International Journal of Computer Science Issues

IJCSI

International Journal of Computer Science Issues

Volume 7, Issue 4, No 4, July 2010 ISSN (Online): 1694-0784 ISSN (Print): 1694-0814

© IJCSI PUBLICATION www.IJCSI.org

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© IJCSI PUBLICATION 2010 www.IJCSI.org

IJCSI Publicity Board 2010

Dr. Borislav D Dimitrov Department of General Practice, Royal College of Surgeons in Ireland Dublin, Ireland

Dr. Vishal Goyal Department of Computer Science, Punjabi University Patiala, India

Mr. Nehinbe Joshua University of Essex Colchester, Essex, UK

Mr. Vassilis Papataxiarhis Department of Informatics and Telecommunications National and Kapodistrian University of Athens, Athens, Greece

EDITORIAL In this fourth edition of 2010, we bring forward issues from various dynamic computer science areas ranging from system performance, computer vision, artificial intelligence, ontologies, software engineering, multimedia, pattern recognition, information retrieval, databases, security and networking among others. Considering the growing interest of academics worldwide to publish in IJCSI, we invite universities and institutions to partner with us to further encourage open-access publications. As always we thank all our reviewers for providing constructive comments on papers sent to them for review. This helps enormously in improving the quality of papers published in this issue. Apart from availability of the full-texts from the journal website, all published papers are deposited in open-access repositories to make access easier and ensure continuous availability of its proceedings. We are pleased to present IJCSI Volume 7, Issue 4, July 2010, split in nine numbers (IJCSI Vol. 7, Issue 4, No. 4). Out of the 179 paper submissions, 57 papers were retained for publication. The acceptance rate for this issue is 31.84%.

We wish you a happy reading!

IJCSI Editorial Board July 2010 Issue ISSN (Print): 1694-0814 ISSN (Online): 1694-0784 © IJCSI Publications www.IJCSI.org

IJCSI Editorial Board 2010

Dr Tristan Vanrullen Chief Editor LPL, Laboratoire Parole et Langage - CNRS - Aix en Provence, France LABRI, Laboratoire Bordelais de Recherche en Informatique - INRIA - Bordeaux, France LEEE, Laboratoire d'Esthétique et Expérimentations de l'Espace - Université d'Auvergne, France Dr Constantino Malagôn Associate Professor Nebrija University Spain Dr Lamia Fourati Chaari Associate Professor Multimedia and Informatics Higher Institute in SFAX Tunisia Dr Mokhtar Beldjehem Professor Sainte-Anne University Halifax, NS, Canada Dr Pascal Chatonnay Assistant Professor MaÎtre de Conférences Laboratoire d'Informatique de l'Université de Franche-Comté Université de Franche-Comté France Dr Karim Mohammed Rezaul Centre for Applied Internet Research (CAIR) Glyndwr University Wrexham, United Kingdom Dr Yee-Ming Chen Professor Department of Industrial Engineering and Management Yuan Ze University Taiwan

Dr Vishal Goyal Assistant Professor Department of Computer Science Punjabi University Patiala, India Dr Dalbir Singh Faculty of Information Science And Technology National University of Malaysia Malaysia Dr Natarajan Meghanathan Assistant Professor REU Program Director Department of Computer Science Jackson State University Jackson, USA Dr Deepak Laxmi Narasimha Department of Software Engineering, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia Dr Navneet Agrawal Assistant Professor Department of ECE, College of Technology & Engineering, MPUAT, Udaipur 313001 Rajasthan, India Dr T. V. Prasad Professor Department of Computer Science and Engineering, Lingaya's University Faridabad, Haryana, India Prof N. Jaisankar Assistant Professor School of Computing Sciences, VIT University Vellore, Tamilnadu, India

IJCSI Reviewers Committee 2010  Mr. Markus Schatten, University of Zagreb, Faculty of Organization and Informatics, Croatia  Mr. Vassilis Papataxiarhis, Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece  Dr Modestos Stavrakis, University of the Aegean, Greece  Dr Fadi KHALIL, LAAS -- CNRS Laboratory, France  Dr Dimitar Trajanov, Faculty of Electrical Engineering and Information technologies, ss. Cyril and Methodius Univesity - Skopje, Macedonia  Dr Jinping Yuan, College of Information System and Management,National Univ. of Defense Tech., China  Dr Alexis Lazanas, Ministry of Education, Greece  Dr Stavroula Mougiakakou, University of Bern, ARTORG Center for Biomedical Engineering Research, Switzerland  Dr Cyril de Runz, CReSTIC-SIC, IUT de Reims, University of Reims, France  Mr. Pramodkumar P. Gupta, Dept of Bioinformatics, Dr D Y Patil University, India  Dr Alireza Fereidunian, School of ECE, University of Tehran, Iran  Mr. Fred Viezens, Otto-Von-Guericke-University Magdeburg, Germany  Dr. Richard G. Bush, Lawrence Technological University, United States  Dr. Ola Osunkoya, Information Security Architect, USA  Mr. Kotsokostas N.Antonios, TEI Piraeus, Hellas  Prof Steven Totosy de Zepetnek, U of Halle-Wittenberg & Purdue U & National Sun Yat-sen U, Germany, USA, Taiwan  Mr. M Arif Siddiqui, Najran University, Saudi Arabia  Ms. Ilknur Icke, The Graduate Center, City University of New York, USA  Prof Miroslav Baca, Faculty of Organization and Informatics, University of Zagreb, Croatia  Dr. Elvia Ruiz Beltrán, Instituto Tecnológico de Aguascalientes, Mexico  Mr. Moustafa Banbouk, Engineer du Telecom, UAE  Mr. Kevin P. Monaghan, Wayne State University, Detroit, Michigan, USA  Ms. Moira Stephens, University of Sydney, Australia  Ms. Maryam Feily, National Advanced IPv6 Centre of Excellence (NAV6) , Universiti Sains Malaysia (USM), Malaysia  Dr. Constantine YIALOURIS, Informatics Laboratory Agricultural University of Athens, Greece  Mrs. Angeles Abella, U. de Montreal, Canada  Dr. Patrizio Arrigo, CNR ISMAC, italy  Mr. Anirban Mukhopadhyay, B.P.Poddar Institute of Management & Technology, India  Mr. Dinesh Kumar, DAV Institute of Engineering & Technology, India  Mr. Jorge L. Hernandez-Ardieta, INDRA SISTEMAS / University Carlos III of Madrid, Spain  Mr. AliReza Shahrestani, University of Malaya (UM), National Advanced IPv6 Centre of Excellence (NAv6), Malaysia  Mr. Blagoj Ristevski, Faculty of Administration and Information Systems Management - Bitola, Republic of Macedonia  Mr. Mauricio Egidio Cantão, Department of Computer Science / University of São Paulo, Brazil  Mr. Jules Ruis, Fractal Consultancy, The Netherlands

 Mr. Mohammad Iftekhar Husain, University at Buffalo, USA  Dr. Deepak Laxmi Narasimha, Department of Software Engineering, Faculty of Computer Science and Information Technology, University of Malaya, Malaysia  Dr. Paola Di Maio, DMEM University of Strathclyde, UK  Dr. Bhanu Pratap Singh, Institute of Instrumentation Engineering, Kurukshetra University Kurukshetra, India  Mr. Sana Ullah, Inha University, South Korea  Mr. Cornelis Pieter Pieters, Condast, The Netherlands  Dr. Amogh Kavimandan, The MathWorks Inc., USA  Dr. Zhinan Zhou, Samsung Telecommunications America, USA  Mr. Alberto de Santos Sierra, Universidad Politécnica de Madrid, Spain  Dr. Md. Atiqur Rahman Ahad, Department of Applied Physics, Electronics & Communication Engineering (APECE), University of Dhaka, Bangladesh  Dr. Charalampos Bratsas, Lab of Medical Informatics, Medical Faculty, Aristotle University, Thessaloniki, Greece  Ms. Alexia Dini Kounoudes, Cyprus University of Technology, Cyprus  Mr. Anthony Gesase, University of Dar es salaam Computing Centre, Tanzania  Dr. Jorge A. Ruiz-Vanoye, Universidad Juárez Autónoma de Tabasco, Mexico  Dr. Alejandro Fuentes Penna, Universidad Popular Autónoma del Estado de Puebla, México  Dr. Ocotlán Díaz-Parra, Universidad Juárez Autónoma de Tabasco, México  Mrs. Nantia Iakovidou, Aristotle University of Thessaloniki, Greece  Mr. Vinay Chopra, DAV Institute of Engineering & Technology, Jalandhar  Ms. Carmen Lastres, Universidad Politécnica de Madrid - Centre for Smart Environments, Spain  Dr. Sanja Lazarova-Molnar, United Arab Emirates University, UAE  Mr. Srikrishna Nudurumati, Imaging & Printing Group R&D Hub, Hewlett-Packard, India  Dr. Olivier Nocent, CReSTIC/SIC, University of Reims, France  Mr. Burak Cizmeci, Isik University, Turkey  Dr. Carlos Jaime Barrios Hernandez, LIG (Laboratory Of Informatics of Grenoble), France  Mr. Md. Rabiul Islam, Rajshahi university of Engineering & Technology (RUET), Bangladesh  Dr. LAKHOUA Mohamed Najeh, ISSAT - Laboratory of Analysis and Control of Systems, Tunisia  Dr. Alessandro Lavacchi, Department of Chemistry - University of Firenze, Italy  Mr. Mungwe, University of Oldenburg, Germany  Mr. Somnath Tagore, Dr D Y Patil University, India  Ms. Xueqin Wang, ATCS, USA  Dr. Borislav D Dimitrov, Department of General Practice, Royal College of Surgeons in Ireland, Dublin, Ireland  Dr. Fondjo Fotou Franklin, Langston University, USA  Dr. Vishal Goyal, Department of Computer Science, Punjabi University, Patiala, India  Mr. Thomas J. Clancy, ACM, United States  Dr. Ahmed Nabih Zaki Rashed, Dr. in Electronic Engineering, Faculty of Electronic Engineering, menouf 32951, Electronics and Electrical Communication Engineering Department, Menoufia university, EGYPT, EGYPT  Dr. Rushed Kanawati, LIPN, France  Mr. Koteshwar Rao, K G Reddy College Of ENGG.&TECH,CHILKUR, RR DIST.,AP, India

 Mr. M. Nagesh Kumar, Department of Electronics and Communication, J.S.S. research foundation, Mysore University, Mysore-6, India  Dr. Ibrahim Noha, Grenoble Informatics Laboratory, France  Mr. Muhammad Yasir Qadri, University of Essex, UK  Mr. Annadurai .P, KMCPGS, Lawspet, Pondicherry, India, (Aff. Pondicherry Univeristy, India  Mr. E Munivel , CEDTI (Govt. of India), India  Dr. Chitra Ganesh Desai, University of Pune, India  Mr. Syed, Analytical Services & Materials, Inc., USA  Dr. Mashud Kabir, Department of Computer Science, University of Tuebingen, Germany  Mrs. Payal N. Raj, Veer South Gujarat University, India  Mrs. Priti Maheshwary, Maulana Azad National Institute of Technology, Bhopal, India  Mr. Mahesh Goyani, S.P. University, India, India  Mr. Vinay Verma, Defence Avionics Research Establishment, DRDO, India  Dr. George A. Papakostas, Democritus University of Thrace, Greece  Mr. Abhijit Sanjiv Kulkarni, DARE, DRDO, India  Mr. Kavi Kumar Khedo, University of Mauritius, Mauritius  Dr. B. Sivaselvan, Indian Institute of Information Technology, Design & Manufacturing, Kancheepuram, IIT Madras Campus, India  Dr. Partha Pratim Bhattacharya, Greater Kolkata College of Engineering and Management, West Bengal University of Technology, India  Mr. Manish Maheshwari, Makhanlal C University of Journalism & Communication, India  Dr. Siddhartha Kumar Khaitan, Iowa State University, USA  Dr. Mandhapati Raju, General Motors Inc, USA  Dr. M.Iqbal Saripan, Universiti Putra Malaysia, Malaysia  Mr. Ahmad Shukri Mohd Noor, University Malaysia Terengganu, Malaysia  Mr. Selvakuberan K, TATA Consultancy Services, India  Dr. Smita Rajpal, Institute of Technology and Management, Gurgaon, India  Mr. Rakesh Kachroo, Tata Consultancy Services, India  Mr. Raman Kumar, National Institute of Technology, Jalandhar, Punjab., India  Mr. Nitesh Sureja, S.P.University, India  Dr. M. Emre Celebi, Louisiana State University, Shreveport, USA  Dr. Aung Kyaw Oo, Defence Services Academy, Myanmar  Mr. Sanjay P. Patel, Sankalchand Patel College of Engineering, Visnagar, Gujarat, India  Dr. Pascal Fallavollita, Queens University, Canada  Mr. Jitendra Agrawal, Rajiv Gandhi Technological University, Bhopal, MP, India  Mr. Ismael Rafael Ponce Medellín, Cenidet (Centro Nacional de Investigación y Desarrollo Tecnológico), Mexico  Mr. Supheakmungkol SARIN, Waseda University, Japan  Mr. Shoukat Ullah, Govt. Post Graduate College Bannu, Pakistan  Dr. Vivian Augustine, Telecom Zimbabwe, Zimbabwe  Mrs. Mutalli Vatila, Offshore Business Philipines, Philipines  Dr. Emanuele Goldoni, University of Pavia, Dept. of Electronics, TLC & Networking Lab, Italy  Mr. Pankaj Kumar, SAMA, India  Dr. Himanshu Aggarwal, Punjabi University,Patiala, India  Dr. Vauvert Guillaume, Europages, France

 Prof Yee Ming Chen, Department of Industrial Engineering and Management, Yuan Ze University, Taiwan  Dr. Constantino Malagón, Nebrija University, Spain  Prof Kanwalvir Singh Dhindsa, B.B.S.B.Engg.College, Fatehgarh Sahib (Punjab), India  Mr. Angkoon Phinyomark, Prince of Singkla University, Thailand  Ms. Nital H. Mistry, Veer Narmad South Gujarat University, Surat, India  Dr. M.R.Sumalatha, Anna University, India  Mr. Somesh Kumar Dewangan, Disha Institute of Management and Technology, India  Mr. Raman Maini, Punjabi University, Patiala(Punjab)-147002, India  Dr. Abdelkader Outtagarts, Alcatel-Lucent Bell-Labs, France  Prof Dr. Abdul Wahid, AKG Engg. College, Ghaziabad, India  Mr. Prabu Mohandas, Anna University/Adhiyamaan College of Engineering, india  Dr. Manish Kumar Jindal, Panjab University Regional Centre, Muktsar, India  Prof Mydhili K Nair, M S Ramaiah Institute of Technnology, Bangalore, India  Dr. C. Suresh Gnana Dhas, VelTech MultiTech Dr.Rangarajan Dr.Sagunthala Engineering College,Chennai,Tamilnadu, India  Prof Akash Rajak, Krishna Institute of Engineering and Technology, Ghaziabad, India  Mr. Ajay Kumar Shrivastava, Krishna Institute of Engineering & Technology, Ghaziabad, India  Mr. Deo Prakash, SMVD University, Kakryal(J&K), India  Dr. Vu Thanh Nguyen, University of Information Technology HoChiMinh City, VietNam  Prof Deo Prakash, SMVD University (A Technical University open on I.I.T. Pattern) Kakryal (J&K), India  Dr. Navneet Agrawal, Dept. of ECE, College of Technology & Engineering, MPUAT, Udaipur 313001 Rajasthan, India  Mr. Sufal Das, Sikkim Manipal Institute of Technology, India  Mr. Anil Kumar, Sikkim Manipal Institute of Technology, India  Dr. B. Prasanalakshmi, King Saud University, Saudi Arabia.  Dr. K D Verma, S.V. (P.G.) College, Aligarh, India  Mr. Mohd Nazri Ismail, System and Networking Department, University of Kuala Lumpur (UniKL), Malaysia  Dr. Nguyen Tuan Dang, University of Information Technology, Vietnam National University Ho Chi Minh city, Vietnam  Dr. Abdul Aziz, University of Central Punjab, Pakistan  Dr. P. Vasudeva Reddy, Andhra University, India  Mrs. Savvas A. Chatzichristofis, Democritus University of Thrace, Greece  Mr. Marcio Dorn, Federal University of Rio Grande do Sul - UFRGS Institute of Informatics, Brazil  Mr. Luca Mazzola, University of Lugano, Switzerland  Mr. Nadeem Mahmood, Department of Computer Science, University of Karachi, Pakistan  Mr. Hafeez Ullah Amin, Kohat University of Science & Technology, Pakistan  Dr. Professor Vikram Singh, Ch. Devi Lal University, Sirsa (Haryana), India  Mr. M. Azath, Calicut/Mets School of Enginerring, India  Dr. J. Hanumanthappa, DoS in CS, University of Mysore, India  Dr. Shahanawaj Ahamad, Department of Computer Science, King Saud University, Saudi Arabia  Dr. K. Duraiswamy, K. S. Rangasamy College of Technology, India  Prof. Dr Mazlina Esa, Universiti Teknologi Malaysia, Malaysia

 Dr. P. Vasant, Power Control Optimization (Global), Malaysia  Dr. Taner Tuncer, Firat University, Turkey  Dr. Norrozila Sulaiman, University Malaysia Pahang, Malaysia  Prof. S K Gupta, BCET, Guradspur, India  Dr. Latha Parameswaran, Amrita Vishwa Vidyapeetham, India  Mr. M. Azath, Anna University, India  Dr. P. Suresh Varma, Adikavi Nannaya University, India  Prof. V. N. Kamalesh, JSS Academy of Technical Education, India  Dr. D Gunaseelan, Ibri College of Technology, Oman  Mr. Sanjay Kumar Anand, CDAC, India  Mr. Akshat Verma, CDAC, India  Mrs. Fazeela Tunnisa, Najran University, Kingdom of Saudi Arabia  Mr. Hasan Asil, Islamic Azad University Tabriz Branch (Azarshahr), Iran  Prof. Dr Sajal Kabiraj, Fr. C Rodrigues Institute of Management Studies (Affiliated to University of Mumbai, India), India  Mr. Syed Fawad Mustafa, GAC Center, Shandong University, China  Dr. Natarajan Meghanathan, Jackson State University, Jackson, MS, USA  Prof. Selvakani Kandeeban, Francis Xavier Engineering College, India  Mr. Tohid Sedghi, Urmia University, Iran  Dr. S. Sasikumar, PSNA College of Engg and Tech, Dindigul, India  Dr. Anupam Shukla, Indian Institute of Information Technology and Management Gwalior, India  Mr. Rahul Kala, Indian Institute of Inforamtion Technology and Management Gwalior, India  Dr. A V Nikolov, National University of Lesotho, Lesotho  Mr. Kamal Sarkar, Department of Computer Science and Engineering, Jadavpur University, India  Dr. Mokhled S. AlTarawneh, Computer Engineering Dept., Faculty of Engineering, Mutah University, Jordan, Jordan  Prof. Sattar J Aboud, Iraqi Council of Representatives, Iraq-Baghdad  Dr. Prasant Kumar Pattnaik, Department of CSE, KIST, India  Dr. Mohammed Amoon, King Saud University, Saudi Arabia  Dr. Tsvetanka Georgieva, Department of Information Technologies, St. Cyril and St. Methodius University of Veliko Tarnovo, Bulgaria  Dr. Eva Volna, University of Ostrava, Czech Republic  Mr. Ujjal Marjit, University of Kalyani, West-Bengal, India  Dr. Prasant Kumar Pattnaik, KIST,Bhubaneswar,India, India  Dr. Guezouri Mustapha, Department of Electronics, Faculty of Electrical Engineering, University of Science and Technology (USTO), Oran, Algeria  Mr. Maniyar Shiraz Ahmed, Najran University, Najran, Saudi Arabia  Dr. Sreedhar Reddy, JNTU, SSIETW, Hyderabad, India  Mr. Bala Dhandayuthapani Veerasamy, Mekelle University, Ethiopa  Mr. Arash Habibi Lashkari, University of Malaya (UM), Malaysia  Mr. Rajesh Prasad, LDC Institute of Technical Studies, Allahabad, India  Ms. Habib Izadkhah, Tabriz University, Iran  Dr. Lokesh Kumar Sharma, Chhattisgarh Swami Vivekanand Technical University Bhilai, India  Mr. Kuldeep Yadav, IIIT Delhi, India  Dr. Naoufel Kraiem, Institut Superieur d'Informatique, Tunisia

 Prof. Frank Ortmeier, Otto-von-Guericke-Universitaet Magdeburg, Germany  Mr. Ashraf Aljammal, USM, Malaysia  Mrs. Amandeep Kaur, Department of Computer Science, Punjabi University, Patiala, Punjab, India  Mr. Babak Basharirad, University Technology of Malaysia, Malaysia  Mr. Avinash singh, Kiet Ghaziabad, India  Dr. Miguel Vargas-Lombardo, Technological University of Panama, Panama  Dr. Tuncay Sevindik, Firat University, Turkey  Ms. Pavai Kandavelu, Anna University Chennai, India  Mr. Ravish Khichar, Global Institute of Technology, India  Mr Aos Alaa Zaidan Ansaef, Multimedia University, Cyberjaya, Malaysia  Dr. Awadhesh Kumar Sharma, Dept. of CSE, MMM Engg College, Gorakhpur-273010, UP, India  Mr. Qasim Siddique, FUIEMS, Pakistan  Dr. Le Hoang Thai, University of Science, Vietnam National University - Ho Chi Minh City, Vietnam  Dr. Saravanan C, NIT, Durgapur, India  Dr. Vijay Kumar Mago, DAV College, Jalandhar, India  Dr. Do Van Nhon, University of Information Technology, Vietnam  Mr. Georgios Kioumourtzis, University of Patras, Greece  Mr. Amol D.Potgantwar, SITRC Nasik, India  Mr. Lesedi Melton Masisi, Council for Scientific and Industrial Research, South Africa  Dr. Karthik.S, Department of Computer Science & Engineering, SNS College of Technology, India  Mr. Nafiz Imtiaz Bin Hamid, Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT), Bangladesh  Mr. Muhammad Imran Khan, Universiti Teknologi PETRONAS, Malaysia  Dr. Abdul Kareem M. Radhi, Information Engineering - Nahrin University, Iraq  Dr. Mohd Nazri Ismail, University of Kuala Lumpur, Malaysia  Dr. Manuj Darbari, BBDNITM, Institute of Technology, A-649, Indira Nagar, Lucknow 226016, India  Ms. Izerrouken, INP-IRIT, France  Mr. Nitin Ashokrao Naik, Dept. of Computer Science, Yeshwant Mahavidyalaya, Nanded, India  Mr. Nikhil Raj, National Institute of Technology, Kurukshetra, India  Prof. Maher Ben Jemaa, National School of Engineers of Sfax, Tunisia  Prof. Rajeshwar Singh, BRCM College of Engineering and Technology, Bahal Bhiwani, Haryana, India  Mr. Gaurav Kumar, Department of Computer Applications, Chitkara Institute of Engineering and Technology, Rajpura, Punjab, India  Mr. Ajeet Kumar Pandey, Indian Institute of Technology, Kharagpur, India  Mr. Rajiv Phougat, IBM Corporation, USA  Mrs. Aysha V, College of Applied Science Pattuvam affiliated with Kannur University, India  Dr. Debotosh Bhattacharjee, Department of Computer Science and Engineering, Jadavpur University, Kolkata-700032, India  Dr. Neelam Srivastava, Institute of engineering & Technology, Lucknow, India  Prof. Sweta Verma, Galgotia's College of Engineering & Technology, Greater Noida, India  Mr. Harminder Singh BIndra, MIMIT, INDIA  Dr. Lokesh Kumar Sharma, Chhattisgarh Swami Vivekanand Technical University, Bhilai, India  Mr. Tarun Kumar, U.P. Technical University/Radha Govinend Engg. College, India  Mr. Tirthraj Rai, Jawahar Lal Nehru University, New Delhi, India

 Mr. Akhilesh Tiwari, Madhav Institute of Technology & Science, India  Mr. Dakshina Ranjan Kisku, Dr. B. C. Roy Engineering College, WBUT, India  Ms. Anu Suneja, Maharshi Markandeshwar University, Mullana, Haryana, India  Mr. Munish Kumar Jindal, Punjabi University Regional Centre, Jaito (Faridkot), India  Dr. Ashraf Bany Mohammed, Management Information Systems Department, Faculty of Administrative and Financial Sciences, Petra University, Jordan  Mrs. Jyoti Jain, R.G.P.V. Bhopal, India  Dr. Lamia Chaari, SFAX University, Tunisia  Mr. Akhter Raza Syed, Department of Computer Science, University of Karachi, Pakistan  Prof. Khubaib Ahmed Qureshi, Information Technology Department, HIMS, Hamdard University, Pakistan  Prof. Boubker Sbihi, Ecole des Sciences de L'Information, Morocco  Dr. S. M. Riazul Islam, Inha University, South Korea  Prof. Lokhande S.N., S.R.T.M.University, Nanded (MH), India  Dr. Vijay H Mankar, Dept. of Electronics, Govt. Polytechnic, Nagpur, India  Dr. M. Sreedhar Reddy, JNTU, Hyderabad, SSIETW, India  Mr. Ojesanmi Olusegun, Ajayi Crowther University, Oyo, Nigeria  Ms. Mamta Juneja, RBIEBT, PTU, India  Dr. Ekta Walia Bhullar, Maharishi Markandeshwar University, Mullana Ambala (Haryana), India  Prof. Chandra Mohan, John Bosco Engineering College, India  Mr. Nitin A. Naik, Yeshwant Mahavidyalaya, Nanded, India  Mr. Sunil Kashibarao Nayak, Bahirji Smarak Mahavidyalaya, Basmathnagar Dist-Hingoli., India  Prof. Rakesh.L, Vijetha Institute of Technology, Bangalore, India  Mr B. M. Patil, Indian Institute of Technology, Roorkee, Uttarakhand, India  Mr. Thipendra Pal Singh, Sharda University, K.P. III, Greater Noida, Uttar Pradesh, India  Prof. Chandra Mohan, John Bosco Engg College, India  Mr. Hadi Saboohi, University of Malaya - Faculty of Computer Science and Information Technology, Malaysia  Dr. R. Baskaran, Anna University, India  Dr. Wichian Sittiprapaporn, Mahasarakham University College of Music, Thailand  Mr. Lai Khin Wee, Universiti Teknologi Malaysia, Malaysia  Dr. Kamaljit I. Lakhtaria, Atmiya Institute of Technology, India  Mrs. Inderpreet Kaur, PTU, Jalandhar, India  Mr. Iqbaldeep Kaur, PTU / RBIEBT, India  Mrs. Vasudha Bahl, Maharaja Agrasen Institute of Technology, Delhi, India  Prof. Vinay Uttamrao Kale, P.R.M. Institute of Technology & Research, Badnera, Amravati, Maharashtra, India  Mr. Suhas J Manangi, Microsoft, India  Ms. Anna Kuzio, Adam Mickiewicz University, School of English, Poland  Dr. Debojyoti Mitra, Sir Padampat Singhania University, India  Prof. Rachit Garg, Department of Computer Science, L K College, India  Mrs. Manjula K A, Kannur University, India  Mr. Rakesh Kumar, Indian Institute of Technology Roorkee, India

TABLE OF CONTENTS

1. Nose Tip Region Detection in 3D Facial Model across Large Pose Variation and Facial Expression Anuar L.H., Mashohor S., Mokhtar M. and Wan Adnan W.A.

Pg 1-9

2. Technical Note: Kinds of cluster building/ grid environment with different resources for distributed computing in biomedicine Fred Viezens

Pg 10-14

3. EPS Confidentiality and Integrity mechanisms Algorithmic Approach Ghizlane Orhanou, Saïd El Hajji, Youssef Bentaleb and Jalal Laassiri

Pg 15-23

4. Hybrid Feature Point Based Registration of 2D Abdominal CT Images Asmita A. Moghe, Jyoti Singhai and S. C Shrivastava

Pg 24-29

5. Multi-Agent System Supply Chain Management in Steel Pipe Manufacturing S Srinivasan, Dheeraj Kumar, Vivek Jaglan

Pg 30-34

6. De Bruijn Pseudo Random Sequences Analysis For Modeling of Quaternary Modulation Formats Hadjira Badaoui, Yann Frignac, Petros Ramantanis, Badr Eddine Benkelfat and Mohammed Feham

Pg 35-38

7. PRQS Sequences Characteristics Analysis by Auto-correlation Function and Statistical Properties Hadjira Badaoui, Yann Frignac, Petros Ramantanis, Badr Eddine Benkelfat and Mohammed Feham

Pg 39-43

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 4, No 4, July 2010 ISSN (Online): 1694-0784 ISSN (Print): 1694-0814

1

Nose Tip Region Detection in 3D Facial Model across Large Pose Variation and Facial Expression Anuar L.H., Mashohor S., Mokhtar M. and Wan Adnan W.A. Multimedia Systems Laboratory, Department of Computer and Communication Systems, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia

Abstract

Detecting nose tip location has become an important task in face analysis. Howev er, for a 3D f ace m odel with pr esence of large rotation v ariation, d etecting no se t ip location is cer tainly a challenging task . In this pap er, we propose a method to detect nose tip r egion in large rotation v ariation based on th e geometrical shape of a nose. Nose region has alway s been considered as the most protuberant p art of a face. Bas ed o n convex points of face surface, we use morphological approach to obtain nose tip region candidates consist of highest point density. For each point of each region candidate, a signature is generated and evaluated w ith trained nose tip tolerance band for matching purpose. The region that contains the point which scores the most is chosen as th e final nose tip region. This method can hand le large ro tation v ariation, f acial expression, com bination of all rotations ( yaw, pitch and ro ll) and larg e no n-facial outliers . Combination of two datab ases has been us ed; UP MFace and GavabDB as training data set and test data set. The experimental results show th at 95 .19% nos e tip r egion over 1300 3D face models were correctly detected. Key words: Nose Tip Region Detection, Morphology, 3D Face Model, Point Signature, Tolerance Band.

1. Introduction Human face recognition has received wide attention for the past few years. Recent development of technol ogy in data acquisition has enabled a 3D face to be captured and analyzed. Extensive studies have shown that 3D face data offers better p erformance of face recognition i n disadvantageous co nditions, such a s hea d pose rotation, illumination an d facial exp ression. Metho ds asso ciated with 3D face recogn ition such as face registration, fac e modeling and facial feature s extraction t hat ha ve been proposed are largely based on the geometry shape of a face which greatly relies on appropriate 3D surface desc riptors and accurate facial landmark locations [1]. Compared t o ot her facial l andmarks, nose offers f ew advantages. Due to th e distinct sh ape an d symmetrica l property of a nose, it is frequently used as a key feature

point in 3D faces represe ntation. For e xample, finding nose facilitates the search for other landmarks such as eyes and mouth corners in order to employ robust facial feature extraction [2 ]. Un like nose, other feature s can c hange significantly due to facial expression, e.g., closed eyes and open mouth. In add ition, the ch aracteristics o f th e nose which indicates the center of the face a nd always pointing frontal are found useful for head pose estimation and face registration. Even t hough geometry feat ure-based m ethod seems t o be more straight-forwa rd in re presenting the whole face, i t highly d epends on th e geometrical characteristics of t he face. Any c hanges of pose or a presence of occlusion can cause se vere l ost of i nformation. T his expl ained by Ayyagari [3] that during acquisition process, it is possible that some parts of the face will be unobservable from any given position, either due to occlusion, or limitations in the sensor’s field of vi ew. Heuristically, n ose i s al ways assumed as the nea rest point to the camera, and t herefore the highest value in z-axis. Although it can largely reduce the co mplexity o f an al gorithm, in case of large scale of variation and rotation, th is assumption does no t always hold. Alternatively, a sear ch fo r nose ti p can b e b ased on th e protuberant parts of a face. Although it cannot give exact location of nose tip, it can reduce the searching space quite effectively. This is fo llowed by one or more other steps to finalize a no se tip . In [4 ], no se tip can didates were first obtained base d o n protuberant p oints. Si nce o nly f rontal faces were c onsidered, nose tips were obtained by symmetry calculation, which was carried out based on the direction comparison of norm al vectors. Similar idea was adopted by Xu [5], who considered nose tip as the highest local p oint an d ha ving peaked ca p-like s hape. Nose tip were fi nalized usi ng S upport Vect or M achine (SM V) t o classify between nose-tip and non-nose-tip points.

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 4, No 4, July 2010 www.IJCSI.org

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Fig. 1 Orientation shape of a nose tip.

Sun a nd Yi n [6] t ook t he a dvantage of t he sy mmetrical property of a n ose. B ased on c urvature of faci al range image, two clusters of inner eye corners were obtaine d. With aid of facial re ference pla ne, nose tip can be determined by putting a p lane along eyes line and find the point t hat has t he m aximum di stance f rom pl ane. However, occlusion aro und th e eyes, for ex ample g lasses or hair, ca n affect t he robustness o f t his m ethod. Breitenstein [7] introduced a scheme of nose detection that is ro bust to larg e pose variation. Shape sign ature was computed f or every poi nt of i nput i mage and the corresponding pose hypotheses were generated in parallel. To select nose tip , erro r function is used to co mpare the input range image to the pre-computed pose images of a n average face model.

studying t he geometrical feat ures of a fac e fol lowed b y morphological operations. Nose tip region then finalized using point signatures matching.

In [ 8], Lu a nd Jai n proposed a feature extractor based on the directional maximum to estimate nose tip location and pose a ngle si multaneously. At eac h quantized pose a ngle in the o riginal coordinate syste m, the po int with the maximum p rojection value alon g co rresponding pose direction is selected as the nose tip candidate and therefore the directional maximum of that pose angle. A nose profile model represe nted by s ubspace is used to select the nose tip and t he as sociated pose angle as t he pose e stimation result. Same t echnique was employed later in [9 ] to find nose tip candid ates. Ho wever, a no se tip is fin alized differently, where the nose c heck curve of each candidate is extracted a nd com pared with th at of train ed nose tip check curve for similarity.

2.1 Convex point classification

Despite of ha ving significantly different nos e structure from o ne person to ano ther, th e orientation sh ape ar ound nose t ip i s however preserved, e ven wi th the p resence of rotation. This shape is considered unique, if measured by appropriate ra dius ca n be represented by fo ur pea ks of nose ridge, nose wings (le ft and right) and t he are a between nose and lips which re ferred as above lips a s shown in Fig. 1. Based on this observation, we proposed a new method to locate nose ti p region of 3D face m odel in large v ariation ro tation. Reg ion cand idates are gained by

The remainder o f t his paper i s o rganized as follows. Section 2 describes t he f ramework o f the proposed method. T he pe rformance o f t he p roposed m ethod i s presented and discussed in section 3. Finally, we give our conclusion in section 4.

2. Nose Tip Region Detection This section describes the nose tip region algorithm in four stages as briefly illustrated by Fig. 2.

In our proposed method framework, point signature is t he key step in verifying th e no se tip. However, it is computationally expensive to calcu late signature for every point on a face. Therefore, it is always practical to perform elimination steps, giving fewer number of nose tip region candidate. H ere, we consider a n ose tip as a region rather than one e xact poi nt and any part of a face is treated as potential nose tip region. From geometry point of view, a face can be broadly represented as convex, concave or flat points to describe the curviness of a face. Inspired by this, a searching of nose tip ca ndidate can be done by learning the convexity of a face. T his method will not give exact location of the no se tip reg ion, but it will effectiv ely reduce the searching space and computational time. For each point , it can b e i dentified as con vex po int or others by making use o f t he known dot product o f neighboring unit vectors, and its surface normal, : cos

_

(1)


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Fig. 2 Framework of the proposed nose tip region detection.

where al l ve ctors magnitude n ormalized t o 1. We de fine as th e 8-conn ected po ints the n eighboring po ints of surrounding t he ori gin. I f is co nvex, t hen we have more than 90 and cos always being negative. In theory, surface normal is perpe ndicular t o its s urface vectors, meaning t hat and cos should be exactly 90 an d 0 respectively. However, in ou r exp eriments, w e fou nd out that the calculated cos is sl ightly sm aller and bi gger than 0. Th ough t he d ifference is v ery sm all, it is good enough for us to differentiate between convex surface and others. During conve x classification, a part from nose , conve x points are m ost lik ely to lie on th e ch in, jaw lin e, ch eek bone, eyeb rows, h air and sh irt co llar. Thu s, to eli minate these non-nose poi nts, we ca lculate the sum of theta for each , and 3 0 perce nt of p oints of the hi ghest sum are selected representing most protuberant points on face. We chose 30 percent of points because the te st data set use d contain large non-facial out liers t hus any number l ower than that ca n cause nose-tip not fully selected on certain faces.

2.2 Search for candidate regions Some peo ple ha ve a wide n ose t ip or b ulbous n ose, producing flat area on middle of nose tip. Thus, this area

will not be picked up during convex classification, causing a hole in the middle of nose tip region. As th e solution to this p roblem, we propo se to u se a mathematical morphology ap proach kno wn as clo sing. Clo sing is a combination of ex panding (d ilation) an d sh rinking (erosion) operations, wi dely use d i n i mage p rocessing t o fill g aps ex ist within image d ata. In ord er to do t his, we treat the face as binary data of set A, where convex points denoted as 1 and non-convex points denoted as 0. Dilation is p erformed first fo llowed by ero sion b y usin g th e sam e 8-connected structuring element B ·

(2)

where and denote dilation and erosion respectively. Knowing t hat n ose t ip region i s am ong hi ghest point density regions, we can na rrow down t he searching space by getting rid of reg ions with lower point d ensity. Th e morphology operation used specifically for this purpose is erosion which applied twice to produ ce less nu mber of regions. At th is point, the output im age is still in bin ary form of convex an d non-convex po ints. To ex tract meaningful regions, se gmentation i s c ompulsory. E ach region i s partitioned and labeled with different number and the nose tip region is finalized using point signatures.


2.3 Point signature Point signature, was first i ntroduced as a n ew presentation of free -form 3D o bject rec ognition [ 10], describes the structural neighborhood of a point on a face represented by sets of distance profiles. It is later used in [11-12] for face recognition. B eing inv ariant to ro tation an d tran slation, registration c an be ac complished by matching t he signatures of data points of sensed surface to the signatures of data points representing the reference surface. Although point si gnature ge nerally kn own as fa ce rec ognition algorithm, we use it specifically to reco gnize a nose tip. This is due t o its ab ility to efficien tly d escribe and represent the unique shape of a nose tip. The definition of point signature is summarized here based on [10]. For a given point p, we place a sphere of radius r, centered at p. Th e i ntersection of th e sp here with the object s urface is a 3D s pace curve C, w hose orientation can be defined by a normal vector, , a “reference” vector , an d th e vecto r cro ss-product of and . is defined as th e unit normal vector of a p lane fitted through the spac e curve C. A ne w plane P´ is defi ned by translating t he fitted p lane t o t he point p in a d irection . T he perpendicular p rojection of C to P´ parallel to

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forms a ne w planar curve C´ with t he projection d istance of poi nts on C´ form ing a si gned di stance pr ofile. T his followed by an gular sam pling of ev ery po int of C by a from the refe rence clockwise r otation a ngle a bout direction . This distance profile may now be represented by a discrete set of values d( ) for i=1,…, , 0< 64 GB ) with technology of Vi rtual Machines (VMware) included three com puting system s. The choice of operat ing sy stem i s not so i mportant by

 argument  /etc/motd  /argument   stdout  /tmp/stdout  /stdout   /job 

2.2 Virtualization of Grid Node - Integrated Network Structures with external Connection


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These job calls the program under /bin/cat with /etc/motd as p arameter an d th e file /tmp/stdout as st andard out put file. It will b e read an d sto red the message of the day. Alternative would be the following job call:

globusrun - ws - submit - c /bin/cat /etc/motd - so /tmp/stdout Analogue to (stdout) let be redi rected t he st andard i nput file (stdin) and standard erro r output file (stderr). It is possible to specify multiple tags argument. Then it can be send a job, but i t runs onl y on t he front end. B y sending jobs to a different computer can be used the command-line parameter -F:

globusrun - ws - submit - f job.xml - F https : //${KNOTEN} : 8443/wsrf/services /ManagedJobFactoryService Job Script: 01 : # ! /bin/sh 02 : 03 : HOSTS  cat /usr/bin/machines 04 : OUTDIR  /clusterwork/mo 05 : 06 : for HOST in $HOSTS 07 : do 08 : cat  EOF  job.xml 09 :  job  10 :  executable  /bin/hostname  /executable  11 :  stdout  $OUTDIR/hostnames  /stdout  12 :  factoryEndpoint  13 :  wsa : Address  14 : https : //$HOST : 8443/wsrf/services/ManagedJobFactoryService 15 :  /wsa : Address  16 :  /factoryEndpoint  17 :  /job  18 : EOF 19 : echo " starting job on $HOST"... 20 : globusrun - ws - submit - f job.xml 21 : 22 : done 23 : 24 : echo " ready. now displaying $OUTDIR/hostnames :" 25 : cat $OUTDIR/hostnames

Script 1 Grid-Job executing on available Worker Nodes.

Fig. 4 Virtualization with ESX Server and bootable Instant-Grid Image.

The ad vantage o f v irtualization is th e possibility to store each update of the grid system or the proceeded data, e.g. downloading and i nstalling of addi tional soft ware packages for grid services application devel opment l ike portal software (Gridsphere [11]).

2.3 Integration of an Apple XServer Cluster into a Grid with heterogeneous Operating Systems Software st ack for gri d m iddleware Gl obus Tool kit with Linux operating system was installed at existing Enclosure of B lade Technol ogy wi th m ore t han one hundred processor cores and st orage capacity of approximate twenty Tera Byte. At that Apple XServers with more than sixty processors and the B SD - Uni x based M ac OS X 10.5 should be included. In a R atio of 1: 1 it isn’t able to install the equal software stack on both architectures. The conventional installation process abort ed wi th error messages.


$OS  uname a | cut - d” ” f1 or  uname - a | awk (print $1) if ($OS - / - “Darwin”) ./set_env_osx fi else ./set_env_linux Screenshot 1 Error Message Maui (scheduler) Installation.

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(1)

or arg uments (see 2 -3) co uld d epending v ary av ailability the Linux or the Mac OS version to execute the scheduled job. The following arguments are possible:

qsub option argument (nodes/ opsys) nodes  machine name (2) opsys  Linux/ Darwin The arguments (see 2) realized a m apping between application, resources and operat ing sy stem. Num eral 3 shows an exam ple for runni ng an i nteractive job on t he particular pl atform. Not e: Thi s i s onl y possi ble under the grid user; the root user can’t operate this. Screenshot 2 Error Message Maui and Torque (batch system) with RPM.pkg support.

With the aid of the application packages Xcode, Fink and RPM for Mac OS X was it p ossible to co mpile an d execute to rque, a p art o f th is stack . To rque is the batch system also used in the en closure. With t he Schedul er (maui) at t he gri d head node of t he encl osure com puting jobs are abl e to run on bot h platforms with the especially environment. The IP Addresses of t he Appl e XServers have to be written at the enclosure grid node in the pbs_mom-file (Portable Batch System ). Th e pbs_server listened at signals of the pbs_mom clients running on t he xservers. Th e p bs_server h ave to written in var/spool/torque at cl ient si de. Loggi ng messages are written in /spool/pbs/mom.logs. With th e co mmand lin e instruction pbsnodes –a (server side) the log report shows the status information of al l worker nodes. The at tribute opsys showed in t his case Li nux or Darwi n (avai lable nodes with the kind of Operating System) and respectively the machine name of the worker nodes.

3. Results At th e n etwork file system (NFS) two installations of an image processi ng appl ication [12] i n t he case Linux and Mac OS compilations are stored. With bash scripts queried arguments, such as:

qsub I (interactive) q (job queue) l arguments (3) The following t erminal out put shows such a workfl ow, a script to calculate a statistic program written in R: fviezens@medinfogrid9b :~  qsub - I - q dgiseq - l nodes  ibmi - mac63930 qsub : waiting for job 225.medinfogrid9b to start . /mnt/opt/ mac/r_skript qsub : job 225.medinfogrid9b ready . /mnt/opt/ mac/r_skript ibmi - mac63930 :~ fviezens$ . /mnt/opt/ mac/r_skript

Some computer centers adopt this technology with a small dimensioned Uni x Server t o m anaged a cluster heterogeneously nature with job subm ission on com mand line.

4. Conclusions It i s possi ble t o creat e grid computing environments in each IT Infrastructure, e.g. intra-, extra- and global IT architectures. The logical and phy sical di sconnection allows d istributed co mputing also in su ch sensitive areas like the health care secto r. Th e p ossibility o f sh aring an d processing data as an addi tional benefit in points medical care and research opens new way s for col laboration and utilization/ provision existing compute resources.

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 4, No 4, July 2010 www.IJCSI.org Acknowledgments This work was support ed from t he Germ an Federal Ministry of Education and R esearch (B MBF) i n t he Projects Instant-Grid, M ediGRID and M edInfoGRID, grants 01AK807, 01AK803 and 01G07016.

References

[1] MediGRID-Applikationsporta l, "MediGRID-Applikationsportal," 2006. [2] pug, "Rechenleistung soll wie Strom aus der Steckdose fließen - Göttinger Zentrum will innovative Netzwerktechnologien für Grid Computing entwickeln," in Göttinger Tageblatt, vol. 119. Göttingen, 2008, pp. 22. [3] F. Viezens, "Grid-Com puting in der Biom edizin," in GridComputing in der Biomedizinischen Forschung – Datenschutz und Datensicherheit, vol. 90, Medizinische Informatik, Biometrie und Epid emiologie, U. Sax, Y. Mohammed, F. Viezens, and O. Rienhoff, Eds. München: Urban&Vogel, 2006, pp. 56-62. [4] R. Beisse, M. Bettag, H. Gassen, W. Höppner, F. Koch, S. Nikol, D. Schmidt, D. Schno rr, A. Schrattenholz, M. Schumacher, and W. Siebert, Medizin im 21.Jahrhundert, Laubach, E.,M au, F .,Mau, Th. ed: S pringer-Verlag Berlin Heidelberg New York, 2002. [5] S. Kottha , K. Pe ter, T . Ste inke, J. Ba rt, J. Fa lkner, A. Weisbecker, F . Viezens , Y. M ohammed, U. Sax, A. Hoheisel, T. Ernst, D. Somme rfeld, D. Krefting, and M. Vossberg, "M edical Im age P rocessing in MediGRID," presented at Germ an e-S cience Conference, Baden-Baden, 2007. [6] GT4, "The Globus Toolkit 4 Programmer’s Tutorial," 2006. [7] C. Boehme, A. Félix, B. Ne umair, and U. Schwardmann, "Instant-Grid: Demonstra tion, Entwicklung und Test von Grid-Anwendungen," in GWDG-Nachrichten, vol. 29, Gesellschaft für wissenschaf tliche Datenverarbeitung mbH Göttingen ed, 2006, pp. 5-13. [8] T. Rings, A. Aschenbrenner, J. Grabowski, T. Kalman, G. Lauer, J . M eyer, A. Quadt, U. S ax, and F . Viezens , "An Interdisciplinary Practical Course on the Application of Grid Computing," presented at 1st Annual IEEE Engineering Education Conference – The Future of Global Learning in Engineering Education (EDUCON 2010), Madrid, Spain, 2010. [9] T. Rings, F. Viezens, J. Meyer, and A. As chenbrenner, "Ein interdisziplinäres Grid-Anwe nderpraktikum basierend auf Instant-Grid," GWDG-Bericht, pp. 19-28, 2009. [10]F. Viezens, A. Barz, and K. Lorberg, "Pseudony misierung von Daten in einem Grid," presented at 52. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie., Augsburg, 2007. [11]GridSphere, "GridSphere," 2006. [12]Oxford, "FMRIB Software Library ," Analy sis Group FMRIB, 2009.

Fred Viezens is a member of the IEEE (German Section), studied 1988-1993 Informatics at the Technical University „Otto-vonGuericke“, Magdeburg. 1994-1997 Lecturer at the DEKRA Academy GmbH, Area: Logistics. 1997-2001 Freelance Activity, IT

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Consulting and Software Development. 2001-2002 Research Assistant at the Otto-von-Guericke-University Magdeburg, Medical Faculty. 2003 Research Assistant in the MBR Computing Centre GmbH, Magdeburg. 2004-2005 Research Assistant at the Ottovon-Guericke University Magdeburg, Faculty of Mechanical Engineering. 2006-2008 Research Assistant at the Georg-August University Göttingen, Department of Medical Informatics at the University Medical Center. Since 2008 Research Assistant at the Otto-von-Guericke University Magdeburg, Medical Faculty. His Research Interest are located on Security Mechanisms, Smartcard Technology, Distributed Computing, Service Oriented Architecture and Computer-Integrated Manufacturing.

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 4, No 4, July 2010 ISSN (Online): 1694-0784 ISSN (Print): 1694-0814

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EPS Confidentiality and Integrity mechanisms Algorithmic Approach Ghizlane ORHANOU, Saïd EL HAJJI, Youssef BENTALEB and Jalal LAASSIRI Département Mathématiques et Informatique, Laboratoire Mathématiques Informatique et Applications, Université Mohammed V - Agdal, Faculté des Sciences, BP 1014, Rabat, Maroc

Abstract

The Long Term Evolution of UMTS is one of the latest steps in an advancing series of mobile telecom munications s ystems. Many articles have already been published on the LTE subject but these publications have view ed the s ubject from particular perspectives. In the pres ent paper, a different approach has been taken. W e are interes ted in the s ecurity features and the cryptographic algorithm s used to ensure confidentiality and integrity of the transmitted data. A closer look is taken to the two EPS confidentiality and integrity algorithm s based on the block cipher algorithm AES: the confidentiality algorithm EEA2 and the integrity algorithm EIA2. Furthermore, we focused on the implementation of both algorithms in C language in respect to the specifications requirement s. We have tested our implementations according to the tes tsets given by the 3rd Generation Partnership Proj ect (3GP P) im plementation document. S ome exam ples of the im plementation tes ts are presented bellow. Keywords: LTE, Confidentiality, Integrity, AES, EEA, EIA, Implementation

1. EPS Security Mechanisms EPS ( Evolved Packet System) rep resents th e v ery latest evolution of the UMTS standard. EPS i s al so known by other acrony ms rel ated t o t echnical st udy items being worked on at 3GPP com mittees: LTE (Long Term Evolution), which is dedicated to the evolution of the radio interface, and SAE (Service Architecture Evolution) which focuses on Core Network architecture evolution. EPS is specified as part of the 3GPP family and proposes a significant im provement step, with a new radio interface and an evolved architecture for bot h t he Access and t he Core Network parts.

Security i s anot her i mportant feat ure of the 3GPP family and i ts evol ution i s an i mportant i ssue. EPS provides security features in a sim ilar way as its predecessors UMTS (Uni versal M obile Tel ecommunication System) and GSM (Gl obal Sy stem M obile). In addi tion to the mutual authentication functionality of the network and the user, t wo ot her securi ty funct ions are provi ded to ensure data security during its transm ission over the air interface and through the LTE-SAE system: ciphering of both user plane d ata an d co ntrol p lane d ata (in th e RRC (Rad io Resource Control) layer), and integrity protection which is used for cont rol pl ane dat a onl y. For t he NAS (NonAccess Stratum) network, both ciphering and integrity are provided. Ciphering is used in order to protect the data streams from being received by a third part y, while integrity protection allows the receiver to detect packet insertion or replacement. In th e p resent p aper, we will fo cus o n th e stu dy of EPS confidentiality an d in tegrity m echanisms an d o n th e cryptographic alg orithms u sed to fu lfill th ese secu rity features.

1.1 LTE Confidentiality and Integrity Layer User and signaling data are co nsidered sensitive and their confidentiality and integrity should be protected between the UE ( User Equipment) and t he servi ng net work. For this reason and i n contrast with UMTS (Universal Mobile Telecommunication System) where the data confidentiality and integrity had been ensu red only in the air interface (between UE and RNC (Radio Network Controller)), these

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 4, No 4, July 2010 www.IJCSI.org features i n t he EPS have been i mplemented i n different levels to ensure more data security.

1.1.1 Overview of LTE control and user plane protocol stacks Confidentiality and Integrity p rotection fo r RRC ( Radio Resource Control) and UP ( User Plane) data i s provi ded between the UE and the e-NB (Evolved Node-B) in th e Access Stratum (AS). These secu rity features are applied at the PDCP ( Packet Data Convergence Protocol) lay er, and no layers below PDCP are co nfidentiality p rotected. For the NAS signaling protection, the security controls are done between UEs and M MEs ( Mobility Management Entity) by the NAS protocol [1, 2].

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After p resenting co ntrol an d u ser p lane stack , we will present in t he fol lowing subsect ion an overvi ew of t he implementation of integrity and confidentiality features in the PDCP layer.

1.1.2 PDCP security functions The PDCP layer m anages data stream s for the user plane, as well as for the control plane. The architecture of the PDCP l ayer di ffers for user pl ane dat a and cont rol pl ane data, as shown i n t he fi gures Fi g. 3 and Fi g. 4 respectively.

The PDC P l ayer i s t he upper sub-l ayer of Lay er 2 i n t he LTE Protocol stack. Fig.1 bellow shows the user plane protocol st ack i n t he Enhanced UM TS Terrestrial Radio Access Network (E-UTRAN) named also LTE.

Fig. 3 Overview of user-plane PDCP [1, 4]

Fig. 1 LTE User plane protocol stack [3]

On the other hand, Fig. 2 bellow shows the emplacement of the RRC and NAS signaling in the EPS control plane.

The ciphering funct ion perform ed by t he PDC P l ayer includes bot h ci phering and deci phering. For t he user plane, the data unit that is cip hered is th e data part of the PDCP PDU, as shown in the figure Fig. 3 above. For th e co ntrol p lane, th e d ata u nit th at is cip hered is the data part of t he PDC P PDU and M AC-I. Indeed, PDC P Data PDUs for control plane data comprise a MAC-I field of 32-bit length for integrity protection as presented in Fig. 4 bellow.

Fig. 2 EPS Control plane protocol stack [3] Fig. 4 Overview of control-plane PDCP [1, 4]


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Moreover, it is im portant to m ention th at th e NAS, independently, appl ies i ntegrity prot ection and ciphering in the NAS layer.

second algorithm EIA2, based on AES al gorithm, which will b e stu died in d etail in sectio n 3 o f th e present paper.

The ciphering and integrity algorithms and keys to be used by the PDCP entity are configured by upper layers.

 Integrity key (IK) agreement;  Data integrity feature: th e receiving entity (ME or SN) must be able to check that the signaling data wasn't modified during its transition over the network access link and to check the expected origin of the message (SN (Serving Network) or UE).

1.2 Confidentiality and Integrity mechanisms 1.2.1 User and signaling data Confidentiality To ensure th e d ata co nfidentiality, th e fo llowing procedures are provided [5]:  Cipher al gorithm EEA (EPS Encryption Algorithm) agreement: To en sure th e co nfidentiality o f u ser and signaling data in LTE-SAE ( Long Term Evolution Service Architecture Evolution), 3GPP has maintained the use of the UM TS al gorithm UEA2 based on SNOW 3G al gorithm [1, 6] , and has nam ed i t EEA1. In addi tion, a new al gorithm EEA2, based on AES algorithm used in the CTR mode (Counter Mode), has been adopted. W e will be interested in its operation study i n det ail i n sect ion 2. of the present paper. Besides, the UE and the EPS can securely negotiate the algorithm to use in their mutual communication.

1.3 EPS Algorithms Identification As seen in the subsection 1.2.2 bellow, there are nowadays two set s of securi ty al gorithms used i n t he Long Term Evolution network. The fi rst set i s based on t he st ream cipher algorithm SNOW 3G, which is inherited for UMTS, the 3 rd generat ion of m obile t elecommunication. The second set i s based on the well-known block cipher algorithm AES. Each alg orithm is id entified b y an identifier. The following subsections present the EEA and the EIA alg orithms id entification u sed b y 3 GPP specifications documents [1].

1.3.1 EEA Algorithm Identification

 Cipher key agreement: the agreement is done bet ween the UE and the network during the Authentication and Key Agreement procedure;

The EPS Encry ption Al gorithms (EEA) are algorithm s work with internal 128-bi t bl ocks under t he cont rol of a 128-bit input key except Null ciphering algorithm.

 Encryption/Decryption of user and signaling data;

To each EEA algorithm is a ssigned a 4-bit identifier. Currently, the following values have been defi ned for NAS, RRC and UP ciphering:

1.2.2 Signaling data Integrity Data integrity in the EPS network ensures the protection of th e sig naling d ata in tegrity an d allo ws the authentication of the sig naling m essages tran smitted between the user and t he serving network [5, 7] . User data is not integrity protected. Integrity prot ection, and repl ay protection, shall be provided to all NAS an d RRC-sig naling messages ex cept those explicitly listed in 3GPP documents [1]. The following security features are provided to ensure the signaling data integrity on the LTE and SAE:  Integrity algorithm (EIA) ag reement: as fo r th e d ata confidentiality, there are actually two v ariants o f th e integrity algorithm for LTE: EIA1 based on SNOW 3G algorithm (named UIA2 in UM TS net work) [1, 6] . It was used since 2006 in t he UM TS net work and was maintained in the LTE-SAE network and the

 “00002”: EEA0 Null ciphering algorithm. The EEA0 algorithm is im plemented in th e way th at it h as th e same effect as if it generates a keystream of all zeroes. The length of t he generated keystream has to be equal to the LENGTH i nput param eter. Apart from t his, al l processing performed in association with ciphering are exactly the sam e as with any of the ciphering algorithms [2].  “00012”: 128-EEA1. The EEA1 i s a st ream ci pher based on another stream cipher named SNOW 3G [6, 8, 9, 10] . As m entioned before, EEA1 is an inheritance from UTMS and was i ntroduced as 3GPP st andard on 2006.  “00102”: 128-EEA2. The EEA2 i s a st ream ci pher based on t he bl ock ci pher AES al gorithm used in its CTR (CounTeR mode) mode.


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UEs and eNBs shall i mplement EEA0, 128-EEA1 and 128-EEA2 for both RRC signaling ciphering and UP ciphering.

receiving the data to encrypt, which help to save tim e. Furthermore, it i s based on bi twise operat ions whi ch are carried out quickly.

Besides, UEs and M MEs shal l i mplement EEA0, 128EEA1 and 128-EEA2 for NAS signaling ciphering.

Fig. 5 b ellow illu strates th e En cryption/Decryption operations using the algorithm EEA.

It is important to note that the security functions are never deactivated, al though i t i s possi ble to apply a NULL ciphering algorithm; The NULL al gorithm may be used in certain special cases, such as for m aking an emergency call without a USIM.

1.3.2 EIA Algorithm Identification Like EPS Confidentiality alg orithms, all EPS In tegrity Algorithms (EIA) works under cont rol of a 128-bi t input key, and for each one a 4-bit identifier is assigned. Currently, the following values have been defined [2]:  "00002": EIA0 Null Integrity Protection algorithm. The EIA0 use i s onl y al lowed for unauthenticated emergency calls.

Fig. 5 Encryption/Decryption of user and signaling data

The i nput param eters of EEA are t he sam e as for t he UMTS encryption function f8. They are as follow:

 "00012": 128-EIA1. The EIA1 i s based on t he st ream cipher SNOW 3G [6, 8, 9, 10].

 COUNT-C: Fram e dependent i nput used t synchronize the sender and the receiver;

 "00102": 128-EIA2. The EIA2 i s based on t he block cipher AES used i n i ts CMAC (Cipher-based MAC) mode.

 BEARER : Service bearer identity;

o

 DIRECTION : Direction of the transmission;

The remaining values have been reserved for future use.

 LENGTH : Number of bits to be encrypted /decrypted;

UEs and eNBs shall i mplement 128-EIA1 and 128-EIA2 for RRC sig naling in tegrity protection. Both algorithms shall be i mplemented al so by UEs and M MEs for NAS signaling integrity protection [2].

 Key: The cipher key. Unlike the UMTS where the encryption entities use o nly o ne cip her k ey fo r eith er user data or signaling data, in the EPS (LTE-SAE), different cipher keys are used depending on the data to protect:

In the present paper, we are in terested in studying the two algorithms EEA2 and EIA2 based on AES. R egarding the first set of cryptographic algorithms inherited from UMTS, their study has been subject of previous works [9, 10].

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KUPenc: an 128-bit cipher key for User Pl confidentiality;

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KRRCenc: a 128-bit cipher key for RRC signaling data confidentiality, provided b y th e PDCP layer between UE and eNB (in the LTE network).

-

KNASenc: a 128-bi t ci pher Key for NAS (NonAccess Stratum) signaling confidentiality, provided as part of the NAS protocol between UE and MME (in the SAE part).

2. LTE Confidentiality algorithm EEA2 2.1 Encryption function EEA The needs fo r a co nfidentiality p rotected m ode o f transmission are fulfilled by an LTE confidentiality cryptographic algorithm EEA [2, 5] which is a sy mmetric synchronous stream cipher. This type of ci phering has t he advantage to generat e t he m ask of dat a before even

ane

Fig. 6 bel low shows t he correspondence bet ween security keys and information flows in the network [3].


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cipher. Dat a i s encry pted and decry pted by usi ng an exclusive-OR operation between intput data (the plaintext) and t he key stream produced by encrypting sequential counter block values by the AES algorithm.

2.2.1 Initialization and keystream generation The sequence of 128-bi t count er bl ocks needed for C TR mode T1, T2, …, Ti, … is constructed as follows:

Fig. 6 Correspondence between security keys and information flows in the network [3]

For LTE, as seen above, two encryption algorithm s EEA1 and EEA2 where adopt ed. The second vari ant EEA2 is based on the AES algorithm used i n t he C TR operat ion Mode (CTR Mode).

The most significant 64 bits of T1 consist of COUNT[0] .. COUNT[31] || BEARER[0] .. BEARER[4] || DIRECTION || 026 (i.e. 26 zero bits). These input values are written from most significant bit on the left to least significant bit on the right, so for example COUNT[0] is the most significant bit of T 1. The l east significant 64 bi ts of T1 are all 0. Fig. 7 bellow shows the count er const ruction from t he EEA2 input data.

In the subsection bellow, we will b e interested in studying the EEA2 operation and implementation.

2.2 Encryption Algorithm EEA2 Operation Fig. 7 First Counter block T1

The second EPS confidentiality algorithm EEA2 uses the block cipher AES as a kernel. The argum ents for the choice of AES as a core algorithm for the second LTE Confidentiality alg orithm (co mpared to UEA1 in th e UMTS which uses the ci pher bl ock KASUM I) are gi ven bellow. But apart from these, UEA1 based on KASUMI and EEA2 based on AES were perceived as equal ly good choices [1]:

Subsequent counter blocks are t hen obt ained by appl ying the standard integer increm enting function (according to Appendix B 1 i n [12] ) mod 264 to th e least sig nificant 6 4 bits of the previous counter block [2].

 The eNB needs to support AES in any case because the eNB needs t o support NDS/ IP (Network Domain Security/Internet Protocol), which uses AES.

To encrypt a payload with AES-CTR, the encryptor partitions the plaintext, PT, into 128-bit blocks. The final block needs not to be 128 bits; it can be less.

 The l icensing condi tions on t he core of UEA1/UIA1 (Kasumi) do not make it free for use for other purposes than 3GPP access protection.

PT = PT[1] PT[2] ... PT[n]

 Similarity with other non-3GPP accesses. In th is sectio n, we will fo cus o n th e stu dy o f th e EEA2 structure and operation to encrypt and decry pt messages. Indeed, 128-EEA2 is based on 128-bi t AES [11] in CTR mode [12]. AES-CTR has many properties that make it an attractive encryption algorithm for high-speed networking. AES-CRT uses the AES block cipher to create a stream

2.2.2 Encryption/Decryption

Each PT block is XORed with a block of the keystream to generate the ciphertext, CT. The AES encryption of each counter block resul ts i n 128 bi ts of key stream. The m ost significant 64 bits of the counter block T are initialized as seen b efore, fo llowed b y 6 4 b its th at are all 0 . This least significant 64 bits part of the counter T is the value that is incremented by one m od 264 t o generat e subsequent counter blocks, each resulti ng in another 128 bits of keystream. The encry ption of n pl aintext bl ocks can be summarized as:


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26 || T0 T := COUNT || BEARER || DIRECTION || 0 (T0 indicate 64 bi ts all equal 0. It presents the part of the Counter block T that will be incremented mod 264).

We have tested our implementation by performing all the TestSets given by 3GPP in the specification document [2], the results correspond t o 3GPP expect ed results. We give you bellow one TestSet with the obtained result.

FOR i := 1 to n-1 DO

Input Data:

CT[i] := PT[i] XOR AES(T) T0 := T0 + 1 END CT[n] := PT[n] XOR TRUNC(AES(T)) The AES() funct ion perform s AES encry ption under the control of the confidentiality key. The TRUNC() function t runcates t he l ast out put of t he AES encrypt o peration to th e sam e len gth as th e fin al plaintext block, returning the most significant bits.

Key = 0a8b6bd8 d9b08b08 d64e32d1 817777fb Count = 544d49cd Bearer = 04 Direction = 0 Length = 310 bits Plaintext = fd40a41d 370a1f65 74509568 7d47ba1d 36d2349e 23f64439 2c8ea9c4 9d40c132 71aff264 d0f24800 Expected Ciphertext = 75750d37 b4bba2a4 dedb3423 5bd68c66 45acdaac a48138a3 b0c471e2 a7041a57 6423d292 7287f000 Fig. 9 bellow shows the Test result which meets the 3GPP Test implementation data.

Fig. 8 b ellow illu strates th e EEA encryption/decryption mechanism.

Fig. 9 EEA2 TestSet

Fig. 8 EEA2 structure

The decryption operation is similar to the encryption and it is important to note that the AES-CTR u ses the only AES encrypt operation (for both encryption and decryption), making AES-CTR im plementation sm aller than implementations of many other AES modes.

2.3 EEA2 Implementation Unlike the first EPS alg orithm EEA1 where the codes where given by the 3GPP specification, we have coded the EEA2 al gorithm i n C l anguage wi th respect t o endi aness issues to avoid the m emory reading problem s faced with EEA1.

3. LTE Integrity algorithm EIA2 3.1 Integrity function EIA Integrity protection is realized by adding a fi eld known as “Message Authentication Code for Integrity ” (M AC-I) to each RRC or NAS m essage whose integrity has to be protected. Thi s fi eld i s cal culated by one of the EPS Integrity al gorithms defi ned gl obally by the integrity function EIA. The i nput param eters t o t he i ntegrity algorithm are the following:  a 32-bit COUNT;

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 4, No 4, July 2010 www.IJCSI.org  a 5-bit bearer identity called BEARER;  the 1-bit direction of the transmission i.e. DIRECTION. The DIRECTION bi t shal l be 0 for upl ink and 1 for downlink;  the message itself i.e. MESSAGE. The bit length of the MESSAGE is LENGTH;  a 128-bit integrity key. As for t he encry ption key , i n the EPS (LTE-SAE) system , different integrity keys are used, depending on t he level where i t is used. Fi g. 6 presented in subsection 2.1 shows the different levels and keys used in each one. - KRRCint: a 128-bit Integrity Key for the protection of the RRC sig naling d ata in tegrity, provided by the PDCP layer between UE and eNB (in the LTE network). - KNASint: a 128-bi t Int egrity Key for NAS (NonAccess St ratum) si gnaling i ntegrity, provi ded as part of t he NAS prot ocol bet ween UE and MME (SAE network); Fig. 10 illustrates the use of the integrity algorithm EIA to protect the messages integrity.

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In the subsections bellow, we will b e in terested in th e study of the EIA2 operation and then its implementation.

3.2 Integrity Algorithm EIA2 Operation In this section, we will fo cus o n th e stu dy o f th e EIA2 structure and operat ion t o generat e t he M AC-I (Message Authentication Code for Integrity). Indeed, EIA2 i s based on 128-bi t AES [11] i n t he C MAC (ci pher-based MAC) mode for Au thentication [1 3]. CMAC, lik e an y welldesigned M AC al gorithm, provi des st ronger assurance of data integrity than a ch ecksum or an error det ecting code. The verification of a checksum or an error det ecting code is designed to detect only acci dental modifications of the data, wh ile CMAC is d esigned to d etect intentional, unauthorized m odifications of t he dat a, as well as accidental modifications [13]. In the following subsections, we will p resent the different EIA2 operation steps.

3.2.1 Subkey Generation Before proceeding to the MAC calculation, the EIA2 algorithm uses a subkey genera tion funct ion t o generat e two subkeys needed during the MAC generation process. Indeed, the integrity key K is used to derive two additional secret values, called the subkeys, denoted K1 and K2. The length of each subkey is 128 bits. The two subkeys are fixed once for any i nvocation of C MAC wi th t he gi ven key. So, for our i mplementation, t he subkey s are precomputed and stored with the key for repeated use [13].

Fig. 10 Derivation of MAC-I/NAS-MAC (or XMAC-I/XNAS-MAC)

Based on t hese i nput param eters t he sender com putes a 32-bit message aut hentication code (M AC-I/NAS-MAC) using th e in tegrity alg orithm EIA. The message authentication code is then appended to the message when sent. The receiver com putes the expected message authentication code (X MAC-I/XNAS-MAC) for the received message in the same way as the sender computed its m essage aut hentication code for t he sent message and verifies the data integrity o f th e message b y co mparing it to the received m essage authentication code, i.e. MACI/NAS-MAC.

One of t he elements of t he subkey generation process is a bit string R which depends on t he used bl ock cipher AES size, which is in our case 128. So, R128 = 012010000111 We present bel low, t he speci fication of t he subkey generation process of CMAC [13]: L = AESK(0128) (128 is the block size of t he bl ock ci pher AES used i n EIA2) IF MSB1 (L) = 0 THEN K1 = L