LEASTMEANSQUARE ADAPTIVE FILTERS


 Walter Long
 3 years ago
 Views:
Transcription
1 LEASTMEANSQUARE ADAPTIVE FILTERS
2 LEASTMEANSQUARE ADAPTIVE FILTERS Edited by S. Haykin and B. Widrow A JOHN WILEY & SONS, INC. PUBLICATION
3 This book is printed on acidfree paper. Copyright q 2003 by John Wiley & Sons Inc. All rights reserved. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate percopy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, (978) , fax (978) Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, New Jersey 07030, (201) , fax (201) , For ordering and customer service, call CALLWILEY. Library of Congress CataloginginPublication Data: Leastmeansquare adaptive filters/edited by S. Haykin and B. Widrow p. cm. Includes bibliographical references and index. ISBN (cloth) 1. Adaptive filters Design and construction Mathematics. 2. Least squares. I. Widrow, Bernard, II. Haykin, Simon, TK7872.F5L dc21 Printed in the United States of America
4 This book is dedicated to Bernard Widrow for inventing the LMS filter and investigating its theory and applications Simon Haykin
5 CONTENTS Contributors Introduction: The LMS Filter (Algorithm) Simon Haykin ix xi 1. On the Efficiency of Adaptive Algorithms 1 Bernard Widrow and Max Kamenetsky 2. TravelingWave Model of Long LMS Filters 35 Hans J. Butterweck 3. Energy Conservation and the Learning Ability of LMS Adaptive Filters 79 Ali H. Sayed and V. H. Nascimento 4. On the Robustness of LMS Filters 105 Babak Hassibi 5. Dimension Analysis for LeastMeanSquare Algorithms 145 Iven M. Y. Mareels, John Homer, and Robert R. Bitmead 6. Control of LMSType Adaptive Filters 175 Eberhard Hänsler and Gerhard Uwe Schmidt 7. Affine Projection Algorithms 241 Steven L. Gay 8. Proportionate Adaptation: New Paradigms in Adaptive Filters 293 Zhe Chen, Simon Haykin, and Steven L. Gay 9. SteadyState Dynamic Weight Behavior in (N)LMS Adaptive Filters 335 A. A. (Louis) Beex and James R. Zeidler vii
6 viii CONTENTS 10. Error Whitening Wiener Filters: Theory and Algorithms 445 Jose C. Principe, Yadunandana N. Rao, and Deniz Erdogmus Index 491
7 CONTRIBUTORS A. A. (LOUIS) BEEX, Systems Group DSP Research Laboratory, The Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA Department of Mechanical and Aerospace Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA ROBERT R. BITMEAD, Technische Universiteit Eindhoven, Faculteit Elektrotechniek, EH 5.29, Postbus 513, 5600 MB Eindhoven, Netherlands HANS BUTTERWECK, ZHE CHEN, Department of Electrical and Computer Engineering, CRL 102, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada L8S 4K1 DENIZ ERDOGMUS, Computational NeuroEngineering Laboratory, EB 451, Building 33, University of Florida, Gainesville, FL Acoustics and Speech Research Department, Bell Labs, Room 2D531, 600 Mountain Ave., Murray Hill, NJ STEVEN L. GAY, Institute of Communication Technology, Darmstadt University of Technology, Merckstrasse 25, D Darmstadt, Germany PROF. DR.ING. EBERHARD HÄNSLER, BABAK HASSIBI, Department of Electrical Engineering, 1200 East California Blvd., M/C , California Institute of Technology, Pasadena, CA Department of Electrical and Computer Engineering, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada L8S 4K1 SIMON HAYKIN, JOHN HOMER, School of Computer Science and Electrical Engineering, The University of Queensland, Brisbane 4072 Stanford University, David Packard Electrical Engineering, 350 Serra Mall, Room 263, Stanford, CA MAX KAMENETSKY, IVEN M. Y. MAREELS, Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne Vic 3010 ix
8 x CONTRIBUTORS V. H. NASCIMENTO, Department of Electronic Systems Engineering, University of São Paulo, Brazil JOSE C. PRINCIPE, Computational NeuroEngineering Laboratory, EB 451, Building 33, University of Florida, Gainesville, FL YADUNANDANA N. RAO, Computational NeuroEngineering Laboratory, EB 451, Building 33, University of Florida, Gainesville, FL Department of Electrical Engineering, Room A Engineering IV Bldg, University of California, Los Angeles, CA ALI H. SAYED, Institute of Communication Technology, Darmstadt University of Technology, Merckstrasse 25, D Darmstadt, Germany GERHARD UWE SCHMIDT, BERNARD WIDROW, Stanford University, David Packard Electrical Engineering, 350 Serra Mall, Room 273, Stanford, CA JAMES R. ZEIDLER, Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92092
9 INTRODUCTION: THE LMS FILTER (ALGORITHM) SIMON HAYKIN The earliest work on adaptive filters may be traced back to the late 1950s, during which time a number of researchers were working independently on theories and applications of such filters. From this early work, the leastmeansquare ðlmsþ algorithm emerged as a simple, yet effective, algorithm for the design of adaptive transversal (tappeddelayline) filters. The LMS algorithm was devised by Widrow and Hoff in 1959 in their study of a patternrecognition machine known as the adaptive linear element, commonly referred to as the Adaline [1, 2]. The LMS algorithm is a stochastic gradient algorithm in that it iterates each tap weight of the transversal filter in the direction of the instantaneous gradient of the squared error signal with respect to the tap weight in question. Let ^wðnþ denote the tapweight vector of the LMS filter, computed at iteration (time step) n. The adaptive operation of the filter is completely described by the recursive equation (assuming complex data) ^wðn þ 1Þ ¼ ^wðnþþmuðnþ½dðnþ ^w H ðnþuðnþš*; ð1þ where uðnþ is the tapinput vector, dðnþ is the desired response, and m is the stepsize parameter. The quantity enclosed in square brackets is the error signal. The asterisk denotes complex conjugation, and the superscript H denotes Hermitian transposition (i.e., ordinary transposition combined with complex conjugation). Equation (1) is testimony to the simplicity of the LMS filter. This simplicity, coupled with desirable properties of the LMS filter (discussed in the chapters of this book) and practical applications [3, 4], has made the LMS filter and its variants an important part of the adaptive signal processing kit of tools, not just for the past 40 years but for many years to come. Simply put, the LMS filter has withstood the test of time. Although the LMS filter is very simple in computational terms, its mathematical analysis is profoundly complicated because of its stochastic and nonlinear nature. Indeed, despite the extensive effort that has been expended in the literature to xi
10 xii INTRODUCTION: THE LMS FILTER (ALGORITHM) analyze the LMS filter, we still do not have a direct mathematical theory for its stability and steadystate performance, and probably we never will. Nevertheless, we do have a good understanding of its behavior in a stationary as well as a nonstationary environment, as demonstrated in the chapters of this book. The stochastic nature of the LMS filter manifests itself in the fact that in a stationary environment, and under the assumption of a small stepsize parameter, the filter executes a form of Brownian motion. Specifically, the small stepsize theory of the LMS filter is almost exactly described by the discretetime version of the Langevin equation 1 [3]: Dn k ðnþ ¼n k ðn þ 1Þ n k ðnþ ¼ ml k n k ðnþþf k ðnþ; k ¼ 1; 2;...; M; ð2þ which is naturally split into two parts: a damping force ml k n k ðnþ and a stochastic force f k ðnþ. The terms used herein are defined as follows: M ¼ order (i.e., number of taps) of the transversal filter around which the LMS filter is built l k ¼ kth eigenvalue of the correlation matrix of the input vector uðnþ, which is denoted by R f k ðnþ ¼kth component of the vector mq H uðnþe* o ðnþ Q ¼ unitary matrix whose M columns constitute an orthogonal set of eigerivectors associated with the eigenvalues of the correlation matrix R e o ðnþ ¼optimum error signal produced by the corresponding Wiener filter driven by the input vector uðnþ and the desired response dðnþ To illustrate the validity of Eq. (2) as the description of small stepsize theory of the LMS filter, we present the results of a computer experiment on a classic example of adaptive equalization. The example involves an unknown linear channel whose impulse response is described by the raised cosine [3] 8 1 h n ¼ 2 1 þ cos 2p < ðn 2Þ ; n ¼ 1; 2; 3; W ð3þ : 0; otherwise where the parameter W controls the amount of amplitude distortion produced by the channel, with the distortion increasing with W. Equivalently, the parameter W controls the eigenvalue spread (i.e., the ratio of the largest eigenvaiue to the smallest eigenvalue) of the correlation matrix of the tap inputs of the equalizer, with the eigenvalue spread increasing with W. The equalizer has M ¼ 11 taps. Figure 1 presents the learning curves of the equalizer trained using the LMS algorithm with the stepsize parameter m ¼ 0:0075 and varying W. Each learning curve was obtained by averaging the squared value of the error signal eðnþ versus the number of iterations n over an ensemble of 100 independent trials of the experiment. The 1 The Langevin equation is the engineer s version of stochastic differential (difference) equations.
11 INTRODUCTION: THE LMS FILTER (ALGORITHM) xiii Figure 1 Learning curves of the LMS algorithm applied to the adaptive equalization of a communication channel whose impulse response is described by Eq. (3) for varying eigenvalue spreads: Theory is represented by continuous welldefined curves. Experimental results are represented by fluctuating curves. continuous curves shown in Figure 1 are theoretical, obtained by applying Eq. (2). The curves with relatively small fluctuations are the results of experimental work. Figure 1 demonstrates close agreement between theory and experiment. It should, however, be reemphasized that application of Eq. (2) is limited to small values of the stepsize parameter m. Chapters in this book deal with cases when m is large. REFERENCES 1. B. Widrow and M. E. Hoff, Jr. (1960). Adaptive Switching Circuits, IRE WESCON Conv. Rec., Part 4, pp B. Widrow (1966). Adaptive Filters I: Fundamentals, Rep. SEL (TR ), Stanford Electronic Laboratories, Stanford, CA. 3. S. Haykin (2002). Adaptive Filter Theory, 4th Edition, PrenticeHall. 4. B. Widrow and S. D. Stearns (1985). Adaptive Signal Processing, PrenticeHall.
Statistics for Experimenters
Statistics for Experimenters Design, Innovation, and Discovery Second Edition GEORGE E. P. BOX J. STUART HUNTER WILLIAM G. HUNTER WILEY INTERSCIENCE A JOHN WILEY & SONS, INC., PUBLICATION FACHGEBIETSBGCHEREI
More informationAnalysis of Financial Time Series
Analysis of Financial Time Series Analysis of Financial Time Series Financial Econometrics RUEY S. TSAY University of Chicago A WileyInterscience Publication JOHN WILEY & SONS, INC. This book is printed
More informationStability of the LMS Adaptive Filter by Means of a State Equation
Stability of the LMS Adaptive Filter by Means of a State Equation Vítor H. Nascimento and Ali H. Sayed Electrical Engineering Department University of California Los Angeles, CA 90095 Abstract This work
More informationEnhancing the SNR of the Fiber Optic Rotation Sensor using the LMS Algorithm
1 Enhancing the SNR of the Fiber Optic Rotation Sensor using the LMS Algorithm Hani Mehrpouyan, Student Member, IEEE, Department of Electrical and Computer Engineering Queen s University, Kingston, Ontario,
More informationFundamentals of Financial Planning and Management for mall usiness
E REPRE EUR A F A CE Fundamentals of Financial Planning and Management for mall usiness M.J. Alhabeeb ENTREPRENEURIAL FINANCE The first effective form of investment was realized when the primitive man
More informationAdaptive Equalization of binary encoded signals Using LMS Algorithm
SSRG International Journal of Electronics and Communication Engineering (SSRGIJECE) volume issue7 Sep Adaptive Equalization of binary encoded signals Using LMS Algorithm Dr.K.Nagi Reddy Professor of ECE,NBKR
More informationADAPTIVE ALGORITHMS FOR ACOUSTIC ECHO CANCELLATION IN SPEECH PROCESSING
www.arpapress.com/volumes/vol7issue1/ijrras_7_1_05.pdf ADAPTIVE ALGORITHMS FOR ACOUSTIC ECHO CANCELLATION IN SPEECH PROCESSING 1,* Radhika Chinaboina, 1 D.S.Ramkiran, 2 Habibulla Khan, 1 M.Usha, 1 B.T.P.Madhav,
More informationAnalysis of MeanSquare Error and Transient Speed of the LMS Adaptive Algorithm
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 48, NO. 7, JULY 2002 1873 Analysis of MeanSquare Error Transient Speed of the LMS Adaptive Algorithm Onkar Dabeer, Student Member, IEEE, Elias Masry, Fellow,
More informationTeaching with Classroom Response Systems
DEREK BRUFF Teaching with Classroom Response Systems Creating Active Learning Environments Teaching with Classroom Response Systems Teaching with Classroom Response Systems Creating Active Learning Environments
More informationwww.wileyglobalfinance.com
Wiley Global Finance is a marketleading provider of over 400 annual books, mobile applications, elearning products, workflow training tools, newsletters and websites for both professionals and consumers
More information4F7 Adaptive Filters (and Spectrum Estimation) Least Mean Square (LMS) Algorithm Sumeetpal Singh Engineering Department Email : sss40@eng.cam.ac.
4F7 Adaptive Filters (and Spectrum Estimation) Least Mean Square (LMS) Algorithm Sumeetpal Singh Engineering Department Email : sss40@eng.cam.ac.uk 1 1 Outline The LMS algorithm Overview of LMS issues
More informationBackground 2. Lecture 2 1. The Least Mean Square (LMS) algorithm 4. The Least Mean Square (LMS) algorithm 3. br(n) = u(n)u H (n) bp(n) = u(n)d (n)
Lecture 2 1 During this lecture you will learn about The Least Mean Squares algorithm (LMS) Convergence analysis of the LMS Equalizer (Kanalutjämnare) Background 2 The method of the Steepest descent that
More informationMANAGEMENT OF DATA IN CLINICAL TRIALS
MANAGEMENT OF DATA IN CLINICAL TRIALS Second Edition ELEANOR MCFADDEN Frontier Science, Ltd. Kincraig, Invernessshire, Scotland WILEYINTERSCIENCE A JOHN WILEY & SONS, INC., PUBLICATION MANAGEMENT OF
More informationLecture 5: Variants of the LMS algorithm
1 Standard LMS Algorithm FIR filters: Lecture 5: Variants of the LMS algorithm y(n) = w 0 (n)u(n)+w 1 (n)u(n 1) +...+ w M 1 (n)u(n M +1) = M 1 k=0 w k (n)u(n k) =w(n) T u(n), Error between filter output
More informationSTUDY GUIDE LINEAR ALGEBRA. David C. Lay University of Maryland College Park AND ITS APPLICATIONS THIRD EDITION UPDATE
STUDY GUIDE LINEAR ALGEBRA AND ITS APPLICATIONS THIRD EDITION UPDATE David C. Lay University of Maryland College Park Copyright 2006 Pearson AddisonWesley. All rights reserved. Reproduced by Pearson AddisonWesley
More informationHUMAN RESOURCES MANAGEMENT FOR PUBLIC AND NONPROFIT ORGANIZATIONS
HUMAN RESOURCES MANAGEMENT FOR PUBLIC AND NONPROFIT ORGANIZATIONS Essential Texts for Public and Nonprofit Leadership and Management The Handbook of Nonprofit Governance, by BoardSource Strategic Planning
More informationModeling, Analysis, and Control of Dynamic Systems
Modeling, Analysis, and Control of Dynamic Systems Second Edition William J. Palm III University of Rhode Island John Wiley Sons, Inc. New York Chichester Weinheim Brisbane Singapore Toronto To Louise.
More informationFinal Year Project Progress Report. FrequencyDomain Adaptive Filtering. Myles Friel. Supervisor: Dr.Edward Jones
Final Year Project Progress Report FrequencyDomain Adaptive Filtering Myles Friel 01510401 Supervisor: Dr.Edward Jones Abstract The Final Year Project is an important part of the final year of the Electronic
More informationBy choosing to view this document, you agree to all provisions of the copyright laws protecting it.
This material is posted here with permission of the IEEE Such permission of the IEEE does not in any way imply IEEE endorsement of any of Helsinki University of Technology's products or services Internal
More informationInternational Marketing Research
International Marketing Research Third edition C. SAMUEL CRAIG and SUSAN P. DOUGLAS Leonard N. Stern School of Business, New York University Allie International Marketing Research Third edition Allie International
More informationComponent Ordering in Independent Component Analysis Based on Data Power
Component Ordering in Independent Component Analysis Based on Data Power Anne Hendrikse Raymond Veldhuis University of Twente University of Twente Fac. EEMCS, Signals and Systems Group Fac. EEMCS, Signals
More informationA STUDY OF ECHO IN VOIP SYSTEMS AND SYNCHRONOUS CONVERGENCE OF
A STUDY OF ECHO IN VOIP SYSTEMS AND SYNCHRONOUS CONVERGENCE OF THE µlaw PNLMS ALGORITHM Laura Mintandjian and Patrick A. Naylor 2 TSS Departement, Nortel Parc d activites de Chateaufort, 78 ChateaufortFrance
More informationGraph Analysis and Visualization
Graph Analysis and Visualization Graph Analysis and Visualization DISCOVERING BUSINESS OPPORTUNITY IN LINKED DATA Richard Brath David Jonker Graph Analysis and Visualization: Discovering Business Opportunity
More informationIslamic Finance and the New Financial System
Islamic Finance and the New Financial System The Wiley Finance series contains books written specifically for finance and investment professionals as well as sophisticated individual investors and their
More informationAnalysis of Filter Coefficient Precision on LMS Algorithm Performance for G.165/G.168 Echo Cancellation
Application Report SPRA561  February 2 Analysis of Filter Coefficient Precision on LMS Algorithm Performance for G.165/G.168 Echo Cancellation Zhaohong Zhang Gunter Schmer C6 Applications ABSTRACT This
More informationNEURAL NETWORKS A Comprehensive Foundation
NEURAL NETWORKS A Comprehensive Foundation Second Edition Simon Haykin McMaster University Hamilton, Ontario, Canada Prentice Hall Prentice Hall Upper Saddle River; New Jersey 07458 Preface xii Acknowledgments
More informationAppendix H: Control System Computational Aids
E1BAPP08 11/02/2010 11:56:59 Page 1 Appendix H: Control System Computational Aids H.1 Step Response of a System Represented in State Space In this section we will discuss how to obtain the step response
More informationCANCELLATION OF WHITE AND COLOR NOISE WITH ADAPTIVE FILTER USING LMS ALGORITHM
CANCELLATION OF WHITE AND COLOR NOISE WITH ADAPTIVE FILTER USING LMS ALGORITHM 1 Solaiman Ahmed, 2 Farhana Afroz, 1 Ahmad Tawsif and 1 Asadul Huq 1 Department of Electrical and Electronic Engineering,
More informationThe Filteredx LMS Algorithm
The Filteredx LMS Algorithm L. Håkansson Department of Telecommunications and Signal Processing, University of Karlskrona/Ronneby 372 25 Ronneby Sweden Adaptive filters are normally defined for problems
More informationAdaptive Variable Step Size in LMS Algorithm Using Evolutionary Programming: VSSLMSEV
Adaptive Variable Step Size in LMS Algorithm Using Evolutionary Programming: VSSLMSEV Ajjaiah H.B.M Research scholar Jyothi institute of Technology Bangalore, 560006, India Prabhakar V Hunagund Dept.of
More informationAdvanced Signal Processing and Digital Noise Reduction
Advanced Signal Processing and Digital Noise Reduction Saeed V. Vaseghi Queen's University of Belfast UK WILEY HTEUBNER A Partnership between John Wiley & Sons and B. G. Teubner Publishers Chichester New
More informationNumerical Methods for Engineers
Steven C. Chapra Berger Chair in Computing and Engineering Tufts University RaymondP. Canale Professor Emeritus of Civil Engineering University of Michigan Numerical Methods for Engineers With Software
More informationScientific Computing: An Introductory Survey
Scientific Computing: An Introductory Survey Chapter 10 Boundary Value Problems for Ordinary Differential Equations Prof. Michael T. Heath Department of Computer Science University of Illinois at UrbanaChampaign
More informationNANOCOMPUTING. Computational Physics for Nanoscience and Nanotechnology
NANOCOMPUTING Computational Physics for Nanoscience and Nanotechnology NANOCOMPUTING Computational Physics for Nanoscience and Nanotechnology James J Y Hsu National Cheng Kung University, Taiwan National
More informationEffective Methods for Software and Systems Integration
Effective Methods for Software and Systems Integration Boyd L. Summers CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 334872742 CRC Press is an imprint of Taylor
More informationAcoustic Echo Cancellation For Speech And Random Signal Using Estimated Impulse Responses
Adaptive Filter International Journal of Recent Development in Engineering and Technology Acoustic Echo Cancellation For Speech And Random Signal Using Estimated Impulse Responses S. I. M. M. Raton Mondol
More informationCOVERS ALL TOPICS IN LEVEL I CFA EXAM REVIEW CFA LEVEL I FORMULA SHEETS
2016 CFA EXAM REVIEW COVERS ALL TOPICS IN LEVEL I LEVEL I CFA FORMULA SHEETS Copyright 2016 by John Wiley & Sons, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published
More informationArt Direction for Film and Video
Art Direction for Film and Video This page intentionally left blank Art Direction for Film and Video SECOND EDITION Robert L. Olson Focal Press Taylor & Francis Croup NEW YORK AND LONDON First published
More informationThis page has been left blank intentionally
Project Governance This page has been left blank intentionally Project Governance Ralf Müller PM Concepts, Sweden Ralf Müller 2009 All rights reserved. No part of this publication may be reproduced, stored
More informationPraise for Agile Contracts
Agile Contracts Praise for Agile Contracts Agile development is starting to become popular in Japan, though Japanese companies have used all in one contracts for the last three decades. In this movement,
More informationDetailed simulation of mass spectra for quadrupole mass spectrometer systems
Detailed simulation of mass spectra for quadrupole mass spectrometer systems J. R. Gibson, a) S. Taylor, and J. H. Leck Department of Electrical Engineering and Electronics, The University of Liverpool,
More informationNONLINEAR TIME SERIES ANALYSIS
NONLINEAR TIME SERIES ANALYSIS HOLGER KANTZ AND THOMAS SCHREIBER Max Planck Institute for the Physics of Complex Sy stems, Dresden I CAMBRIDGE UNIVERSITY PRESS Preface to the first edition pug e xi Preface
More informationContents. Preface. xiii. Part I 1
Contents Preface xiii Part I 1 Chapter 1 Introduction to FrequencyModulated ContinuousWave 3 Radar 1.1 Brief History 3 1.2 Examples of Use of FMCW Radar 5 1.2.1 Radio Altimeters 5 1.2.2 LevelMeasuring
More informationProgramming Interviews Exposed: Secrets to Landing Your Next Job
Programming Interviews Exposed: Secrets to Landing Your Next Job Preface.... xxv Introduction....xxix Chapter 1 Before the Search... 1 Chapter 2 The Job Application Process....9 Chapter 3 Approaches to
More informationCCNY. BME I5100: Biomedical Signal Processing. Linear Discrimination. Lucas C. Parra Biomedical Engineering Department City College of New York
BME I5100: Biomedical Signal Processing Linear Discrimination Lucas C. Parra Biomedical Engineering Department CCNY 1 Schedule Week 1: Introduction Linear, stationary, normal  the stuff biology is not
More informationCOMPUSCIENCE. Subject Coverage. File Type. Features Thesaurus None. Record Content. File Size. Coverage 19722002. Updates.
Subject Coverage File Type Algorithms Artificial intelligence Computers and education Computer graphics, image processing, pattern recognition Computer systems organization Information systems Software
More informationProbability and Statistics
Probability and Statistics Syllabus for the TEMPUS SEE PhD Course (Podgorica, April 4 29, 2011) Franz Kappel 1 Institute for Mathematics and Scientific Computing University of Graz Žaneta Popeska 2 Faculty
More informationSystem Identification for Acoustic Comms.:
System Identification for Acoustic Comms.: New Insights and Approaches for Tracking Sparse and Rapidly Fluctuating Channels Weichang Li and James Preisig Woods Hole Oceanographic Institution The demodulation
More informationData Visualization. Principles and Practice. Second Edition. Alexandru Telea
Data Visualization Principles and Practice Second Edition Alexandru Telea First edition published in 2007 by A K Peters, Ltd. Cover image: The cover shows the combination of scientific visualization and
More informationAN INTRODUCTION TO OPTIONS TRADING. Frans de Weert
AN INTRODUCTION TO OPTIONS TRADING Frans de Weert AN INTRODUCTION TO OPTIONS TRADING The Securities & Investment Institute Mission Statement: To set standards of professional excellence and integrity
More informationApplied Linear Algebra I Review page 1
Applied Linear Algebra Review 1 I. Determinants A. Definition of a determinant 1. Using sum a. Permutations i. Sign of a permutation ii. Cycle 2. Uniqueness of the determinant function in terms of properties
More informationThe pnorm generalization of the LMS algorithm for adaptive filtering
The pnorm generalization of the LMS algorithm for adaptive filtering Jyrki Kivinen University of Helsinki Manfred Warmuth University of California, Santa Cruz Babak Hassibi California Institute of Technology
More informationSoftware and Hardware Solutions for Accurate Data and Profitable Operations. Miguel J. Donald J. Chmielewski Contributor. DuyQuang Nguyen Tanth
Smart Process Plants Software and Hardware Solutions for Accurate Data and Profitable Operations Miguel J. Bagajewicz, Ph.D. University of Oklahoma Donald J. Chmielewski Contributor DuyQuang Nguyen Tanth
More informationDynamic Process Modeling. Process Dynamics and Control
Dynamic Process Modeling Process Dynamics and Control 1 Description of process dynamics Classes of models What do we need for control? Modeling for control Mechanical Systems Modeling Electrical circuits
More informationCore Curriculum to the Course:
Core Curriculum to the Course: Environmental Science Law Economy for Engineering Accounting for Engineering Production System Planning and Analysis Electric Circuits Logic Circuits Methods for Electric
More informationLecture Notes to Accompany. Scientific Computing An Introductory Survey. by Michael T. Heath. Chapter 10
Lecture Notes to Accompany Scientific Computing An Introductory Survey Second Edition by Michael T. Heath Chapter 10 Boundary Value Problems for Ordinary Differential Equations Copyright c 2001. Reproduction
More informationIntroduction to Engineering System Dynamics
CHAPTER 0 Introduction to Engineering System Dynamics 0.1 INTRODUCTION The objective of an engineering analysis of a dynamic system is prediction of its behaviour or performance. Real dynamic systems are
More informationAN INTRODUCTION TO NUMERICAL METHODS AND ANALYSIS
AN INTRODUCTION TO NUMERICAL METHODS AND ANALYSIS Revised Edition James Epperson Mathematical Reviews BICENTENNIAL 0, 1 8 0 7 z ewiley wu 2007 r71 BICENTENNIAL WILEYINTERSCIENCE A John Wiley & Sons, Inc.,
More informationIntroduction to Industrial Organization
Introduction to Industrial Organization The MIT Press. Cambridge, Massachusetts London, England Second printing, 2002 2000 Massachusetts Institute of Technology All rights reserved. No part of this book
More informationPraise for Launch. Hands on and generous, Michael shows you precisely how he does it, step by step. Seth Godin, author of Linchpin
Praise for Launch Launch is your road map to success in an everchanging world. Stelzner shows you how to enchant your customers so that they ll want to help you change the world. Guy Kawasaki, author
More informationBEYOND 401(k)S SMALL BUSINESS OWNERS
BEYOND 401(k)S FOR SMALL BUSINESS OWNERS A Practical Guide to Incentive, Deferred Compensation, and Retirement Plans JEAN D. SIFLEET John Wiley & Sons, Inc. BEYOND 401(k)S FOR SMALL BUSINESS OWNERS BEYOND
More informationForecasting of Economic Quantities using Fuzzy Autoregressive Model and Fuzzy Neural Network
Forecasting of Economic Quantities using Fuzzy Autoregressive Model and Fuzzy Neural Network Dušan Marček 1 Abstract Most models for the time series of stock prices have centered on autoregressive (AR)
More informationLMS is a simple but powerful algorithm and can be implemented to take advantage of the Lattice FPGA architecture.
February 2012 Introduction Reference Design RD1031 Adaptive algorithms have become a mainstay in DSP. They are used in wide ranging applications including wireless channel estimation, radar guidance systems,
More informationUsing quantum computing to realize the Fourier Transform in computer vision applications
Using quantum computing to realize the Fourier Transorm in computer vision applications Renato O. Violin and José H. Saito Computing Department Federal University o São Carlos {renato_violin, saito }@dc.uscar.br
More informationEmpirical ModelBuilding and Response Surfaces
Empirical ModelBuilding and Response Surfaces GEORGE E. P. BOX NORMAN R. DRAPER Technische Universitat Darmstadt FACHBEREICH INFORMATIK BIBLIOTHEK InvortarNf.. Sachgsbiete: Standort: New York John Wiley
More informationADAPTIVE CHANNEL EQUALIZER FOR WIRELESS COMMUNICATION SYSTEMS
International Journal of Electronics and Communication Engineering (IJECE) ISSN(P): 22789901; ISSN(E): 2278991X Vol. 2, Issue 5, Nov 2013, 159166 IASET ADAPTIVE CHANNEL EQUALIZER FOR WIRELESS COMMUNICATION
More informationParameter identification of a linear single track vehicle model
Parameter identification of a linear single track vehicle model Edouard Davin D&C 2011.004 Traineeship report Coach: dr. Ir. I.J.M. Besselink Supervisors: prof. dr. H. Nijmeijer Eindhoven University of
More informationIntegrated Reservoir Asset Management
Integrated Reservoir Asset Management Integrated Reservoir Asset Management Principles and Best Practices John R. Fanchi AMSTERDAM. BOSTON. HEIDELBERG. LONDON NEW YORK. OXFORD. PARIS. SAN DIEGO SAN FRANCISCO.
More informationAdaptive Sampling Rate Correction for Acoustic Echo Control in VoiceOverIP Matthias Pawig, Gerald Enzner, Member, IEEE, and Peter Vary, Fellow, IEEE
IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 58, NO. 1, JANUARY 2010 189 Adaptive Sampling Rate Correction for Acoustic Echo Control in VoiceOverIP Matthias Pawig, Gerald Enzner, Member, IEEE, and Peter
More informationNICK SMITH AND ROBERT WOLLAN WITH CATHERINE ZHOU. John Wiley & Sons, Inc.
NICK SMITH AND ROBERT WOLLAN WITH CATHERINE ZHOU John Wiley & Sons, Inc. Copyright # 2011 by Accenture, LLP. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously
More informationUnivariate and Multivariate Methods PEARSON. Addison Wesley
Time Series Analysis Univariate and Multivariate Methods SECOND EDITION William W. S. Wei Department of Statistics The Fox School of Business and Management Temple University PEARSON Addison Wesley Boston
More informationFinancial Derivatives Futures, Forwards, Swaps, Options, Corporate Securities, and Credit Default Swaps
Financial Derivatives Futures, Forwards, Swaps, Options, Corporate Securities, and Credit Default Swaps World Scientific Lecture Notes in Economics ISSN: 23826118 Series Editor: Dirk Bergemann (Yale University,
More informationComparing Dual Microphone System with Different Algorithms and Distances between Microphones.
Master Thesis Electrical Engineering May 2013 Comparing Dual Microphone System with Different Algorithms and Distances between Microphones. Ariful Islam Shafinaz Shahjahan Nitu This thesis is presented
More informationLeastSquares Intersection of Lines
LeastSquares Intersection of Lines Johannes Traa  UIUC 2013 This writeup derives the leastsquares solution for the intersection of lines. In the general case, a set of lines will not intersect at a
More informationECE438  Laboratory 9: Speech Processing (Week 2)
Purdue University: ECE438  Digital Signal Processing with Applications 1 ECE438  Laboratory 9: Speech Processing (Week 2) October 6, 2010 1 Introduction This is the second part of a two week experiment.
More informationMathematical Modeling and Methods of Option Pricing
Mathematical Modeling and Methods of Option Pricing This page is intentionally left blank Mathematical Modeling and Methods of Option Pricing Lishang Jiang Tongji University, China Translated by Canguo
More informationINTELLIGENT SYSTEMS, CONTROL, AND AUTOMATION: SCIENCE AND ENGINEERING
Robotics International Series on INTELLIGENT SYSTEMS, CONTROL, AND AUTOMATION: SCIENCE AND ENGINEERING VOLUME 43 Editor Professor S. G. Tzafestas, National Technical University of Athens, Greece Editorial
More informationDoptimal plans in observational studies
Doptimal plans in observational studies Constanze Pumplün Stefan Rüping Katharina Morik Claus Weihs October 11, 2005 Abstract This paper investigates the use of Design of Experiments in observational
More informationComputer exercise 2: Least Mean Square (LMS)
1 Computer exercise 2: Least Mean Square (LMS) This computer exercise deals with the LMS algorithm, which is derived from the method of steepest descent by replacing R = E{u(n)u H (n)} and p = E{u(n)d
More informationElementary Differential Equations
Elementary Differential Equations EIGHTH EDITION Earl D. Rainville Late Professor of Mathematics University of Michigan Phillip E. Bedient Professor Emeritus of Mathematics Franklin and Marshall College
More informationExperiment 7: Familiarization with the Network Analyzer
Experiment 7: Familiarization with the Network Analyzer Measurements to characterize networks at high frequencies (RF and microwave frequencies) are usually done in terms of scattering parameters (S parameters).
More informationSocial Services Administration In Hong Kong
Social Services Administration In Hong Kong Tneoretical Issues ana Case Studies This page is intentionally left blank Social Services Administration In Hong Kong Theoretical Issues ana Case Studies Editors
More informationCLUSTER ANALYSIS FOR SEGMENTATION
CLUSTER ANALYSIS FOR SEGMENTATION Introduction We all understand that consumers are not all alike. This provides a challenge for the development and marketing of profitable products and services. Not every
More informationADAPTIVE EQUALIZATION. Prepared by Deepa.T, Asst.Prof. /TCE
ADAPTIVE EQUALIZATION Prepared by Deepa.T, Asst.Prof. /TCE INTRODUCTION TO EQUALIZATION Equalization is a technique used to combat inter symbol interference(isi). An Equalizer within a receiver compensates
More informationSOLVING LINEAR SYSTEMS
SOLVING LINEAR SYSTEMS Linear systems Ax = b occur widely in applied mathematics They occur as direct formulations of real world problems; but more often, they occur as a part of the numerical analysis
More informationLecture 6. Artificial Neural Networks
Lecture 6 Artificial Neural Networks 1 1 Artificial Neural Networks In this note we provide an overview of the key concepts that have led to the emergence of Artificial Neural Networks as a major paradigm
More informationIMPROVED VIRTUAL MOUSE POINTER USING KALMAN FILTER BASED GESTURE TRACKING TECHNIQUE
39 IMPROVED VIRTUAL MOUSE POINTER USING KALMAN FILTER BASED GESTURE TRACKING TECHNIQUE D.R.A.M. Dissanayake, U.K.R.M.H. Rajapaksha 2 and M.B Dissanayake 3 Department of Electrical and Electronic Engineering,
More informationBINOMIAL OPTION PRICING
Darden Graduate School of Business Administration University of Virginia BINOMIAL OPTION PRICING Binomial option pricing is a simple but powerful technique that can be used to solve many complex optionpricing
More informationCloudonomics: The Business Value of Cloud Computing
Additional praise for Cloudonomics: The Business Value of Cloud Computing It is a business imperative to do more with less and do everything faster. Cloudonomics offers a muchappreciated framework for
More informationIMPROVED NETWORK PARAMETER ERROR IDENTIFICATION USING MULTIPLE MEASUREMENT SCANS
IMPROVED NETWORK PARAMETER ERROR IDENTIFICATION USING MULTIPLE MEASUREMENT SCANS Liuxi Zhang and Ali Abur Department of Electrical and Computer Engineering Northeastern University Boston, MA, USA lzhang@ece.neu.edu
More informationFundamentals of Actuarial Mathematics
Fundamentals of Actuarial Mathematics S. David Promislow York University, Toronto, Canada John Wiley & Sons, Ltd Contents Preface Notation index xiii xvii PARTI THE DETERMINISTIC MODEL 1 1 Introduction
More information250325  METNUMER  Numerical Methods
Coordinating unit: 250  ETSECCPB  Barcelona School of Civil Engineering Teaching unit: 751  ECA  Department of Civil and Environmental Engineering Academic year: Degree: 2015 BACHELOR'S DEGREE IN GEOLOGICAL
More informationTableau Your Data! Fast and Easy Visual Analysis with Tableau Software. Daniel G. Murray. Second Edition
Tableau Your Data! Tableau Your Data! Fast and Easy Visual Analysis with Tableau Software Second Edition Daniel G. Murray Tableau Your Data! Fast and Easy Visual Analysis with Tableau Software Published
More informationA Reliability Point and Kalman Filterbased Vehicle Tracking Technique
A Reliability Point and Kalman Filterbased Vehicle Tracing Technique Soo Siang Teoh and Thomas Bräunl Abstract This paper introduces a technique for tracing the movement of vehicles in consecutive video
More informationFounded in 1807, John Wiley & Sons is the oldest independent publishing company in the United States. With offices in North America, Europe,
Islamic Finance Founded in 1807, John Wiley & Sons is the oldest independent publishing company in the United States. With offices in North America, Europe, Australia, and Asia, Wiley is globally committed
More informationCharacterization Of Polynomials Using Reflection Coefficients
Applied Mathematics ENotes, 4(2004), 114121 c ISSN 16072510 Available free at mirror sites of http://www.math.nthu.edu.tw/ amen/ Characterization Of Polynomials Using Reflection Coefficients José LuisDíazBarrero,JuanJosé
More informationTAGUCHI APPROACH TO DESIGN OPTIMIZATION FOR QUALITY AND COST: AN OVERVIEW. Resit Unal. Edwin B. Dean
TAGUCHI APPROACH TO DESIGN OPTIMIZATION FOR QUALITY AND COST: AN OVERVIEW Resit Unal Edwin B. Dean INTRODUCTION Calibrations to existing cost of doing business in space indicate that to establish human
More informationNEURAL NETWORK FUNDAMENTALS WITH GRAPHS, ALGORITHMS, AND APPLICATIONS
NEURAL NETWORK FUNDAMENTALS WITH GRAPHS, ALGORITHMS, AND APPLICATIONS N. K. Bose HRBSystems Professor of Electrical Engineering The Pennsylvania State University, University Park P. Liang Associate Professor
More informationIntroduction to Matrix Algebra
Psychology 7291: Multivariate Statistics (Carey) 8/27/98 Matrix Algebra  1 Introduction to Matrix Algebra Definitions: A matrix is a collection of numbers ordered by rows and columns. It is customary
More informationPredictive Analytics for Human Resources
Predictive Analytics for Human Resources Wiley & SAS Business Series The Wiley & SAS Business Series presents books that help seniorlevel managers with their critical management decisions. Titles in the
More information