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 …

Recently, the logarithmic hyperbolic cosine adaptive filter (LHCAF) was proposed and was
seen to demonstrate excellent robustness against impulsive interference. However, for the …

Proportionate Maximum Versoria Criterion (P-MVC) based adaptive algorithms for unknown
sparse system identification problem are proposed in this brief. The conventional …

The presented system is a three-phase three-wire (3P–3W), seamless, capable, dual-stage
PV power generation system without battery storage for rural residential loads to ensure a …

Sparsity-induced kernel adaptive filters have emerged as a promising candidate for a
nonlinear sparse system identification (SSI) problem. The existing zero-attracting kernel …

We develop a least-mean-squares (LMS) algorithm to identify scattering-sparse systems,
where the few significantly large coefficients of the unknown impulse response are …

This paper introduces an optimized zero-attractor to improve the performance of least mean
square (LMS)-based algorithms for the identification of sparse system. Compared with …

A robust adaptive filter is usually unaffected by spurious disturbances at the error sensor. In
an endeavour to improve robustness of the adaptive filter, a novel modified Champernowne …

The sparse norm constraint (l 0, l 1, l 2 and lp) least mean square algorithm (LMS) is
established technique for modeling sparse systems. However, when applied in target …

Visible light communication (VLC) has emerged as a potential candidate for next generation
wireless communication systems. However, nonlinear characteristics of light emitting diode …