Separation of sources consists of recovering a set of signals of which only instantaneous
linear mixtures are observed. In many situations, no a priori information on the mixing matrix …

With solid theoretical foundations and numerous potential applications, Blind Signal
Processing (BSP) is one of the hottest emerging areas in Signal Processing. This volume …

Independent component analysis (ICA) aims at decomposing an observed random vector
into statistically independent variables. Deflation-based implementations, such as the …

The source separation problem has been addressed in many ways during the last decade,
and one of its instances gave birth to Independent Component Analysis (ICA). Iterative …

In the independent component (IC) model it is assumed that the components of the observed
p-variate random vector x are linear combinations of the components of a latent p-vector z …

This paper is concerned with the problem of separating independent non‐Gaussian
sources. This is done by adaptively maximizing a contrast function based on fourth‐order …

Conventional antenna array processing techniques are based on the use of second order
statistics but rest on restrictive assumptions. Thus, when a priori information about the …

A new approach based on the constant modulus (CM) criterion is proposed to separate
instantaneous linear mixtures of signals using a linear memoryless multiple input multiple …

This paper introduces a new algorithm to approximate non-orthogonal joint diagonalization
(NOJD) of a set of complex matrices. This algorithm is based on the Frobenius norm …

Objective: Independent component analysis (ICA) is now a widely used solution for the
analysis of multi-subject functional magnetic resonance imaging (fMRI) data. Independent …