System identification problems are always challenging to address in applications that
involve long impulse responses, especially in the framework of multichannel systems. In this …

Machine learning and data mining algorithms are becoming increasingly important in
analyzing large volume, multi-relational and multi--modal datasets, which are often …

Linear system identification is a key problem in many important applications, among which
echo cancelation is a very challenging one. Due to the long length impulse responses (ie …

The recursive least-squares (RLS) adaptive filter is an appealing choice in many system
identification problems. The main reason behind its popularity is its fast convergence rate …

We give an overview of recent developments in numerical optimization-based computation
of tensor decompositions that have led to the release of Tensorlab 3.0 in March 2016 (www …

Background The growing interest in neuroimaging technologies generates a massive
amount of biomedical data of high dimensionality. Tensor-based analysis of brain imaging …

In this paper we consider low-rank estimation of room impulse responses (RIRs). Inspired by
a physics-driven room-acoustical model, we propose an estimator of RIRs that promotes a …

Time difference of arrival (TDOA) estimation, which often serves as the fundamental step for
a source localization or a beamforming system, has a significant practical importance in a …

Traditional hyperspectral unmixing methods neglect the underlying variability of spectral
signatures often observed in typical hyperspectral images (HI), propagating these …

Tensor-based signal processing methods are usually employed when dealing with
multidimensional data and/or systems with a large parameter space. In this paper, we …