Modern signal processing (SP) methods rely very heavily on probability and statistics to
solve challenging SP problems. SP methods are now expected to deal with ever more …

In this letter, the RLS adaptive algorithm is considered in the system identification setting.
The RLS algorithm is regularized using a general convex function of the system impulse …

In this paper, we propose novel l 1-regularized space-time adaptive processing (STAP)
algorithms with a generalized sidelobe canceler architecture for airborne radar applications …

Sparse, group-sparse and online channel estimation is conceived for millimeter wave
(mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing …

Denoising of medical images is of great concern as it plays a significant role in performance
of computer aided diagnosis (CAD) systems. In real life scenarios, various conditions like …

In practice, one often encounters systems that have a sparse impulse response, with the
degree of sparseness varying over time. This paper presents a new approach to identify …

In order to improve the performance of least mean square (LMS)-based adaptive filtering for
identifying block-sparse systems, a new adaptive algorithm called block-sparse LMS (BS …

In this paper, we propose adaptive algorithms for system identification of sparse systems.
We introduce a L 1-norm penalty to improve the performance of affine projection algorithms …

This paper presents adaptive link selection algorithms for distributed estimation and
considers their application to wireless sensor networks and smart grids. In particular …

We design and analyse filter bank multicarrier (FBMC) offset quadrature amplitude
modulation (OQAM)-based millimeter wave (mmWave) hybrid multiple-input multiple-output …