Because translating the growing body of knowledge about human disease into treatments
has been slower than expected, the application of machine learning techniques to drug …

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 …

A linear-in-the-parameters nonlinear filter consists of a functional expansion block, which
expands the input signal to a higher dimensional space nonlinearly, followed by an adaptive …

Robust adaptive signal processing algorithms based on a generalized maximum
correntropy criterion (GMCC) suffers from high steady state misalignment. In an endeavour …

Adaptive beamformers use data from sensor arrays to capture signal from a desired
direction without any distortion, in the presence of interfering signals from other directions in …

Recently, nearest Kronecker product (NKP) decomposition based Wiener filter and
Recursive Least Squares (RLS) have been proposed and was found to be a good candidate …

High-dimensional system identification problems can be efficiently addressed based on
tensor decompositions and modelling. In this paper, we design a recursive least-squares …

This paper introduces a new class of nonlinear filters for nonlinear acoustic echo
cancellation (NLAEC) based on Hermite nonlinear filters (HNFs), which is a sub-class of …

Lower precision arithmetic can improve the throughput of adaptive filters, while requiring
less hardware resources and less power. Such benefits are crucial for adaptive filters …

Decentralized systems are appealing due to their reduced complexity and flexibility. A class
of decentralized multi-channel active noise control (MCANC) systems has been developed …