In this paper, we present a partial survey of the tools borrowed from tensor algebra, which
have been utilized recently in Statistics and Signal Processing. It is shown why the …

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

Blind signal separation (BSS) and independent component analysis (ICA) are emerging
techniques of array processing and data analysis that aim to recover unobserved signals or" …

Learning algorithms and underlying basic mathematical ideas are presented for the problem
of adaptive blind signal processing, especially instantaneous blind separation and …

Neural networks (NNs) are able to give solutions to complex problems in digital
communications due to their nonlinear processing, parallel distributed architecture, self …

Recently a number of adaptive learning algorithms have been proposed for blind source
separation. Although the underlying principles and approaches are different, most of them …

In the field of blind source separation, joint-diagonalization-based approaches constitute an
important framework, leading to useful algorithms such as the popular joint approximate …

The field of neuroimaging has embraced the need for sharing and collaboration. Data
sharing mandates from public funding agencies and major journal publishers have spurred …

We consider the problem of recovering blindly (ie, without the use of training sequences) a
number of independent and identically distributed source (user) signals that are transmitted …

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 …