By Cichocki A., Amari Sh.-H.
With good theoretical foundations and various strength purposes, Blind sign Processing (BSP) is among the preferred rising parts in sign Processing. This quantity unifies and extends the theories of adaptive blind sign and snapshot processing and gives sensible and effective algorithms for blind resource separation, autonomous, vital, Minor part research, and Multichannel Blind Deconvolution (MBD) and Equalization. Containing over 1400 references and mathematical expressions Adaptive Blind sign and photo Processing promises an extraordinary selection of beneficial recommendations for adaptive blind signal/image separation, extraction, decomposition and filtering of multi-variable indications and knowledge.
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Extra resources for Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
When signals do not overlap in the time-domain then one signal stops (is silent) before another one begins. Such signals are easily separated when a receiver is accessible only while the signal of interest is sent. This multiple access method is called TDMA (Time Division Multiple Access). 5. The method based on this principle is called FDMA (Frequency Division Multiple Access). Both TDMA and FDMA are used in many modern digital communication systems . Of course, if the source power spectra overlap, the spectral diversity is not sufficient to extract sources, therefore, we need to exploit another kind of diversities.
One of the objectives of this book is to present promising novel approaches and associated algorithms that are more robust with respect to noise and/or that can reduce the noise in the estimated output vector y(k). Usually, it is assumed that the source signals and additive noise components are statistically independent. 5 −1 0 300 600 −1 0 300 600 −1 0 300 600 Mixed signal 4 2 0 −2 −4 0 75 150 225 300 375 450 525 600 (b) Fig. 6 Illustration of exploiting time-frequency diversity in BSS. (a) Original unknown source signals and available mixed signal.
It may well be that one is not interested in separation of the noise sources. 8). Various signal processing methods have been developed for noise cancelling [1236, 1237, 1234] and with some modifications they can be applied to noise cancellation in BSS. In many practical situations, we can measure or model the environmental noise. 7). For example, in the acoustic “cocktail party” problem, we can measure or record the environmental noise by using an isolated microphone. In a similar way, noise in biomedical applications can be measured by appropriately placed auxiliary sensors (or electrodes).
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications by Cichocki A., Amari Sh.-H.