Recently, a family of block-sparse proportionate adaptive filtering has been introduced for the block-sparse system identification. Here, we focus on the family of block-sparse proportionate second-order Volterra filt...
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Recently, a family of block-sparse proportionate adaptive filtering has been introduced for the block-sparse system identification. Here, we focus on the family of block-sparse proportionate second-order Volterra filtering algorithm for nonlinear echo cancellation. It is demonstrated that a multiple block-sparse system is naturally involved in the sparse adaptive Volterra filter, which could be accelerated by applying the block-sparse proportionate idea. Simulation results confirmed the performance improvement compared to both the previous proportionate second-order Volterra and the classical second-order Volterra filters.
This paper proposes an efficient implementation of fast FIR filtering algorithms with useful characteristics for real-time application. They maintain a low processing delay, independent of the filter length. The diffi...
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This paper proposes an efficient implementation of fast FIR filtering algorithms with useful characteristics for real-time application. They maintain a low processing delay, independent of the filter length. The difficulty is to keep as much as possible of the improvement brought by the reduction of the arithmetic complexity of these fast FIR filtering algorithms without exceeding the Digital Signal Processor (DSP) resources (number of registers, pointers, memory, ...). A particular attention is devoted to the heavy use of pointers which represents a crucial problem. It is solved in this paper by an optimal organisation of data in memory. Improvements of more than 70% in actual timings on an ADSP-2100 compared to the classical algorithm of convolution are obtained, even for very short blocks.
Currently, Field Programmable Gate Array (FPGA) goes beyond the low-level line-by-line hardware description language programming in implementing parallel multidimensional image filtering algorithms. High-level abstrac...
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Currently, Field Programmable Gate Array (FPGA) goes beyond the low-level line-by-line hardware description language programming in implementing parallel multidimensional image filtering algorithms. High-level abstract hardware-oriented parallel programming method can structurally bridge this gap. This paper proposes a first step toward such a method to efficiently implement Parallel 2-D MRI image filtering algorithms using the Xilinx system generator. The implementation method consists of five simple steps that provide fast FPGA prototyping for high performance computation to obtain excellent quality of results. The results are obtained for nine 2-D image filtering algorithms. Behaviourally, two Virtex-6 FPGA boards, namely, xc6vlX240Tl-1lff1759 and xc6vlX130Tl-1lff1156 are targeted to achieve; lower power consumption of (1.57 W) and down to (0.97 W) respectively at maximum sampling frequency of up to (230 MHZ). Then, one of the nine MRI image filtering algorithms, has empirically improved to generate an enhanced MRI image filtering with moderate lower power consumption at higher maximum frequency.
Two novel adaptive filtering algorithms are proposed. The algorithms greatly reduce the number of adaptation steps of the LMS algorithm by extrapolating the adaptive filtering coefficients. The extrapolation is done t...
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ISBN:
(纸本)0780336798
Two novel adaptive filtering algorithms are proposed. The algorithms greatly reduce the number of adaptation steps of the LMS algorithm by extrapolating the adaptive filtering coefficients. The extrapolation is done through the time constant and optimal coefficient estimations of the coefficient adaptation process. Simulation results for the system identification and correlation canceller loop show significant reduction of the adaptation steps. The algorithms can be applied to general orthogonal adaptive filters and least-square error optimization problems.
The aim of the present work is to analyse the behaviour of the Fourier, sine and cosine digital filtering algorithms in impedance calculation, considering a distorted current signal due to severe saturation of the CT....
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The aim of the present work is to analyse the behaviour of the Fourier, sine and cosine digital filtering algorithms in impedance calculation, considering a distorted current signal due to severe saturation of the CT. To this aim we consider the effect caused by data window length variation, and the analogue anti-aliasing filter on the algorithm output. The current signals considered in this work have been obtained from a power test in which an intentional short circuit was produced, and the primary and secondary currents of the CT submitted to test were recorded.
Speckle noise in synthetic aperture radar (SAR) images reduces target feature detection. Two of the better known post-processing speckle suppression filters are the Lee (1986) and Kuan (1987) filters. The authors pres...
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Speckle noise in synthetic aperture radar (SAR) images reduces target feature detection. Two of the better known post-processing speckle suppression filters are the Lee (1986) and Kuan (1987) filters. The authors present modifications that have been made to the Lee and Kuan filters, such that there is an improvement in speckle suppression in regions of large back-scattering variations. Improvement over the original algorithms are illustrated using SEASAT images of Flevoland (Netherlands). Quantitative measures of performance for various types simulated terrain are also presented.
In recent years, collaborative filtering becomes one of the most successful recommender systems. Its key technique is to predict new ratings from the known ratings. Unfortunately, in the previous research, the tempora...
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In recent years, collaborative filtering becomes one of the most successful recommender systems. Its key technique is to predict new ratings from the known ratings. Unfortunately, in the previous research, the temporal information was rarely applied. That is to say, the ratings at different time were considered the same. However, from our point of view, not only the mean values of ratings in different periods are different, but users' opinions toward items may change with the passage of time as well. We analyze the influence of the temporal information and introduce three methods to apply the temporal information. Firstly, by mixing user, item and time attributes, we present a regression-based method. Secondly, to guarantee that the ratings in different time can contribute different weights to the predicting rating, we adjust the prediction function by adding a parameter, which is a function of the time between the predicting rating and the known rating. Thirdly, we select different methods to predict ratings of different periods. Experiments on two real large datasets show that our methods are effective and can improve the accuracy.
Analysis of stochastic gradient based adaptive algorithms with general cost functions is carried out. The analysis holds under mild assumptions on the inputs and the cost function. Previous analyses typically consider...
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Analysis of stochastic gradient based adaptive algorithms with general cost functions is carried out. The analysis holds under mild assumptions on the inputs and the cost function. Previous analyses typically consider mean and mean square behavior, we consider almost sure behavior. The parameter estimates are shown to enter a small neighborhood about the optimum value and remain there for a finite length of time. The asymptotic distribution of the parameter estimates is shown to be Gaussian. Adaptive algorithms which fall under the framework of this paper are signed error LMS, dual sign LMS, quantized state LMS, least mean fourth, dead zone algorithms, and momentum algorithms. Some discussion is presented regarding stochastic gradient algorithms where the regressor is replaced with a general function of the regressor.
Electrocardiogram (ECG) can help to diagnose range of diseases including heart arrhythmias, heart enlargement, heart inflammation (pericarditis or myocarditis) and coronary heart disease. ECG consists of noise which i...
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Electrocardiogram (ECG) can help to diagnose range of diseases including heart arrhythmias, heart enlargement, heart inflammation (pericarditis or myocarditis) and coronary heart disease. ECG consists of noise which is non stationary that affects the reliability of ECG waveform. In this paper an adaptive filter for denoising ECG signal based on Least Mean Squares (LMS), Normalized Least Mean Square (NLMS), Affine Projection LMS (APA-LMS) and Recursive least Squares algorithm (RLS) is presented with experimental results and the results are found to be encouraging. The performances of these algorithms are compared in terms of various parameters such as SNR, PSNR, MSE and SD. To validate the proposed methods, real time recorded data from the MIT-BIH database is used. RLS algorithm is found to exhibit lower MSE, and higher SNR compared to other algorithms. Therefore the results demonstrate superior performance of adaptive RLS filter for denoising of ECG signal.
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