Design, Development and Calibration of Virtual ...

Sensors & Transducers Volume 93 Issue 6 June 2008

www.sensorsportal.com

ISSN 1726-5479

Editor-in-Chief: professor Sergey Y. Yurish, phone: +34 696067716, fax: +34 93 4011989, e-mail: [email protected] Editors for Western Europe Meijer, Gerard C.M., Delft University of Technology, The Netherlands Ferrari, Vittorio, Universitá di Brescia, Italy Editors for North America Datskos, Panos G., Oak Ridge National Laboratory, USA Fabien, J. Josse, Marquette University, USA Katz, Evgeny, Clarkson University, USA

Editor South America Costa-Felix, Rodrigo, Inmetro, Brazil Editor for Eastern Europe Sachenko, Anatoly, Ternopil State Economic University, Ukraine Editor for Asia Ohyama, Shinji, Tokyo Institute of Technology, Japan

Editorial Advisory Board Abdul Rahim, Ruzairi, Universiti Teknologi, Malaysia Ahmad, Mohd Noor, Nothern University of Engineering, Malaysia Annamalai, Karthigeyan, National Institute of Advanced Industrial Science and Technology, Japan Arcega, Francisco, University of Zaragoza, Spain Arguel, Philippe, CNRS, France Ahn, Jae-Pyoung, Korea Institute of Science and Technology, Korea Arndt, Michael, Robert Bosch GmbH, Germany Ascoli, Giorgio, George Mason University, USA Atalay, Selcuk, Inonu University, Turkey Atghiaee, Ahmad, University of Tehran, Iran Augutis, Vygantas, Kaunas University of Technology, Lithuania Avachit, Patil Lalchand, North Maharashtra University, India Ayesh, Aladdin, De Montfort University, UK Bahreyni, Behraad, University of Manitoba, Canada Baoxian, Ye, Zhengzhou University, China Barford, Lee, Agilent Laboratories, USA Barlingay, Ravindra, RF Arrays Systems, India Basu, Sukumar, Jadavpur University, India Beck, Stephen, University of Sheffield, UK Ben Bouzid, Sihem, Institut National de Recherche Scientifique, Tunisia Binnie, T. David, Napier University, UK Bischoff, Gerlinde, Inst. Analytical Chemistry, Germany Bodas, Dhananjay, IMTEK, Germany Borges Carval, Nuno, Universidade de Aveiro, Portugal Bousbia-Salah, Mounir, University of Annaba, Algeria Bouvet, Marcel, CNRS – UPMC, France Brudzewski, Kazimierz, Warsaw University of Technology, Poland Cai, Chenxin, Nanjing Normal University, China Cai, Qingyun, Hunan University, China Campanella, Luigi, University La Sapienza, Italy Carvalho, Vitor, Minho University, Portugal Cecelja, Franjo, Brunel University, London, UK Cerda Belmonte, Judith, Imperial College London, UK Chakrabarty, Chandan Kumar, Universiti Tenaga Nasional, Malaysia Chakravorty, Dipankar, Association for the Cultivation of Science, India Changhai, Ru, Harbin Engineering University, China Chaudhari, Gajanan, Shri Shivaji Science College, India Chen, Jiming, Zhejiang University, China Chen, Rongshun, National Tsing Hua University, Taiwan Cheng, Kuo-Sheng, National Cheng Kung University, Taiwan Chiriac, Horia, National Institute of Research and Development, Romania Chowdhuri, Arijit, University of Delhi, India Chung, Wen-Yaw, Chung Yuan Christian University, Taiwan Corres, Jesus, Universidad Publica de Navarra, Spain Cortes, Camilo A., Universidad Nacional de Colombia, Colombia Courtois, Christian, Universite de Valenciennes, France Cusano, Andrea, University of Sannio, Italy D'Amico, Arnaldo, Università di Tor Vergata, Italy De Stefano, Luca, Institute for Microelectronics and Microsystem, Italy Deshmukh, Kiran, Shri Shivaji Mahavidyalaya, Barshi, India Dickert, Franz L., Vienna University, Austria Dieguez, Angel, University of Barcelona, Spain Dimitropoulos, Panos, University of Thessaly, Greece Ding Jian, Ning, Jiangsu University, China Djordjevich, Alexandar, City University of Hong Kong, Hong Kong

Donato, Nicola, University of Messina, Italy Donato, Patricio, Universidad de Mar del Plata, Argentina Dong, Feng, Tianjin University, China Drljaca, Predrag, Instersema Sensoric SA, Switzerland Dubey, Venketesh, Bournemouth University, UK Enderle, Stefan, University of Ulm and KTB Mechatronics GmbH, Germany Erdem, Gursan K. Arzum, Ege University, Turkey Erkmen, Aydan M., Middle East Technical University, Turkey Estelle, Patrice, Insa Rennes, France Estrada, Horacio, University of North Carolina, USA Faiz, Adil, INSA Lyon, France Fericean, Sorin, Balluff GmbH, Germany Fernandes, Joana M., University of Porto, Portugal Francioso, Luca, CNR-IMM Institute for Microelectronics and Microsystems, Italy Francis, Laurent, University Catholique de Louvain, Belgium Fu, Weiling, South-Western Hospital, Chongqing, China Gaura, Elena, Coventry University, UK Geng, Yanfeng, China University of Petroleum, China Gole, James, Georgia Institute of Technology, USA Gong, Hao, National University of Singapore, Singapore Gonzalez de la Rosa, Juan Jose, University of Cadiz, Spain Granel, Annette, Goteborg University, Sweden Graff, Mason, The University of Texas at Arlington, USA Guan, Shan, Eastman Kodak, USA Guillet, Bruno, University of Caen, France Guo, Zhen, New Jersey Institute of Technology, USA Gupta, Narendra Kumar, Napier University, UK Hadjiloucas, Sillas, The University of Reading, UK Hashsham, Syed, Michigan State University, USA Hernandez, Alvaro, University of Alcala, Spain Hernandez, Wilmar, Universidad Politecnica de Madrid, Spain Homentcovschi, Dorel, SUNY Binghamton, USA Horstman, Tom, U.S. Automation Group, LLC, USA Hsiai, Tzung (John), University of Southern California, USA Huang, Jeng-Sheng, Chung Yuan Christian University, Taiwan Huang, Star, National Tsing Hua University, Taiwan Huang, Wei, PSG Design Center, USA Hui, David, University of New Orleans, USA Jaffrezic-Renault, Nicole, Ecole Centrale de Lyon, France Jaime Calvo-Galleg, Jaime, Universidad de Salamanca, Spain James, Daniel, Griffith University, Australia Janting, Jakob, DELTA Danish Electronics, Denmark Jiang, Liudi, University of Southampton, UK Jiao, Zheng, Shanghai University, China John, Joachim, IMEC, Belgium Kalach, Andrew, Voronezh Institute of Ministry of Interior, Russia Kang, Moonho, Sunmoon University, Korea South Kaniusas, Eugenijus, Vienna University of Technology, Austria Katake, Anup, Texas A&M University, USA Kausel, Wilfried, University of Music, Vienna, Austria Kavasoglu, Nese, Mugla University, Turkey Ke, Cathy, Tyndall National Institute, Ireland Khan, Asif, Aligarh Muslim University, Aligarh, India Kim, Min Young, Koh Young Technology, Inc., Korea South

Ko, Sang Choon, Electronics and Telecommunications Research Institute, Korea South Kockar, Hakan, Balikesir University, Turkey Kotulska, Malgorzata, Wroclaw University of Technology, Poland Kratz, Henrik, Uppsala University, Sweden Kumar, Arun, University of South Florida, USA Kumar, Subodh, National Physical Laboratory, India Kung, Chih-Hsien, Chang-Jung Christian University, Taiwan Lacnjevac, Caslav, University of Belgrade, Serbia Lay-Ekuakille, Aime, University of Lecce, Italy Lee, Jang Myung, Pusan National University, Korea South Lee, Jun Su, Amkor Technology, Inc. South Korea Lei, Hua, National Starch and Chemical Company, USA Li, Genxi, Nanjing University, China Li, Hui, Shanghai Jiaotong University, China Li, Xian-Fang, Central South University, China Liang, Yuanchang, University of Washington, USA Liawruangrath, Saisunee, Chiang Mai University, Thailand Liew, Kim Meow, City University of Hong Kong, Hong Kong Lin, Hermann, National Kaohsiung University, Taiwan Lin, Paul, Cleveland State University, USA Linderholm, Pontus, EPFL - Microsystems Laboratory, Switzerland Liu, Aihua, University of Oklahoma, USA Liu Changgeng, Louisiana State University, USA Liu, Cheng-Hsien, National Tsing Hua University, Taiwan Liu, Songqin, Southeast University, China Lodeiro, Carlos, Universidade NOVA de Lisboa, Portugal Lorenzo, Maria Encarnacio, Universidad Autonoma de Madrid, Spain Lukaszewicz, Jerzy Pawel, Nicholas Copernicus University, Poland Ma, Zhanfang, Northeast Normal University, China Majstorovic, Vidosav, University of Belgrade, Serbia Marquez, Alfredo, Centro de Investigacion en Materiales Avanzados, Mexico Matay, Ladislav, Slovak Academy of Sciences, Slovakia Mathur, Prafull, National Physical Laboratory, India Maurya, D.K., Institute of Materials Research and Engineering, Singapore Mekid, Samir, University of Manchester, UK Melnyk, Ivan, Photon Control Inc., Canada Mendes, Paulo, University of Minho, Portugal Mennell, Julie, Northumbria University, UK Mi, Bin, Boston Scientific Corporation, USA Minas, Graca, University of Minho, Portugal Moghavvemi, Mahmoud, University of Malaya, Malaysia Mohammadi, Mohammad-Reza, University of Cambridge, UK Molina Flores, Esteban, Benemérita Universidad Autónoma de Puebla, Mexico Moradi, Majid, University of Kerman, Iran Morello, Rosario, DIMET, University "Mediterranea" of Reggio Calabria, Italy Mounir, Ben Ali, University of Sousse, Tunisia Mukhopadhyay, Subhas, Massey University, New Zealand Neelamegam, Periasamy, Sastra Deemed University, India Neshkova, Milka, Bulgarian Academy of Sciences, Bulgaria Oberhammer, Joachim, Royal Institute of Technology, Sweden Ould Lahoucin, University of Guelma, Algeria Pamidighanta, Sayanu, Bharat Electronics Limited (BEL), India Pan, Jisheng, Institute of Materials Research & Engineering, Singapore Park, Joon-Shik, Korea Electronics Technology Institute, Korea South Penza, Michele, ENEA C.R., Italy Pereira, Jose Miguel, Instituto Politecnico de Setebal, Portugal Petsev, Dimiter, University of New Mexico, USA Pogacnik, Lea, University of Ljubljana, Slovenia Post, Michael, National Research Council, Canada Prance, Robert, University of Sussex, UK Prasad, Ambika, Gulbarga University, India Prateepasen, Asa, Kingmoungut's University of Technology, Thailand Pullini, Daniele, Centro Ricerche FIAT, Italy Pumera, Martin, National Institute for Materials Science, Japan Radhakrishnan, S. National Chemical Laboratory, Pune, India Rajanna, K., Indian Institute of Science, India Ramadan, Qasem, Institute of Microelectronics, Singapore Rao, Basuthkar, Tata Inst. of Fundamental Research, India Raoof, Kosai, Joseph Fourier University of Grenoble, France Reig, Candid, University of Valencia, Spain Restivo, Maria Teresa, University of Porto, Portugal Robert, Michel, University Henri Poincare, France Rezazadeh, Ghader, Urmia University, Iran Royo, Santiago, Universitat Politecnica de Catalunya, Spain Rodriguez, Angel, Universidad Politecnica de Cataluna, Spain Rothberg, Steve, Loughborough University, UK Sadana, Ajit, University of Mississippi, USA

Sadeghian Marnani, Hamed, TU Delft, The Netherlands Sandacci, Serghei, Sensor Technology Ltd., UK Sapozhnikova, Ksenia, D.I.Mendeleyev Institute for Metrology, Russia Saxena, Vibha, Bhbha Atomic Research Centre, Mumbai, India Schneider, John K., Ultra-Scan Corporation, USA Seif, Selemani, Alabama A & M University, USA Seifter, Achim, Los Alamos National Laboratory, USA Sengupta, Deepak, Advance Bio-Photonics, India Shearwood, Christopher, Nanyang Technological University, Singapore Shin, Kyuho, Samsung Advanced Institute of Technology, Korea Shmaliy, Yuriy, Kharkiv National University of Radio Electronics, Ukraine Silva Girao, Pedro, Technical University of Lisbon, Portugal Singh, V. R., National Physical Laboratory, India Slomovitz, Daniel, UTE, Uruguay Smith, Martin, Open University, UK Soleymanpour, Ahmad, Damghan Basic Science University, Iran Somani, Prakash R., Centre for Materials for Electronics Technol., India Srinivas, Talabattula, Indian Institute of Science, Bangalore, India Srivastava, Arvind K., Northwestern University, USA Stefan-van Staden, Raluca-Ioana, University of Pretoria, South Africa Sumriddetchka, Sarun, National Electronics and Computer Technology Center, Thailand Sun, Chengliang, Polytechnic University, Hong-Kong Sun, Dongming, Jilin University, China Sun, Junhua, Beijing University of Aeronautics and Astronautics, China Sun, Zhiqiang, Central South University, China Suri, C. Raman, Institute of Microbial Technology, India Sysoev, Victor, Saratov State Technical University, Russia Szewczyk, Roman, Industrial Research Institute for Automation and Measurement, Poland Tan, Ooi Kiang, Nanyang Technological University, Singapore, Tang, Dianping, Southwest University, China Tang, Jaw-Luen, National Chung Cheng University, Taiwan Teker, Kasif, Frostburg State University, USA Thumbavanam Pad, Kartik, Carnegie Mellon University, USA Tian, Gui Yun, University of Newcastle, UK Tsiantos, Vassilios, Technological Educational Institute of Kaval, Greece Tsigara, Anna, National Hellenic Research Foundation, Greece Twomey, Karen, University College Cork, Ireland Valente, Antonio, University, Vila Real, - U.T.A.D., Portugal Vaseashta, Ashok, Marshall University, USA Vazques, Carmen, Carlos III University in Madrid, Spain Vieira, Manuela, Instituto Superior de Engenharia de Lisboa, Portugal Vigna, Benedetto, STMicroelectronics, Italy Vrba, Radimir, Brno University of Technology, Czech Republic Wandelt, Barbara, Technical University of Lodz, Poland Wang, Jiangping, Xi'an Shiyou University, China Wang, Kedong, Beihang University, China Wang, Liang, Advanced Micro Devices, USA Wang, Mi, University of Leeds, UK Wang, Shinn-Fwu, Ching Yun University, Taiwan Wang, Wei-Chih, University of Washington, USA Wang, Wensheng, University of Pennsylvania, USA Watson, Steven, Center for NanoSpace Technologies Inc., USA Weiping, Yan, Dalian University of Technology, China Wells, Stephen, Southern Company Services, USA Wolkenberg, Andrzej, Institute of Electron Technology, Poland Woods, R. Clive, Louisiana State University, USA Wu, DerHo, National Pingtung University of Science and Technology, Taiwan Wu, Zhaoyang, Hunan University, China Xiu Tao, Ge, Chuzhou University, China Xu, Lisheng, The Chinese University of Hong Kong, Hong Kong Xu, Tao, University of California, Irvine, USA Yang, Dongfang, National Research Council, Canada Yang, Wuqiang, The University of Manchester, UK Ymeti, Aurel, University of Twente, Netherland Yong Zhao, Northeastern University, China Yu, Haihu, Wuhan University of Technology, China Yufera Garcia, Alberto, Seville University, Spain Zagnoni, Michele, University of Southampton, UK Zeni, Luigi, Second University of Naples, Italy Zhong, Haoxiang, Henan Normal University, China Zhang, Minglong, Shanghai University, China Zhang, Qintao, University of California at Berkeley, USA Zhang, Weiping, Shanghai Jiao Tong University, China Zhang, Wenming, Shanghai Jiao Tong University, China Zhou, Zhi-Gang, Tsinghua University, China Zorzano, Luis, Universidad de La Rioja, Spain Zourob, Mohammed, University of Cambridge, UK

Sensors & Transducers Journal (ISSN 1726-5479) is a peer review international journal published monthly online by International Frequency Sensor Association (IFSA). Available in electronic and CD-ROM. Copyright © 2007 by International Frequency Sensor Association. All rights reserved.

Sensors & Transducers Journal

Contents Volume 93 Issue 6 June 2008

www.sensorsportal.com

ISSN 1726-5479

Editorial Sensors Expo & Conference 2008: Main Trends, Newest Sensors and High Technologies Sergey Y. Yurish………………………………………………………………………………………………

I

Research Articles Sensors Expo & Conference 2008: Main Trends, Newest Sensors and High Technologies Sergey Y. Yurish……………………………………………………………………………………………… Design, Development and Calibration of Virtual System for Relative Humidity Measurement Georgi NIKOLOV, Boyanka NIKOLOVA .............................................................................................

1

Fabricating Capacitive Micromachined Ultrasonic Transducers with Wafer Bonding Technique Anil Arora, Ram Gopal, V. K. Dwivedi, Chandra Shekhar, Babar Ahmad, Rudra Pratap, P. J. George ................................................................................................................

15

Remote Measurement of Electric Current Using Magneto-Optic Technique Mahfoozur Rehman, Basem Abdul Jalil, Zaid Abdullah .....................................................................

21

Design and Implementation of Output Feedback Control for Piezo Actuated Structure Using Embedded System R. Maheswari, M. Umapathy, L. R. Karalmarx, D. Ezhilarasi.............................................................

29

A Generic Model for Relative Adjustment between Optical Sensors Using Rigorous Orbit Mechanics B. Islam, Nita H. Shah and B. Gopala Krishna...................................................................................

37

Voltage Control System of a DC Generator Using PLC Subrata Chattopadhyay, Sagarika Pal ...............................................................................................

50

Color Signature of Current: a Novel Concept for Current Level Indication Sarbani Chakraborty, Satish Chandra Bera .......................................................................................

57

Indirect Method of Non Invasive In-vitro Measurement of D+ Glucose Amar Rouane .....................................................................................................................................

69

CuO-Modified WO3 Sensor for the Detection of a ppm Level H2S Gas at Room Temperature N. B. Sonawane, D. R. Patil, L. A. Patil..............................................................................................

82

Evaluation of Aquatic Environments Using a Sensorial System Based on Conducting Polymers and its Potential Application in Electrochemical Sensors Nelson Consolin Filho, Everaldo Carlos Venancio, Eduarda Regina Carvalho, Marcilene Ferrari Barriquello, Marcela B. Cunha-Santino, Irineu Bianchini Jr., Armando A. H. Vieira and Luiz H. C. Mattoso ....................................................................................

92

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Synthesis and Characterization of a Noval Ammonia Gas Sensor Based on PANI-PVA Blend Thin Films D. B. Dupare, P. Ghosh, K. Datta, A. S. Aswar and M. D. Shirsat.....................................................

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International Frequency Sensor Association (IFSA).

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Sensors & Transducers ISSN 1726-5479 © 2008 by IFSA http://www.sensorsportal.com

Design, Development and Calibration of Virtual System for Relative Humidity Measurement Georgi NIKOLOV, Boyanka NIKOLOVA Faculty of Electronics and Technologies, Technical University of Sofia, Studenstki Grad, TU-Sofia, block 1, room 1366, 1797, Sofia, Bulgaria E-mail: [email protected], [email protected]

Received: 13 May 2008 /Accepted: 24 June 2008 /Published: 30 June 2008 Abstract: In the present paper a frequency mode data acquisition technique, which converts directly relative humidity into a frequency signal, is considered. The system is realized using low cost oscillator, multifunction data acquisition board and capability of virtual instrumentation. In order to automate calibration procedures appropriate software (so called virtual instrument) is developed and described in brief. Using possibility of virtual instrumentation or to be more precise the build in LabVIEW probability, statistics and interpolation functions a method to increase calibration accuracy is suggested and put into practice. Following this approach the polynomial calibration equation for capacitive sensor is extracted and saved in transducer electronic data sheets file. As example of proposed approach a virtual system based on relative humidity capacitive sensor is realized. The main objective of this paper is to demonstrate how with virtual instrumentation can be solved some difficult engineering problems such as relative humidity calibration and measurement. Copyright © 2008 IFSA. Keywords: Capacitive sensors, Frequency measurement, Graphical programming, RH calibration, Virtual instrumentation

1. Introduction Electronic methods for measuring relative humidity (RH) are based either on dew point or on the behaviour of moist materials. Methods that use moist materials are the simplest to use, but they need to be calibrated at intervals if they are used for precise measurement. Recent developments in semiconductor technology have made possible humidity sensors that are highly accurate, durable, and cost effective [1]. The most common humidity sensors are capacitive, resistive, and thermal conductivity. 1

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Capacitive RH sensors have proven themselves to be a practical sensor technology in a wide variety of applications. These relative humidity sensors are designed for high volume, cost sensitive applications such as office automation, automotive cabin air control, home appliances, and industrial process control systems. They are also useful in all applications where humidity compensation is needed. These RH sensors are the only types of full-range measuring devices capable of operating accurately down to 0% RH and up to 100% RH over wide temperature ranges. Besides necessity of periodical calibration the additional disadvantage of the capacitive sensors is that methods and techniques for measuring capacitance are complicated and expensive in general. For this reason in recent years low cost methods that convert capacitance into corresponding voltage, time intervals or frequency become popular, especially for sensor application. Then, the frequency can be converter into corresponding relative humidity by logical circuits, microcontroller or by virtual counting techniques [2, 5, 7, 9, 13]. Due to the remarkable improvement of data processing ability of personal computer and the digitalization of measuring instrument, virtual instrumentation becomes a total system realized all functions of measuring instrument through a graphical user interface. The advantages of using virtual instrumentation to perform measurements and automate data collection are obvious. With this concept, the system developers design and integrate the system that takes measurements, controls processes, implements calibration and linearization routines, displays information to the end user or connects with other applications. In addition it is imperative that the virtual instruments provide high-level, intuitive development paradigms so that a wide variety of users can rapidly build measurement and control systems. They include comprehensive statistical and numerical analysis functions, as well as highperformance signal processing and control algorithms common in measurement applications. Data acquisition system plays a fundamental role in a lot of virtual measurement solutions. The purpose of such a system is generally the analysis of the logged data and the improvement of the object of measurements. The data acquisition system can be divided in two main parts: hardware and software. The hardware part is made of sensors, cables, measuring instrument (usually data acquisition board) and computer. The software part is made of the instrumental drivers and the analysis software. Following this consideration in this paper a design, calibration and implementation of relative humidity system is presented. The block diagram of the proposed virtual system is shown in Fig. 1. In order to convert RH sensor’s capacitance changes into appropriate frequency a capacitive sensor oscillator circuits using low cost comparator is suggested. The proposed circuits provide a frequency output that is proportional to relative humidity and can be easily integrated into system with a multifunction data acquisition board (DAQ). To accomplish accurate relative humidity measurements the capability of virtual instrumentation is take to account. As application development environment the LabVIEW, one of an industry-leading for designing and implementing virtual systems is selected. LabVIEW is a graphically-based programming language optimized for test and measurement, automation, instrument control, data acquisition, and data analysis applications. The number of advanced mathematic blocks for functions such as integration, filters, and other specialized capabilities usually associated with data capture from hardware sensors is immense. Additional benefit of LabVIEW over other development environments is the extensive support for accessing instrumentation hardware. Drivers and abstraction layers for many different types of instruments and buses are included or are available for inclusion. The abstraction layers offer standard software interfaces to communicate with hardware devices. The provided driver interfaces save program development time and even people with limited coding experience can write programs and deploy test solutions in a reduced time frame when compared to more conventional or competing systems. In presented elaboration as data acquisition board is selected National Instrument’s DAQPad 6020, which is modular device with USB connection to the computer platform. One of the benefits of using 2

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National Instruments boards is the availability of NI-DAQ drivers for the boards. In the presented development the Traditional NI DAQ is used because DAQPad 6020 is not compatible with newer DAQmx drivers. Although the support for traditional NI-DAQ will continue, for new DAQ boards is advised to use DAQmx.

Relaxation Oscillator

RH Sensor

signal

Virtual Counter (DAQ)

frequency 101.33 kHz

Software Environment (LabVIEW)

RH, %

time Fig. 1. Block diagram of system for relative humidity measurement.

2. Design Considerations for Virtual System for RH Measurement The presented design approach is divided in four base steps: design of measurement hardware, develop of appropriate software, choose calibration procedures and put the system into practice. As sensing element a MiniCap 2 capacitive sensor is selected but can be used any similar capacitive RH sensor. The Panametrics MiniCap 2 relative humidity sensor combines small size and relatively high accuracy with long-term stability (typical ±2% RH over 24 months) [8]. This general-purpose RH sensor is user calibrated and has good sensitivity, fast wet-up and dry-down response. The dielectric permittivity of this thin-film polymer capacitive type sensor changes linearly as a function of atmospheric relative humidity. With MiniCap 2 relative humidity can be measured in the range of 0 to 100%, with linearity and hysteresis of +/-1% RH, and a typical response time of less than 60 s for 90% of total range. This sensor is ideally suited to such applications as HVAC management, control systems, humidistat and RH measurement instrumentation. The nominal capacitance of the sensor is 207 pF ±15% at 25°C 33% RH. Typical capacitance change provoked by relative humidity variation from 10% to 90% RH is 12%. It is easy to calculate that capacitance of the sensor will change approximately with 0.31 pF for 1% change of relative humidity. Usually, the manufacturers of capacitive sensors recommended in their data sheets and application notes to use transmitters with linear current or voltage output proportional to relative humidity [8]. Instead, in the present paper is proposed approach of frequency mode data acquisition technique, which converts sensor output into a frequency-based signal by placing the sensor element into a resistance-capacitance (RC) oscillator. Similar approach for direct humidity to frequency conversion is considered in [7]. In reference [2] the same RC oscillator is used but for temperature measurement with resistive temperature detector. The difference here is that emphasis is done upon virtual instrumentation instead embedded system. Oscillators offer several advantages over the constant current and voltage sensing circuits. The first advantage is that an analog to digital converter is not required. The second one is that these circuits can produce an accuracy and resolution that is better than an analog output voltage circuit. 3

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There are many references and application notes that describe various oscillator circuits in details [2, 3, 4, and 7]. In this paper the implementation of single comparator relaxation oscillator (or so called regenerative oscillator) is suggested as shown in Fig. 2. In order to illustrate the design approach in present paper the well-known National Semiconductor’s comparator LM393 is selected. The RC group is configured with a variable capacitance (humidity sensor CRH) and fixed resistor RRH. The circuit converts the time-varying sensor signal into a square wave with a varying time period. This signal can be counted and digitized into a value that is proportional to the oscillator frequency, and in turn, proportional to the relative humidity measured. VCC 5V

VCC R2A 100kΩ 2

R4A 3.2kΩ

R3A 100kΩ U1A

8 3

outRH

1 2

R1A 100kΩ

LM393N

0

4

RRH 41.2kΩ 4 CRH 220pF

0 0

Fig. 2. Single comparator relaxation oscillator.

The output frequency for circuit is given by [5]:

f OUT =

1 ⎡ ⎛ V − VOH RC ⎢ln⎜⎜ THL ⎣ ⎝ VTLH − VOH

⎞ ⎛ V − VOL ⎞⎤ ⎟⎟ + ln⎜⎜ THL ⎟⎟⎥ ⎠ ⎝ VTLH − VOL ⎠⎦

,

(1)

where VOH is high level output voltage and VOL is low level output voltage. The trip voltage, which triggers the output to swing from VOH to VOL or vice versa, is referred to as VTHL and VTLH, respectively. In this equation the input offset voltage (VOS) and the input bias current (IB) terms of the comparator is ignored for simplification. Detailed theoretical error analysis of similar circuits is done in [2]. According consideration done in this reference, a major limitation in the inaccuracy of the relaxation oscillator is the comparator’s output current drive capability. However, in a practical realization of the oscillator, the threshold levels (VTHL, VTLH) of the comparator are subject to noise. The exact moment, at which the capacitor voltage equals the threshold level, the comparator's output voltage can be change by the random noise. Thus, noise on the threshold level results in phase noise or jitter. Apart from noise on the threshold level 4

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there are some additional sources of error, concerning the jitter of the circuit that is theoretically analyzed in [10]. A practical approach of analyzing the oscillator’s jitter is proposed in present investigation. Suggested approach is based on statistical capability of virtual instrumentation. The result of analyses is applied in the end of paragraph after consideration of implemented technique for frequency measurement. The generated frequency from relaxation oscillator is measured with virtual frequency meter based on multifunctional DAQ board and appropriate software. An important component of any sophisticated data acquisition board is the manipulation and coordination of various timing parameters and events. Typically, this requires some type of hardware mechanism for the implementation of high-precision system-level timing. The basic building block of such a mechanism is the counter/timer circuits. Combined with a highly accurate frequency source and the ability to operate in various modes, a counter can be used for a wide variety of timing and counting applications. In many of National Instruments production counters are realized as specialized circuit called DAQ-STC. This circuit contains nine function groups for timing control of the analog input and output, digital input and output, interrupt control, synchronization of programmable function inputs etc. For frequency measurements can be used the general-purpose counter/timer module (GPCT). This module contains two identical 24-bit binary up/down counters with associated load and save registers, and a control structure for implementing some common counting and timing input and output functions. With contribution of the save register current count value can be read without affecting circuit operation supported in hardware. There are many possible modes available with this versatile counter module. For frequency measurement the three base methods can be implemented in a programming way without any hardware manipulations. The first method is period measurement that is useful for low frequencies. The second one is standard direct counting method applicable for relatively high frequencies. The last one, reciprocal counting method is usable as for low as for high frequencies. From data sheet of MiniCap 2, taking into account the manufacturer’s tolerance, for 0% relative humidity the minimal capacitance of the sensor is calculated as 165.7 pF. Using equation (1), datasheet of the comparator and R value of 41.2 kΩ, the maximum frequency generated from the relaxation oscillator is solved equal to 105.5 kHz. Respectively maximal capacitance is 261.87 pF and minimal frequency will be 66.8 kHz. Similar results with slight difference are achieved with simulation. Obviously the appropriate method for frequency measurement is standard direct counting. To measure frequency using this method, the two counters are connected as is shown in Fig. 3. Each counter have a SOURCE input, a GATE input, and an output called OUT. The input Source 0 of counter 0 is connected to an onboard clock fref (usually there are choice of several frequencies depending on the DAQ board, for the NI-6020E − 100 kHz or 20 MHz). Onboard clock is divided in Counter 0 and in the Output 0 is generated known amount of time T0 which gating the counter 1. The Counter 1 count how many edges N of the signal being measured occur in this time.

f fref

Source 1

Out 1

Counter 1 Source 0

Out 0

T0

Gate 1

Counter 0 Gate 0

N = fT0

Fig. 3. The connection of the counters for frequency measurement in standard direct counting.

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The frequency of the waveform equals the count divided by the known amount of time T0: f =

f N = N ref , T0 k

(2)

where k is coefficient of frequency division and fref is internal base counter clock. If the trigger noise error is neglected, the relative error of the frequency to digital conversion is [4, 11]: δf =

⎛ ∆f ⎛ 1⎞ 1 ⎞ ∆f ⎟⎟ , = ±⎜ ref + ⎟ = ±⎜⎜ δ fref + ⎜ f ⎟ f N fT 0 ⎠ ⎝ ⎝ ref ⎠

(3)

where δfref is base accuracy of internal clock (for the case of DAQPad 6020 δfref is ±0.01%). If T0 is programmed to be 1 s and minimal frequency generated by oscillator is approximately 50 kHz the relative quantization error would be less than 0.002% i.e. the relative reference error would be dominant. Because relative humidity sensor’s error and oscillator’s jitter is much more (hundred times) greater than calculated above, this simple standard direct counting method for frequency conversion is sufficient. Additionally with this method is possible to measure two frequencies without any hardware reconfigurations that are useful for temperature monitoring during calibration as is shown in the next paragraph. Either traditional NI-DAQ drivers or newer one NI DAQmx can easy configure counters. Tight integration with measurement and control drivers is one of the biggest strengths of LabVIEW. With DAQ drivers and other measurement analysis tools it is easy to input real-world, time-domain signals directly from data acquisition hardware and provide results ready for charting, graphing, or further processing. With build in LabVIEW mathematics functions, the user can determine data characteristics such as random error distribution, standard deviation etc. In presented paper these capability of virtual instrumentation are applied in order to estimate the measurement error of designed system. As is considered, if counting time T0 is programmed to be 1 s, frequency measurement error would be less than 0.01%. Therefore source of dominant error is expected to be relaxation oscillator. To estimate expected error the capacitor with constant value of 220 pF is connected instead RH capacitive sensor. The oscillator is turn on to generate, and approximately 1400 numbers of measurements are done during two hours. The distribution of measured data is shown as histogram on Fig. 4. Bottom of the histogram are placed indicators for arithmetic mean value of frequency, its standard deviation and relative error. The last one is calculated using equation [14]:

δf =

∆f OUT ( f − f min ) = 2 max , f OUT ( f max + f min )

(4)

where fmax and fmin are the maximum and minimum values of the measuring frequencies in the working time period. From previously done considerations follow that in order to achieve measurement accuracy of relative humidity better than 1% the relative error of generated frequency must be less than 0.15%. As can be seen from the figure 4 the histogram shows that most of measured frequencies are close to the mean value. The distribution of the frequency instability is close to the normal (Gaussian) distribution. This means that these random variations of frequency are caused by noise produced in the active and passive components of the oscillator circuit and with careful design, the frequency change can be held to less than 0.1 percent over a wide range of temperature and power-supply variation. 6

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Fig. 4. The normal distribution of measured data from the oscillator.

3. Calibration of the Virtual System for RH Measurement The manufacturers for almost all capacitive RH sensors noted that the sensor couldn’t be used directly without preliminary calibration. Calibration means the determination of specific variables that describe the transfer function of entire measurement system, including the sensor, the interface circuit, and the DAQ board. It is known and proven that relative humidity calibration is a difficult task that requires specialized knowledge and systems. The main problem is to generate humidity with high accuracy and stability especially for calibration outside a special laboratory. There are different methods to generate humidity − divided flow generators, mixing reactors, two-temperature reactors, two-pressure system etc. [1]. All these methods require specialized equipment and are difficult to execute in ordinary electronic laboratory. For educational laboratories a very convenient method to calibrate humidity sensors is the use of saturated salt solutions. This method is based on the fact that certain salt solutions generate a certain relative humidity in the air above them. By putting a known solution of distilled water and chemically pure salt inside the glass container, a known humidity is established at equilibrium. RH sensor can be inserted through a hole in the box, which is made reasonably airtight with a split rubber bung. The generated actual RH value above salt solution is a function of the chemical properties of the salt when mixed with water. With different salts yielding different values. Greenspan compiled, from the literature, data on 28 saturated salt solutions for ambient temperatures 0 to 50°C to cover the entire range of relative humidity [12]. The salt solutions suitable for presented investigation are lithium chloride LiCl (11.3% RH), magnesium chloride MgCl2 (32.8% RH), magnesium nitrate Mg(NO3)2 (52.9% RH) and sodium chloride NaCl (75.3% RH). The values in brackets are taken from Greenspan’s table [12] for salt solutions in 25°C. Obtaining equilibrium conditions is one of the most critical requirements of the method. This means that there should be no difference of temperature between the humidity sensor, the solution and the 7

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headspace above the solution. To avoid error due to temperature differences in this investigation is used continuously temperature monitoring. The monitoring is realized with similar relaxation oscillator circuit but instead capacitive sensor and constant resistor are used constant capacitor and NTC thermistor. The thermistor is calibrated for three reference point around 20°C. Because calibration is made in narrow temperature interval and for equilibrium the important is temperature difference not the exact temperature value this method for thermistor calibration is sufficient. However it is obligatory that the glass container be thermally isolated form its surroundings and for correct calibration, it is essential that salt chambers are tightly closed. The humidity level should be continuously monitored until a stable level is obtained. Where the salt is changed must be allowed about 20 minutes for equilibration [12]. To achieve continuously monitoring of temperature difference and relative humidity during calibration the LabVIEW based software application is created. In addition this software application guided the user for order of calibration procedure. When the sensor is put in glass container with one of the salt solutions the software wait programmed time for equilibration and then begin to monitor and save the data for temperature, and generated frequency. When the data from all selected salt solution is collected the software calculates arithmetic mean value of frequency for each salt and draws the transfer characteristic − frequency versus relative humidity. In the front panel is indicated also the frequency standard deviation and maximal temperature difference during process of calibration. To calculate relative humidity above each of saturated salts, the mean value of temperature during calibration time is used. Preliminary from Greenspan’s data the functional relationship between temperature and relative humidity above each of salt solutions is extracted. The front panel of created LabVIEW virtual instrument for RH capacitive sensor calibration with saturated salt solutions is shown in Fig. 5. In the figure can be seen a set of controls and indicators used for maintaining and monitoring of the calibration procedures.

Fig. 5. Front Panel of virtual instrument for RH capacitive sensor calibration.

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Once the all calibration data is obtained a dialog window is opened urged the user to select the type of regression analyses to best fit the experimental data points to some equation. Regression is a powerful technique that can be used to represent observed data and trends with equations. This technique acts to minimize the sum of the squares deviations of the experimental values from values calculated using some theoretical equation. Usually solving the regression equation is difficult task. Using capability of virtual instrumentation in this development solving the regression equation is quite easy. For this purpose the LabVIEW virtual instrument called “Regression solver” is used with slight adaptation. The dialog window of regression solver is shown on the Fig. 6. As can be seen from the figure acquired during calibration process data is well described with polynomial equation. In other words with suggested virtual approach a polynomial curve describing the frequency-to-relative humidity (electrical-to-physical) transfer function of the transducer is achieved automatically.

Fig. 6. Dialog window of virtual instrument for regression solving.

Good LabVIEW application design can take advantage of software design patterns. Design patterns represent techniques that have proven themselves and typically have evolved through the efforts of many developers. These software templates usually have been fine-tuned for simplicity, maintainability and readability. There are many design patterns that can be used in LabVIEW. For the relatively low speed events and in the absence of any parallel processing the most appropriate is Standard State Machine design pattern (called also Test Executive State Machine). Moreover, by employing Standard State Machine architecture the programs become easier to extend in functionality, to maintain and most importantly allow for the control by a separate program and consequently automation. This design pattern is selected for realization of virtual instrument for automated calibration of relative humidity transducer. Because this is only a high-level discussion, this paper only touches on significant features of the developed source code. The part of this code or so called block diagram is shown in Fig. 7. A State Machine, implemented in this development, consists from two case structures inside a “While loop”. The “While loop” provides the ability to continuously execute until the conditional operator in low left 9

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corner is set to “false”. Upper case structure contains the main steps of calibration procedures – wait for equilibration after inserting sensor above saturated salt solution, take measurement data and display the arithmetic mean value of frequency and temperature deviation. A Standard State Machine allows for the code to determine the next state to execute given data generated in the current state. The hardest part of creating a State Machine is to differentiate between possible states in the state diagram. In this development a special type of control “type definition” is used. The benefit of using type definitions with state machines is the flexibility allowed in terms of modifications. The lower case structure has only two states one for control the counters and to save measured data and one idle state. There are two identical subVI for DAQ’s counters control, which are consecutively executed. First one subVI measures frequency in Source 0 for relative humidity, and second one measures frequency in Source 1 for temperature. Frequency from Source 1 is transformed into temperature with equation nested in “Formula node”. Frequency from source 0 is stored into file for further manipulations. Detailed consideration of created LabVIEW software code is out of possibility of this paper. Software code can be downloaded from http://ecad.tu-sofia.bg/education/RH virtual system.llb.

Fig. 7. Block Diagram of virtual instrument for RH capacitive sensor calibration.

4. Creating Virtual TEDS for Calibrated Humidity Sensor With medium and large data acquisition systems, there is a need for self-identification of sensors. This feature provides substantial savings in the labor required to record the serial numbers, calibration factors, and calibration dates for all the sensors. The standard for sensors IEEE 1451.4 provides a relatively simple mechanism for building plug and play technology into traditional analog sensors. The heart of IEEE 1451.4 standard is the TEDS (Transducer Electronic Data Sheet) that specify the format and content of self-identifying parameters. TEDS contains pertinent information such as serial numbers, calibration dates, and scale factors. The TEDS for a given sensor can have as many as four sections, containing different types of data. The Basic TEDS data simply identifies the device. An IEEE Standard TEDS that contains the specific 10

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information for the sensors – typically the data needed to properly configure the electrical interface and convert the measurement data into engineering units, may follow the Basic TEDS. The Standard TEDS section describes everything needed to make a measurement using the sensors. Following Standard TEDS is Calibration TEDS. There are two Calibration Templates that could apply. The first is a point-to-point template (Template ID = 40) that would hold the calibration data as taken by the calibration system. The second Calibration Curve Template (Template ID = 41) is designed for polynomial curve fits and allows for multiple curve sections to be defined. The calibration templates provide a means of characterizing transducers on an individual basis. This detailed calibration can lead to increased accuracy beyond the published specifications for a given sensor type. Finally, the last portion of the TEDS User Area is available to store custom data and information that describes the use for the sensor or some other text. This is a very useful feature for storing sensor location, function on the measurement system, additional maintenance information, or other custom data. The TEDS can be deployed for a sensor in two ways – reside in embedded EEPROM memory or exist as a separate file. First way is originally defined in the IEEE 1451.4 standard. Second way establishes Virtual TEDS (VTEDS) and extends the benefits of plug and play sensors to traditional analog sensors. VTEDS files provide the same Transducer Electronic Data Sheet in file format. As a part of virtual measurement technology VTEDS takes advantage of the fact that the data about a sensor does not have to physically reside on the sensor. Instead, that data can be located in a database on a local or networked computer to which the data acquisition system is attached. With a VTEDS, the sensor identifies and describes itself to the data acquisition system to which it is connected. Virtual TEDS files are also valuable in applications where sensor operating conditions prevent the use any electronics in near the sensor. In order to accommodate such a small memory and still be able to contain significant information in the TEDS, it is necessary to provide a very precise definition of what every bit means. The language the templates are written in is the Template Description Language (TDL). The TDL encapsulates a large portion of the software needed in any IEEE 1451.4 implementation. This Description Language is a scripted and tagged language, which provides a standard method to describe the functionality of an IEEE 1451.4 transducer. In order to be able to read data from a transducer, a system must be able to parse a template written in TDL. LabVIEW is one of the first general-purpose application software that supports the TDL. This graphical programming environment has a TEDS library that incorporates the IEEE P1451.4 standard and is available free. With this library, LabVIEW users can read and parse TEDS data from sensors, as well as edit and write TEDS. The functions available in LabVIEW also provide for the TEDS data stream to be retrieved as a bit stream. The ability to reprogram TEDS from LabVIEW is useful for updating calibration information or to digitally store user-defined information including the physical location of the sensor. In order to provide plug and play capability for presented virtual system the specific subVI is created. This subVI is nested in next state of State Machine right after programming code for regression solving. After obtaining sensor’s calibration curve the user is prompted to fill in the Basic, the Calibration and the User templates for RH sensor and save data in appropriate VTEDS file. The block diagram of the subVI is shown in Fig. 8. As can be seen programming code is accomplished only with functions from LabVIEW TEDS library. This virtual approach will enable computer-based measurement systems to become more automated, easier to setup and configure, and more robust. In addition TEDS sensors are created in the same development environment. This is a good solution for laboratories that use different sensors on a daily basis. For recalibrations, the calibrated sensors would have the latest TEDS data entered in from the calibration software, ready to plug in.

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Fig. 8. Block Diagram of subVI for creating RH capacitive sensor TEDS.

5. Implementation of the Virtual System In order to illustrate the benefits of using virtual instrumentation for relative humidity measurement and monitoring a simple data-logging system is created. The term "logging" describes the measurement, manipulation, collection, storage and display the information from sensor. Data logging is used in a variety of situations. An easy example is a weather system logger used by meteorologists to detect relative humidity, temperature and pressure in order to determine upcoming weather conditions. The purpose of the created software is to enable users to control the collection, display and storage of the data obtained from the calibrated RH capacitive sensor. The base function is to take readings from the sensors at regular intervals. It is possible to take many readings at time intervals ranging from a few seconds to hours or even days. Actually the logging system measure frequency and convert it into relative humidity using calibration curve stored in a VTEDS file supporting the sensor. To read VTEDS the TEDS add-on library for LabVIEW is used as is shown in block diagram from Fig. 9.

Fig. 9. The block diagram for reading the TEDS.

With the development of virtual and network technologies, it is possible to deploy the presented logging system as web-based networked virtual station. The system can only be configured and operated by one client at any given time. However, multiple clients can view data from the virtual system simultaneously. There are many network technologies compatible with LabVIEW. More of them require advanced programming techniques and development of custom data handling mechanisms. In this development the Remote Panel Connection technology is selected. To implement this technology additional programming code is not needed. Only the web server located on networked computer that is running the LabVIEW must be configured and enabled. With LabVIEW, remote front panels not only can be viewed, but can also be controlled from either the LabVIEW environment or a web browser. The front panel of created data logging system that is controlling by distance user using 12

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web browser is shown in Fig. 10. The experiment indicates that the web-based logging system works well and is easy to maintain, adapt and extend.

Fig. 10. The front panel of the created data logging system.

6. Conclusions An approach of design, development and implementation of virtual system for relative humidity measurement and monitoring is considered in this paper. The virtual system is based on capacitive relative humidity sensor, relaxation oscillator, multifunction DAQ and graphical application development environment. A method for estimation of frequency shift of an RC oscillator connected to the capacitance sensor based on virtual measurement techniques is suggested. An innovative approach for capacitive sensor calibration by using statistical and regression solving capability of LabVIEW is proposed and considered. Results of experiments for calibration with saturated salt solution conducted under laboratory conditions are provided, showing the applicability of the presented approach. By using build in LabVIEW library functions and by creating additional software modules the Plug and Play capability is included into the capacitive sensor. This approach updates a legacy sensor to be capable of plugging into IEEE 1451.4 standard using VTEDS. Such a VTEDS holds the calibration information of a transducer, which may be best described by a nonlinear relationship. It is possible to 13

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store the coefficients of a 3rd order polynomial and to do on line calculation for the displayed physical result. Measurements have shown a significantly better behavior compared to a two point linearization. Finally the data logging system is created and deployed to distance control and monitoring using build in LabVIEW Remote Panel Connection technology. The presented approach can be applied for any capacitive RH sensors and DAQs with counter/timers blocks to realize virtual system for relative humidity measurement and monitoring. However to achieve better measurement accuracy the more precise oscillator must be used because in present development the long term stability of the oscillator is not investigated.

References [1]. J. Fraden, Handbook of modern sensors: physics, designs, and applications, ISBN 0-387-00750-4, Springer-Verlag New York, 2004. [2]. E. Haile, J. Lepkowski, Oscillator Circuits For RTD Temperature Sensors, Microchip Technology Inc., AN895, 2004, http://www.e-sonic.com/whatsnew/Microchip/signal/AN895.pdf [3]. Sensor Platforms Inc., SSP1492 Sensor Signal Processor Chip, Product Datasheet, V2. 0 – December 13, 2005, http://www.sensorplatforms.com/docs/SSP1492ProductDatasheetV2.0_.pdf [4]. G. Nikolov, I. Stoyanov, B. Nikolova, Virtual Universal Counter, Proceedings of 14th International Conference Electronics '2005, Sozopol, Sept. 21-23, 2005, Vol. 3, pp. 75-80. [5]. G. Nikolov, I. Stoyanov, B. Nikolova, RLC Measurement Using Virtual Counting Techniques, Proceedings of the Technical University – Sofia, Vol. 56, book 2, 2006, pp 242-247. [6]. National Instruments, DAQ-STC Technical Reference Manual, Part Number 340934A-01, May 1995. [7]. S. Yurish, Digital Humidity Sensors and Data Logger Design Based on Modern Frequency-to-Digital Converter, Sensors & Transducers Journal, Vol. 59, Issue 9, September 2005, pp. 419-425. [8]. Panametrics, MiniCap-2 Miniatursensor zur Messung der Relativen Feuchte, Technische Daten, 2001. [9]. S. Yurish, Data Acquisition Systems for Quasi-Digital Temperature Sensors Based on Universal Frequency-to-Digital Converter, Sensors & Transducers Journal, Vol. 57, Issue 7, July 2005, pp. 341-351. [10].S. L. J. Gierkink, Control Linearity and Jitter of Relaxation Oscillators, ISBN: 90-36513170, Philips Research Laboratories, Eindhoven, 1999, http://doc.utwente.nl/14152/1/t000000f.pdf [11].S. Yurish, F. Reverter, R. Pallas-Areny, Measurement error analysis and uncertainty reduction for periodand time-interval-to-digital converters based on microcontrollers, Measurement Science and Technology, 16, 2005, pp. 1660–1666. [12].B. Patissier, D. Walters, Basics of Relative Humidity Calibration for Humirel HS1100/HS1101 Sensors, HUMIREL, Application Note HPC020VO, December 1999, http://www.springerlink.com/content/ x72g6h1678l8j272/fulltext.pdf [13].Jim Williams, Instrumentation Applications for a Monolithic Oscillator, Linear Technology, Application Note 93, February 2003, http://www.linear.com/pc [14].S. Y. Yurish, N. V. Kirianaki, N. O. Shpak, Reference’s Accuracy in Virtual Frequency – to – Code Converters, XVI IMEKO World Congress, Wien, AUSTRIA, 26-28 September, 2000, Vol. X, pp. 325-330. ___________________

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Guide for Contributors Aims and Scope Sensors & Transducers Journal (ISSN 1726-5479) provides an advanced forum for the science and technology of physical, chemical sensors and biosensors. It publishes state-of-the-art reviews, regular research and application specific papers, short notes, letters to Editor and sensors related books reviews as well as academic, practical and commercial information of interest to its readership. Because it is an open access, peer review international journal, papers rapidly published in Sensors & Transducers Journal will receive a very high publicity. The journal is published monthly as twelve issues per annual by International Frequency Association (IFSA). In additional, some special sponsored and conference issues published annually.

Topics Covered Contributions are invited on all aspects of research, development and application of the science and technology of sensors, transducers and sensor instrumentations. Topics include, but are not restricted to: • • • • • • • • • • • • •

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