Computer Vision Pattern Recognition

Blind Source Separation: Advances in Theory, Algorithms and by Ganesh R. Naik, Wenwu Wang

By Ganesh R. Naik, Wenwu Wang

Blind resource Separation intends to document the recent result of the efforts at the research of Blind resource Separation (BSS). The booklet collects novel examine rules and a few education in BSS, self sufficient part research (ICA), man made intelligence and sign processing purposes. additionally, the examine effects formerly scattered in lots of journals and meetings all over the world are methodically edited and awarded in a unified shape. The booklet is perhaps of curiosity to college researchers, R&D engineers and graduate scholars in desktop technology and electronics who desire to study the middle rules, equipment, algorithms and functions of BSS.

Dr. Ganesh R. Naik works at college of expertise, Sydney, Australia; Dr. Wenwu Wang works at collage of Surrey, UK.

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Additional resources for Blind Source Separation: Advances in Theory, Algorithms and Applications

Example text

The main idea is as follows: in each iteration, one first fixes the dictionary and uses OMP [17] or FOCUSS [23] to update the sparse coefficients, then fixes the obtained sparse coefficients and updates the dictionary in the next stage. MOD was later modified to iterative least squares algorithm (ILS-DLA) [24] and recursive least squares algorithm (RLS-DLA) [25]. Aharon et al. developed the K-SVD algorithm [26], which can be viewed as a generalization of the K-means algorithm.

This signal z 12 may therefore be used to subsequently select the value of v. To this end, let us consider the covariance of the RVs Z 12 and Z 3 defined by z 12 and z 3 for a single random value of the initial states of the two qubits. ) stands for variance. 71) can be shown to yield v2 − v2 cov(Z 12 , Z 3 ) = var(Z 12 ) . 73) 1 − v2 v Considering the nondegenerate case when var(Z 12 ) ⊗= 0, Eq. 74) with ε = ±1. 21) yield y3 = εs3 . Moreover, the sign indeterminacy on v and therefore on y3 may be avoided by using physical knowledge about the sign of v, as explained in Sect.

Zhao et al. imaging, and communication systems. Typically, a linear mixture model is assumed where the mixtures Z ∈ Rr×N are described as Z = AS + V. Each row of S ∈ Rs×N is a source and A ∈ Rr×s models the linear combinations of the sources. The matrix V ∈ Rr×N represents additive noise or interference introduced during mixture acquisition and transmission. Usually in the BSS problem, the only known information is the mixtures Z and the number of sources. , mathematically, one needs to solve min ∈Z − AS∈2F .

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