A neural network design – the adaptive resonance theory least mean square (ART-LMS) neural network – is proposed for the restoration of images corrupted by impulse noise. The network design is based on the concept of a counterpropagation network (CPN). The ART network automatically uses a vigilance parameter to generate the cluster layer node for the Kohonen learning algorithm in CPN. In additi...

The goal of this paper is to introduce the joined LMS and LMF adaptive algorithm (LMFSF) for data echo cancellation. The idea is to use an error estimate as smooth as possible, and after comparison with the selected thresholds to decide whether for the next step we apply LMS or LMF. In order to avoid oscillations from LMS to LMF and back, we use two hysteresis loops. It will be shown through si...

The present system of speech enhancement is developing by adaptive filtering approach in digital filters. The adaptive filter utilizes the least mean square algorithm for noise removal, but in practical application of LMS algorithm, a key parameter is the step size. As it is known, if the step size is large, the convergence rate of LMS algorithm will be rapid, but the steady-state mean square e...

This paper presents a simulation study of a smart antenna (SA) system based on direction-of-arrival (DOA) estimation is based on the MUltiple SIgnal Classification (MUSIC) algorithm for finding the Position Location (PL) of the desired user. The beamformer steer the main beam towards the desired user and nullify all other interferer, through adaptive beamforming using Least Mean Square (LMS) al...

The adaptive interference cancellation algorithms(AICA) is proposed in this paper. The Matlab simulation results of the performance of six different kinds of AICA are presented. According to the comparison of the simulation results, especially the usage of resource, convergence rate and effects of cancellation, the NLMS adaptive interference cancellation algorithm and frequency-domain block LMS...

The stochastic-gradient-descent LMS adaptive filtering algorithm provides a powerful and computationally efficient means of realizing adaptive filters. In this work, we present architectural synthesis of low-power computational engines (or hardware accelerators) for LMS adaptive filtering. This engine could be configured for delayed LMS/delayed normalized LMS, full-band/subband adaptive filteri...

The paper proposed the adapter filter design based on the improved LMS algorithm. The paper first introduced the minimum mean square (LMS) algorithm, which is a very useful and very simple estimated gradient method. This algorithm has been widely used since the early 1960s quickly, its advantage is that the small amount of calculation, and the rapid development of the digital signal processor a...

Though the Leaky Least-Mean-Square (LLMS) algorithm mitigates the drifting problem of the LMS algorithm, its performance is similar to that of the LMS algorithm in terms of convergence rate. In this paper, we propose a new LLMS algorithm that has a better performance than the LLMS algorithm in terms of the convergence rate and at the same time solves the drifting problem in the LMS algorithm. T...