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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.)

说明：  利用ENVI/IDL，对两张影像进行直方图规范化，均衡化。(The histogram of the two images is normalized and balanced.)

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)

离散小波变换程序 实现了4层离散小波变换 可运行的程序代码(The discrete wavelet transform procedure achieved a 4-layer discrete wavelet transform operable program code)

多波束、波束形成、DSP实现、多波束分析(Multibeam、Multi-beam forming、Multi-beam analysis、DSP、)

Tetrolet变换的原代码，一种稀疏表示的小波变换，由haar变换改进得到(Tetrolet transform the original code, a sparse representation of the wavelet transform, haar transform improved)

非下采样小波变换的一个实现程序，对边缘和纹理提取效果较好(Nonsubsampled wavelet transform of an implementation program, better edge and texture extraction effect)

基于小波变换的图像压缩编码，实现了多种小波变换，并且实现了12:1的高压缩率。(Based on Wavelet Transform Image Coding, to achieve a variety of wavelet transform, and achieved a high 12:1 compression ratio.)

对仿真信号进行复morlet小波变换后，绘制的时频图。(After the simulated signal multiplexing morlet wavelet transform, time-frequency diagram drawn.)

matlab实现的多相滤波器，cic抽取D=5时例子，及级联型的cic的程序。(matlab implementation of the polyphase filter, cic examples taken D = 5 时, and the cascade of cic procedures.)