Data Acquisition edited by Dr. Michele Vadursi SC I YO Data Acquisition Edited by Dr. Michele Vadursi Published by Sciyo Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2010 Sciyo All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by Sciyo, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Jelena Marusic Technical Editor Teodora Smiljanic Cover Designer Martina Sirotic Image Copyright PeJo, 2010. Used under license from Shutterstock.com First published November 2010 Printed in India A free online edition of this book is available at www.sciyo.com Additional hard copies can be obtained from publication@sciyo.com Data Acquisition, Edited by Dr. Michele Vadursi p. cm. ISBN 978-953-307-193-0 SC I YO.CO M WHERE KNOWLEDGE IS FREE free online editions of Sciyo Books, Journals and Videos can be found at www.sciyo.com Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Preface IX Noise, Averaging, and Dithering in Data Acquisition Systems 1 Filippo Attivissimo and Nicola Giaquinto Bandpass Sampling for Data Acquisition Systems 23 Leopoldo Angrisani and Michele Vadursi Clock Synchronization of Distributed, Real-Time, Industrial Data Acquisition Systems 41 Alessandra Flammini and Paolo Ferrari Real Time Data Acquisition in Wireless Sensor Networks 63 Mujdat Soyturk, Halil Cicibas and Omer Unal Practical Considerations for Designing a Remotely Distributed Data Acquisition System 85 Gregory Mitchell and Marvin Conn Portable Embedded Sensing System using 32 Bit Single Board Computer 109 R. Badlishah Ahmad, Wan Muhamad Azmi Mamat Microcontroller-based Data Acquisition Device for Process Control and Monitoring Applications 127 Vladimír Vašek, Petr Dostálek and Jan Dolinay Java in the Loop of Data Acquisition Systems 147 Pedro Mestre, Carlos Serodio, João Matias, João Monteiro and Carlos Couto Minimum Data Acquisition Time for Prediction of Periodical Variable Structure System 169 Branislav Dobrucký, Mariana Marčoková and Michal Pokorný Wind Farms Sensorial Data Acquisition and Processing 185 Inácio Fonseca, J. Torres Farinha and F. Maciel Barbosa Contents Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Chapter 16 Data Acquisition System for the PICASSO Experiment 211 Jean-Pierre Martin and Nikolai Starinski Data Acquisition Systems for Magnetic Shield Characterization 229 Leopoldo Angrisani, Mirko Marracci, Bernardo Tellini and Nicola Pasquino Microcontroller-based Biopotential Data Acquisition Systems: Practical Design Considerations 245 José Antonio Gutiérrez Gnecchi, Daniel Lorias Espinoza and Víctor Hugo Olivares Peregrino Data Acquisition for Interstitial Photodynamic Therapy 265 Emma Henderson, Benjamin Lai and Lothar Lilge Critical Appraisal of Data Acquisition in Body Composition: Evaluation of Methods, Techniques and Technologies on the Anatomical Tissue-System Level 281 Aldo Scafoglieri, Steven Provyn, Ivan Bautmans, Joanne Wallace, Laura Sutton, Jonathan Tresignie, Olivia Louis, Johan De Mey and Jan Pieter Clarys High-Effi ciency Digital Readout Systems for Fast Pixel-Based Vertex Detectors 313 Alessandro Gabrielli, Filippo Maria Giorgi and Mauro Villa VI The book is intended to be a collection of contributions providing a bird’s eye view of some relevant multidisciplinary applications of data acquisition. While assuming that the reader is familiar with the basics of sampling theory and analog-to-digital conversion, the attention is focused on applied research and industrial applications of data acquisition. Even in the few cases when theoretical issues are investigated, the goal is making the theory comprehensible to a wide, application-oriented, audience. In detail, the fi rst chapter examines the effects of noise on the performance of data acquisition systems, and the performance improvements achievable thanks to dithering and averaging techniques. The second chapter presents some practical solutions for the acquisition of band- pass signals. The following chapters deal with distributed data acquisition systems, wireless sensor networks, and data acquisition systems architectures: they address synchronization, design and performance evaluation issues. Finally, a series of chapters present some multidisciplinary applications of data acquisition for sensing and on-line monitoring, ranging from energy and power systems to biomedical system, from nuclear and particle physics to magnetic shields characterization. Editor Dr. Michele Vadursi University of Naples “Parthenope” Department of Technologies Naples, Italy Preface [...]... not limited to data acquisition) , in order to improve the performance of the system itself In the field of data acquisition, besides adding an external signal, other kinds of dithering are possible and used, aiming at different performance improvements For example, an effective anti-aliasing filter can be obtained, without increasing the sampling rate and without introducing 8 Data Acquisition physical...1 Noise, Averaging, and Dithering in Data Acquisition Systems Filippo Attivissimo and Nicola Giaquinto1 Dipartimento di Elettrotecnica ed Elettronica Politecnico di Bari, Italia 1 Introduction In any data acquisition system (DAS) many error effects, both of systematic nature (e.g nonlinearity) and of random nature (e.g electronic... of the averaged samples 17 Noise, Averaging, and Dithering in Data Acquisition Systems σn = 0.3 LSB 17 16 15 14 be 13 12 X: 18 Y: 10.92 11 10 9 simulations approx 1 approx 2 8 7 0 2 4 6 8 log2N 10 12 14 16 18 Fig 15 ENOB of an 8-bit linear DAS with input WGN ( σ n = 0.3 LSB ), as a function of the number N of the averaged samples 18 Data Acquisition σn = 0.5 LSB 16 15 X: 18 Y: 15.26 14 13 be 12 11... number N of the averaged samples 19 Noise, Averaging, and Dithering in Data Acquisition Systems σn = 0.1 LSB 13 simulations approx 1 approx 2 X: 18 Y: 12.59 be 12.5 12 11.5 0 2 4 6 8 log2N 10 12 14 16 18 Fig 17 ENOB of a 12-bit linear DAS with input WGN ( σ n = 0.1 LSB ), as a function of the number N of the averaged samples 20 Data Acquisition Fig 18 Variation in the ENOB (with respect to the nominal... Bandpass Sampling for Data Acquisition Systems 1University Leopoldo Angrisani1 and Michele Vadursi2 of Naples Federico II, Department of Computer Science and Control Systems 2University of Naples “Parthenope”, Department of Technologies Italy 1 Introduction A number of modern measurement instruments employed in different application fields consist of an analogue front-end, a data acquisition section,... front-end, a data acquisition section, and a processing section A key role is played by the data acquisition section, which is mandated to the digitization of the input signal, according to a specific sample rate (Corcoran, 1999) The choice of the sample rate is connected to the optimal use of the resources of the data acquisition system (DAS) This is particularly true for modern communication systems, which... the effects of bandpass sampling with a non-ideal data acquisition system Let s(t) be a generic bandpass signal, characterized by a bandwidth B and a central frequency fc As well known, the spectrum of the discrete-time version of s(t) consists of an infinite set of replicas of the spectrum of s(t), centered at frequencies 25 Bandpass Sampling for Data Acquisition Systems fλ,ν = λ fc + ν fs (1) where... subject for a possible future expanded version of the chapter 1 corresponding author: http://dee.poliba.it/DEE/Giaquinto.html 2 Data Acquisition 2 Effective number of bits If x(t) is the analogue input of a DAS and yn are the output samples, the evaluation of the overall acquisition fidelity takes into account, customarily, only transformations involving the shape of x(t) Therefore, the fidelity evaluation... however, the same: to obtain each output sample by a (weighted) average of many samples of the input, in order to reduce the acquisition error This is the principle of oversampling, i.e trading bandwidth (and possibly sampling frequency) for accuracy, e.g in terms of ENOB 6 Data Acquisition As a side note, it must be highlighted that oversampling is implemented by design in a wide class of analog-to-digital... ∫−Q /2 Fig 6 Ideal quantization error eq ( x ) and dithered quantization error eqd ( x ) (for the case σ n = 0.1 LSB ) x 2 σ qd y eqd Fig 7 Additive model of the dithered quantization of Fig 5 (14) 10 Data Acquisition 2 (If eq ( x ) is substituted to eqd ( x ) , the result is trivially σ q = Q 2 / 12 ) Of course the result of integration (14) with integrand given by (13) depends only on the standard deviation . Data Acquisition edited by Dr. Michele Vadursi SC I YO Data Acquisition Edited by Dr. Michele Vadursi Published by Sciyo Janeza. 10 Preface IX Noise, Averaging, and Dithering in Data Acquisition Systems 1 Filippo Attivissimo and Nicola Giaquinto Bandpass Sampling for Data Acquisition Systems 23 Leopoldo Angrisani and Michele. Synchronization of Distributed, Real-Time, Industrial Data Acquisition Systems 41 Alessandra Flammini and Paolo Ferrari Real Time Data Acquisition in Wireless Sensor Networks 63 Mujdat Soyturk,