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 …

We propose two adaptive filtering algorithms that combine sparsity-promoting schemes with
data-selection mechanisms. Sparsity is promoted via some well-known nonconvex …

In the context of acoustic echo cancellation (AEC), it is shown that the level of sparseness in
acoustic impulse responses can vary greatly in a mobile environment. When the response is …

A general zero attraction (GZA) proportionate normalized maximum correntropy criterion
(GZA-PNMCC) algorithm is devised and presented on the basis of the proportionate-type …

Proportionate adaptive filters, such as those based on the improved proportionate
normalized least-mean-square (IPNLMS) algorithm, have been proposed for echo …

This paper presents a proportionate normalized least-mean-square (PNLMS) algorithm
using individual activation factors for each adaptive filter coefficient, instead of a global …

Stochastic approximation techniques play an important role in solving many problems
encountered in machine learning or adaptive signal processing. In these contexts, the …

It is known that the conventional adaptive filtering algorithms can have good performance for
non-sparse systems identification, but unsatisfactory performance for sparse systems …

In this paper, we provide an overview of the major developments in the area of sparse
adaptive filters, starting from the celebrated works on PNLMS algorithm and its several …

To make use of the sparsity property of broadband multipath wireless communication
channels, we mathematically propose an lp‐norm‐constrained proportionate normalized …