By Cichocki A., Amari Sh.-H.

With strong theoretical foundations and diverse capability functions, Blind sign Processing (BSP) is likely one of the most well liked rising components in sign Processing. This quantity unifies and extends the theories of adaptive blind sign and snapshot processing and offers sensible and effective algorithms for blind resource separation, self sustaining, significant, 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 remarkable choice of beneficial options for adaptive blind signal/image separation, extraction, decomposition and filtering of multi-variable signs and knowledge.

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In fact, BSP techniques can be successfully applied to efficiently solve this problem and the first results are very promising [230, 232, 883]. Ordinary filtering and signal processing techniques have great difficulties with this problem [1286]. 26 INTRODUCTION TO BLIND SIGNAL PROCESSING: PROBLEMS AND APPLICATIONS (a) BSP Extracted signals Noise (b) BSP } FECG MECG Noise (c) BSP } EMG independent components Noise Fig. 16 Exemplary biomedical applications of blind signal processing: (a) A multi-recording monitoring system for blind enhancement of sources, cancellation of noise, elimination of artifacts and detection of evoked potentials, (b) blind separation of the fetal electrocardiogram (FECG) and maternal electrocardiogram (MECG) from skin electrode signals recorded from a pregnant women, (c) blind enhancement and independent components of multichannel electromyographic (EMG) signals.

Similarly, in the basic adaptive inverse control problem [1286], we attempt to estimate a form of adaptive controller whose transfer function is the inverse (in some sense) of that of the plant itself. The objective of such an adaptive system is to make the 5 PROBLEM FORMULATIONS – AN OVERVIEW plant to directly follow the input signals (commands). A vector of error signals defined as the difference between the plant outputs and the reference inputs are used by an adaptive learning algorithm to adjust parameters of the linear controller.

3). The above problems are often referred to as BSS (blind source separation) and/or ICA (independent component analysis): the BSS of a random vector x = [x1 , x2 , . . , xm ]T is obtained by finding an n × m, full rank, linear transformation (separating) matrix W such that the output signal vector y = [y1 , y2 , . . , yn ]T , defined by y = W x, contains components that are as independent as possible, as measured by an information-theoretic cost function such as the Kullback-Leibler divergence or other criteria like sparseness, smoothness or linear predictability.

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