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 [...]... 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 simulations approx 1 approx 2 10 9 8 7 0 2 4 6 8 log2N 10 12 14 16 18 Fig 16 ENOB of an 8-bit linear DAS with input WGN ( σ n = 0.5 LSB... order of 2 13 σn = 0.05 LSB simulations approx 1 approx 2 8.6 8.4 X: 18 Y: 8.274 be 8.2 8 7.8 7.6 0 2 4 6 8 log2N 10 12 14 16 18 Fig 14 ENOB of an 8-bit linear DAS with input WGN ( σ n = 0.05 LSB ), as a function of the number N 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... of the 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... g1 max abs dev g1 = 0.004 01 LSB 0.3 g2 max abs dev g2 = 0.000 21 LSB σqd [LSB] 0.25 0.2 0 .15 0 .1 0.05 0 0 0 .1 0.2 0.3 0.4 0.5 σn [LSB] 0.6 0.7 0.8 0.9 1 Fig 10 Comparison between the numerically evaluated points of the function g(⋅) , the asymptotic expression (16 ), and the approximations g1 (⋅) and g2 (⋅) 2 σ qr x quantd( x ) y eqr Fig 11 Equivalent representation of the noisy quantization in Fig 1. .. is: ⎧ 1 −σn ⎪ ⎪ 12 g(σ n ) ≅ g1 (σ n ) = ⎨ 2 2 ⎪ 1 ⋅ e −2π σ n ⎪ 2 ⋅π ⎩ for σ n ≤ 0 .11 LSB (17 ) for σ n > 0 .11 LSB (the threshold 0 .11 LSB achieves a nearly optimal approximation of g(⋅) for this formula) A more accurate, even if less elegant approximation, is given by the expression (a refinement of that proposed in [AGS08]): g(σ n ) ≅ g2 (σ n ) = k ϕ (σ n , μ 1 , σ 1 ) Φ (σ n , μ 2 ,σ 2 ) (18 ) x... is the Gaussian pdf (12 ), and Φ ( x , μ ,σ ) = ∫ ϕ ( x ', μ ,σ )dx ' is the Gaussian −∞ cumulative distribution function The five parameters k , 1 ,σ 1 , μ 2 ,σ 2 , are determined by a nonlinear LS fitting and have the values: k = 0.0774; 1 = 0. 019 0; σ 1 = 0 .15 43; μ2 = −0.0587; σ 2 = 0 .12 01 (19 ) Both the approximations are quite good (Fig 10 ): in the range σ n ∈[0 ,1] LSB , g1 ( x ) approximates... n Fig 2 Additive model equivalent to that in Fig .1 2 2 σ e2 = σ n + σ q (10 ) Taking into account the normalization convention ( Q = 1 ), the term in (10 ) becomes 2 2 12 σ e = 1 + 12 σ n , and therefore in this elementary case the ENOB of the DAS is: be = b − ( 1 2 log 2 1 + 12 σ n 2 ) (11 ) A simple numerical simulation (performed for b in the range 8 16 bits) confirms the formula (Fig 3) It is interesting... σ n + σ q = σ n + 1 / 12 = σ e : be ≅ b − ( ) 1 1 2 log 2 1 + 12 ⋅ σ n + log 2 N 2 2 (24) The exact formula is derived by substituting σ qd = g(σ n ) : be = b − 2 ⎛ 1 + 12 [σ n − g 2 (σ n )] ⎞ 1 log 2 ⎜ 12 g 2 (σ n ) + ⎟ ⎜ ⎟ 2 N ⎝ ⎠ (25) Figs 14 -16 show the result of numerical simulations of an 8-bit quantizer with various levels of input WGN (ranging from 0.05 to 0.5 LSB), and Fig 17 shows the result... rectangle is represented in Fig 9 11 Noise, Averaging, and Dithering in Data Acquisition Systems σ qd σn 0 0.2887 0 .1 0 .19 21 0.2 0 .10 23 0.3 0.03 81 0.4 0.0096 0.5 0.0 016 Tab 1 Some points of the function σ qd = g(σ n ) (both in LSB units) 0. 01 0.009 0.008 0.007 σqd [LSB] 0.006 0.005 0.004 0.003 X: 0.5 Y: 0.0 016 22 0.002 0.0 01 0 0.4 0.42 0.44 0.46 0.48 0.5 σn [LSB] 0.52 0.54 0.56 0.58 0.6 Fig 9 Zoom of... figures 1 For a given σ n , the maximum (asymptotic) increase of performance is given by (Fig 18 ): Δb = − log 2 12 − log 2 g(σ n ) (26) An accurate evaluation of (26) for low σ n can be obtained by using the approximation g2 (⋅) given by (18 ) (values in Tab 2) The approximation g1 (⋅) given by (17 ) is also usable, obtaining an extension of the formula given in [CP94]: ⎧− log 2 (1 − 12 σ n ) for σ n ≤ 0 .11 . Sensorial Data Acquisition and Processing 18 5 Inácio Fonseca, J. Torres Farinha and F. Maciel Barbosa Contents Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Chapter 16 Data Acquisition. Naples “Parthenope” Department of Technologies Naples, Italy Preface 1 Noise, Averaging, and Dithering in Data Acquisition Systems Filippo Attivissimo and Nicola Giaquinto 1 Dipartimento. Additive model equivalent to that in Fig .1. 222 en q σ σσ =+. (10 ) Taking into account the normalization convention ( 1 Q = ), the term in (10 ) becomes 22 12 1 12 en σ σ =+ , and therefore in this