Table Of ContentAdvanced Data Acquisition
and Intelligent Data Processing
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The NEC and You Perfect Together:
Advanced Data Acquisition
A Comprehensive Study of the
and IntNealtiloingael nEltecDtriacatla CoPdre ocessing
Editors
Vladim´ırHaasz
Gregory P. Bierals
CzechTechnicalUniversity
Prague,CzechRepublic
Electrical Design Institute, USA
and
KuroshMadani
University Paris-EstCre´teil(UPEC)
Paris,France
River Publishers
Published2014byRiverPublishers
RiverPublishers
Alsbjergvej10,9260Gistrup,Denmark
www.riverpublishers.com
DistributedexclusivelybyRoutledge
4ParkSquare,MiltonPark,Abingdon,OxonOX144RN
605ThirdAvenue,NewYork,NY10017,USA
Advanced Data Acquisition and Intelligent Data Processing/by Vladimır Haasz,
KuroshMadani.
© 2014 River Publishers. All rights reserved. No part of this publication may be
reproduced, stored in a retrieval systems, or transmitted in any form or by any
means, mechanical, photocopying, recording or otherwise, without prior written
permissionofthepublishers.
RoutledgeisanimprintoftheTaylor&FrancisGroup,aninforma
business
ISBN978-87-93102-73-6(print)
While every effort is made to provide dependable information, the
publisher, authors, and editors cannot be held responsible for any errors
oromissions.
Table of Contents
1.Introduction 1
Vladim´ırHaaszandKuroshMadani
2.Waveformacquisition withresolutions exceeding thoseoftheADCs
employed 5
HeYin,MohammadMani,OmarS.Sonbul and
AlexanderN.Kalashnikov
3.Differentappliance identification innon-intrusive appliance load
monitoring 31
KrzystofLiszewski,RobertŁukaszewski, RyszardKowalik,
ŁukaszNogalandWiesławWiniecki
4.Designandtestingofanelectronic nosesensitivetothearoma
oftruffles 59
DespinaZampioglou andJohnKalomiros
5.DAQacquisition forultrasonic transducer evaluation underspread
spectrum excitation 83
L.Svilainis, A.Chaziachmetovas, D.KybartasandV.Erdukyas
6.Optimalinformation fusioninstochastic unknowninput
observersnetwork 127
LeonidM.Lyubchyk
7.Odorclassification byneuralnetworks 159
SigeruOmatu
v
vi TableofContents
8.ANFISbasedapproach forimprovedmultisensors signalprocessing 181
NataliyaRoshchupkina, AnatoliySachenko, OleksiyRoshchupkin,
VolodymyrKochanandRadislavSmid
9.FPGA-basedANFISlinearizer formeasurementsystems 213
MourirBouhedda
10.Identification ofsystemsusingVolterramodelintimeand
frequencydomain 233
VitalyiPavlenko, SergeiPavlenkoandViktorSperanskyy
11.Trainingcellularautomataforhyperspectral imagesegmentation 271
B.Priego,D.Souto, F.BellasandR.J.Duro
Index 293
AbouttheEditors 297
1
Introduction
Vladimir Haasz1 and Kurosh Madani2
1
Czech Technical University in Prague, Faculty of Electrical Engineering
2Images, Signals & Intelligent Systems Lab, IUT Senart-FB, University Paris-Est Créteil
DAQ and data processing is a basic part of all automated production
systems, diagnostic systems, watching over quality of production, energy
distribution, transport control and in various other areas. Demands on the
speed, accuracy and reliability increase in general. It is possible to fulfill
these demands not only using superior (nevertheless more expensive)
hardware, but also applying advanced data acquisition and intelligent data
processing. It deals e.g. with optimal data fusion of a number of sensors,
new stochastic methods for accuracy increasing, new algorithms for
acceleration of data processing, etc. These are the grounds for publishing
this book.
In the first part of the book (chapters 2-5) advanced methods of data
acquisition in non-routine applications are presented. It concerns e.g.
methods of increasing the vertical and horizontal resolutions of acquired
waveforms. The applicability of these methods depends on the nature of
the waveform of interest. The best enhancements seem to be achievable for
repeatable waveforms; the available measurement time budget can
constrain the enhancement obtainable in practice.
The following chapter deals with disaggregation algorithms in
conjunction with smart meters providing consumers with detailed statistics
of electrical energy consumption. The detailed feedback could lead to a
reduction in consumption, including increased consumer awareness of a
consumption structure.
The intelligent data fusion enables to achieve good results also with
low-cost sensors. An “electronic nose” based on a low-cost array of gas-
sensors is presented as an example. Preliminary results show that an
intelligent odor-discriminating system based on a gas sensor array can
contribute to identification and classification of truffles.
V. Haasz and K. Madani (Eds.), Advanced Data Acquisition and Intelligent Data
Processing, 1–3.
(cid:3)c 2014RiverPublishers.Allrightsreserved.
2 Introduction
Describing of an ultrasonic DAQ system for the ultrasonic transducer
properties investigation closes this first part. Discussion on signal and
transducer types is presented. Spread spectrum (SS) signals and
conventional pulse, step and burst signals were used in comparison.
Measured transducer impedance interaction with excitation generator was
evaluated for attainable power delivery efficiency and bandwidth.
The second part attends to measured data fusion using up-to-date
advanced data processing. The problem of optimal data fusion in the
network of stochastic unknown-input observer ensuring the state and input
signal estimation for dynamic systems under the presence of stochastic
disturbance and random measurement noise is presented in Chap. 6.
Optimal stochastic unknown-input observer design method is proposed and
the equations for observer optimal parameters are founded as well as the
conditions of steady-state observer existence are established. For the
problem of state and input estimates fusion in decentralized stochastic
observers the optimal estimates aggregation is obtained.
The following chapter deals with detection of an odor in the human
living space. It presents an array sensing system of odors and adopts a
layered neural network for classification. All measurement data obtained
from fourteen metal oxide semiconductor gas (MOG) sensors are applied.
In order to classify odors, the data from all fourteen sensors are used even
if some of them are not sensitive so much. Three methods of using the data
by insensitive sensors to find the features of odors Have been proposed.
Then, applying those features to a layered neural network, the classification
results are compared.
The last chapter of this part concerns multisensors signal processing.
An improved algorithm of intelligent data processing by integrating the
modified identification method of individual characteristic curve along
with the adaptive neuro-fuzzy inference system (ANFIS) is presented. The
results of data prediction with training the ANFIS system and the different
number of training epochs are described. The proposed approach provides
the high accuracy of data prediction as well as low neural network training
error.
The last three chapters of the book are focused on nonlinear systems
and multidimensional image processing. Nonlinear sensors and digital
solutions are used in many embedded system designs. As the input/output
characteristic of most sensors is nonlinear in nature, obtaining data from a
nonlinear sensor by using an optimized device has always been a design
challenge. A new Adaptive Neuro-Fuzzy Inference System (ANFIS) digital
architecture based Field Programmable Gate Array (FPGA) was proposed
to linearize the sensor’s characteristic. The performance of the developed
Introduction 3
architecture was examined by comparison to ANFIS software model and to
two other designed architectures based on FPGA using classical techniques.
The accuracy of the interpolation method of nonlinear dynamical
systems identification based on the Volterra model in the frequency
domain is described in the following chapter 10. To extract the n–th partial
component in the response of the system to the test signal the n–th partial
derivative of the response using the test signal amplitude is found and its
value is taken at zero. The polyharmonic signals have been used as the test
ones. The algorithmic and software toolkit in Matlab was developed for
identification processes. This toolkit is used for constructing the
informational model of the test system.
The book is ended by the chapter which relates to multidimensional
image processing. By involving the usage of a Cellular Automata (CA)
based approach the presented work offers an original policy for
overcoming problems relating hyperspectral images’ segmentation. The
application of CA over the hyperspectral cube leads to homogeneous
regions easing the segmentation task. This way of proceeding avoids any
form of projection and thus preserves the spectral character of the
information in the segmentation process. It is also shown that the
evolutionary process ruling the CA can be carried out over RGB images
overcoming the need of appropriately labeled ground truth images
(required in other usually used techniques). The effectiveness of the
proposed approach has been verified as well over synthetic hyperspectral
images as on segmentation of real-world hyperspectral images leading to
competitive results.
