Blind Source Signal Separation Method of Compressed Sensing Sparse Component Analysis

Sparse Kernel Independent Component Analysis for Blind Source Separation

We address the problem of Blind Source Separation (BSS) of superimposed signals in situations where one signal has constant or slowly varying intensities at some consecutive locations and at the corresponding locations the other signal has highly varying intensities. Independent Component Analysis (ICA) is a major technique for Blind Source Separation and the existing ICA algorithms fail to est...

Extraction of Sensory part of Ulnar Nerve Signal Using Blind Source Separation Method

A recorded nerve signal via an electrode is composed of many evokes or action potentials, (originated from individual axons) which may be considered as different initial sources. Recovering these primitive sources in its turn may lead us to the anatomic originations of a nerve signal which will give us outstanding foresights in neural rehabilitations. Accordingly, clinical interests may be r...

Compressed Sensing and Source Separation

Separation of underdetermined mixtures is an important problem in signal processing that has attracted a great deal of attention over the years. Prior knowledge is required to solve such problems and one of the most common forms of structure exploited is sparsity. Another central problem in signal processing is sampling. Recently, it has been shown that it is possible to sample well below the N...

Independent component analysis and blind source separation

Independent Component Analysis and Blind Source Separation