Extracted-from-the-ECG-signal-

基于matlab小波变换的轴承故障外圈点蚀分析。(Based on wavelet transform matlab punctuate eclipse outer bearing failure analysis.)

说明：  广义S变换及其逆变换代码，可以直接进行属性分析(Generalized S-transform and its inverse transform code can be used for attribute analysis directly)

在labview中进行小波分析程序设计，内容最全、最好的文件，参照本文件可以直接一步步编写小波分析程序，适合初学者。(The labview wavelet analysis program design, the most comprehensive, best file reference in this document can step directly Step preparation of a wavelet analysis program for beginners.)

Le Gall 5/3 小波变换及其反变换，可用于JPEG2000无损压缩中(Le Gall 5/3 wavelet transform and its inverse transform，which can be used for lossless compression in JPEG2000)

降水小波分析 用于分析降水序列的周期变化规律(Wavelet analysis of rainfall series for the analysis of the cycle variation of precipitation)

这是一个多维小波变换工具箱,列举了很多的实例.(This is a multi-dimensional wavelet transform toolbox, cited many examples.)

MATLAB实现的高斯平滑滤波源代码，用了高斯函数的定义来做，附实例，效果很好(gauss filter by matlab)

说明：  LeGall(5，3)小波滤波器，其计算复杂度小、存储开销低、压缩效率高，已被JPEG2000标准采用。提升小波实例c语言实现(Lifting Wavelet Example C Language Implementation)

用迭代法生成haar子程序上 以及信息隐藏的详细功能、包含内容说明(Iterative method to generate a the haar subroutine and information hidden features, including content description)

Block-based random image sampling is coupled with a projectiondriven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously with a smooth reconstructed image. Both contourlets as well as complex-valued dual-tree wavelets are considered for their highly directional representation, while bivariate shrinkage is adapted to their multiscale decomposition structure to provide the requisite sparsity constraint. Smoothing is achieved via a Wiener filter incorporated into iterative projected Landweber compressed-sensing recovery, yielding fast reconstruction. The proposed approach yields images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation. Additionally, reconstruction quality is substantially superior to that from several prominent pursuits-based algorithms that do not include any smoothing