An exhaustive review of adaptive signal processing schemes which are robust, sparsity-
aware and robust as well as sparsity-aware has been carried out in this paper. Conventional …

We propose an adaptive diffusion mechanism to optimize global cost functions in a
distributed manner over a network of nodes. The cost function is assumed to consist of a …

We introduce a novel family of adaptive filtering algorithms based on a relative logarithmic
cost inspired by the “competitive methods” from the online learning literature. The …

Adaptive filters are at the core of many signal processing applications, ranging from acoustic
noise supression to echo cancelation [1], array beamforming [2], channel equalization [3], to …

Adaptive networks rely on in-network and collaborative processing among distributed
agents to deliver enhanced performance in estimation and inference tasks. Information is …

In this work, we analyze the mean-square performance of different strategies for distributed
estimation over least-mean-squares (LMS) adaptive networks. The results highlight some …

The combination of filters concept is a simple and flexible method to circumvent various
compromises hampering the operation of adaptive linear filters. Recently, applications which …

Although the LMS algorithm is often preferred in practice due to its numerous positive
implementation properties, once the parameter space to estimate becomes large, the …

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

We study the compressive diffusion strategies over distributed networks based on the
diffusion implementation and adaptive extraction of the information from the compressed …