paper1

谐波小波包分解程序，并且将其应用于声发射信号特征提取(Harmonic wavelet packet decomposition procedure, and will be applied to acoustic emission signal feature extraction)

小波变换阈值去噪的文章，基于最大最小准则计算(Minimax Estimation, Adaptive Estimation, Nonparametric Regression, Density Estimation, Spatial Adaptation, Wavelet Orthonormal bases, BesovSpaces, Optimal Recovery.)

给出了小波变换的具体java实现，小波变换的java程序，共包括waveletgen.java 和transformation.java(given wavelet transform to achieve specific java, java wavelet transform procedures, it includes waveletgen.java and transformation.java)

有关小波包分解和介绍的PPT，适合于初学者快速掌握小波包分解相关问题。(the PPT of wavelet packet decomposition and the introduction , suitable for beginners to quickly master the wavelet packet decomposition.)

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

说明：  用c++实现小波变换，参考matlab实现(Using C + + to realize wavelet transform, referring to matlab to realize)

使用傅里叶变换和小波变换提取心电信号中的呼吸信号(Using the Fourier transform and the wavelet transform to extract the breathing signal ECG)

小波分解，得到低频系数并求特征熵，可用来对信号进行分选识别(Wavelet decomposition, low coefficient and entropy, which can be used for signal sorting and recognition)

说明：  小波变换技术与工程实践：图像的小波变换(MFC框架下)(Wavelet transform technology and engineering practice: Image Wavelet Transform (MFC framework))

说明：  有小波分解求小波熵，还有小波包技术求解信号特征，适应与分析脑电信号特征(Seeking a wavelet decomposition wavelet entropy, and wavelet packets technique to solve the signal characteristics, and analysis of EEG characteristics of adaptation)