The principal issue in acoustic echo cancellation (AEC) is to estimate the impulse response between loudspeaker and microphone of a hands-free communication device. This application can be addressed as system identification problem, which solved by using an adaptive filter. most common one for AEC normalized least-mean-square (NLMS) algorithm. It known that overall performance this algorithm co...

The block-sparse normalized least mean square (BS-NLMS) algorithm which takes advantage of sparsity, successfully shows fast convergence in adaptive system identification, control, and other industrial informatics applications. It is also attractive acoustic processing where long impulse response, highly correlated sparse echo path are encountered. However, the major drawback BS-NLMS largely co...

Partial-update algorithms reduce adaptive filter complexity by updating only a subset of taps at each iteration. However, they suffer a processing overhead in tap selection that can substantially reduce the computational advantages of partial-update schemes. Short-sort M-Max NLMS (SM-NLMS) addresses this problem by having the advantages of other partial-update schemes but with very low computat...

The normalized least-mean-square (NLMS) algorithm is one of the most common choices for echo cancellation. Nevertheless, an NLMS algorithm has to compromise between several performances criteria (e.g., convergence rate versus misadjustment, tracking capabilities versus robustness). Thus, a variable step-size NLMS (VSS-NLMS) algorithm represents a more reliable solution. Recently, several VSS-NL...

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...