Adaptive Blind Signal and Image Processing
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. It offers a broad coverage of blind signal processing techniques and algorithms both from a theoretical and practical point of view. It presents more than 50 simple algorithms that can be easily modified to suit the reader's specific real world problems. It provides a guide to fundamental mathematics of multi input, multi output and multi sensory systems. It includes illustrative worked examples, computer simulations, tables, detailed graphs and conceptual models within self contained chapters to assist self study. An accompanying CD-ROM features an electronic, interactive version of the book with fully coloured figures and text. C and MATLAB[registered] user friendly software packages are also provided. MATLAB[registered] 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.
Why Read This Book
You should read this book if you want a unified, mathematically grounded introduction to blind source separation and adaptive blind processing with many practical algorithms and extensive references. It gives you algorithmic detail and theoretical insight so you can implement and adapt ICA, PCA/MCA, and multichannel blind deconvolution methods for real signal and image problems.
Who Will Benefit
Graduate students, researchers, and DSP engineers working on source separation, multichannel deconvolution, audio/biomedical signal processing, or image decomposition who need both theory and implementable algorithms.
Level: Advanced — Prerequisites: Linear algebra (eigen/singular-value decompositions), probability and statistics, basic signal processing (filters, convolution), and familiarity with optimization/gradient methods.
Key Takeaways
- Implement core blind source separation algorithms including ICA and principal/minor component methods.
- Derive and apply adaptive update rules (gradient, natural gradient, Hebbian-type) for online separation and tracking.
- Design and apply multichannel blind deconvolution and blind equalization methods for convolutive mixtures.
- Extend blind-processing approaches to image and multidimensional data, including preprocessing and performance measures.
- Evaluate identifiability and performance of blind algorithms and choose appropriate criteria and cost functions.
- Adapt BSS/ICA algorithms to practical issues such as noise, nonstationarity, complex-valued signals, and high-dimensional data.
Topics Covered
- 1. Introduction to Blind Signal and Image Processing
- 2. Statistical Preliminaries and Problem Formulation
- 3. Principal and Minor Component Analysis
- 4. Independent Component Analysis: Theory and Identifiability
- 5. Adaptive Algorithms: Gradient, Natural Gradient and Hebbian Rules
- 6. Nonlinear, Higher-Order and Non-Gaussian Methods
- 7. Multichannel Blind Deconvolution and Blind Equalization
- 8. Complex-valued Signals and Extensions
- 9. Blind Image Separation and Multidimensional Data
- 10. Regularization, Sparsity and Overcomplete Representations
- 11. Performance Measures, Convergence and Practical Issues
- 12. Applications: Audio, Communications, Biomedical and Radar Examples
- 13. Implementation Notes and Numerical Considerations
- Appendices and Extensive Bibliography
Languages, Platforms & Tools
How It Compares
Closely related to Hyv arinen, Karhunen & Oja's 'Independent Component Analysis' (2001) but broader in adaptive algorithm derivations and multichannel/image processing; complements Jean-Fran ois Cardoso's work on joint diagonalization and BSS.
