In this paper, we present a general construction framework of parameterizations of masks for tight wavelet frames with two symmetric/antisymmetric generators which are of arbitrary lengths and centers. Based on this i...
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In this paper, we present a general construction framework of parameterizations of masks for tight wavelet frames with two symmetric/antisymmetric generators which are of arbitrary lengths and centers. Based on this idea, we establish the explicit formulas of masks of tight wavelet frames. Additionally, we explore the transform applicability of tight wavelet frames in image compression and denoising. We bring forward an optimal model of masks of tight wavelet frames aiming at image compression with more efficiency, which can be obtained through SQP (Sequential Quadratic Programming) and a GA (Genetic algorithm). Meanwhile, we present a new model called Cross-Local Contextual Hidden Markov Model (CLCHMM), which can effectively characterize the intrascale and cross-orientation correlations of the coefficients in the wavelet frame domain, and do research into the corresponding algorithm. Using the presented CLCHMM, we propose a new image denoising algorithm which has better performance as proved by the experiments. (C) 2010 Elsevier B.V. All rights reserved.
Despite the active exploration of linear and nonlinear modeling of exchange rates, there is no consensus on the optimal forecasting model other than the traditional random walk and ARMA benchmark models in the literat...
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Despite the active exploration of linear and nonlinear modeling of exchange rates, there is no consensus on the optimal forecasting model other than the traditional random walk and ARMA benchmark models in the literature. Given the increasing recognition of heterogeneous market structure, this paper proposes an alternative Slantlet denoising based hybrid methodology that attempts to incorporate the linear and nonlinear data features. The recently emerging Slantlet analysis is introduced to separate the linear data features as it constructs filters with varying lengths at different scales and has more appealing time localization features than the normal wavelet analysis. Meanwhile, the Least Squares Support Vector Regression (LSSVR) is used to model and correct for the empirical errors nonlinear in nature. As empirical studies were conducted in the Euro exchange rate market, the performance of the proposed algorithm was compared with those of benchmark models including random walk, ARMA and LSSVR models. The proposed algorithm outperforms the benchmark models. More importantly the proposed methodology explores and offers deeper insights as to the underlying data generating process.
Speech recognition accuracy tends to drop down in strong noisy conditions. Accordingly, useful signals may be eliminated if the coefficients are processed with the same wavelet threshold. To solve this problem, a weig...
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ISBN:
(纸本)9780769536996
Speech recognition accuracy tends to drop down in strong noisy conditions. Accordingly, useful signals may be eliminated if the coefficients are processed with the same wavelet threshold. To solve this problem, a weighting threshold optimization method is proposed. The weighting is optimized according to the relativity of wavelet coefficients in each decomposed layer so that the denoising threshold can be more accurate. The useful speech signals can be reserved completely. The signals anti-interference ability can also be enhanced. an get a better performance especially at adverse conditions.
The authors present a denoising algorithm based on Gabor-like speech features extracted by independent component analysis, that demonstrates much improved signal-to-noise ratio and recognition rates.
The authors present a denoising algorithm based on Gabor-like speech features extracted by independent component analysis, that demonstrates much improved signal-to-noise ratio and recognition rates.
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