小波对图像的二次压缩.

D5/3提升整数小波，可实现图像的无损压缩，包括一维和二维图像整数小波变换(D5/3 integer wavelet upgrade can realize lossless image compression, including the one-dimensional and two-dimensional image integer wavelet transform)

小波包分析提取振动信号中的分解，系数重构特征频率，以及能量谱分析计算(Wavelet packet analysis to extract the vibration signal decomposition coefficient reconstruction of the characteristic frequency, and calculate the energy spectrum analysis)

说明：  地震勘探中小波变换，去噪，对初学者容易上手，运用maltlab编写，运行也方便，(Wavelet transform and denoising in seismic exploration)

心电信号的读入，滤波，以及小波变换并重构。(ECG reads, filtering, and wavelet transform and reconstruction.)

图像的5/3小波变换的matlab代码。程序简短，易懂。(Image 5/3 wavelet transform matlab code. Program brief and easy to understand.)

OFDM系统完整程序，小波/小波包变换在OFDM中的应用的程序。(Complete program of the OFDM system, wavelet/wavelet packet transform in OFDM applications program.)

小波去噪,通过对含噪信号进行四层小波分解，此处利用db3小波系列，通过仿真发现可以很好的消除信号中的噪声(Wavelet denoising, the signal through four layers of noisy wavelet decomposition, where the use db3 wavelet series, the simulation found in the well to eliminate the noise signal)

基于matlab的小波包分解应用于机械振动信号的故障分析(Used in failure analysis of mechanical vibration signals based on the Matlab wavelet packet decomposition)

说明：  该函数用于读取ecg信号，找到小波变换模极大序列(function to read the ECG signal, find wavelet transform modulus maxima sequence)

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.)