By Cichocki A., Amari Sh.-H.
With stable theoretical foundations and diverse strength functions, Blind sign Processing (BSP) is likely one of the preferred rising parts in sign Processing. This quantity unifies and extends the theories of adaptive blind sign and snapshot processing and gives useful and effective algorithms for blind resource separation, self sufficient, important, Minor part research, and Multichannel Blind Deconvolution (MBD) and Equalization. Containing over 1400 references and mathematical expressions Adaptive Blind sign and picture Processing supplies an unheard of selection of beneficial recommendations for adaptive blind signal/image separation, extraction, decomposition and filtering of multi-variable indications and knowledge.
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Additional info for Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Yn (t)]T and sensor signals as well as some a priori knowledge of the mixing system. 1). , when the inverse system does not exist or the number of observations is less than the number of source signals) and then estimate source signals implicitly by exploiting some a priori information about the system and applying a suitable optimization procedure. In many cases, source signals are simultaneously linearly filtered and mixed. The aim is to process these observations in such a way that the original source signals are extracted by the adaptive system.
N). 3). 1 In this book, unless otherwise mentioned, we assume that the source signals (and consequently output signals) are zero-mean. Non zero-mean source can be modelled by zero-mean source with an additional constant source. This constant source can be usually detected but its amplitude cannot be recovered without some a priori knowledge. There are several definitions of ICA. In this book, depending on the problem, we use different definitions given below. 1 (Temporal ICA) The ICA of a noisy random vector x(k) ∈ IRm is obtained by finding an n × m, (with m ≥ n), a full rank separating matrix W such that the output signal vector y(k) = [y1 (k), y2 (k), .
21). , the number of outputs of the system is equal to the number of sensors, although in practice the number of sources can be less than the number of sensors (m ≥ n). Such a model is justified by two facts. First of all, the number of sources is generally unknown and may change over time. Secondly, in practice we have additive noise signals that can be considered as auxiliary unknown sources; therefore, it is also reasonable to extract these noise signals. In the ideal noiseless case, the redundant (m−n) output signals yj should decay during adaptive learning process to zero and then only n outputs will correspond to the recovered sources.