Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications, Volume 1

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John Wiley & Sons, Jun 14, 2002 - Science - 586 pages
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With solid theoretical foundations and numerous potential applications, Blind Signal Processing (BSP) is one of the hottest emerging areas in Signal Processing. This volume unifies and extends the theories of adaptive blind signal and image processing and provides practical and efficient algorithms for blind source separation: Independent, Principal, Minor Component Analysis, and Multichannel Blind Deconvolution (MBD) and Equalization. Containing over 1400 references and mathematical expressions Adaptive Blind Signal and Image Processing delivers an unprecedented collection of useful techniques for adaptive blind signal/image separation, extraction, decomposition and filtering of multi-variable signals and data.
  • Offers a broad coverage of blind signal processing techniques and algorithms both from a theoretical and practical point of view
  • Presents more than 50 simple algorithms that can be easily modified to suit the reader's specific real world problems
  • Provides a guide to fundamental mathematics of multi-input, multi-output and multi-sensory systems
  • Includes illustrative worked examples, computer simulations, tables, detailed graphs and conceptual models within self contained chapters to assist self study
  • Accompanying CD-ROM features an electronic, interactive version of the book with fully coloured figures and text. C and MATLAB user-friendly software packages are also provided
    MATLAB is a registered trademark of The MathWorks, Inc.

By providing a detailed introduction to BSP, as well as presenting new results and recent developments, this informative and inspiring work will appeal to researchers, postgraduate students, engineers and scientists working in biomedical engineering, communications, electronics, computer science, optimisations, finance, geophysics and neural networks.

 

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Contents

Processing
24
Solving a System of Algebraic Equations and Related Problems
43
Inhibition Principles
64
PrincipalMinor Component Analysis and Related Problems
87
Blind Decorrelation and SOS for Robust Blind Identification
129
Technique
162
Statistical Signal Processing Approach to Blind Signal Extraction
177
Kurtosis
184
signals two fetal signals and two noise signals b Detailed
297
Robust Techniques for BSS and ICA with Noisy Data
305
with noise cancellation It is assumed that the reference
311
Appendix A Cumulants in Terms of Moments
333
Estimating Functions and Superefficiency
383
Blind Filtering and Separation Using a StateSpace Approach
423
Nonlinear State Space Models SemiBlind Signal Processing
443
Gradient Approach
451

Reference Signals
205
Natural Gradient Approach to Independent Component Analysis
231
Locally Adaptive Algorithms for IC A and their Implementations
273
learning algorithm 7 23
280
Appendix Mathematical Preliminaries
535
Glossary of Symbols and Abbreviations
547
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Page 486 - Space or time adaptive signal processing by neural network models," in Neural Networks for Computing: AIP Conference Proceedings 151, JS Denker, Ed., American Institute of Physics, New York, 1986.

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About the author (2002)

Cichocki, Microelectronics & Computer Technology Corp. (MCC), Austin, TX, USA.

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