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 …

We address the problem of in-network distributed estimation for sparse vectors. In order to
exploit the underlying sparsity of the vector of interest, we incorporate the ℓ 1-and ℓ 0-norm …

This work provides a comprehensive overview of adaptive diffusion networks, from the first
papers published on the subject to state-of-the-art solutions and current challenges. These …

We propose a normalized least mean squares algorithm with variable step size. Unlike other
solutions, it has low computational cost, only three parameters that are simple to choose …

This chapter provides an introduction to adaptive signal processing, covering basic
principles through the most important recent developments. After a brief example for, we …

This paper addresses the problem of distributed in-network estimation for a vector of interest,
which is sparse in nature. To exploit the underlying sparsity of the considered vector, the ℓ 1 …

Blind equalizers avoid the transmission of pilot sequences, allowing a more efficient use of
the channel bandwidth. Normally, after a first rough equalization is achieved, it is necessary …

This paper presents a novel scheme for nonlinear acoustic echo cancellation based on
adaptive Volterra Filters with linear and quadratic kernels, which automatically prefers those …

Sparsity phenomena in learning processes have been extensively studied, since their
detection allows to derive suited regularized optimization algorithms capable of improving …

Distributed signal processing has attracted widespread attention in the scientific community
due to its several advantages over centralized approaches. Recently, graph signal …