Since the discovery of functional connectivity in fMRI data (ie, temporal correlations between
spatially distinct regions of the brain) there has been a considerable amount of work in this …

Starting with a simple generative model and the assumption of statistical independence of
the underlying components, independent component analysis (ICA) decomposes a given …

In this paper, we consider the joint blind source separation (JBSS) problem and introduce a
number of algorithms to solve the JBSS problem using the independent vector analysis (IVA) …

Matrix decompositions such as the eigenvalue decomposition (EVD) or the singular value
decomposition (SVD) have a long history in signal processing. They have been used in …

Fusion of information from multiple sets of data in order to extract a set of features that are
most useful and relevant for the given task is inherent to many problems we deal with today …

Complex-valued signals occur in many areas of science and engineering and are thus of
fundamental interest. When developing signal processing methods in the complex domain …

In this paper, we consider the problem of blind estimation of states and topology (BEST) in
power systems. We use the linearized dc model of real power measurements with unknown …

The presence of blanket jamming, a typical form of airborne synthetic aperture radar (SAR)
jamming, causes incoherent signals with strong power to enter the airborne SAR receiver …

Recently, an extension of independent component analysis (ICA) from one to multiple
datasets, termed independent vector analysis (IVA), has been a subject of significant …

The smoothness of graph signals has found desirable real applications for processing
irregular (graph-based) signals. When the latent sources of the mixtures provided to us as …