Издательство John Wiley, 2000, -587 pp.
Signal processing has always played a critical role in science and technology and development of new systems like computer tomography, wireless communication, digital cameras etc. As demand of high quality and reliability in recording and visualization systems increases, signal processing has an even more important role to play.
Blind signal processing is now one if the hottest and emerging areas in signal processing with solid theoretical foundation and many potential applications.
Introduction to BSP.
Solving a System of Algebraic Equations.
Principal/Minor Component Analysis.
Blind Decorrelation and SOS for Robust Blind Identification.
Sequential Blind Signal Exnraction.
Natural Gradient Approach to Independent Component Analysis.
Locally Adaptive Algorithms for ICA.
Robust Techniques for BSS and ICA with Noisy Data.
Multichannel Blind Decomposition.
Estimating Functions and Superefficiency for ICA and Deconvolution.
Blind Filtering and Separation Using a State-Space Approach.
Nonlinear State Models – Semi-Blind Signal Processing.
Signal processing has always played a critical role in science and technology and development of new systems like computer tomography, wireless communication, digital cameras etc. As demand of high quality and reliability in recording and visualization systems increases, signal processing has an even more important role to play.
Blind signal processing is now one if the hottest and emerging areas in signal processing with solid theoretical foundation and many potential applications.
Introduction to BSP.
Solving a System of Algebraic Equations.
Principal/Minor Component Analysis.
Blind Decorrelation and SOS for Robust Blind Identification.
Sequential Blind Signal Exnraction.
Natural Gradient Approach to Independent Component Analysis.
Locally Adaptive Algorithms for ICA.
Robust Techniques for BSS and ICA with Noisy Data.
Multichannel Blind Decomposition.
Estimating Functions and Superefficiency for ICA and Deconvolution.
Blind Filtering and Separation Using a State-Space Approach.
Nonlinear State Models – Semi-Blind Signal Processing.