HarmoniousWavelet

双树复小波程序，具有平移不变心，改进了小波变换(The DTCWT program with shift-invariant heart improve wavelet transform)

小波分析在心电信号去噪中的应用,小波变换在心电图数据处理中的应用研究(Wavelet analysis of ECG signal denoising)

利用小波变换，EMD以及两者的综合分别进行轴承信号背景噪声去噪分析(Using wavelet transform, EMD and the combination of the two separate bearing signal denoising of background noise)

2.1 傅里叶变换 2.1.1 经典傅里叶变换 2.1.2 傅里叶变换的基本性质 2.1.3 快速傅里叶变换 2.1.4 短时傅里叶变换 2.2 小波分析与多分辨率分析的历史 2.3 小波分析与傅里叶变换的对比 2.4 小波变换 2.4.1 连续小波变换 2.4.2 离散小波变换 2.4.3 高维小波连续变换 2.5 常用小波基函数 2.5.1 小波函数 2.5.2 小波函数系 2.5.3 复数小波 2.6 构造紧支撑正常小波基 2.7 多分辨率分析与小波构造 2.8 分析小波包 2.8.1 小波包的定义及性质 2.8.2 分解小波包的空间 2.8.3 小波包算法(2.1 Fourier transform 2.1.1 classical Fourier transform 2.1.2 The basic properties of the Fourier transform 2.1.3 Fast Fourier Transform 2.1.4 short time Fourier transform 2.2 wavelet analysis and multi-resolution analysis of the history of 2.3 Wavelet Analysis and comparison of the Fourier transform 2.4 Wavelet Transform 2.4.1 Continuous Wavelet Transform 2.4.2 Discrete Wavelet Transform 2.4.3 Continuous high-dimensional wavelet transform 2.5 Common wavelet basis function 2.5.1 wavelet function 2.5.2 Department of wavelet function 2.5.3 Complex Wavelet 2.6 Construction of compactly supported wavelets normal More than 2.7 resolution analysis and wavelet construction 2.8 Analysis of Wavelet Packet 2.8.1 Definition and properties of wavelet packet 2.8.2 wavelet packet decomposition space 2.8.3 Wavelet Packet Algorithm)

说明：  用MATLAB编程实现实现滤波，与数字信号处理相关(Implementing filtering)

《小波分析理论与MATALB R2007 实现》第10章源码(wavelet analysis using matlab)

基于多级树集合分裂排序（spiht）的图像压缩算法(Image compression algorithm based on a collection of multi-level tree split-Sort (spiht) of)

实现盲源信号的分离，在一个信号源中提取出有用的信号。(Blind source signal separation, in a signal source to extract useful signals.)

小波变换，反变换，以及提升小波变换，包括97小波，及53小波(Wavelet transform, inverse transform, as well as the lifting wavelet transform, including the 97 wavelet, and 53 wavelet)

利用小波分析检测超高压输电线路故障和故障分类(fault identification and classifiction of EHV transmission lines based on wavelet transform)