Cichocki A., Amari Sh.-H.'s Adaptive Blind Signal and Image Processing: Learning PDF

By Cichocki A., Amari Sh.-H.

With good theoretical foundations and diverse capability purposes, Blind sign Processing (BSP) is among the most well liked rising parts in sign Processing. This quantity unifies and extends the theories of adaptive blind sign and photograph processing and gives useful and effective algorithms for blind resource separation, self sufficient, central, Minor part research, and Multichannel Blind Deconvolution (MBD) and Equalization. Containing over 1400 references and mathematical expressions Adaptive Blind sign and picture Processing gives you an unheard of selection of necessary ideas for adaptive blind signal/image separation, extraction, decomposition and filtering of multi-variable signs and information.

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Additional resources for Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

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Biomedical source signals are usually weak, nonstationary signals and distorted by noise and interferences. Moreover, they are usually mutually superimposed. Besides classical signal analysis tools (like adaptive supervised filtering, parametric or non-parametric spectral estimation, time-frequency analysis, and higher-order statistics) intelligent blind signal processing techniques (IBSP) can be used for preprocessing, noise and artifact reduction, enhancement, detection and estimation of biomedical signals by taking into account their spatio-temporal correlation and mutual statistical dependence.

Independent and identically-distributed) sequence that is independent of all the other source sequences. 11. In this book, many such extensions and generalizations are described. 11) p=0 is described by a multichannel finite-duration impulse response (FIR) adaptive filter at discrete-time k [612, 657]. 11 (a)) m ∞ yj (k) = wjip xi (k − p), (j = 1, 2, . . 13) p=−∞ where y(k) = [y1 (k), y2 (k), . . , yn (k)]T is an n-dimensional vector of outputs and W(k) = {Wp (k), −∞ ≤ p ≤ ∞} is a sequence of n × m coefficient matrices used at time k, and the matrix transfer function is given by ∞ Wp (k) z −p .

We derive, review and extend the existing adaptive algorithms for blind and semi-blind signal processing with a special emphasis on robust algorithms with equivariant properties in order to considerably reduce the bias caused by measurement noise, interferences and other parasitic effects. Moreover, novel adaptive systems and associated learning algorithms are presented for estimation of source signals and reduction of influence of noise. , Gaussian, Laplacian and uniformly-distributed noise assuming a generalized Gaussian distributed and other models.

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Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications by Cichocki A., Amari Sh.-H.


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