Let {vij}, i, j = 1, 2, …, be i.i.d, random variables with Ev11 = 0, Ev11^2 = 1 and a1 = （ai1,…, aiM） be random vectors with {aij} being i.i.d, random variables. Define XN =（x1,…, xk） and SN =XNXN^T,where xi=ai...
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Let {vij}, i, j = 1, 2, …, be i.i.d, random variables with Ev11 = 0, Ev11^2 = 1 and a1 = （ai1,…, aiM） be random vectors with {aij} being i.i.d, random variables. Define XN =（x1,…, xk） and SN =XNXN^T,where xi=ai×si and si=1/√N（v1i,…, vN,i）^T. The spectral distribution of SN is proven to converge, with probability one, to a nonrandom distribution function under mild conditions.
A method of single channel speech enhancement is proposed by de-noising using stationary wavelet transform. The approach developed herein processes multi-resolution wavelet coefficients individually and then recovery ...
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A method of single channel speech enhancement is proposed by de-noising using stationary wavelet transform. The approach developed herein processes multi-resolution wavelet coefficients individually and then recovery signal is reconstructed. The time invariant characteristics of stationary wavelet transform is particularly useful in speech de-noising. Experimental results show that the proposed speech enhancement by de-noising algorithm is possible to achieve an excellent balance between suppresses noise effectively and preserves as many target characteristics of original signal as possible. This de-noising algorithm offers a superior performance to speech signal noise suppress.
A method of single channel speech enhancement is proposed by de-noising using stationary wavelet transform. The approach developed herein processes multi-resolution wavelet coefficients individually and then recovery ...
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A method of single channel speech enhancement is proposed by de-noising using stationary wavelet transform. The approach developed herein processes multi-resolution wavelet coefficients individually and then recovery signal is reconstructed. The time invariant characteristics of stationary wavelet transform is particularly useful in speech de-noising. Experimental results show that the proposed speech enhancement by de-noising algorithm is possible to achieve an excellent balance between suppresses noise effectively and preserves as many target characteristics of original signal as possible. This de-noising algorithm offers a superior performance to speech signal noise suppress.
A novel and efficient speckle noise reduction algorithm based on Bayesian wavelet shrinkage using cycle spinning is proposed. First, the sub-band decompositions of non-logarithmically transformed SAR images are shown....
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A novel and efficient speckle noise reduction algorithm based on Bayesian wavelet shrinkage using cycle spinning is proposed. First, the sub-band decompositions of non-logarithmically transformed SAR images are shown. Then, a Bayesian wavelet shrinkage factor is applied to the decomposed data to estimate noise-free wavelet coefficients. The method is based on the Mixture Gaussian Distributed (MGD) modeling of sub-band coefficients. Finally, multi-resolution wavelet coefficients are reconstructed by wavelet-threshold using cycle spinning. Experimental results show that the proposed despeckling algorithm is possible to achieve an excellent balance between suppresses speckle effectively and preserves as many image details and sharpness as possible. The new method indicated its higher performance than the other speckle noise reduction techniques and minimizing the effect of pseudo-Gibbs phenomena.
In this paper, a new image classification method is developed. This approach applies graph decomposition and probabilistic neural networks(PNN) to the task of supervised image classification. We use relational graphs ...
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In this paper, we propose a dimension reduction method of locality preserving projections based on QR-decomposition of training data matrix, namely LPP/QR. It is efficient and effective in under-sampled recognition of...
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Quotient space theory of problem solving, a formal model of granular computing, is generalized in the sense that topological structure is replaced by Cech's closure space. Some basic issues of granular computing, ...
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The theoiy of the quotient space is a new mathematical tool for the study of the different granularity *** uses a triple(X,f,T)to describe a problem,among which X stands for the domain of the problem,f stands for the ...
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The theoiy of the quotient space is a new mathematical tool for the study of the different granularity *** uses a triple(X,f,T)to describe a problem,among which X stands for the domain of the problem,f stands for the attribute of the domain,and T stands for the structure of the *** analysis and solution of the problem(X,f,T),along with the further analysis and study of the domain and its structure and attribute,help to the description of the different granularity world based upon the complete *** paper firstly introduces the theory of quotient space,and then focuses on the application of this theoiy through the granularity analysis of the searching in the WWW,which has successfully come to the definite result of different *** about the search engine also are presented.
World Wide Web has developed to an inherently distributed information system,which brings people great trouble in finding needed information although huge amount of information available on the *** search engine is a ...
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World Wide Web has developed to an inherently distributed information system,which brings people great trouble in finding needed information although huge amount of information available on the *** search engine is a very important tool for people to obtain information on the webs,but the low-precision and low-recall exist widely in current search *** effective and accurate intelligent search engine based on the expert systems technology has become the most important research *** paper analyzes the WWW with different granularities according to quotient space theory,gives summarization of search engine and describes an expert system built by Visual Prolog.
Multivariate t-mixture modelling is more robust than Gaussian mixture modelling to a set of data containing a group or groups of observations with longer than Gaussian tails or a typical observations. To alleviate the...
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ISBN:
(纸本)0780384032
Multivariate t-mixture modelling is more robust than Gaussian mixture modelling to a set of data containing a group or groups of observations with longer than Gaussian tails or a typical observations. To alleviate the problem of local convergence of the traditional EM algorithm, a split-and-merge operation is introduced into the EM algorithm for multivariate t-mixtures. The split-and-merge equations are first presented theoretically and then a new merge method is acquired. Accordingly, a modified EM algorithm is constructed. Experiments of data clustering and unsupervised color image segmentation are given.
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