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希尔伯特变换和小波变换检测相位差的matlab程序和一些资料(Hilbert transform and wavelet transform to detect the phase difference matlab program and some of the information)

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)

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)

wavelet analysis of an image

这是采用小波变换以及压缩感知的理论编写的图像融合程序(This is the wavelet transform and the theory of compressed sensing image fusion program written in)

与labview之间的联系 在小波变换方面(Contact between labview in terms of the wavelet transform)

基于DB4 的小波变换，实现了小波的分解与重构，可根据实际需要修改阈值的选取方法(Based DB4 wavelet transform to achieve wavelet decomposition and reconstruction, according to the actual need to modify the threshold selection method)

小波变换对电能质量暂态信号的分析和运用，包含电压骤升、骤降等(Wavelet transform for power quality analysis and the use of transient signals, including voltage dips, swells, etc.)

利用CVX进行优化，得到期望加权矢量，从而的得到需要的波束形成(CVX using optimized to give the desired beamforming)

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.)