This paper presents enhancement of image reconstruction by comprehensively exploiting the phase diversity of adaptive synthesis filter bank. A novel approach of selection of synthesis delay filters in various reconstruction groups and possible placing configurations of optimal groups in adaptive synthesis filter bank have been explored. In contrast with linear phase synthesis filters, synthesis...

a critical protection requirement for grid connected distributed generators (dg) is anti-islanding protection. in this paper, a new islanding detection method for any possible network loading is proposed based on utilizing and combining various system parameter indices. in order to secure the detection of islanding, eight intentional disturbances are imposed to the system under study in which t...

This paper presents novel adaptive reduced-rank filtering algorithms based on joint iterative optimization of adaptive filters. The novel scheme consists of a joint iterative optimization of a bank of fullrank adaptive filters that constitute the projection matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe least squares (LS) expressions f...

An adaptive pole-zero filter comprised of a bank of bandpass filters is presented. The bandpass filters permit the adaptive filter to be realized in a parallel form of first-order sections. Simple monitoring of the filter poles during adaptation is therefore possible so that stability can be ensured. This paper focuses on bandpass filters which are implemented by a frequency-sampling structure;...

Digital image is often degraded by many kinds of noise during the process of acquisition and transmission. To make subsequent processing more convenient, it is necessary to decrease the effect of noise. There are many kinds of noises in image, which mainly include salt and pepper noise and Gaussian noise. This paper focuses on median filters to remove the salt and pepper noise. After summarizin...

In this paper, we will review partial update adaptive algorithms with special emphasis on data−dependant algorithms. We then demonstrate that the same approach applied in the MMax LMS partial update algorithm for linear adaptive filters [4] can be extended to the class of nonlinear filters known as Volterra filters. The impact of the fact that the input vector is no longer a set of delayed inpu...

This chapter provides an overview of interesting phenomena pertaining to the learning capabilities of stochastic-gradient adaptive filters, and in particular those of the least-mean-squares (LMS) algorithm. The phenomena indicate that the learning behavior of adaptive filters is more sophisticated, and also more favorable, than was previously thought, especially for larger step-sizes. The discu...

The performed job, in this study, consists in studying adaptive filters and higher order statistics (HOS) to ameliorate their performances, by extension of linear case to non linear filters via Volterra series. This study is, principally, axed on:  Choice of the adaptation step and convergence conditions.  Convergence rate.  Adaptive variation of the convergence factor, according to the inpu...

An Adaptive varying window size Recursive Weighted Median Filter [ARWMF] for removing the impulse noise in Color images is presented. The weights for the RWMF are calculated by using the median controlled algorithm. By applying Region of Interest [ROI] on selected windows the weight calculation’s efficiency can be increased and the memory will be reduced because of the ROI. In median controlled...

Parallel combinations of adaptive filters have been effectively used to improve the performance of adaptive algorithms and address typical trade-offs, such as the one between convergence rate and steady-state error. In these combinations, the component filters are usually run independently and then combined, which leads to a well known convergence stagnation effect. Conditional transfers of coe...