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Unsupervised Adaptive Filtering : Blind Deconvolution by Simon Haykin

mottom47
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Condition:
Good
“Some wear on covers, previous owners stamp on outer page edges and on cover page. Pages have some ”... Read moreAbout condition
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eBay item number:284603408043
Last updated on 01 Nov, 2023 15:31:32 GMTView all revisionsView all revisions

Item specifics

Condition
Good
A book that has been read, but is in good condition. Minimal damage to the book cover eg. scuff marks, but no holes or tears. If this is a hard cover, the dust jacket may be missing. Binding has minimal wear. The majority of pages are undamaged with some creasing or tearing, and pencil underlining of text, but this is minimal. No highlighting of text, no writing in the margins, and no missing pages. See the seller’s listing for full details and description of any imperfections. See all condition definitionsopens in a new window or tab
Seller notes
“Some wear on covers, previous owners stamp on outer page edges and on cover page. Pages have some ...
Country/Region of Manufacture
United States
Subjects
Engineering & Technology
ISBN
9780471379416

About this product

Product Identifiers

Publisher
Wiley & Sons, Incorporated, John
ISBN-10
0471379417
ISBN-13
9780471379416
eBay Product ID (ePID)
1632253

Product Key Features

Number of Pages
200 Pages
Language
English
Publication Name
Unsupervised Adaptive Filtering, Blind Deconvolution Vol. 2
Publication Year
2000
Subject
Signals & Signal Processing, Electrical
Type
Textbook
Subject Area
Technology & Engineering
Author
Simon Haykin
Series
Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control Ser.
Format
Hardcover

Dimensions

Item Height
0.8 in
Item Weight
16.4 Oz
Item Length
9.5 in
Item Width
6.3 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
99-029980
Dewey Edition
21
Series Volume Number
Volume 2
Illustrated
Yes
Volume Number
Volume 2
Dewey Decimal
621.3/815324
Table Of Content
Contributors vii Preface xi 1 Introduction 1 Simon Haykin 1.1 Why Adaptive Filtering? 1 1.2 Supervised and Unsupervised Forms of Adaptive Filtering 2 1.3 Two Important Unsupervised Signal-Processing Tasks 3 1.4 Three Fundamental Approaches to Unsupervised Adaptive Filtering 6 1.5 Organization of Volume II 10 References 11 2 The Core of FSE-CMA Behavior Theory 13 C. R. Johnson, Jr., P. Schniter, I. Fijalkow, L. Tong, J. D. Behm, M. G. Larimore, D. R. Brown, R. A. Casas, T. J. Endres, S. Lambotharan, A. Touzni, H. H. Zeng, M. Green, and J. R. Treichler 2.1 Introduction 14 2.2 MMSE Equalization and LMS 22 2.3 The CM Criterion and CMA 41 2.4 CMA-Adapted-Equalizer Design Issues with Illustrative Examples 75 2.5 Case Studies 89 2.6 Conclusions 106 References 108 3 Relationships between Blind Deconvolution and Blind Source Separation 113 Scott C. Douglas and Simon Haykin 3.1 Introduction 113 3.2 Problem Descriptions 117 3.3 Algorithmic Relationships 122 3.4 Structural Relationships 129 3.5 Extensions 140 3.6 Conclusions 142 References 142 4 Blind Separation of Independent Sources Based on Multiuser Kurtosis Optimization Criteria 147 Constantinos B. Papadias 4.1 Introduction 148 4.2 Problem Formulation and Assumptions 150 4.3 Review: The Single-User Equalization Problem 154 4.4 Necessary and Su½cient Conditions for BSS 160 4.5 Unconstrained Criteria: The MU-CM Approach 162 4.6 Constrained Criteria: The MUK Approach 165 4.7 Numerical Examples 171 4.8 Conclusions 175 References 176 Index 181
Synopsis
A complete, one-stop reference on the state of the art of unsupervised adaptive filtering While unsupervised adaptive filtering has its roots in the 1960s, more recent advances in signal processing, information theory, imaging, and remote sensing have made this a hot area for research in several diverse fields. This book brings together cutting-edge information previously available only in disparate papers and articles, presenting a thorough and integrated treatment of the two major classes of algorithms used in the field, namely, blind signal separation and blind channel equalization algorithms. Divided into two volumes for ease of presentation, this important work shows how these algorithms, although developed independently, are closely related foundations of unsupervised adaptive filtering. Through contributions by the foremost experts on the subject, the book provides an up-to-date account of research findings, explains the underlying theory, and discusses potential applications in diverse fields. More than 100 illustrations as well as case studies, appendices, and references further enhance this excellent resource. Following coverage begun in Volume I: Blind Source Separation, this volume discusses: * The core of FSE-CMA behavior theory * Relationships between blind deconvolution and blind source separation * Blind separation of independent sources based on multiuser kurtosis optimization criteria, A complete, one-stop reference on the state of the art of unsupervised adaptive filtering While unsupervised adaptive filtering has its roots in the 1960s, more recent advances in signal processing, information theory, imaging, and remote sensing have made this a hot area for research in several diverse fields., Un berwachte adaptive Filterung bedeutet, da das System automatisch auf Ver nderungen der Bedingungen reagiert: Die Filter k nnen sich verschiedenen Situationen anpassen, ohne da ein Mensch eingreifen m te. Hunderte von Beitr gen zu diesem u erst aktuellen Forschungsfeld sind in der Fachpresse erschienen. Dieser Band fa t den derzeitigen Erkenntnisstand zusammen und erspart Ihnen damit eine zeitraubende Recherche. (04/00), A complete, one-stop reference on the state of the art of unsupervised adaptive filtering While unsupervised adaptive filtering has its roots in the 1960s, more recent advances in signal processing, information theory, imaging, and remote sensing have made this a hot area for research in several diverse fields. This book brings together cutting-edge information previously available only in disparate papers and articles, presenting a thorough and integrated treatment of the two major classes of algorithms used in the field, namely, blind signal separation and blind channel equalization algorithms. Divided into two volumes for ease of presentation, this important work shows how these algorithms, although developed independently, are closely related foundations of unsupervised adaptive filtering. Through contributions by the foremost experts on the subject, the book provides an up-to-date account of research findings, explains the underlying theory, and discusses potential applications in diverse fields. More than 100 illustrations as well as case studies, appendices, and references further enhance this excellent resource. Following coverage begun in Volume I: Blind Source Separation, this volume discusses: ∗ The core of FSE-CMA behavior theory ∗ Relationships between blind deconvolution and blind source separation ∗ Blind separation of independent sources based on multiuser kurtosis optimization criteria

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