fcmMATLAB

说明：  用Maple和MATLAB解决科学计算问题（第三版）.pdf(Using Maple and MATLAB to solve problems in scientific computing (third edition). Pdf)

用MATLAB去除移动车辆行驶过程的阴影，并对视频进行优化(Remove the shadow of the mobile vehicles using MATLAB, and to optimize the video)

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Compressive sampling is an emerging technique that promises to effectively recover a sparse signal from far fewer measurements than its dimension. The compressive sampling theory assures almost an exact recovery of a sparse signal if the signal is sensed randomly where the number of the measurements taken is proportional to the sparsity level and a log factor of the signal dimension. Encouraged by this emerging technique, this thesis briefly reviews the application of Compressive sampling in speech processing. It comprises the basic study of two necessary condition of compressive sensing theory: sparsity and incoherence. In this thesis, various sparsity domain and sensing matrix for speech signal and different pairs that satisfy incoherence condition has been compiled.

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求解分数阶倒数混沌系统的matlab程序(Solving fractional order chaotic systems matlab countdown procedure)

This paper gives a comprehensive survey on 3D mesh watermarking, which is considered an effective solution to the above two emerging problems. Our survey covers an introduction to the relevant state of the art, Many researchers, from both the academic and the industrial sectors, have become aware of their intellectual property protection and authentication problems arising with their increasing use. In this paper we spacialy discus the advantages and disadvantages of the transformed domain methods and the spatial domain methods.

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该源码是基于matlab编写Asaoka算法，用于高速铁路沉降预测(The source code is written in matlab Asaoka based algorithm for high-speed railway settlement prediction)

小波包变换可以对高频部分提供更精细的分解，而且这种分解既无冗余，也无疏漏，所以对包含大量中、高频信息的信号能够进行更好的时频局部化分析。(Wavelet packet transform can provide the high frequency part of the finer decomposition, and this decomposition is neither redundant nor oversight, so the signal contains a large amount of high frequency information can be a better time-frequency localization analysis.)