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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)

在实数域 ,对称、 正交的紧支集非平凡单小波基不存在 ,而多小波把紧支集，对称性、 正交性完美地结合在一起 ,使小波理论从标量扩展到矢量范畴。利用上述特点将多小波变换应用于 SAR 图像压缩。(In the real fields, symmetric, orthogonal set of nontrivial compactly supported wavelet basis does not exist alone, but more than the compact support wavelet, symmetry, orthogonality perfect combination, So wavelets theory extended Dao vector from scalar context. Use of these features will be more than SAR image compression wavelet transform.)

说明：  实现k-l变换，代码短，效率高，适合初学者进行上机练习，原理简单，比较容易懂和实现。(Achieve kl transformation, the code short, high efficiency, suitable for beginners to practice on the machine, simple in principle, relatively easy to understand and achieve.)

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)

1、求冲激函数、阶跃函数及三角函数的小波变换 2、模极大值曲线 3、求它们的李氏指数(one, seeking impulse function, step function and trigonometry two wavelet transform, modulus maxima curve 3, Lee seeking their index)

小波模极大值去噪程序，希望对大家有用哈！(modulus maxima wavelet denoising procedures in the hope that it may be useful for Kazakhstan.)

wavelet transforms in advanced forms

小波变换(wavelet transform，WT)是一种新的变换分析方法，它继承和发展了短时傅立叶变换局部化的思想，同时又克服了窗口大小不随频率变化等缺点，能够提供一个随频率改变的"时间-频率"窗口，是进行信号时频分析和处理的理想工具。它的主要特点是通过变换能够充分突出问题某些方面的特征，能对时间(空间)频率的局部化分析，通过伸缩平移运算对信号(函数)逐步进行多尺度细化，最终达到高频处时间细分，低频处频率细分，能自动适应时频信号分析的要求，从而可聚焦到信号的任意细节，解决了Fourier变换的困难问题，成为继Fourier变换以来在科学方法上的重大突破。(Wavelet transform (WT) is a new transform analysis method. It inherits and develops the idea of localization of short-time Fourier transform and overcomes the shortcomings that window size does not vary with frequency. It can provide a frequency-dependent The Time-Frequency window is ideal for time-frequency analysis and processing of signals. Its main feature is that by transforming some features that can fully highlight some aspects of the problem, the localization of the time (space) frequency can be analyzed, and the signal (function) can be gradually and multi-scale refined by the scaling operation, finally reaching the high frequency Time subdivision, frequency subdivision at low frequencies, can automatically adapt to the requirements of time-frequency signal analysis, which can focus on any details of the signal to solve the difficulties of Fourier transform has become a major breakthrough in the scientific method since Fourier transform.)

基于小波变换的图像分析，它是在matlab中运行的(Image analysis based on wavelet transform, which is run in matlab)

小波变换、EMD以及EMD结合小波变换，几种常用的时频去噪方法在脑电预处理中运用的的对比分析，算法过程解释详细，有利于初学者入门学习；小波变换、EMD以及EMD结合小波变换，几种常用的时频去噪方法在脑电预处理中运用的的对比分析，算法过程解释详细，有利于初学者入门学习