A regularized least mean mixed norm multichannel image restoration algorithm

Recently, in the world wide the use of power electronics devices increases sharply. As a result, harmonic pollution becomes a vital problem than before. Harmonics rotate in the power system network and interfere with the system equipments, disturbing their normal operation which can deteriorate the quality of the delivered power. Therefore, efficient method with low computational time is a critical tool to estimate and quantify the harmonic that can be used in online control and mitigation of harmonics. Least Mean Square (LMS) is simple and popular algorithm that has been applied in many applications, but, noise can affect its performance. This paper presents and compare the performance of Least Mean Square (LMS), Least Mean Fourth (LMF) and a combined (LMS/LMF) in estimation harmonic component for the signal corrupted with noise contain low signal to noise ratio (SNR). The results show that LMF and LMS/LMF have better steady state performance as compared to LMS.

In this paper, we propose an iterative mixed norm image restoration algorithm. A functional which combines the least mean squares (LMS) and the least mean fourth (LMF) functionals is proposed. A function of the kurtosis is used to determine the relative importance between the LMS and the LMF functionals. An iterative algorithm is utilized for obtaining a solution and its convergence is analyzed. Experimental results demonstrate the capability of the proposed approach.

In this paper, we propose an iterative mixed norm image restoration algorithm. A functional which combines the least mean squares (LMS) and the least mean fourth (LMF) functionals is proposed. A function of the kurtosis is used to determine the relative importance between the LMS and the LMF functionals. An iterative algorithm is utilized for obtaining a solution and its convergence is analyzed. Experimental results demonstrate the capability of the proposed approach.

Least-Mean Square algorithm, or simply the LMS, is generally one of the most commonly used techniques for optimized solution in adaptive schemes especially in the Gaussian environment. However, least mean fourth (LMF) algorithm and its variants, such as normalized LMF (NLMF), perform better in the non-Gaussian environment. The conventional LMF algorithms usually diverge in non-Gaussian environment with dynamic input. Conventionally, regularization is archived by using a small constant compensation term in the denominator of the learning rate to protect the algorithm from divergence. This paper introduces an efficient time-varying regularized normalized least mean fourth (R-NLMF) algorithm. In the proposed algorithm, the regularization term is made time varying and gradient adaptive according to steepest descent approach. Thus the proposed algorithm adapts its learning rate according to the environment and the input signal dynamics. A similar approach, namely Generalized Normalized Gradient Descent (GNGD) algorithm, has been previously applied to the normalized least mean-square (NLMS) algorithm in the Gaussian environment. However, due to its dependence on NLMS, the GNGD algorithm performance degrades in non-Gaussian environment. In order to overcome this problem, an efficient regularized NLMF algorithm for non-Gaussian environment is proposed. The algorithm shows promising results and achieves faster convergence while maintaining lesser steady-state miss-adjustment. Simulations are carried out to support the theoretical development.

In this work, the convergence analysis of the variable weight mixed-norm least-mean squares (LMS)-least mean-fourth (LMF) adaptive algorithm is derived. The proposed algorithm minimizes an objective function defined as a weighted sum of the LMS and LMF cost functions, where the weighting factor is time varying and adapts itself to allow the algorithm to keep track of the variations in the environment. As a by-product of this novel approach, new necessary and sufficient conditions for the LMF algorithm have been derived. Furthermore, a more general expression for the excess steady-state error for the LMF algorithm has been derived.

In this paper, we propose a new mixed norm LMS-LMF adaptive algorithm. The algorithm minimizes an objective function defined as a weighted sum of the least mean fourth (LMF) and least mean square (LMS) cost functions. The weighting factor is time varying and adapts itself so as to emphasize one cost function over the other based on proximity to the optimum. Improved convergence is illustrated by examples. Bounds on the step size to ensure mean convergence are also derived.

An adaptive filtering algorithm that minimizes the variance on the squared error subject to a constraint on the mean-squared error (MSE) is proposed. For the adaptive-linear-combiner signal-processing configuration, this algorithm, called the LVCMS algorithm (for least variance subject to a constraint on mean squared error) has a gradient that is a weighted linear combination of the least-mean-square (LMS) and least-mean-fourth (LMF) algorithm gradients. Theoretical expressions are derived for the LVCMS algorithm convergence factor and misadjustment, and comparisons are made with the LMS and LMF adaptive rules for Gaussian, Laplacian, and uniform plant noise nd driving term distributions. Performance comparisons of the LVCMS, LMS, and LMF algorithms based on Monte Carlo simulation studies indicate that the LVCMS adaptation rule not only can yield a small variance of the squared error but it also produces favorable values of the mean-squared error and the mean-squared coefficient error. >

Adaptive equalization is undoubtedly one of the cornerstones of digital communication as it allows for fast and reliable data transmission over practical channels that are non-ideal and that are plagued with serious problems such as signal dispersion and non-Gaussian interferences. In such cases, the performance of the well-known least-mean square (LMS) algorithm or that of any of its variants remains sub-optimal at best. As a remedy to this, the least-mean fourth (LMF) algorithm was later proposed but its high computational load was found to be a constraining factor in its practical implementation, particularly for real-time applications. Inspired from a previous work done on the LMS [1], we recently proposed a new algorithm [2], termed LMF-PTQ, combining the power of the LMF with the implementational advantages of the power-of-two quantization scheme. In this work, we demonstrate, through an extensive simulation study, the effectiveness of our proposed LMF-PTQ algorithm in the equalization of a time-varying channel in a CDMA system which suffers from both signal dispersion and non-Gaussian disturbances. The results obtained so far are very encouraging and provide us with ample enthusiasm to investigate the proposed algorithm further.

This paper presents a robust generalized modified Blake-Zisserman adaptive filter (GMBZAF)-based control scheme for a three-phase grid-tied single-stage photovoltaic system (GTPVS) to improve power quality (PQ). The GMBZAF-based control scheme enables GTPVS to integrate maximum PV power into the grid with power quality (PQ) improvement at the grid side, such as maintaining balanced sinusoidal grid currents, current harmonics mitigation, reactive power assistance to the inductive load at steady-state, and even during dynamic conditions (variable load and irradiance). The GMBZAF is implemented to accurately calculate the active and reactive weight components of load current. The GTPVS is modeled using MATLAB/Simulink, and its response is observed at steady-state, unbalanced loading, and variable solar irradiance conditions. The comparative assessment between the proposed control scheme and the existing adaptive filtering-based control schemes such as least mean square (LMS) and least mean fourth (LMF) is done.

This paper surveys recent advances related to sparse least mean square (LMS) algorithms. Since standard LMS algorithm does not take advantage of the sparsity information about the channel being estimated, various sparse LMS algorithms that are aim at outperforming standard LMS in sparse channel estimation are discussed. Sparse LMS algorithms force the solution to be sparse by introducing a sparse penalty to the standard LMS cost function. Under the reasonable conditions on the training datas and parameters, sparse LMS algorithms are shown to be mean square stable, and their mean square error performance and convergence rate are better than standard LMS algorithm. We introduce the sparse algorithms under Gaussian noises model. The simulation results presented in this work are useful in comparing sparse LMS algorithms against each other, and in comparing sparse LMS algorithms against standard LMS algorithm.

This paper presents a new statistical analysis of the least mean fourth (LMF) adaptive algorithm behavior. Nonlinear recursive equations are derived which predict the behavior of the first and second order moments of the adaptive weights for Gaussian inputs. These recursions can be used to predict the mean square error (MSE) behavior. The new model improves the available models in that it predicts both the transient and steady-state behaviors for measurement noise having any zero-mean probability density function (pdf) and for any signal-to-noise ratio. This is important because the LMF algorithm is known to outperform the LMS algorithm for non-Gaussian noise distributions and for large signal-to-noise ratios. In addition, the new model explicitly shows the dependence of the algorithm's dynamics on the initial weight conditions. Computer simulations illustrate the accuracy of the new model in predicting the algorithm behavior.

portion of the traffic carried by high speed packet-switched broadband integrated services digital networks (B-ISDN). The high peak-to-mean ratio of VBR traffic, however, makes allocation of network resources, such as bandwidth, quite difficult. If bandwidth is allocated based on the peak rate, it will be highly inefficient and would, in fact, mitigate the gains of statistical multiplexing. On the other hand, choosing the mean rate means of allocating bandwidth would lead to congestion in the network, specially if multiple sources become active at the same time and start delivering video traffic into the network at their peak rates. The dynamics of the VBR traffic, coupled with the delay-bandwidth product of the high-speed networks, add to the complexity of congestion control and render conventional reactive based congestion control schemes as highly inefficient. Bandwidth allocation, based on the mean rate aided by a preventative congestion control scheme that could predict the onset of congestion, well in advance, is the desired solution. One such technique is proposed that estimates the current traffic state, predicts the future traffic states and forecasts the occurrence of congestion in the network. The emphasis is on demonstrating the applicability of least mean square (LMS) filters in estimating the current state of the VBR traffic sources accessing the networks. Some simulation results are presented.

A major complaint of individuals with normal hearing and hearing impairments is a reduced ability to understand speech in a noisy environment. This paper is proposed to remove the noise from the speech signal in various real time environments and to make the hearing impaired to understand the information by transforming speech in to text format. The quality of audio signal can be improved by filtering the degraded speech signal through adaptive filters. For Noise cancellation widrow & hoff’s Least Mean Square (LMS) algorithms are being used for simplicity in implementation. The LMS algorithm was simple in it's derivation and robust in a number of applications but it also have limitation in selection of a certain values such as step size and results in computational complexity. Hence to overcome all the limitations and to present an enhanced speech signal, a novel approach named VSSNDLMS algorithm is proposed. The performance of algorithms like Variable Step Size Normalised LMS , Normalised Differential LMS with proposed VSSNDLMS with different input signals are analysed in this paper . Finally, through simulation results the proposed VSSNDLMS algorithm converges fastly with minimum mean square error and it is useful in predicting the adaptive filter performance of various algorithms and the implementation indicate the improvement in quality of the speech signal and it appears to be favourable for hearing impaired. Keywords: VSSNDLMS, LMS Algorithm, Noise Cancellation, Speech Processing, Adaptive Filter

본 논문에서 ICS(Interference Cancellation System) 중계기에서 간섭제거를 위한 새로운 CSS(Complex Signed-Signed) CMA(Constant Modulus Algorithm) 알고리즘을 제안한다. 중계기가 궤환 신호를 가질 때 CSS CMA 알고리즘은 궤환 경로로부터 간섭 신호를 제거하기 위한 디지털 신호 처리를 사용한 ICS 중계기에 대해 제안한다. 제안된 Complex Signed Signed CMA 알고리즘은 스텝 사이즈를 조절함에 따라 하드웨어 복잡도와 성능이 개선된다. 제안된 Complex Signed Signed CMA 알고리즘에서 스텝사이즈가 0.00043인 경우 수렴상태에서 평균 자승 에러는 기존 CMA 알고리즘보다 약 4dB정도 더 낮다. 그리고, 평균 자승 에러 -25dB를 기준으로 LMS(Least Mean Square)와 Signed LMS(Normalized Least Mean Square)보다 수렴회수가 1950 ~ 2150회 정도 줄어든다. In the paper, we propose a new CSS(Complex Signed-Signed) CMA(Constant Modulus Algorithm) algorithm for ICS(Interference Cancellation System). When the repeater get the feedback signal, the CSS CMA algorithm is proposed at the ICS repeater using DSP(Digital Signal Processing) for the removal of interfering signals from the feedback paths. The proposed CSS CMA algorithm improved performances and hardware complexity by adjusting step size values. the steady state MSE(Mean Square Error) performance of the proposed CSS CMA algorithm with step size of 0.00043 is about 4dB better than the conventional CMA algorithm. And the proposed Complex Signed Signed CMA algorithm requires 1950 ~ 2150 less iterations than the LMS(Least Mean Square) and Signed LMS(Normalized Least Mean Square) algorithms at MSE of -25dB.

Multikernel adaptive filtering over graphs based on normalized LMS algorithm