC Fuzzy logic is a problem-solving control system methodology that lends itself to implementation in systems ranging from simple, small, embedded microcontrollers to large, networked, multi-channel PC or workstation-based data acquisition and control systems. It can be implemented in hardware, software, or a combination of both. Fuzzy logic provides a simple way to arrive at a definite conclusion based upon vague, ambiguous, imprecise, noisy, or missing input information. Fuzzy logic 's approach to control problems mimics how a person would make decisions, only much faster. Human thinking and decisions are based on ‘yes’/’no’ reasoning, or 1’/ 0” logic. Accordingly Boolean logic was developed, and Expert System principles were formulated based on Boolean logic. II FUZZY CONTROL In general, a control system based on AI is defined as intelligent control. A fuzzy control system essentially embeds the experience and intuition of a human plant operator, and sometimes those of a designer and/or researcher of a plant. The design of a conventional control system is normally based on the mathematical model of a plant. If an accurate model is available with known parameters, it can be analyzed, for example by abode or Nyquist plot, and a controller can be designed for the specified performance. Such a procedure is tedious and time testing, although CAD programs are available for such design. Unfortunately, for complex process, such a cement plants, nuclear reactors, and the like reasonably good mathematical model is difficult to find. On the other hand, the plant operator may have good experience for controlling the process. Power electronics system models are often ill-defined. Even if a plant model is multi variable, complex, and non-linear, such as the dynamic d-q model of an ac machine.
III.FUZZY ASSOCIATED MATRIX (FAM) Fuzzy Associated Matrix is used to imply the fuzzy rules of the learning rate and momentum parameter .Fuzzy controllers will get the two parameters at the same time evaluate the error and change in the error coefficient. The change in error is the difference between the actual error and last error calculated.
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Fuzzy sets are: 1.High Positive(HP) 2.Low positive(LP) 3.zero(z) 5.Low Negative(LN)
E\CE
HP(- LP(- Z(0) LN(1) H 2) 1) N( 2)
HP(2)
HP
LP
LP
LN
Z
LP(1)
HP
HP
HP
Z
LN
Z(0)
HP
HP
Z
LN
H N
LN(1) HP
Z
LN
LN
H N
HN(2) Z
LP
LN
HN
H N
6.High Negative(HN) N=number of fuzzy sets={HP,LP,Z,LN.HN}=5 Number of numerical values=n=N/2-0.5=2 n value ranges between +2 to -2 From this numerical base fuzzy matrix the rule matrix can be drawn as follows IV DEFUZZIFICATION Defuzzification is the process of producing a quantifiable result in fuzzy logic, given fuzzy sets and corresponding membership degrees. It is typically needed in fuzzy control systems. These will have a number of rules that transform a number of variables into a fuzzy result, that is, the result is described in terms of membership in fuzzy sets. For example, rules designed to decide how much pressure to apply might result in "Decrease Pressure (15%), Maintain
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Pressure (34%), and Increase Pressure (72%)". Defuzzification is interpreting the membership degrees of the fuzzy sets into a specific decision or real value. The simplest but least useful defuzzification method is to choose the set with the highest membership, in this case, "Increase Pressure" since it has a 72% membership, and ignore the others, and convert this 72% to some number. The problem with this approach is that it loses information. The rules that called for decreasing or maintaining pressure might as well have not been there in this case. A common and useful defuzzification technique is Centre of Gravity (COG) Defuzzificztion. First, the results of the rules must be added together in some way. The most typical fuzzy set membership function has the graph of a triangle. Now, if this triangle were to be cut in a straight horizontal line somewhere between the top and the bottom, and the top portion were to be removed, the remaining portion forms a trapezoid. The first step of defuzzification typically "chops off" parts of the graphs to form trapezoids (or other shapes if the initial shapes were not triangles). For example, if the output has "Decrease Pressure (15%)", then this triangle will be cut 15% the way up from the bottom. In the most common technique, all of these trapezoids are then superimposed one upon another, forming a single geometric shape. Then, the centroid of this shape, called the fuzzy centroid, is calculated. The x coordinate of the centroid is the defuzzified value. V SPEED CONTROL OF DC MOTOR The above speed control system is low cost and suitable for learning at home where being rigorously, mathematically correct is not required. It is important to be aware that this speed controller is only an experimental controller to get familiar with the fuzzy logic concept. It is not what engineers call a rigorous, technically correct application of fuzzy logic. 1. Determine the control system input. Examples: The temperature is the input for your home air conditioner control system. Speed of the car is the input for your cruise control. In our case, input is the speed in Rpm of the DC motor, for which we are going to regulate the speed. See Figure 3 above. Speed error between the speed measured and the target speed of 2,420 Rpm is determined in the program. Speed error may be positive or negative. We measure the DC output voltage from the generator. This voltage is proportional to speed. This speed-proportional voltage is applied to an analog input channel of our fuzzy logic controller, where the analog to digital converter and the personal computer, including appropriate software, measure it. RULES: Translate the above into plain English rules (called "linguistic" rules by Dr. Zadeh). These Rules will appear in the BASIC computer program as "If- Then" statements: Rule 1: If the motor is running too slow, then speed it up. Rule 2: If motor speed is about right, then not much change is needed. Rule 3: If motor speed is too fast, then slow it down. The next three steps use a charting technique that will lead to a computer program. The purpose of the computer Program is to determine the voltage to send to the speed controlled motor. Associate the above inputs and outputs as causes and effect with a Rules Chart, as in Figure shown, below. The chart is made with triangles, the use of which will be explained. Triangles are used, but other shapes, such as bell curves, could also be used. Triangles work just fine and are easy to work with. Width of the triangles can vary. Narrow triangles provide tight control when operating conditions are in their area. Wide triangles provide looser control. Narrow triangles are usually used in the center, at the set point (the target speed). For our example, there are three triangles, as can be seen in
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Figure below (three rules, hence three triangles).
The above shown figure is derived from the previously discussed Rules and Results in the following regarding voltage to the speed controller: a. If speed is about right then not much change needed in voltage to the speed controller. b. If speed is too slow then increase voltage to the speed controller to Speed up. c. If speed is too fast then decrease voltage to the speed controller to slow down. The vertical line intersects the about right triangle at .4 and the Too fast Triangle at .3. This is determined by the ratio of sides of congruent triangles from Plane Geometry. The next step is to draw "effect" (output determining) triangles with their height "h" determined by the values obtained in Step, above. The triangles to be drawn are determined by the rules in Step 6. Since the vertical 2,437.4 Rpm speed line does not intersect the too slow triangle, we do not draw the Speed up triangle. We draw the Not much change and the Slowdown triangles because the vertical speed line intersects the about right and Too fast triangles. These "effect" triangles will be used to determine controller output that is the voltage to send to the speed control transistor. The result is affected by the widths we have given the triangles and will be calculated. See Figure above. The Not much change triangle has a height of .4 and the Slowdown triangle has a height of .3, because these were the intersect points for their matching "cause" triangles; see Figure above. CONCLSION In this paper we have given the introduction of the fuzzy logic and also the Advantages compared with the conventional control methods. Mainly we have explained how the fuzzy logic is used in the speed control of the DC motor. REFERENCES 1. Modern power electronics and AC drives By Bimal K.Bose. 2. Fuzzy Logic with Engineering Application By Timothy J.Ross. 3. Principles of soft computing by S.N.Sivanandam,S.N Deepa 4. Intelligent Control Systems with Labview by pedro pance-cruz,Fernando D,Ramirez-Figueroa 5.Introduction to Fuzzy Sets,Fuzzy logic and Fuzzy control system by G.chen,Trung Tatph
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Device & Voice Control System Based On Gesture Recognition Using 3d Mems Accelerometers R.Mukunthan, B.Mohamed Ibrahim Gani,G.Palani Kumar, P.Sivakumar B.Tech Scholar, ECE Department, Kalasalingam University Senior Assistant Professor, ECE Department, Kalasalingam University Abstract-The advent of MEMS technology and the subsequent surge of various transducers using the MEMS technology have opened up many avenues for new applications, MEMS based transducers today find applications in applications ranging from automotive electronics to aviation. But one possible application domain where MEMS technology can make significant advances is in the various types of modalities that can be designed to assist people with different types of physical disabilities. Using the MEMS technology assistance can be provided to physically handicapped people who are deprived of the usage of their limbs to move or to control things around them. Dumb people can be provided with modalities to communicate with people without having to depend on sign language. Even partially paralyzed people who are either limited to their rooms or beds can be equipped to control things around them in their day to day life. This project is an attempt in this direction to design modalities that can assist people who are limited by either handicaps or diseases to have a better quality of life. In this project a low cost device will be designed that used MEMS accelerometers to recognize different gestures made by people. Once recognized these gestures can be used to do various functions.
KEYWORDS: Gesture recognition; handwritten recognition; MEMS accelerometers. I. INTRODUCTION Human gestures are expressive, meaningful body motions involving physical movements of the fingers, hands, arms, head or body with the intent to convey meaningful information or to communicate with the environment. With the rapid development of computer technology, human computer interaction has become a ubiquitous activity in our daily life. More attention has been focused on translating these human gestures into computerunderstandable language in the past few years. Many gesture tracking and recognition technologies have been proposed. In general, these current gesture tracking technologies derive pose estimates from electrical measurements received from mechanical, magnetic, acoustic, inertial, optical, radio or microwave sensors Each sensor has its advantages and limitations. During this work a miniature accelerometer based recognition systems which acknowledge hand gestures in 3-D is constructed by using gestures, numeric and alphabets will be recognized the digital format MEMS is termed as a micro electro mechanical system where mechanical parts like membranes have been manufactured at microelectronics circuits. It uses micro-fabrication technology. It has channels, cantilevers, membranes, holes, cavity, and additionally mechanical parts. Miniaturization of the device reduces cost by decreasing material consumption. MEMS also increases the applicability by reducing size and mass. Integrated MEMS already includes data acquisition, filtering, data storage, communication interfacing and networking. MEMS technology makes the things smaller and better. A typical example is brought by the MEMS accelerometer development. An accelerometer is a device that measures the physical acceleration. The physical parameters are temperature, pressure, force, light, etc.. It measures the weight per unit mass. MEMS accelerometers can measure is g-force. MEMS accelerometer can detect the acceleration change of three directions in space. MEMS based inertial sensors are lightweight, good for fast motion tracking, and can offer a large sensing range, but they lack long term stability due to the problem of severe zero drift.
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Fig. 1 . Sensing direction of the triaxial MEMS accelerometer In this paper, we proposed the portable embedded device consists of triaxial accelerometer, microcontroller (PIC18LF45K22) and Zigbee wireless communication modules.Users can utilize this portable device to write digits and make hand gestures.In this research MEMS accelerometers measure the acceleration of the signal in three co-ordinates such as x-axis, y axis and z-axis. These co-ordinates are displayed on the LCD using the PIC microcontroller. II RELATED WORK MEMS accelerometer measures the acceleration of the signal in three co-ordinates such as x-axis, y-axis, and zaxis. To capture the hand motions online, the general MEMS sensor which can be operated without any external reference and limitation in working conditions is used. However, motion recognition is comparatively tough for different users since they have different styles and speeds to generate various motion trajectories. Thus, several researchers have tried to avoid this type of problem for increasing the accuracy of handwriting recognition systems .By manipulating the acceleration signals and angular velocities of sensors, several researchers have reduced the error of handwriting trajectory reconstruction. However, these trajectory reconstructions suffer from different errors due to the usage of inertial sensors. Hence, Dong et al. Proposed optical tracking calibration method to obtain accelerations of the MEMS inertial sensors based proposed device by calibrating two dimension trajectories and to obtain accurate attitude angle by using multiple camera calibration. Yang et al. Proposed a digital pen to track motion in three dimension space by MEMS accelerometer and gyroscopes to improve the recognition accuracy by introducing the efficient acceleration error compensation algorithm which is based on zero velocity compensation. Luo et al proposed an extended kalman filter with magnetometers to compensate the orientation of the MEMS motion sensor based digital writing device.
Fig 2. Accelerometer sensor
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III HARDWARE DESIGN AND DESCRIPTION There are two parts to the device one is the portable device that is provided to the user (user module), the second is the device that does the control function (control module). The user module has a 3D accelerometer, interface to a microcontroller with an inbuilt multi-channel ADC. The microcontroller is in turn connected to a low cost short range RF transmitter. The accelerometer is attached to any body part of the person that he can move under their voluntary control. When the person moves his hand the accelerometers produce analog voltages corresponding to the acceleration experienced by its various axes. The microcontroller is preprogrammed with the signals corresponding to certain gestures. When the gesture made by the user matches any of the gestures made by the user, the microcontroller generates a certain code uniquely identifying the gesture.
Fig 3. Pin diagram of Pic Microcontroller The control module has an RF receiver, which can receive the code from the user module. The RF receiver is in turn connected to a microcontroller, which is programmed with specific functions that can be done in the corresponding unique gesture code. The function that is done could be decided depending on the nature of the user
Fig4.Microcontroller
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User Module Antenna
Embedded Microcontroller
Acceleromoter(s)
Wireless Transceiver
(MEMS)
Control Module (Devices Control)
Relay Drive
Relay Drive
Antenna
Microcontroller
Wireless Transceiver
Voice Processor
Speaker
- Output Devices
- Input Devices
- Central Processing
- Communication Devices
Fig. 5. block diagram
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IV EXPERIMENTAL RESULTS
MEMS accelerometers measure the acceleration of the signal in three co-ordinates such as x-axis, yaxis, and z-axis. An accelerometer based portable device can also be used as mouse by selecting the mouse mode in the system. The each and specific gesture of the accelerometer based mouse is used to recognize the specific mouse functions
Fig.6. Display Co-ordinates (X, Y, Z) on LCD
Fig.7. Gestures for generating the handwritten digits
Fig.8. Handwritten recognition
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CONCLUSION The development of the MEMS accelerometer based portable device used to generate desired commands by hand motions to control electronic devices without any space limitations. The acceleration made by the hand motion is measured by the MEMS accelerometer are wirelessly transmitted to the computer by using the Zigbee wireless module. MEMS accelerometer based portable device is used to control the mouse cursor on a computer. Also the usage of simple sensor in the process of authentication using the same MEMS accelerometer based device helps for simple, accurate and efficient way of verification. MEMS accelerometer based recognition system provides an efficient and strong password protection REFRENCES
[1] Shengli Zhou, Fei Fei, Guanglie Zhang, John D. Mai, Yunhui Liu, Jay Y. J. Liou and Wen J. Li, Fellow, “IEEE 2D Human Gesture Tracking and Recognition by the Fusion of MEMS Inertial and Vision Sensors,vol 14,pg 1160-1170,2014 [2] R. Xu, S. Zhou, and W. J. Li, “MEMS accelerometer based nonspecific-user hand gesture recognition,” IEEE Sensors Journal, vol. 12, no. 5, pp. 1166-1173, 2012. [3] R. Z. Khan, and N. A. Ibraheem, “Comparative study of hand gesture recognition system,” in Computer Science & Information Technology, pp. 203-213, 2012
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Wireless Power Transmission M.Hare Sudhan Dept of Electronics and Communication Engneering Sri Sairam Engineering College,Chennai-44 Abstract:—In this paper, we present the concept of transmitting power without using wires i.e., transmitting power as microwaves from one place to another is in order to reduce the transmission and distributionl losses. This concept is known as Microwave Power transmission (MPT). We also discussed the technological developments in Wireless Power Transmission (WPT). The advantages, disadvantages, biological impacts and applications of WPT are also presented. Index Terms—Microwave Power transmission (MPT), Solar Power Satellites (SPS), Wireless Power transmission (WPT)
I.INTRODUCTION: Wireless energy transmission (WPT) or WiTricity is the process of transmission of energy from one place to another without using wires. Mostly energy transfer is done using wires. The energy loss is mostly during transmission. The energy loss exceed 40% (in India) [1]. So the efficiency counts about to 50-60%. The main reason for the energy loss is due to resistance of the wire. It can be improved to a certain level by using underground cables made of high temperature superconductors. But the transmission is still inefficient. A solution for this problem is the method of wireless power transmission (i.e. without wires). Its efficiency is around 80%.The methods of achieving WPT are resonant inductive coupling(RIC), electromagnetic radiations such as microwaves or lasers. RIC can be used only for a short distance transmission. As lasers are hazardous it is not used. But microwaves are safe for living beings upto a certain frequency. Hence microwaves are mostly used. II.WIRELESS POWER TRANSMISSION: In this paper, we are going to discuss about wireless power transmission using microwaves. Microwave power Transmission is the process of using microwaves in transmission of power through outer space without the need of wires. Following World War II which saw the development of high power microwave emitters also known as cavity magnetrons, the idea of using microwaves to transmit power was researched. The only condition that has to be satisfied is the line of sight between the source and the receiver. The steps involved in MPT are as follows: i. Converting electrical energy into microwave energy. ii. Capturing microwaves using rectenna (rectifying antenna) iii. Converting microwave energy back to electrical energy. III.WIRELESS POWER TRANSMISSION SYSTEM The concept of Wireless Power Transmission System can be explained as follows: In the transmission side, the microwave power source generates microwave power and the output power is controlled by electronic control circuits. The wave guide ferrite circulator which protects the microwave source from reflected power is connected with the microwave power source through the Coax – Waveguide Adaptor. The tuner matches the impedance between the transmitting antenna and the microwave source. The attenuated signals will be then separated based on the direction of signal propagation by Directional Coupler. The transmitting antenna radiates the power uniformly through free space to the rectenna. In the receiving side, a rectenna receives the transmitted power and converts the microwave
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power into DC power. The impedance matching circuit and filter is provided to setting the output impedance of a signal source equal to the rectifying circuit. The rectifying circuit consists of Schottky barrier diodes converts the received microwave power into DC power.
3.1 Components of WPT System The Primary components of Wireless Power Transmission are Microwave Generator, Transmitting antenna and Receiving antenna (Rectenna). The components are described in this chapter. 3.1.1 Microwave Generator The microwave transmitting devices are classified as Microwave Vacuum Tubes (magnetron, klystron, Travelling Wave Tube (TWT), and Microwave Power Module (MPM)) and Semiconductor Microwave transmitters (GaAs MESFET, GaN pHEMT, SiC MESFET, AlGaN/GaN HFET, and InGaAS). Magnetron is widely used for experimentation of WPT. The microwave transmission often uses 2.45GHz or 5.8GHz of ISM band. The other choices of frequencies are 8.5 GHz [2], 10 GHz [3] and 35 GHz [4]. The highest efficiency over 90% is achieved at 2.45 GHz [4] among all the frequencies. 3.1.2 Transmitting antenna The slotted wave guide antenna, micro strip patch antenna, and parabolic dish antenna are the most popular type of transmitting antenna. The slotted waveguide antenna is ideal for power transmission because of its high aperture efficiency (> 95%) and high power handling capability. 3.1.3 Rectenna The rectenna is a passive element consists of antenna, rectifying circuit with a low pass filter between the antennas and rectifying diode. The antenna used in rectenna may be dipole, microstrip or parabolic dish antenna.[5] The patch dipole antenna achieved the highest efficiency among the all. The performance of various printed rectenna . Schottky barrier diodes (GaAs-W, Si, and GaAs) are usually used in the rectifying circuit due to the faster reverse recovery time and much lower forward voltage drop and good RF characteristics. IV: ADVANTAGES, DISADVANTAGES, AND BIOLOGICAL IMPACTS OF WPT 4.1 Advantages: Wireless Power Transmission system would completely eliminates the existing high-tension power transmission line cables, towers and sub stations between the generating station and consumers and facilitates the interconnection of electrical generation plants on a global scale. It has more freedom of choice of both receiver and transmitters. Even mobile transmitters and receivers can be chosen for the WPT system. The cost of transmission and distribution become less and the cost of electrical energy for the consumer also would be reduced. The power could be transmitted to the places where the wired transmission is not possible. Loss of transmission is negligible level in the Wireless Power Transmission; therefore, the efficiency of this method is very much higher than the wired transmission. Power is available at the rectenna as long as the WPT is operating. The power failure due to short circuit and fault on cables would never exist in the transmission and power theft would be not possible at all. 4.2 Disadvantages: The Capital Cost for practical implementation of WPT seems to be very high and the other disadvantage of the concept is interference of microwave with present communication systems.
4.3 Biological Impacts:
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Common beliefs fear the effect of microwave radiation. But the studies in this domain repeatedly proves that the microwave radiation level would be never higher than the dose received while opening the microwave oven door, meaning it is slightly higher than the emissions created by cellular telephones.[6] Cellular telephones operate with power densities at or below the ANSI/IEEE exposure standards .[7] Thus public exposure to WPT fields would also be below existing safety guidelines. V. APPLICATIONS OF WPT Generating power by placing satellites with giant solar arrays in Geosynchronous Earth Orbit and transmitting the power as microwaves to the earth known as Solar Power Satellites (SPS) is the largest application of WPT. Another application of WPT is moving targets such as fuel free airplanes, fuel free electric vehicles, moving robots and fuel free rockets. The other applications of WPT are Ubiquitous Power Source (or) Wireless Power Source, Wireless sensors and RF Power Adaptive Rectifying Circuits (PARC). CONCLUSION The concept of Microwave Power transmission (MPT) and Wireless Power Transmission system is presented. The technological developments in Wireless Power Transmission (WPT), the advantages, disadvantages, biological impacts and applications of WPT are also discussed. This concept offers greater possibilities for transmitting power with negligible losses and ease of transmission than any invention or discovery heretofore made. Dr. Neville of NASA states “You don’t need cables, pipes, or copper wires to receive power. We can send it to you like a cell phone call – where you want it, when you want it, in real time”. We can expect with certitude that in next few years’ wonders will be wrought by its applications if all the conditions are favorable. REFERENCES: [1] http://cleantechindia.wordpress.com/2008/07/16/indiaselectricity-transmission-and-distribution-losses/ [2] L.W. Epp, A.R. Khan, H.K. Smith, and R.P. Smith, “A compact dual-polarized 8.51-GHz rectenna for high-voltage (50 V) actuator applications,” IEEE Trans. Microwave Theory Tech., vol. 48, pp. 111-120, 2000. [3] T-WYoo and K. Chang, “Theoretical and experimental development of 10 and 35 GHz rectennas,” IEEE Trans. Microwave Theory Tech., vol. 40, pp. 1259-1266, 1992. [4] P. Koert and J.T. Cha, “35 GHz rectenna development,” in Proc. 1st Annu. Wireless Power Transmission Conf., San Antonio, TX, 1993, pp. 457-466. [5] Brown, W.C, “The History of the Development of the Rectenna” Proc. Of SPS microwave systems workshop, pp.271- 280, Jan 1980. [6] www.howstuffworks.com (How Micro Ovens Work – A Cooking Oven for the 21st century. By Gabriel Gache) [7] J.C. Lin, “Biological aspects of mobile communication fields,” Wireless Networks, vol. 3, pp. 439-453, 1997
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E-Crashcorder Next Generation Vehicular Black Box Device integrated with Airbag Control System that records Audio and Visual Footage of the Crash Scenario in addition to Vital Vehicle Parameters KAVITHA S, JAYANDRAN S, NAVAS UG-Student B.E-ECE, Saveetha University, E PREMKUMAR S Asst professor,Saveetha University Abstract-The project aim is to design a next generation Vehicle Black Box (EDR), named as E-Crash corder, (Enhanced Crash Data Recorder) that is the combination of all the advantages of previous Black Boxes, Event Data Recorders (EDRs) and standalone Digital Video/Audio recorders. The E-Crash corder is integrated within the Electronic Control Unit (ECU) which is responsible for the airbag KEYWORDS-GPS,SCCB CONTROL,EDR I.
INTRODUCTION
The project aim is to design a next generation Vehicle Black Box (EDR), named as E-Crashcorder, (Enhanced Crash Data Recorder) that is the combination of all the advantages of previous Black Boxes, Event Data Recorders (EDRs) and standalone Digital Video/Audio recorders. The E-Crashcorder is integrated within the Electronic Control Unit (ECU) which is responsible for the airbag control and deployment and stores the status of vehicle gathered from different sensors. It is equipped with camera that records the video snapshots in front of the vehicle. It also records the audio inside the vehicle, using a microphone. The E-Crashcorder has Global Positioning System(GPS) receiver for reading the current latitude and longitude of the vehicle point. The 6 Degrees of Freedom (DOF) of inertial sensor, which is a Triple-axis accelerometer sensor and Triple-axis magnetometer sensor, is integrated with ECrashcorder to read the velocity, acceleration and orientation of vehicle using which we analyze the stability of vehicle during the travel. After collecting and synchronizing all data, the E-Crashcorder saves them in Secure Digital (SD) Card. It has a USB port which is used to transfer the recorded data to a PC/Laptop.the crash event. Immediately after a crash event, the recorder automatically stops after a few seconds. EDR data can be retrieved and analyzed to determine the driver’s actions and how the vehicle performed at the time of a crash. A real time clock running in the microcontroller is used to timestamp every data that will be recorded.Here is the list of the crash data parameters that will be recorded. The system can be integrated with even bicycles/two wheelers with a few modifications.40 seconds of data recording that exceeds the traditional EDR limit of 10 seconds.A high performance 32-bit ARM Cortex-M3 microcontroller, consuming very low power.
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SOFTWARE USED: FAT-32 embedded file system library for memory card via SPI protocol.USB 2.0 library with CDC device class acting as virtual COM. Digital Camera Driver with SCCB control protocol. Digital MEMS Compass device driver via I2C protocol. MEA protocol decoder for GPS via UART protocol.Cortex-M3 peripheral device driver library,CMSIS from ARM
EXISTING TECHNOLOGY: Modern vehicles use a number of onboard computers to control driving systems, including acceleration, braking and airbag deployment. The computers are connected to sensors throughout the vehicle and send the sensor data to EDR (Event Data Recorder aka Vehicle Black Box). But these EDRs have limitation, for example, in the recently alleged unintended-acceleration incidents involving Toyota, the expert witness said that EDR itself went wrong during the crash event and as a result the data that was stored is not good enough to figure out whether the car or the driver is to blame.
PROPOSED TECHNOLOGY: IS to design a next generation Vehicle Black Box (EDR), named as E-Crash corder, (Enhanced Crash Data Recorder) that is the combination of all the advantages of previous Black Boxes, Event Data Recorders (EDRs) and standalone Digital Video/Audio recorders. The E-Crash corder is integrated within the Electronic Control Unit (ECU)
BLOCK DIAGRAM:
II ARM CORTEX M3 Next Generation 32-bit ARM Processor for Embedded Applications based on ARMv7-M Architecture. Harvard architecture. Separate I & D buses allow parallel instruction fetching & data storage3-stage pipeline with branch speculation. Fetch, Decode & Execute Integrated bus matrix. Configurable nested vectored interrupt controller (NVIC).Advanced debug and trace components (DAP, SWV, ETM).Wakeup Interrupt Controller (WIC) Memory Protection Unit (MPU)
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USART Baud Rate Generator (BRG) The BRG supports both the asynchronous and synchronous modes of the USART. It is a dedicated 8-bit baud rate generator. The SPBRG register controls the period of a free running 8-bit timer. In asynchronous mode, bit BRGH (TXSTA) also controls the baud rate. In synchronous mode, bit BRGH is ignored. Table shows the formula for computation of the baud rate for different USART modes which only apply in master mode (internal clock). Given the desired baud rate and Fosc, the nearest integer value for the SPBRG register can be calculated using the formula in Table. From this, the error in baud rate can be determined.
CAMERA: OV7670 VGA/OVGA Resolution.30 frames/sec frame rate.DSP based image processing.Standard SCCB Interface for Control and Configuration.8-pin parallel interface for data.Low voltage low power CMOS technology.A high speed FIFO for data buffering.3Mbit FIFO size (384 kb) . III. GLOBAL POSITIONING SYSTEM (GPS) The Global Positioning System (GPS) is a location system based on a constellation of about 24 satellites orbiting the earth at altitudes of approximately 11,000 miles. GPS was developed by the United States Department of Defense (DOD), for its tremendous application as a military locating utility. The DOD's investment in GPS is immense. GPS has proven to be a useful tool in non-military mapping applications as well. The smart antenna can track upto 66 satellites at a time.Fast time to first fix, Superior sensitivity, and Low power.Less than 10m Accuracy.57600bps UART interface.Up to 10Hz update rate.Built-in micro battery to preserve system data for rapid satellite acquisition.LED indicator for fix or no fix.GPS satellites are orbited high enough to avoid the problems associated with land based systems, yet can provide accurate positioning 24 hours a day, anywhere in the world. Uncorrected positions determined from GPS satellite signals produce accuracies in the range of 50 to 100 meters. Today, many industries are leveraging off the DOD's massive undertaking. As GPS units are becoming smaller and less expensive, there are an expanding number of applications for GPS. In transportation applications, GPS assists pilots and drivers in pinpointing their locations and avoiding collisions.
IV MAGNETOMETER Magnetometers, which measure magnetic fields, are distinct from metal detectors, which detect hidden metals by their conductivity. When used for detecting metals, a magnetometer can detect only magnetic (ferrous) metals, but can detect such metals buried much deeper than a metal detector. Magnetometers are capable of detecting large objects like cars at tens of meters, while a metal detector's range is unlikely to exceed 2 meters.The magnetometer is based on the idea that the magnetic
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flux moving through a coil depends on the orientation of the with respect to the magnetic field lines of the earth.It consists of three spokes which share a single exciter coil. The complete suspend in order to ensure that only the horizontal component of the magnetic field is measured. When zooming down to one of the spokes the following sketch can be made:
How ever with the inclusion of an external field (that of the earth for examples), changes in the total flux can be observed. This change in flux is caused by the saturation of the perm alloy used. When a magnetizable material is fully saturated. The material is completely magnetized and a stronger magnetic force as no effect on the magnetic flux density.
V DC SERVO METER Used for position and speed control.Operated with PWM pulses @ 50Hz.Dutycyle variation controls the desired parameter.Operates with low current, ideal for battery powered applications. In any electric motor, operation is based on simple electromagnetism.A current-carrying conductor generates a magnetic field; when this is then
placed in an external magnetic field, it will experience a force proportional to the current in the conductor, and to the strength of the external magnetic field. As you are well aware of from playing with magnets as a kid, opposite (North and South) polarities attract, while like polarities (North and North, South and South) repel. The internal configuration of a DC motor is designed to harness the magnetic interaction between a current-carrying conductor and an external magnetic field to generate rotational motion.Let's start by looking at a simple 2-pole DC electric. winding with a "North" polarization, while green represents a magnet or winding with a "South" polarization).Let's start by looking at a simple 2-pole DC electric motor (here red represents a magnet or winding with a "North" polarization, while green represents a magnet or winding with a "South"polarization
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Again, disassembling a coreless motor can be instructive -- in this case, my hapless victim was a cheap pager vibrator motor. The guts of this disassembled motor are available for you to see here (on 10 lines / cm graph paper). This is (or more accurately, was) a 3-pole coreless DC motor.
VI PROJECT ADVANTAGES Existing EDR technology doesn’t integrate audio and video into its functionality. The addition of audio and video into the recorded parameters would help to find out the true happenings and make the investigation much easier one.Records all the vehicle parameters starting from 30 seconds before crash and 10 seconds after crash.Recording the latitude and longitude of the vehicle will provide the true vehicle position and motion during a crash event.USB access to the black box device helps easy retrieval of information on any PC/Laptop.A 2GB memory card is used as the main storage memory providing enough space for all the audio, video and other vehicle parameters.The system can be integrated with even bicycles/two wheelers with a few modifications.40 seconds of data recording that exceeds the traditional EDR limit of 10 seconds.A high performance 32-bit ARM Cortex-M3 microcontroller, consuming very low power. CONCLUSION The project Recorders and standalone Digital Video/Audio recorders. The E-Crashcorder is integrated within the Electronic Control Unit. Responsible for the airbag control and deployment and stores the status of vehicle gathered from different sensors. is the combination of all the advantages of previous Black Boxes, Event Data Recorders. FAT-32 embedded file system library for memory card via SPI protocol.USB 2.0 library with CDC device class acting as virtual COM. Next Generation 32-bit ARM Processor for Embedded Applications based on ARMv7-M Architecture. Harvard architecture. Separate I & D buses allow parallel instruction fetching & data storage3-stage pipeline with branch speculation. High Performance RISC CPU.Greater performance efficiency, without increasing the frequency or power requirements. ARM Cortex-M3 processor, running at frequencies of up to 100 MHz .A Memory Protection Unit (MPU) supporting eight regions is included. Four general purpose timers / counters, with a total of eight capture inputs and ten compare outputs. DSP based image processing.Standard SCCB Interface for Control and Configuration.8-pin parallel interface for data.Low voltage low power CMOS technology. Earth has 24 GPS satellites, atleast 4 are always visible .GPS receiver calculates location using Triangulation method. Allows FAT-32 formatting for easy file management.Used as mass storage device in portable embedded system.Developed by SD Card Association. Windows compatible FAT-32 file system.Ported to Cortex-M3 and Cortex-M0. serial protocol connecting a microcontroller with a host computer such as a PC/Laptop. Used for position and speed control.Operated with PWM pulses @ 50Hz. Recording the latitude and longitude of the vehicle will provide the true vehicle position and motion during a crash event.
REFRENCE [1].Box and N. R. Draper. Empirical Model Building and Response Surfaces. John Wiley & Sons, 1987. T. Ranney,W. Garrott, and M. Goodman, NHTSA Driver Distraction Research: Past, Present, and Future, National Highway Traffic Safety Administration, 2001, Tech. Rep. Paper No. 2001-06-0177.[2] V. Neale, T. Dingus, S. Klauer, J. Sudweeks, and M. Goodman, An Overview of the 100-Car Naturalistic Study and Findings, National Highway Traffic Safety Administration, 2005, Tech. Rep. Paper No. 05-0400. M. Kutila, M. Jokela, G. Markkula, and M. Rue, “Driver distraction detection with a camera vision system,” in Proc. IEEE Int. Conf. Image Processing (ICIP 2007), SanAntonio, TX,USA, Sep. 2007, vol. 6, pp. 201–204. [7] C.-T. Lin, R.-
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C. Wu, S.-F. Liang, W.-H. Chao, Y.-J. Chen, and T.-P. Jung, “EEG-based drowsiness estimation for safety driving using independent component analysis,” IEEE Trans. Circuits Syst. I: Reg. Papers, vol. 52, no. 12, pp. 2726–2738, Dec. 2005. E. Biham et al., “How to Steal Cars — A Practical Attack on Keeloq,” CRYPTO 2007, 2010. [2] A. W. M. Bonnick, Automotive Computer Controlled Systems: Diagnostic Tools and Techniques, Automobile Electronics, Taylor & Francis Group, 2001. A. Martínez-Ballesté, P. A. Pérez-Martínez, and A. Solanas, “The Pursuit of Citizens’ Privacy: A Privacy-Aware Smart City Is Possible,” IEEE Commun. Mag., vol. 51, no. 6, June 2013.
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Power Production in Satellites by utilising Solar Energy using Ultra capacitors A.TAMIL ILAKKIYA, J.SUGANYA Abstract:Ultracapacitor is a new way of storing electric energy eclipsing chemical batteries. Instead of storing energy electrochemically, it stores it in an electric field having a capacitance in series with an equivalent resistance. Ultracapacitors having multiple advantages over conventional batteries, including a lifetime of over 10 years, resistance to change in temperature, shock, overcharging and discharging efficiency makes it elite in the field of electronics .Eco-friendly, light in weight, no maintenance and low cost brings in an idea that it could be used for power production in satellites in space. Power production being important in satellite this paper reveals a way to store energy and utilise it to optimum level. The ultracapacitors can be charged using renewable solar energy and can be used in satellites for maximum energy requirements. Keywords: Terminologies used for Ultra capacitor (i)EDLC-Electric double layer capacitor (ii)Super capacitor (iii) Standard oil of Ohio research centre I.INTRODUCTION
Ultracapacitor originated in the work of Standard Oil of Ohio research centre (SOHIO) in early 1960’s. Ultracapacitors have an cycle efficiency of 95% plus and lower internal resistance. Ultracapacitor being such an efficient device can be used to provide power for satellites by the energy obtained from solar source. This paper throws light on storing the energy obtained from the sun in the form of electric energy in the ultracapacitors and transmitting it to the satellites in the form of microwaves. II. SOLAR ENERGY AND ULTRACAPACITOR
Let us take a glance at the principle and construction of ultracapacitor. 2.1.PRINCIPLE Ultracapacitor uses two pieces of activated carbon immersed in an aqueous electrolyte solution connected across the terminals of a battery to work as a capacitor. Applying a potential across the ultracapacitor electrodes polarizes the electrolyte, with roughly half of the electrolyte molecules transferring an electron to the other half. The resulting positive and negative ions migrate via the impressed electric field to the respective electrodes. The model of conduction-band electrons in metals helps explain what happens inside the carbon when the voltage is applied. All of the involuted surface area of each electrode becomes an energy-level boundary. Just beneath the surface of the negative electrode, for example, in fig.1 conduction band occupied by a horde of roving electrons that lack the energy to escape from the surface in a similar band at the positive electrode, ”holes” or “electron vacancies “,rove beneath the surface but are unable to capture electrons from outside. Here the applying potential is obtained from solar energy.
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2.2.CONSTRUCTION
Fig.1Ultracapacitor
Fig.2 Basic Construction Jelly roll assembly of two carbon electrodes with paper separator. • Foil tab ends to aluminium collector plates • Plates leads to can and terminal ends The super capacitor active part is made in most of the case of two identical electrodes. There is a spacer between the electrodes, the separator, which functions to provide an electronic insulation between the electrodes, while leaving the ions moving through its porosity to ensure the ionic conduction. The active part is impregnated with an electrolyte made up of a solvent containing a dissociated salt and is closed in a tight package.
Fig.3 Solar Panels
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Fig.4 Butterfly wing structure From the above fig 3, A solar panel or photovoltaic module, is composed of individual PV cells. This crystalline silicon panel has an aluminium frame and glass on the front. These solar panels are placed in areas where there is direct contact of sunlight. These panels absorb the energy from the sun and convert it to electrical energy by the use of PV cells in each panel. SOLAR COLLECTOR Solar panels use light energy (photons) from the sun to generate electricity through the photovoltaic effect. The majority of modules use wafer based crystalline silicon cells or thin-film cells based on cadmium telluride.
III. ULTRA CAPACITOR STORAGE AND SATELLITE
THE ENERGY FROM THE SOLAR PANEL IS CONNECTED TO THE ULTRA-CAPACITOR WHERE IT IS STORED IN THE FORM OF ELECTRICAL ENERGY, FROM WHERE IT IS RETRIEVED FOR CONVERSION INTO MICROWAVES.
These microwaves are transmitted to the satellite for power production. 3.1. WORKING The basic block diagram of ultra-capacitor working for satellite is shown in fig.5 SUN
RECEIVER IN THE SATELLITE
SOLAR PANELS
MICRO
MICRO
WAVE
WAVE
CONVERTER
GENERATO R
ULTRA VO LTAGE CAPACITO R REGULATO R
Fig.5 Block diagram of Ultracapacitors
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SOLAR PANELS
Fig.6Solar energy is collected in solar panels VOLTAGE REGULATOR A voltage regulator is an electrical regulator designed to automatically maintain a constant voltage level. It operates by comparing the actual output voltage to some internal fixed reference voltage. Any difference isamplified and used to control the regulation element in such a way as to reduce the voltage error. ULTRACAPACITOR The supply from the voltage regulator is connected to the suitable terminals of the ultracapacitor. EDLC’s do not have a conventional dielectric rather than two separate plates separated by an intervening substance, these capacitors use “plates” that are in fact two layers of the same substrate and the electrical properties, the so-called “Electrical Double Layer”.
Fig.7 Electrical double layer From the fig.7, When positively charged electrolyte ions form a layer on the surface on the negative electrode, electrons beneath the surface pair up with them. These two layers of separated chargers, then, are a capacitor storing static charge. Similarly, at the positive electrode, holes pair up with negative ions, forming a second electronic double layer that itself is a capacitor. This molecular-scale charge-separation distance, coupled with the great surface area of the activated carbon electrodes, yields the ultracapacitors extreme storage capabilities. This stored energy is transferred to the next stage.
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MICROWAVE CONVERTER
Fig.8 Conversion of Electrical Energy into Microwaves The microwave converter resembles a cathode ray tube. It receives the energy continuously from one end and emits the generated microwaves through antennas. The above fig.8 shows the conversion of beam of electrons into microwaves by a microwave converter. MICROWAVE TRANSMISSION
Fig.9 Microwave Transmission A depiction of a solar satellite that could send electric energy by microwaves to a space vessel or planetary surface.Power transmission via radio waves can be made more directional, allowing longer distance power beaming, with shorter wavelengths of electromagnetic radiation, typically in the microwave range. Wireless high power transmission using microwaves is shown in fig.9.Experiments in the tens of kilowatts have been performed at Goldstone in California in 1975 and more recently (1997) at Grand Bassin on Reunion Island. These methods achieve distances on the order of a kilometer.
POWER RECEIVING END FOR SATELLITE
Fig.10 Power Receiving End for Satellite Thus, the transmitted microwaves are received at the receiving end of the satellite and utilized for power production is shown in the above fig.10
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ADVANTAGES OF ULTRACAPACITORS Low cost Light in weight No maintenance More reliability Cycle efficiency is above 95% Low internal resistance Broad temperature range (-30C0 to 65+C0) Higher power density, faster
CONCLUSION This paper reveals the advantages of using ultra capacitors for storing energy and utilising it to optimum level and transmitting it to the satellite for maximum power production.
REFERENCES [1] The solrayo website available at http://www.solrayo.com/ [2] CDE Cornell Dubilier available at http://www.cde.com/ [3] Glaser, Peter E. (22 November 1968). "Power from the Sun: Its Future" (PDF).Science Magazine162 (3856): 857–861. [4] Satellite Power System Concept Development and Evaluation Program July 1977 - August 1980. DOE/ET- 0034, February 1978. 62 pages [5] Satellite Power System Concept Development and Evaluation Program Reference System Report. DOE/ER-0023, October 1978. 322 [6] Statement of John C. Mankins U.S. House Subcommittee on Space and Aeronautics Committee on Science, Sep 7, 2000 [7] Satellite Power System (SPS) Resource Requirements (Critical Materials, Energy, and Land). HCP/R-4024-02, October 1978. [8] Satellite Power System (SPS) Financial/Management Scenarios. Prepared by J. Peter Vajk. HCP/R-4024-03, October 1978. 69 pages [9] Satellite Power System (SPS) Financial/Management Scenarios. Prepared by Herbert E. Kierulff. HCP/R-402413, October 1978. 66 pages. [10] Satellite Power System (SPS) Public Acceptance. HCP/R-4024-04, October 1978. 85 pages.
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[11] Satellite Power System (SPS) State and Local Regulations as Applied to Satellite Power System Microwave Receiving Antenna Facilities. HCP/R-4024-05, October 1978. 92 pages. [12] Satellite Power System (SPS) Student Participation. HCP/R-4024-06, October 1978. 97 pages. [13] Potential of Laser for SPS Power Transmission. HCP/R-4024-07, October 1978. 112 pages. [14] Satellite Power System (SPS) International Agreements. Prepared by Carl Q. Christol. HCP-R-4024-08, October 1978. 283 pages. [15] Satellite Power System (SPS) International Agreements. Prepared by Stephen Grove. HCP/R-4024-12, October 1978. 86 pages. [16] Satellite Power System (SPS) Centralization/Decentralization. HCP/R-4024-09, October 1978. 67 pages. [17] Satellite Power System (SPS) Mapping of Exclusion Areas For Rectenna Sites. HCP-R-4024-10, October 1978. 117 pages. [18] Economic and Demographic Issues Related to Deployment of the Satellite Power System (SPS). ANL/EES-TM23, October 1978. 71 pages. [19] Some Questions and Answers About the Satellite Power System (SPS). DOE/ER-0049/1, January 1980. 47 pages. [20] Satellite Power Systems (SPS) Laser Studies: Meteorological Effects on Laser Beam Propagation and Direct Solar Pumped Lasers for the SPS. NASA Contractor Report 3347, November 1980. 143 pages.
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Neuromorphic VLSI Implementation of Analog Inner Hair Cell and Auditory Nerve IC Using Non-Invasive Technique Saranya.SA II M.E (VLSI Design) Dr.J.Raja., Proffessor, ECE Department, Adhiparasakthi Engineering College Melmaruvathur,TN,India Abstract—An analog inner hair cell and auditory nerve IC has been implemented using noninvasive techniques. Differential topology is used to remove the reverberations caused. A fully-differential current-mode architecture is used and the ability to correct channel mismatch is evaluated with matched layouts as well as with digital current tuning. Current mode architecture is developed to avoid CMOS mismatch. Brain machine interface based on noninvasive technique is implemented. The designed IC is used for ultra-low power biomimetic system. Speech synthesis is used for speech recognition in noisy environment. Neural activity is measured using Brain machine interface (EEG).
Index Terms- Non-Invasive Technique, spectrogram ,hearing aids, low recogniation,neuromrophic,fully differential topology, Very large scale integration(VLSI)
power,
speech
I. INTRODUCTION Neuromorphic that mimics the design and implementation of micro systems that emulates the the structure and function of the brain. Recognition of audio cues in mammalian cochlea for hearing in profoundly deaf patients.Neuromorphic engineering is an interdisciplinary discipline that takes inspiration from biology, physics, mathematics, computer science and engineering to design artificial neural systems, the physical architecture and design principles of which are based on those of biological nervous systems. In neuromorphic engineering, technology and neuroscience cross-fertilize each other.The designed processor Can be used for next-generation implants will be fully implanted inside the body of the patient and consequently have very stringent requirements on the power consumption used for signal processing. This IC is intended for use in such next-generation implants. Neuromorphic improves the performance of artificial systems through the development of chips and systems that process information collectively using primarily analog circuits. This paper presents the central concepts required for the creative and successful design of analog VLSI circuits. The discussion is weighted toward novel circuits that emulate natural signal processing. Unlike most circuits in commercial or industrial applications, these circuits operate mainly in the subthreshold or weak inversion region Moreover, their functionality is not limited to linear operations, but also encompasses many interesting nonlinear operations similar to those occurring in natural systems. It includes noise analysis, and process technology. The architecture they have designed works in a similar way to neurons and can therefore be used to test various ways of reproducing the brain’s processing ability. In addition, they are significantly more energy efficient than conventional chips. This involves in neuroscience, medicine, and computing a new foundation, to explore and understand the brain and the neural acyivity of the disabled person, and to use that knowledge to build new computing technologies.For these auditory systems, portions of the mammalian cochlea are often implemented using analog very large scale integration (VLSI).
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Fig 1:Human ear and inner hair cell(IHC)and Outer Hair Cell (OHC).The electrical impulses are sent to the auditory nerve.Mammalian cochlear hair cells come in two anatomically and functionally distinct types: the outer and inner hair cells. Damage to these hair cells results in decreased hearing sensitivity, i.e. sensorineural hearing loss.The loss in ability to communicate is considered one of the most disabling condition.A major cause of why acquired hearing loss is permanent in mammals lies in the incapacity of the sensory cells (epithelia) of the inner ear to replace damaged mechanoreceptor cells, or hair cells. Sensory hair cells are mechanoreceptors that transduce fluid movements generated by sound into electrochemical signals interpretable by the brain. VLSI analog electronic circuits that implement or include the functionality of the Inner Hair Cell (IHC) and Auditory Nerve’s (AN) adaptation have targeted mostly monaural system and multi-binaural systems and ultralow-power monaural systems. Usually, hearing loss that is called "sensorineural" or "nerve deafness" is actually caused by problems with the cochlea, instead of the actual auditory nerve, but a very small percentage of hearing loss is caused by problems with the nerve, itself,usually related to acoustic neuromas (tumors) on the nerve covering. The auditory nerve and the vestibular nerve, which carries balance information from the semicircular canals to the brain, join together as they pass through the bony canals of your skull. Together, they are called the 8th cranial nerve, or the Vestibulocochlear nerve. Also passing through the same bony canals of your skull is the 7th cranial nerve, or the facial nerve, which supports facial expression and sensation. It's interesting to note that while many of the nerve fibers in this bundle do carry the sound signal to the brain, most (some estimates are as much as 2 thirds) of the nerve fibers actually carry information BACK to the cochlea from the brain. The cochlea can then use this information to suppress sound you are not interested in like background noise. This explains why hearing aids (which amplify sounds) can help you hear better, but they do not completely correct a hearing loss.It also explains why one of the biggest problems that hard of hearing people face is the effect of background noise. Even the best hearing aid can only amplify sound; it can't converse with your brain and help your brain eliminate background noise the way a normal working ear can do.Brain machine interfaced with EEG aims for giving nois less resolution.Some hearing aids use multiple microphones to to (somewhat) suppress background noise by suppressing omni directional sound and enhancing sounds from the front of the wearer, and that works well but not nearly as well as your cochlea does by communicating with your brain over the auditory nerve. In this paper, we present an analog IHC and AN (AIHCAN) integrated circuit (IC) for use in low-power monaural and mulitiple binaural acoustic applications. The biomimetic IHC and AN we choose to implement in non invasive technique. Brain-machine-interface-based speech prostheses usage in IHC and AN function is a potentially important component of a front-end preprocessor for use in neuromorphic systems that perform sound localization resulted by a spectrogram..The developed IHC and AN circuit uses an average power less than 6.9 mW and minimizing the area and can result in reduced CMOS mismatch. This capability makes fabrication of large AIHCAN circuits for use in embedded systems practical. II. NON-INVASIVE TECHNIQUE A medical procedure is strictly defined as non-invasive when no break in the skin is created and there is no contact with the skin break, or internal body cavity beyond a natural or artificial body orifice. For example, deep palpation and percussion is non-invasive but a rectal examination is invasive. Likewise, examination of the eardrum or inside the nose falls the definition of non-invasive procedure. There are many non-invasive procedures, ranging from simple observation, to specialized forms of surgery, such as radio surgery. BMIs based on electroencephalography (EEG) have been shown to give some sort of communication capability to the patients. Unfortunately, EEG systems use electrodes placed on the scalp to measure neural activity occurring deep inside the brain; as a consequence, the recorded information is noisy. BMIs based on functional magnetic resonance imaging (fMRI) overcome this problem because they can record neural activity occurring deep inside the brain with high spatial resolution. Currentlly, fMRI systems are large and impractical and cannot create a viable speech prosthesis. In addition fMRI measurements are inherently slow and, consequently, it is almost impossible to achieve real-time production of speech. Perhaps noise suppression algorithm is developed to degrade the background noise.The discovery of the first modern non-invasive techniques based on physical methods, electrocardiography and X-rays, dates back to the end of the 19th century. Since then, non-invasive methods which penetrate the body nonetheless, but by electromagnetic or particle radiation rather than a scalpel have continuously enlarged the scope of medical technology. Non-invasive techniques commonly used for diagnosis of hearing aids are MEG signals were first measured to reduce the magenetic background noise,
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covering most of the head. In this way, MEGs of a subject or patient can now be accumulated rapidly but is impractical. The EEG signals derive from the net effect of ionic currents flowing in the dendrites of neurons during synaptic transmission be thought of as current dipoles, i.e.currents with a position, orientation, and spatial extent..Many patients with physiological disorders such as Amyotrophic Lateral Sclerosis (ALS) or injuries such as high-level spinal cord injury suffer from disruption of the communication path between the brain and the body. People with severe motor disabilities may lose much of their voluntary muscle control. The disabled people with the above mentioned problems are forced to accept a reduced quality of life, resulting in dependence on caretakers and escalating social costs. Most of the existing assistive technology devices for these patients are not usable because these devices are dependent on motor activities from specific parts of the body. Alternative control paradigms for these individuals are thus desirable. The electrophysiological signals generated from the brain can be used to command different devices, provided that the person who will control the device should also be able to control the generation of these signals. Studies showed that with sufficient training, people can control the generation of certain brain signals. Having generated these signals, they can be conditioned and processed to perform the specific work for which they are generated. In other words, the interface can be made able to adapt and understand the meaning of these signals and work accordingly. If this type of Brain-Machine Interface (BMI) is successfully implemented, they can be used in developing sophisticated assistive devices(such as, hearing machine ) to help the people with distortionless signal.Previous works in development of BMI show that the signal acquisition and processing are getting complicated with the growing availability of more sophisticated recording devices.To overcome these complexities rather simple method is required to couple easily recordable neuronal signals with the neuromorphic micro devices III. ELECTROENCEPHLOGRAPHY BMI based EEG signals originate from the same neurophysiological processes, there are important differences. Electric fields are less distorted than magnetic fields by the skull and scalp, which results in a resolution with noise. Whereas this paper presents spectogrm image of the sound heared by the disabled person in scalp EEG is sensitive to both tangential and radial components of a current source in a spherical volume conductor, MEG detects only its tangential components. Scalp EEG can, therefore, detect activity both in the sulci and at the top of the corti, EEG is, therefore, sensitive to activity in more brain areas,but the neural activity of the brain resulted in Electroencephalography (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations resulting from ionic current flows within the neurons of the brain. In clinical contexts, EEG refers to the recording of the brain's spontaneous electrical activity over a short period of time, usually 20–40 minutes, as recorded from multiple electrodes placed on the scalp. Diagnostic applications generally focus on the spectral content of EEG, that is, the type of neural oscillations that can be observed in EEG signals. EEG is most often used to diagnose,in EEG readings but this use has decreased with the advent of highresolution anatomical imaging techniques such as MRI and CT. Despite limited spatial resolution, EEG continues to be a valuable tool for research and diagnosis, especially when millisecond-range temporal resolution (not possible with CT or MRI) BMI with speech synthesis is required. II NEUROMORPHIC NOISEATTENUATOR For speech enhancement, a neuromorphic noise attenuator is used to preserve speech and attenuate background noise depending on the characteristics of human hearing system and the onset feature of consonant. In addition, the neuromorphic noise attenuator also employs multiplication for time - domain gain smoothing to reduce the artificial noise problem of traditional spectral subtraction algorithm. Finally, considering the power limitation of the systems and the latency tolerance of user (about 10 ms 15 ms) the proposed algorithm is simplified to reduce the computational complexity is in process for futher work.
Fig 2.Neuromorphic Noise Attenuator
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III. CURRENT MODE IMPLEMENTAION OF AIHCAN IC 3.1. INNER HAIR CELL In the biological cochlea, the inner hair cells transduce vibra-tion in the cochlea into a neural signal. This function is modeled by a novel inner hair cell circuit shown in Fig. 3. A transconduc-tance amplifier transforms A curthe differential cochlear output into a single ended current, to which a dc offset may be added using rent mirror rectifies the current signal before passing it through a low-pass filter. This half wave-rectified current is as a first ap-proximation given from the circuit.
Fig.3 Inner Hair Cell Circuit This half wave-rectified current is as a first approximation given by
I HWR = max(0,gm(v c1- v c2) +I OFF) where
is controlled by
and
by
. off. The bias currents
and
, respectively. C3,
implemented as a pMOS capacitor, should have been implemented with an nMOS capacitor because the voltage . Nonetheless, the circuit still operates cor-rectly as the voltage swing is small and the is quite close to pMOS capacitor acts almost linearly. The cutoff was set around 1 kHz as in the real inner hair cell, modeling the reduction in phase-locking ob-served on real auditory nerves at frequencies greater than 1 kHz. The two control and are slightly below to allow the two pMOS transistors providing to operate in signals and will show up as a current gain depending satu-ration. Any voltage difference between exponentially on this voltage difference. In the results shown in this paper, both voltages were equal to 4.5 V, resulting in a gain of 1.The biological inner hair cell exhibits adaptation to an on-going stimulus, therefore it responds more strongly to the onset of stimulation than the sustained part of the stimulus and its response is suppressed temporarily after the offset of stimulation. This adaptation has been modeled in but it was considered too complex and too large for inclusion on the current chip. However, we intend to include this in future versions. 3.2. DUAL AGC MODEL The dual AGC model has been implemented on a variety of electronic platforms including computer software simulations, discrete analog electronics,DSPs using Analog Device’s Tiger SHARCs, FPGAs, and digital ASICs. The current power envelopes of these platforms limit their usage in mobile applications where acoustic sensors can have the most impact. To overcome this power constraint, an analog VLSI inner hair cell and auditory nerve circuit, known as the AI-HCAN circuit, is implemented using current-mode CMOS cir-cuits. The dual AGC model is and having analogous terms in the current transformed into the current do-main with the model constants domain and the time constants[ , , and taken directly from the dual AGC model. Instead of relying on
the copious amounts of gain that voltage-mode circuits provide, we rely on the matching of CMOS currentmode circuits to implement low-power elec-tronics with sufficient dynamic range despite today’s low supply voltage technology. It should be stated that most current-mode IHC circuits, use weak inversion MOSFETs in log-domain fi lters to achieve large dynamic ranges and to perform translinear multi-plication. Precisely because of the poor matching properties of weak inversion MOSFET’s, which cannot be overcome with compensated layout techniques such as common-centroid layouts, usage was kept to a minimum by only using weak inversion MOSFETs in the low-pass filter’s operational transconductance amplifier (OTA). By focussing on current-mode CMOS circuits that are not operating in weak inversion, we primarily concern ourselves with the non-ideal current mirroring of CMOS transistors. This has the beneficial effect of allowing differential-mode
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circuit topology because we can rely on the higher degree of current mirroring as compared to weak inversion MOSFETs. By using a differential structure for the entire circuit, we can reject unwanted common-mode signals. No IHC and A circuits to date have been reported to use a fully differential current-mode architec-ture. The current mirror is the fundamental building block for most, if not all, current-mode circuits. Its precision determines many design requirements such as necessary power, required area, and interface types. Passive techniques to increase the precision of current mirroring can be accomplished via large transistor sizes. Peduncles and in the
AIHCAN IC the FBCM is used to buffer almost every circuit. The FBCM’s transfer function is shown
Fig. 4. The fully differential dual AGC current-domain circuit begins with an envelope follower that consists of a half-wave rectifier (HWR) that is buffered with a fully-balanced current mirror (FBCM). The buffered signal feeds two stages of low-pass filtering. Adaptation is performed in the dual AGC stage where both pre- and post- synaptic effects are replicated. Fully-balanced current mirrors (FBCM) provide impedance matching for the multiplier (MULT) and low-pass fi lter (LPF) circuits. Subtraction (SUB) circuits in the AGC provide a reference current level, or , from which the filtered feedback signal is compared.
3.3. FULLY BALANCED CURRENT MIRROR The fully balanced current mirror (FBCM), shown in Fig. 4, is a general current-mode building block that does not require voltage-mode op- amps for high linearity, which makes it useful for current-mode signal processing. The FBCM, by Karl [45], makes a small modification to the differential current mirror by Zele et al. [46]. Specifically, the and , which were connected to constant current bias sources in [46], were replaced source connections of through to . For the negative with mirrored signal currents. For the positive half, current is mirrored from through to . These connections enhance common-mode rejection half, current is mirrored from
and .Through the combination of its low input and high by reducing the voltage swing at the sources of output impedances, the FBCM has demonstrated upwards of 60 dB of common-mode signal rejection [45]. This allows the FBCM to act as a buffer for circuits that do not have high output. Fig. 5. The FBCM circuit is composed of two halves, each containing a Wilson mirror input connected to a complementary current mirror on the output. By cross-coupling the output stages, the differential output affects , , , and are the both sides and the signal path becomes fully balanced because the transistors ratios of 1/2; this configuration results in a current gain of one. The circuit is also fully only ones with balanced because the positive and negative, or left and right, signal paths are identical and controlled by one or both halves of the circuit. Finally it should be noted that the FBCM’s regularity allows mismatch-compensated layouts that are made using interdigitated NMOS and PMOS mirrors.
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Fig.6 Output waveform : It shows the FBCM circuit act as a buffer circuit which has boost up the low input current.
Fig7.Power consumption of FBCM circuit.
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3.4. BIPOLAR CMOS The advent technologies which also include BiCMOS combines the strengths of two different process technologies into a single chip: Bipolar transistors offer high speed and gain in the IHC circuit, which are critical for high-frequency analog sections, whereas CMOS technology excels for constructing simple, lowpower logic gates.The analog and digital parts on a single chip, BiCMOS SiGe (Silicon-Germanium) technology drastically reduces the number of external components while optimizing power consumption. Fig 7. Shows Bicmos circuit used in AIHCAN IC offers high speed.
Fig.8 Transient Response of Bicmos shows the measured curves were the power reduces in linearrange of operation.
IV RESULT 4.1. Testing Platform The AIHCAN IC has been modified to reduce the power using Analog CADENCE tool with equal width and ) . The Tanner eda tool used supply voltage for the AIHCAN IC is 2 A length of PMOS and NMOS ( 0.35 and the average current used by the AIHCAN circuit is less than 3.36 mA.Signals are generated, acquired, and displayed at the sample rate channel via a graphical application running in the CADENCE environment. Synchronized input signals to the AIHCAN IC are provided via analog voltage output,DC response is selected and the obtained output gives transient reponse. 4.2. System Response The AIHCAN IC’s response to the frequency tone bursts to examine outputs at the FBCM circuits because they can easily be used to mirror output currents via independent transistors. The dynamic range of the system was measured to be 43 dB. This value is lower than the simulated system response of 60 dB, but is still a useful range for an IHC and A circuit. When com-pared to the IHC and A models of AIHCAN IC’s performance have typical dynamic ranges of 40-to-50 dB. It is possible that the measurements from the test setup described are under representing the capabilities of the AIHCAN IC in biometrics. 4.3.CMOS Mismatch The matching of many AIHCANs is a critical design criteria. To illustrate the importance of input channel matching we can reliably extract important binaural cues. The AIHCAN IC was also an exercise in understanding the use of passive techniques such as using very large MOSFET areas and regular layout methodologies to minimize CMOS mismatch. CMOS mismatch has a dramatic effect on current mirroring and can compromise the performance of electronic analog circuits. The machines that have evolved over time to target the rapid growth of digital CMOS devices..To minimize the current mismatch from variations in voltages,widths, and lengths the large size of a single AIHCAN circuit it demonstrates the event that matching of large area current-mode circuits was unable to prevent DC mismatch, the AIHCAN IC was designed to be able to correct this DC mismatch after the fabrication.
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4.4. Area and Energy Efficiency The use of the AIHCAN circuit, had an estimated order of magnitude reduction in power consumption as shown in Table I. Additionally, by using a differential signal path, power dissipation remains constant under varying signal conditions for relevant frequen-cies. An additional reduction in power of over 10% could be realized if one were to remove additional structures and output blocks that were used for testing the circuit. To eliminate bias circuitry concerns while studying the effects of mismatch on the fabricated IC, the bias current circuitry was designed to min-imize current variation.The AIHCAN IC was designed to use a purposely large area with the intent to understand use in acoustic applications. It was possible to re-duce mismatch effects, but it was not sufficient to rely on passive techniques alone. Mismatch will create DC offsets, thus solutions like the DACCOR circuit, must be used, especially before any signal multiplication. The DACCOR circuit currently of area. AIHCAN circuit would occupy an area of only 78% of a single DACCOR uses 8-bits and 0.02 circuit used in the AIHCAN IC.Therefore, minimization of the DACCOR circuit is possible. The AIHCAN area does not allow audi-tory processors with a reasonable number of microphone and circuit’s 2.3 frequency channels to be fabricated, but with DACCOR, it may be possible to reduce the circuit to less than so that 64 AIHCAN circuits can be placed in an area of 5.76 .Accordingly, transistor, 0.09 capacitor, and interconnect dimensions would be reduced by a factor of fi ve. The widths and lengths of the transistors on the AIHCAN IC are so large that this reduction will still leave the dimensions of the transistors in the multi-mi-cron range.These simulation results imply that scaling of the AIHCAN IC is feasible with the inclusion of DACCOR tuning until tran-sistor dimensions approach the sub-micron range. By removing any DC offsets, a miniaturized AIHCAN circuit could be used on a mixed-signal auditory processor with tens to hundreds of channels. CONCLUSION This Brain-machine interface based on the EEG demonstrated here is a proof-of-principle to reduce the noise using algorithm,that is gradually prevailing upon the very potential field of rehabilitation. By applying this technique it is possible to provide mobility to the disabled people. We also described an IC that can be used for hearing synthesis. The IC will consume less power,lower than 6.9 mW of average power,and can be integrated into an implantable/wearable prosthetic system.The dual AGC model has been transformed into the current domain using differential current -mode circuits and can be used with ultra-low power. The ability to tune the system to remove DC offsets allows CMOS mismatch to be compensated for in the signal pipeline. The lowpower envelope, real-time processing, and behavior flexibility from various levels of tenability make the AIHCAN IC a practical module for use in biomimetic processing, especially in mobile and battery powered applications where the AIHCAN IC can be easily integrated into existing and future systems. It is very much possible to adapt and extend the BMI model to use any other type of signal (voluntary or involuntary) through proper signal processing techniques capable of transforming the input signal to a binary decision signal. The work in progress is to extend this technique to control assistive devices for the disabled. REFERENCES [1] David.S Freedman, Member, IEEE, Howard I. CohenSocrates Deligeorges, Christian Karl, and Allyn E. Hubbard “An analog VLSI implementation of inner hair cell and auditory nerve using dual AGC model” IEEE Trans.Biomed.Circuits Syst.,vol.5,no.4,pp.240-256,2014 [2] K. H. Wee, L. Turicchia, and R. Sarpeshkar,“An articulatory silicon vocal tract for speech and hearing prostheses,” IEEE Trans. Biomed.Circuits Syst., vol. 5, no. 4, pp. 339–346, 2011 [3] A. Hubbard, H. Cohen, C. Karl, D. Freedman, D. Mountain, L.Ziph-Schatzberg, M. Nourzad Karl, S. Kelsall, T. Gore, Y. Pu,Z. Yang, X. Xing, and S.Deligeorges, “Biologically inspiredcircuitry that mimics mammalian hearing,” in Proc. SPIE Conf.Ser., 2009, vol.7321. [4] Y. Shao, Z. Jin, D. Wang, and S.Srinivasan, “An auditory-based feature for robust speech recognition,” in Proc. IEEE Int. Conf. Acoustics,Speech, and Signal Processing, 2009, pp. 4625–4628. [5] V. Chan, S.-C. Liu, and A. van Schaik, “AEREAR: A matched silicon cochlea pair with address event representation interface,” IEEE Trans.Circuits Syst. I, Reg. Papers, vol. 54, no. 1, pp. 48–59, 2007. [6] R. Sarpeshkar, C. Salthouse, J.-J. Sit, M. Baker, S. Zhak, T.-T. Lu, L.Turicchia, and S. Balster, “An ultralow-power programmable analog bionic ear processor,” IEEE Trans. Biomed. Eng., vol. 52, no. 4, pp.711– 727,2005
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[7] A. McEwan and A. van Schaik, “An alternative analog VLSI implementationof the Meddis innerhair cell model,” in Proc. IEEE Int.Symp. Circuits and Systems, 2004, vol. 4, pp.928–931.
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Multimodel Authentication System Using Artificial Neural Network R.Sherline jesie Assistant Professor Satyabama University Chennai. Abstract-Security and authentication of a person is a crucial part of any industry. There are many techniques used for this purpose. One of them is face and iris recognition. Face and iris recognition is an effective means of authenticating a person. The advantage of this approach is that, it enables us to detect changes in the face and iris image pattern of an individual to an appreciable extent. The recognition system can tolerate local variations in the face or iris image of an individual. Here the performance of both the recognition system is evaluated by comparing its recognition rate and accuracy. Hence face and iris recognition can be used as a key factor in crime detection mainly to identify criminals. There are several approaches to face and iris recognition of which Principal Component Analysis (PCA) and Neural Networks have been incorporated in this paper.
General Terms: Face and iris recognition, Principal Component Analysis (PCA) and Neural Networks Keywords: Principal Component Analysis (PCA) and Neural Networks. I.
INTRODUCTION
The demand for reliable personal identification in computerized access control has resulted in an increased interest in biometrics to replace password and identification (ID) card. The password and ID card can be easily breached since the password can be divulged to an unauthorized user, and the ID card can be stolen by an impostor. Thus, the emergence of biometrics has addressed the problems that plague the traditional verification methods. Biometric which make use of human features such as iris, retina, face, fingerprint, signature dynamics, and speech can be used to verify a person's identity. The biometrics data have an edge over traditional security methods since they cannot be easily stolen or shared. The face recognition system has the benefit of being a passive, non-intrusive system for verifying personal identity. The proposed face recognition system consists of face verification, and face recognition tasks. In verification task, the system knows a priori the identity of the user, and has to verify this identity, that is, the system has to decide whether the a priori user is an impostor or not. In face recognition, the a priori identity is not known: the system has to decide which of the images stored in a database resembles the most to the image to recognize. The primary goal of this paper is to present the performance evaluation carried out using artificial neural network for face verification and recognition. There has been a rapid increase in the need of accurate and reliable personal identification infrastructure in recent years. Biometrics has become an important technology for security. Iris is the colored part round the pupil of the eye, which is unique, stable, and inoffensive and can be collected easily. Iris recognition is an identification method based on texture features of the human eye iris to determine the identity, it is one of the most accurate biological recognition methods, and it has been applied in the security domains such as identity authentication. Compared with other biological specificity such as face and fingerprint, iris patterns are more stable and reliable. Furthermore, iris recognition system is non-invasive to the users. So the iris recognition technology has become the research focus in the current biological recognition region.
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II. SYSTEM OVERVIEW The face and iris recognition system consists of two phases which are the training phase and recognition phase.
Fig.1 Block diagram for face and iris recognition system First, the image acquired is trained and stored in the database. The acquired image are normalized from which the characteristic features are extracted and the feature vectors known as eigenvectors are formed. The system is trained on these eigenvectors which means adjusting the weights of hidden and output neurons to minimize the output error. Minimizing the error to an acceptable level marks the end of the training phase after which the recognition phase starts. The unknown input image is fed into the system for recognition the main features are extracted and computed to find the distance between the input image and the stored images. Then it is compared with a threshold value to decide upon whether it is a known or unknown image in identifying a person.
III. METHODOLOGY 3.1. Preprocessing The purpose of the pre-processing module is to reduce or eliminate some of the variations in image due to illumination. The image is acquired using a web camera. The acquired image may have some gaussian noise present in it. So, the image has to be trained. By training, the image is preprocessed and original image is restored. Here we are applying the algorithm based on neural network called shunting inhibitory cellular neural network to restore the image.
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Fig.2 Test image taken from a web camera representing face and iris with noise
Fig.3 Test image of face and iris after preprocessing the image using neural network Shunting inhibitory Cellular Neural Network is a model of visual processing, which can provide contrast and edge enhancement, Image restoration, reconstruction and dynamic range compression. A new class of Artificial Neural Network, called Shunting inhibitory Cellular Neural Network (SICNN) based on the physiologically plausible mechanism of shunting inhibition. A Shunting Inhibitory Cellular Neural Network (SICNN) is an interconnected group of artificial neurons that uses a mathematical model or computational model for information processing based on a connectionist approach to computation. In most cases an SICNN is an adaptive system that changes its structure based on external or internal information that flows through the network. A SICNN also known as a parallel distributed processing network, is a computing solution that is loosely modeled after cortical structure of the brain. It consists of interconnected processing elements call nodes or neurons that work together to produce an output function. The output of a neural network relies on the cooperation of the individual neurons within the network to operate. Processing of information by neural networks is characteristically done in parallel rather than in series (or sequentially) as in earlier binary computers or Von Neumann machines. Since it relies on its member neurons collectively to perform its function, a unique property of a neural network is that it can still perform its overall function even if some of the neurons are not functioning. In other words it is robust to tolerate error or failure. Additionally, SICNN are more readily adaptable to fuzzy logic computing tasks.
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3.2. Feature extraction The purpose of the feature extraction is to extract the feature vectors or information which represents the image. The feature extraction algorithm used are Principal Component Analysis (PCA). Principal component analysis (PCA) for face and iris recognition used is based on the information theory approach. It extracted the relevant information in an image and encoded as efficiently as possible. It identifies the subspace of the image space spanned by the training image data and decorrelates the pixel values. The classical representation of a image is obtained by projecting it to the coordinate system defined by the principal components. The projection of images into the principal component subspace achieves information compression, decorrelation and dimensionality reduction to facilitate decision making. In mathematical terms, the principal components of the distribution of faces or the eigenvectors of the covariance matrix of the set of images, is sought by treating an image as a vector in a very high dimensional face space. 3.3. Classification The purpose of the classification sub-module is to map the feature space of a test data to a discrete set of label data that serves as template. The classification is done using Correlation technique. Correlation is a robust and general technique for pattern recognition and is used in many applications, such as automatic target recognition, biometric recognition and optical character recognition. The design, analysis and use of correlation pattern recognition algorithms require random variables and processes, matrix or vector methods, detection and estimation theory. Here the image stored in the database and test image from the web camera is considered. The correlation of the trained image and the test image are taken. The two images are multiplied (pixel-wise) and the values in the resulting product array are summed to obtain the correlation value of the trained image with the test image for that relative location between the two. This calculation of correlation values is then repeated by shifting the trained image to all possible centerings of the trained image with respect to the test image. Euclidean distances between the projected test image and the projection of all centered training images are calculated. Test image is supposed to have minimum distance with the corresponding image in the training database. And thus the test image is detected whether it is a known image or unknown image. CONCLUSION The paper has presented a face and iris recognition system using artificial neural networks. The performance of both the system is evaluated. By evaluating the performance of both face and iris recognition, both the systems have high accuracy and 100% recognized result.
Fig.4 Face recognition system
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Fig.5 Iris recognition system 5. RESULT Therefore the simulated result for face recognition is shown below:
Fig.6 Test image given as input in text format for face recognition
Fig.7 Test image for face recognition The value of weight is entered and PSNR ratio is calculated in order to obtain the output whether it is matched or unmatched image.
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Fig.8 Matched image ( Face detected ) The simulated result for iris recognition is shown below:
Fig.9 Test image given as input in text format for iris recognition
Fig.10 Test image for iris recognition
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Fig.11 Matched image ( Iris detected ) The experimental results shows that both face and iris recognition obtain a recognized result but iris recognition system have higher recognition rate and accuracy than face recognition system in evaluating a persons identity. Because in case of twins, both of them may look identical. Facial identification reads the nodal points of an individual’s facial features sometimes both the twins may have same distance between the nodal points while face recognition is considered. But it is not in the case of iris recognition system, the iris varies from one individual to another and even in case of twins. Thus the evaluation of both face and iris recognition is performed. ACKNOWLEDGMENTS I would like to thank God, my parents and all those who supported me in all aspects towards the development of this paper. REFERENCES [1] Stefano Arca, Paola Campadelli, Elena Casiraghi, Raaella Lanzarotti, "An Automatic Feature Based Face Authentication System", 16th Italian Workshop on Neural Nets(WIRAN), 2005, pp. 120-126 [2] Shahrin Azuan Nazeer, Nazaruddin Omar, Marzuki Khalid, "Face Recognition System using Artificial Neural Networks Approach", IEEE-ICSCN 2007,pg.420-425. [3] T. Chen, W. Yin, X.-S. Zhou, D. Comaniciu, T. S. Huang, "Total Variation Models for Variable Lighting Face Recognition and Uneven Background Correction", IEEE Transactions on Pattern Analysis and Machine Intelligence,vol. 28(9), 2006, pp.1519-1524 [4] Bruce A. Draper, Kyungim Baek, Marian Stewart Bartlett, J. Ross Beveridge, "Recognizing faces with PCA and ICA."Computer Vision and Image Understanding, vol. 91(1-2), 2003, pp.115-137 [5] S. Lawrence, C. L. Giles, A. Tsoi, and A. Back, "Face recognition: A convolutional neural-network approach," IEEE Trans. on Neural Networks, vol. 8, pp. 98--113, January 1997. [6] Johnny Ng, Humphrey Cheung, "Dynamic Local Feature Analysis for Face Recognition", International Conference Biometric Authentication, (ICBA), 2004, pp. 234-240 [7] M. Villegas and R. Paredes. "Comparison of illumination normalization methods for face recognition.", In Mauro Falcone Aladdin Ariyaeeinia and Andrea Paoloni, editors, Third COST 275 Workshop - Biometrics on the Internet,2005,pp. 27-30
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[8] Javier Ruiz-del-Solar, Pablo Navarrete, "Eigenspace-based Face Recognition: A comparative study of different approaches", IEEE Trans. on Sys., Man. & Cyb. C., vol. 16(7), pp.817-830. [9] Wendy S Yambor, Bruce A. Draper J. Ross Beveridge, "Analyzing PCA-based Face Recognition Algorithms: Eigenvector Selection and Distance Measures", Proc. 2nd Workshop on Empirical Evaluation in Computer Vision, 2000. [10]Zhou Zhiping, Hui Maomao, Sun Ziwen, "An Iris Recognition Method Based on 2DWPCA and Neural Network", Chinese control and Decision Conference,2009. [11] Leila Fallah Araghi, Hamed Shahhosseini, Farbod Setoudeh, "Iris Recognition using Neural Network", Proceedings of the International MultiConference of Engineers and Computer Scientists,2010,Vol 1. [12]Ahmed M. Sarhan, " Iris Recognition using Discrete Cosine Transform and Artificial Neural Network", Journal of Computer Science, May 2009. [13] Reaz M.B.I, Sulaiman M.S, Yasin F.M, Leng T.A, " Iris Recognition using Neural Network based on VHDL prototyping", IEEE, international Conference,2004.
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A Public Safety Offline Application of Smartphone’s and Android OS M.Palanisamy, Lecturer/CSE, M Kumarasamy College of Engineering, Karur. Abstract: Android is a Linux kernel based open source operating system developed by Google. Android primarily designed for smart phones and tablets with touch screen. We explored the Android Operating System and development of software environment and evaluated several of its capabilities by constructing a working application. This is an offline application which collects the speed of the bike/car through Bluetooth transceiver with help of digital speedometer of the bike to determine the location of nearby schools, and if a driver drove over the speed limit in school/college zone, it sounded an alarm to the driver. We believe android smart phones have broad applicability to public safety problems. Keywords – Android, Smartphone, Public safety
I.INRODUCTION Smartphone’s becomes like one part of human body which has greater features with less expensive. While a person driving he/she may distracted by talking on the phone or sending text messages. In our research study of smart phone technology, we developed a proof-of-concept system that tackled public and traffic safety in restricted zones. Our system tackles the necessity for drivers to pay complete visual attention to the road while still being alerted to the speed of the bike/car. The system integrates the android application and Bluetooth communication to make the driver to being alerted while crossing the speed limit and restricted zones. The rest of the skeleton of the paper as follows. We review the relevant technology in Section II. In Section III, we discuss a proof-of-concept system to increase public safety and its implementation. Section IV is the conclusion including a discussion of our future direction. II.BACKGROUND 3.1. Android Software Android is a middleware software stack mobile operating system. In November 2007 Google bought Android from Android Inc[1]. Android uses a DVM (Dalvik Virtual Machine) which is designed under the constraint of slow CPU and little RAM. So, it will run on OS without swap space.[2] Google provides the user manual guide to develop android application step by step in their official website of android. Using Android SDK and eclipse plug-in, easily can develop the android application. Android SDK comprises of Android emulator, command line tools such as AAPT (Android Asset Packaging Tools), ADB (Android Debug Bridge) and AIDL (Android IDL Compiler). Figure 1 show android Emulator and Figure 2 shows the android eclipse plug-in.
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3.2...Android Offline Application: Android offline application is designed which receives the signal from the bike/car through Bluetooth. The particular city map will be taken and it will be stored in SQLite database. Using simcard’s latitude and longitude value, the corresponding user location will be determined. Whenever the driver inserts bike/car key into the keyhole, automatically Bluetooth will get turned on to determine the speed of the vehicle in restricted area such as schools and colleges using Android offline application which works similar to the online GPS system. 3.3...Android Hardware: To develop the android offline application only 512 MB of RAM and does not need any additional hardware to run the above application. 3.4. Bluetooth: Bluetooth is a short-range standard wireless technology for exchanging the data from fixed mobile devices. It was standardized as IEEE 802.15.1, but this was no longer maintained by Bluetooth Special Interest Group (SIG). It is compatible for smart phones, personal computers, laptop and Tablet PC. It supports up to 60 meters. This project uses Bluetooth to obtain the speed of the bike/car using speedometer of the respective vehicle and transmits to the driver’s mobile phone when the speed limit is crossed. 3.5. Speedometer: Speedometer is a gauge which measures and displays the corresponding speed of the vehicle. In digital speedometer, the digital information of the speed of the bike/car will be taken and it will be transmitted to the driver’s smart phone through Bluetooth to sound an alarm.
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IV.PROOF-OF-CONCEPT
Most of the restricted zones such as school/colleges in and around Asia have a speed limit of 20 MPH. Hurried drivers will try to go as fast as possible while driving even when they are crossing restricted area. Some of the drivers may not conscious and not aware of problem when they are crossing the restricted area with no speed limit. We are proposing an application that that uses Android’s functionality to help solve this problem by sounding an alarm if device was in restricted area. The map of the particular city has to be taken and restricted areas are considered as nodes which have the value of 1 using the graph datastructure. Using the simcard’s latitude and longitude, whenever the particular vehicle crosses the restricted area beyond the speed limit, alarm will be sounded using digital speedometer value which will be transferred to Android Offline Application through Bluetooth. Fig.4 depicts the output of the normal speed outside the restricted zone. Fig.5 depicts the output when the driver drove beyond the speed limit. An audible alarm also played from mobile when driver goes beyond the speed limit. In a restricted zones, the intensity of the alarm increased as the driver drove beyond the speed limit. As the driver slowed, the alarm intensity will decrease. This sound system depicts that the vehicle was travelling at an acceptable speed. Testing the application through emulator shows the output as expected.
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IV.CONCLUSION AND FUTUREWORK
The Android platform proved to be capable of supporting different kinds of application. Our example application showed that receiving data from the bike alerts user by sound when driver droves vehicle beyond the speed limit. Many more novel applications are possible in Android by its great support by the official website. In future, the same application can be developed to cover the entire state or country. The same application can be developed with extended functionality to know the current road status and traffic information using GPS. Using the sensor when the driver met accident, from android mobile an emergency call can be made automatically. REFERENCES
[1]. Google bought Android from Android Inc[http://moss.csc.ncsu.edu/~mueller/g1/introduction-toandroid-845.pdf] [2].Dalvik Virtual machine [http://sites.google.com/site/io/dalvik-vm-internals] [3] US Government, Global Positioning System, http://gps.gov/ [last accessed on Oct 16, 2014]. [4] 3G, Assisted-GPS Test Calls for 3G WCDMA Networks, http://www.3g.co.uk/PR/November2004/8641.htm [last accessed on Oct 16, 2014]. [5] Open Handset Alliance, Open Handset Alliance Announces 14 New Members, http://www.openhandsetalliance.com/press_120908.html [last accessed on Oct 16, 2014]. [5] Google, Inc., What is Android?, http://code.google.com/android/what-isandroid. html [last accessed on Oct 16, 2014]. [6] Computerworld, Inc., iPhone Apps distribution still an Issue for Business, http://www.computerworld.com/action/article.do?command=viewArticle Basic&articleId=9095398 [last accessed on Oct 16, 2014]. [7] The Apache Foundation, Licenses, http://www.apache.org/licenses/ [last accessed on Oct 17, 2014].
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A Novel Method of Violated Constraint Prediction with Modified Spatial Analysis Based Fuzzy Sorting K.Nithiya, M.E Student, A.Vinoth Kannan Assistant Professor ECE Dept Applied Electronics, IFET college of Engineering Villupuram , Tamilnadu Abstract:Mobility Prediction of a Moving Node and Network Delay is an important performance characteristic of a wireless network. The Data delivery Delay of a network specifies how long it takes for a data to travel across the network from one node or endpoint to another. It is typically measured in multiples or fractions of seconds.The work presented here belongs to domain of data mining cum wireless network , the Real Time Early Prediction of network delay based on mobility is done using the proposed spatial analysis for constraint violation prediction method. A New application is presented concerning the Delivery delays of UDP packets in GPRS network.The GPS points that are collected from GPS module is analyzed using proposed spatial analysis, for future location prediction using Timestamps as primary data . INDEX TERMS: Monitoring system, Delay Analysis, GPRS, GPS, UDP/IP, Time Constraint, Map matching I.INTRODUCTION: Real Time Applications usually impose strict time constraints which affect the Grade of service. Time constraints Restricts the Time gap between 2 locations. IP network delays can range from just a few milliseconds to several hundred milliseconds. When two devices communicate with each other using a packet-switched network(GPRS), it takes a certain amount of time for information to transmit and receive the data. The total time that it takes for this chunk of information, commonly called a packet, to travel end-to-end is called network delay. In this Proposed Violation Prediction method ,Communication or operating delays between 2 datas are bounded and are taken into account by verifying a global time constraint. The uncertainty induced by these delays generates an uncertainty on the verification that results in a possibility measure associated with constraint verification. Freschet Distance (1999) based prediction lack of True path of Moving Object[1]. The performance of Kalman filter approach depends only on the quality of electronic map data and error sources (2011) associated with positioning devices were not considered[13]. Coorelation ananlysis (2012) shift the received signal by delay and multiply it with other series[12]. Even Fuzzy Logic based matching does not consider error sources when estimating the location. Dynamic time windows (2011) based delay estimation based on Kalman Filter restricted to stastitical data [6]. Our Objective is therefore to study the particular problem that whenever vehicle location request is made its current position will not be retrieved accurately,instead its previous position will not be retrieved accurately,instead its previous position alone sent to requested client. For that, we suppose that communication delays between devices are bounded. This uncertainty on communication delays induces an uncertainty on the time constraint verification. The exploitation of the obtained results allows recognising in a distributed way, the occurrence of the failure symptom with a certain possibility. If a target node moves linearly, through zone prediction method we can predict the location accurately. However, on the other side, when a target does not move direction such as though spiral way. When a device on a packet switching network sends information to another device, it takes a certain amount of time for that information, or data, to travel across the network and be received at the other end.This delay still becomes worst when using Unreliable UDP packets.On low sampling rate GPS trajectories with ≥ 5 and maximum Backoff attempt is 15 times
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a.) A GPS log Is a collection of GPS points = { 1, 2, … } .,Each GPS point ∈ contains latitude . , longitude. and timestamp . .
b.) GPS Trajectory A GPS Trajectory is a sequence of GPS points with the time interval between any consecutive GPS points not exceeding a certain threshold, i.e.: c) Road Segment A road segment 𝑒𝑒 is a directed edge that is associated with an id 𝑒𝑒. 𝑒𝑒𝑖𝑖𝑑𝑑, a typical travel speed 𝑒𝑒. 𝑣𝑣, a length value 𝑒𝑒. 𝑙𝑙, a starting point 𝑒𝑒. 𝑠𝑠𝑡𝑡𝑎𝑎𝑟𝑟𝑡𝑡, an ending point 𝑒𝑒. 𝑒𝑒𝑛𝑛𝑑𝑑 and a list of intermediate points that describes the road using a polyline. d) Network A network is a directed graph𝐺𝐺(𝑉𝑉, 𝐸𝐸), where 𝑉𝑉 is a set of vertices representing the intersections and terminal points of the road segments, and 𝐸𝐸 is a set of edges representing road segments. e.) Path Given two vertices, in a road network, a path is a set of connected road segments that start at and end at
f.) Timestamps Start time of each iterations represent the Timestamp of that data set. This elapsed time for each trajectory is obtained from Tic function using MATLAB. II .ANALYSIS ON SPATIAL CONSTRAINTS: Existing localization techniques which mostly rely on GPS technology are not able to provide reliable positioning accuracy in all situations. This spatial based map matching technique will satisfy the real time constraints to reduce data delivery time and further provide accuracy than existing methods. The Important terms used are described in this section. III. HARDWARE AND SOFTWARE REQUIREMENTS: GPS and GPRS module BU353 USB receiver and SIM 300which is a Triband GSM/GPRS engine works on frequencies EGSM 900 MHz, DCS 1800 MHz and PCS 1900 MHz is preferred for Getting the Location data such as Latitude Longitude and Timestamps are Analyzed in MATLAB Tool and command using lat_calc(i)=str2double(lat(i,:)) function for 10 iterations. Obtained values are used as references for improving Map matching accuracy.Tic and toc commands are used for internal stopwatch timer interval recordings for each trajectory set. IV PROPOSED ARCHITECTURE: In this spatial based location prediction method , the target location at an instance of time is predicted based on previous good locations using an iterative process. Once the zone of the target is predicted with respect a relative origin, the Previous location Points from trajectory data is used to find future location and packet delivery speed enhancement . This is done using Timestamps from which the data is sent and time of its arrival. The values are Recorded for each transmission of a packet to find out the transmission time which is the difference between Arrival and sent. WGS 84 coordinate datum is converted to Decimal Degrees as first step to enhance accuracy in prediction. Consequential Movements is given as Tj, Tj+1 TJ+1 - Tj ≤ Maximum Time gap.
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Proposed Spatio Temporal analysis System 1. Candidate Preparation 2 Analysis
3. Match Results Best path
GPS logs
Road network
Spatial search Analysis Temporal
Candidate Preparation
Match result
Analysis Membership User Interface Candidate sets
Degree preparation for Fuzzy based location prediction
Fig 1 :Proposed Spatial Analysis Based Constraint Prediction. The strict Timing constraints has to be satisfied for achieving best map matching accuracy and packet delay analysis. To fulfill this need in networks in which the topology changes frequently, these changes should not affect the Quality of Service (QoS) for data delivery. The maximum gap defined by Floating point Time difference between 2 successive GPS points. The IP configuration for sending and receiving packets is carried out using UDP socket creation in DOS prompt, since this prediction is carried over Client , Server Architecture.
Fig 1(b) –IP configuration using IP Address.
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Fuzzy Sorting- It is not always possible to do computations with real values.,due to unknown GPS noise and error values obtained, if interval between the values related to uncertained sequences are considered for fuzzy sorting.Rules related to satisfy those time Time constraints are created manually and checked for all sequent points.If number„n‟of intervals is of form[ ai,bi], permutation is created Cj contains all [aij,bij] to satisfy all constraints c1 ≤ c3 ≤c2cn≤. If the Times point when compared with current GPS point is smaller than timestamp of last data of previous trajectory , then Each rule assigned a measure degree α and β source using Rectangular Grid over tracking region. Error point over each edge of grid is considered for future mobility prediction.
Fig 2 - Trajectory Of Gps Assisted vehicle path Constraints are expressed by timing relationships between Trajectory(GPS points) A constraint can, for example, express a transport time window between two locations. To determine the Time window constraints consider the observed sample and characteristics of the correctly operating system are used to create a confidence space of possible timing relationships between Gps Trajectory obtained from the system. To execute simulation for zone finding, MATLAB is used as a simulation tool. The UDP packet socket is created using Send and Receive Arguments with IP configuration. From the given point P, Within radius „r‟ canditate projection is a line drawn from point P to the Road side of the segment.Line segment is projected from the point P to the road segment e, and named as C. shortest distance between p and c is the road that vehicle is choosen to travel as an assumption for this the proposed concept. In spatial analysis, both geometric and topological information of the road network is used to evaluate the candidate points are used for Time instance evaluation. If any value greater than max Gap is obtained ,
The observation probability(geometric information) is defined as the likelihood that a GPS sampling point matches a candidate point computed based on the distance between the two points Transmission Probability(topological information) is used to identify the true path if a cross path is located wrongly. In this if a cross path is identified wrongly, then the previous P point is compared and the path is follower regarding it. The nodal delays accumulate and give an end-to-end delay, Time difference = Timestamp of packet Received –Packet Sent Time End- End delay = Arrival time –Received time / number of iterations used. Here 10 The Total Number of iterations used is 10. One of the major concerns of this research is to keep track the moving target as well as stationary target. As the target can move any direction dynamic references used for applying proper geometry in triangulation based map matching method instead of stationary references. That‟s‟why modified spatia done here to reduce the execution time of proposed method .The iteration process earned the execution time of 0.0019 sec with respect to The Algorithm defines t timing relationship (Timestamps) and their Sequencing relationship.
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V. PROPOSED ALGORITHM BASED ON TIME CONSTRAINTS
STEP 1: Initialize list of candidate points an an empty list STEP 2: For i= 1 to n do STEP3: Get candidate values for observed GPS node positions. From the given point P, Within radius r canditarete projection is a line drawn from point P to the road side of the segment. Line segment is projected from the point P to the road segment e, and named as C. STEP 4: Time Difference between successive GPS points are recorded in Mantissa format to include temporally similarity with respect to current point received. ( Here VB event driven programming language is used ) STEP5: Line segment is projected from the point P to the road segment e, and named as C. shortest distance between p and c is the road that vehicle is choosen to travel STEP6: The observation probability(geometric information) is defined as the likelihood that a GPS sampling point matches a candidate point computed based on the Time difference between the two points STEP7: Transmission Probability (topological information) is used to identify the true path if a cross path is located wrongly. In this if a cross path is identified wrongly, then the previous P point is compared and the path is follower regarding it. STEP8: Binary exponential backoff (truncated exponential backoff) is used to transmit datas data with number of attempts restricted to 15. STEP9: The Candidate graph is constructed to find the relative sequence to be matched for prediction Violations of Constraints due to Measurement errors. STEP10: Return the matched sequence with less delay using membership functions based on Fuzzy sorting It takes for last Iteration of data received with 2.400 sec delay and total execution time is 0.0019 sec which is less then the existing map match method Hidden markov model and kalman filter implementation. AT commands such as CIPSTART, CIPCLOSE are used to Initiate and stop GPS device and Time Difference between points obtained from OSM or Google Map can be used to visually represent the violated constraint.
Values of Final Iteration Sample no
Latitude
1
2400.000 12100.000 2.1000 00 00 sec
2
2400.010 12100.000 0.7000 00 00 sec
0.0002 8
3
2400.020 12100.000 1.3000 00 00 sec
0.0002 0
4
2400.0300 12100.010 0.4000 0 00 sec
0.0001 9
5
2400.0400 12100.000 4.7000 0 00 sec
0.0001 9
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Longitude
Delay(s Process ec) Time 0.00037
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6
2400.0500 12100.000 3.9000 0 00 sec
0.0001 9
7
2400.0600 12100.020 1.4000 0 00 sec
0.0001 9
8
2400.0700 12100.000 4.3000 0 00 sec
0.0001 9
9
2400.0800 12100.000 2.4000 0 00 sec
0.0001 9
Thus, Delivery delay is determined using Absolute departure time of 1st data and last data by monitoring the time windows.This can be further expanded By Fuzzy based sorting using Membership functions in near future. samples are analysed with MATLAB for defined spacing relationship of GPS points with various sampling rates.If one constraint is found to be violated therefore delay occurs on forthcoming Data deliveries.Hence all the forthcoming coming constraints are to checked on the assumed route with constant sampling rate trajectories.
Fig 4 -Time Difference execution using Matlab
Fig 3 –Delay Obtained for packet through Iterated Time constraint analysis Fig 5 –Observed Sample vs Execution Time for delay prediction
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Map matching is a Error correction technique done for pattern identification of latitude, Longitude points and also for packet delivery delay constraints formulation. This method can be useful for any kind of Physical network in Future mainly for Distributed systems, Peer-peer Networks to determine the END-END delay before packet reaches destination in network.
Fig 6 –Time constaint violation monitoring in Google Map near Villupuram,Tamilnadu VI.CONCLUSION: This Paper clearly examine the performance of algorithm and datas from correctly observed operating system to predict the delivery delay before the packet Reaches the destination. The time from when the packet is sent and time it received at the Receiver is recorded and it becomes crucial fator more than the threshold value here assumed Maximum Gap of 0.00019 seconds, the time difference is further applied using Fuzzy sorting to observe Error sources. So far,Spatial Analysis Results that are found is presented in this paper and membership function Estimation for Fuzzy based Interval value sorting process is in progress and presented in Future. Delays are predicted earlier within the reach of it to destination using Advanced Time Constraint violation Prediction Algorithm , at the same time by plotting the obtained values in Google map using appropriate interface,up-to date data receival with less delay and more accuracy is achieved by the proposed method.The Future Scope implies that Delay prediction is possible for GPRS, 3G Networks and delay diagnosis can be done by expanding the concept using FUZZY Sorting by proposed Algorithm by 2016. REFERENCES [1] Jensen C. S. Capturing the uncertainty of moving-object representations based on Freschet Distance, in Proceedings of the 6th international Symposium on Advances in Spatial Databases, 111-132, 1999 [2] Samet, H., Sankaranarayanan, J., and Alborzi, H. Scalable network distance browsing in spatial databases. In Proceedings of the 2008 ACM SIGMOD international Conference on Management of Data, 43-54, 2008 [ 3] Zheng, Y., Chen, Y., Xie, X., and Ma, W. GeoLife2.0: a location-based social networking service.In proceedings of International Conference on Mobile Data Management, 2009. [4] H. Jula, M. Dessouky, and P. A. Ioannou,“Truck route planning in no networks with time windows at customer locations,”IEEETrans.Intell. Transp. Syst., vol.7, no. 1, pp. 51–62, Mar. 2006. [5] S. Murugananham andeal P. R time web based vehicle tracking using J.GPS,” World Acad. Sci. Eng. Technol., vol. 61, pp. 91– 99,Jan. 2010.
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