Non Linear Blind Source Separation Using Different Optimization Techniques

The Independent Component Analysis technique has been used in Blind Source separation of non linear mixtures. The project involves the blind source separation of a non linear mixture of signals based on their mutual independence as the evaluation criteria. The linear mixer is modeled by the Fast ICA algorithm while the Non linear mixer is modeled by an odd polynomial function whose parameters are updated by four separate optimization techniques which are Particle Swarm Optimization, Real coded Genetic Algorithm, Binary Genetic Algorithm and Bacterial Foraging Optimization. The separated mixture outputs of each case was studied and the mean square error in each case was compared giving an idea of the effectiveness of each optimization technique

Attenuated total reflection enhanced photoejection from cathodes Final report

Optical equations governing interaction between radiation and interface of two media using metallic cathode

Computing the Partial Correlation of ICA Models for Non-Gaussian Graph Signal Processing

[EN] Conventional partial correlation coefficients (PCC) were extended to the non-Gaussian case, in particular to independent component analysis (ICA) models of the observed multivariate samples. Thus, the usual methods that define the pairwise connections of a graph from the precision matrix were correspondingly extended. The basic concept involved replacing the implicit linear estimation of conventional PCC with a nonlinear estimation (conditional mean) assuming ICA. Thus, it is better eliminated the correlation between a given pair of nodes induced by the rest of nodes, and hence the specific connectivity weights can be better estimated. Some synthetic and real data examples illustrate the approach in a graph signal processing context.This research was funded by Spanish Administration and European Union under grants TEC2014-58438-R and TEC2017-84743-P.Belda, J.; Vergara Domínguez, L.; Safont Armero, G.; Salazar Afanador, A. (2019). 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