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The maximum correntropy criterion (MCC) algorithm has attracted much attention due to its capability of combating impulsive noise. However, its performance depends on choice of the kernel width, which is a hard issue. Several variable kernel width schemes based on various error functions have been proposed to address this problem. Nevertheless, these methods may not provide an optimal kernel width because they do not contain any knowledge of the background noise that actually has influence on the optimization of the kernel width. This paper proposes an improved variable kernel width MCC algorithm, which is derived by minimizing the squared deviation at each iteration. We also design a reset mechanism for the proposed algorithm to improve its tracking capability when the estimated vector encounters a sudden change. Simulations for system identification and echo cancellation scenarios show that the proposed scheme outperforms other variable kernel width algorithms.

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To further improve the performance of the variable step size continuous mixed p-norm (VSS-CMPN) adaptive filtering algorithm in the presence of impulsive noise, a generalized VSS-CMPN algorithm (GVSS-CMPN) is proposed in this paper. Instead of assuming the probability density-like function () p λ to be uniform, a linear function is proposed for () p λ to control the mixture of various error norms. The influence of the selection of the regulating factor (slope of the linear function) is discussed. Besides, the computational complexity as well as the mean-square convergence analysis is presented. Simulations conducted in the system identification scenario demonstrate the superiority of the proposed algorithm over known algorithms.

ObjectiveTo compare the iterative decomposition of water and fat with echo asymmetry and the least-squares estimation (IDEAL) method with a fat-saturated T2-weighted (T2W) fast recovery fast spin-echo (FRFSE) imaging of the spine.Materials and MethodsImages acquired at 3.0 Tesla (T) in 35 patients with different spine lesions using fat-saturated T2W FRFSE imaging were compared with T2W IDEAL FRFSE images. Signal-to-noise ratio (SNR)-efficiencies measurements were made in the vertebral bodies and spinal cord in the mid-sagittal plane or nearest to the mid-sagittal plane. Images were scored with the consensus of two experienced radiologists on a four-point grading scale for fat suppression and overall image quality. Statistical analysis of SNR-efficiency, fat suppression and image quality scores was performed with a paired Student's t test and Wilcoxon's signed rank test.ResultsSignal-to-noise ratio-efficiency for both vertebral body and spinal cord was higher with T2W IDEAL FRFSE imaging (p < 0.05) than with T2W FRFSE imaging. T2W IDEAL FRFSE demonstrated superior fat suppression (p < 0.01) and image quality (p < 0.01) compared to fat-saturated T2W FRFSE.ConclusionAs compared with fat-saturated T2W FRFSE, IDEAL can provide a higher image quality, higher SNR-efficiency, and consistent, robust and uniform fat suppression. T2W IDEAL FRFSE is a promising technique for MR imaging of the spine at 3.0T.

The shrinkage linear complex-valued least mean squares (SL-CLMS) algorithm with a variable step-size (VSS) overcomes the conflicting issue between fast convergence and low steady-state misalignment. To the best of our knowledge, the theoretical performance analysis of the SL-CLMS algorithm has not been presented yet. This letter focuses on the theoretical analysis of the excess mean square error (EMSE) transient and steady-state performance of the SL-CLMS algorithm. Simulation results obtained for identification scenarios show a good match with the analytical results.

Recently, the leaky diffusion least-mean-square (DLMS) algorithm has obtained much attention because of its good performance for high input eigenvalue spread and low signal-to-noise ratio (SNR). However, the leaky DLMS algorithm may suffer from performance deterioration in the sparse system. To overcome this drawback, the leaky zero attracting DLMS (LZA-DLMS) algorithm is developed in this paper, which adds an 1 -norm l penalty to the cost function to exploit the property of sparse system. The leaky reweighted zero attracting DLMS (LRZA-DLMS) algorithm is also put forward, which can improve the estimation performance in the presence of time-varying sparsity. Instead of using the 1 -norm l penalty, in the reweighted version, a log-sum function is employed as the substitution. Based on the weight error variance relation and several common assumptions, we analyze the transient behavior of our findings and determine the stability bound of the step-size. Moreover, we implement the steady state theoretical analysis for the proposed algorithms. Simulations in the context of distributed network system identification illustrate that the proposed schemes outperform various existing algorithms and validate the accuracy of the theoretical results.INDEX TERMS leaky, low SNR, zero attracting, sparse system, weight error variance VOLUME XX, 2017

This paper is devoted to investigating the numerical solution for a class of fractional diffusion-wave equations with a variable coefficient where the fractional derivatives are described in the Caputo sense. The approach is based on the collocation technique where the shifted Chebyshev polynomials in time and the sinc functions in space are utilized, respectively. The problem is reduced to the solution of a system of linear algebraic equations. Through the numerical example, the procedure is tested and the efficiency of the proposed method is confirmed.