This paper proposes a verilog implementation of a normalised Least Mean Square (NLMS) adaptive algorithm. The envisaged application in the wireless communication identification system. The good convergence of NLMS algorithm has made us to choose it. It also has good stability. Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application...

We present the general framework for mean-square performance analysis of the selective partial update affine projection algorithm (SPU-APA) and the family of SPU normalized least mean-squares (SPU-NLMS) adaptive filter algorithms in nonstationary environment. Based on this the tracking performance of Max-NLMS, N-Max NLMS and the various types of SPU-NLMS and SPU-APA can be analyzed in a unified...

Normalized least mean square (NLMS) was considered as one of the classical adaptive system identification algorithms. Because most of systems are often modeled as sparse, sparse NLMS algorithm was also applied to improve identification performance by taking the advantage of system sparsity. However, identification performances of NLMS type algorithms cannot achieve high-identification performan...

This paper, presents a new Normalized Least Mean Square (NLMS) algorithm, tailored for adaptive identification of invariant systems impulse responses with speech inputs. The proposed Square Root NLMS (SR-NLMS) algorithm is based on a specific normalization of the LMS adaptive filter input, by the Euclidean norm of the tap-input. In fact, we cancel the term involving the statistics of the input ...

We conduct an extensive study of nonlinear localized modes (NLMs), which are temporally periodic and spatially structures, in a two-dimensional array repelling magnets. In our experiments, we arrange lattice hexagonal configuration with light-mass defect, harmonically drive the center chain tunable excitation frequency, amplitude, angle. use damped, driven variant vector Fermi- Pasta-Ulam-Tsing...

NLMs is a state-of-art image denoising method; however, it sometimes oversmoothes anatomical features in low-dose CT (LDCT) imaging. In this paper, we propose a simple way to improve the spatial adaptivity (SA) of NLMs using pointwise fractal dimension (PWFD). Unlike existing fractal image dimensions that are computed on the whole images or blocks of images, the new PWFD, named pointwise box-co...

We present a normalized LMS (NLMS) algorithm with robust regularization. Unlike conventional NLMS with the fixed regularization parameter, the proposed approach dynamically updates the regularization parameter. By exploiting a gradient descent direction, we derive a computationally efficient and robust update scheme for the regularization parameter. In simulation, we demonstrate the proposed al...

In this paper, normalized least mean (NLMS) square and recursive least squares (RLS) adaptive channel estimator are described for multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. These CE methods uses adaptive estimator which are able to update parameters of the estimator continuously, so that the knowledge of channel and noise statistics are not ...