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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Solving a System of Algebraic Equations and Related Problems
Inhibition Principles
PrincipalMinor Component Analysis and Related Problems
Blind Decorrelation and SOS for Robust Blind Identification
Statistical Signal Processing Approach to Blind Signal Extraction
signals two fetal signals and two noise signals b Detailed
Robust Techniques for BSS and ICA with Noisy Data
with noise cancellation It is assumed that the reference
Appendix A Cumulants in Terms of Moments
Estimating Functions and Superefficiency
Blind Filtering and Separation Using a StateSpace Approach
Nonlinear State Space Models SemiBlind Signal Processing

Reference Signals
Natural Gradient Approach to Independent Component Analysis
Locally Adaptive Algorithms for IC A and their Implementations
learning algorithm 7 23
Gradient Approach
Appendix Mathematical Preliminaries
Glossary of Symbols and Abbreviations

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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)

Andrzej Cichocki received the M.Sc. (with honors), Ph.D. and Dr.Sc. (Habilitation) degrees, all in electrical engineering, from Warsaw University of Technology in Poland.

Since 1972, he has been with the Institute of Theory of Electrical Engineering, Measurement and Information Systems, Faculty of Electrical Engineering at the Warsaw University of Technology, where he obtain a title of a full Professor in 1995.

He spent several years at University Erlangen-Nuerenberg in Germany, at the Chair of Applied and Theoretical Electrical Engineering directed by Professor Rolf Unbehauen, as an Alexander-von-Humboldt Research Fellow and Guest Professor. In 1995-1997 he was a team leader of the laboratory for Artificial Brain Systems, at Frontier Research Program RIKEN (Japan), in the Brain Information Processing Group.

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