wavelet

MATLAB实现小波变换中的图像拼接技术，可实现水平拼接(In MATLAB wavelet transform in image stitching technology, can achieve the level of splicing)

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.)

自己编写的基于指数遗忘快速算法改进的morlet小波变换程序(I have written forgotten fast algorithm based on the index improved wavelet transform morlet procedure)

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.)

图像小波变换，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。(Image Wavelet Transform)

Resumé de l’article: "Early Detection of Fatigue Damage on Rolling Element Bearings Using Adapted Wavelet" par Xavier Chiementin, Fabrice Bolaers, Jean-Paul Dron en franç ais

说明：  双数复数小波变换的Matlab源码的A部分，全部解压到一个文件夹后直接调用函数。(Double the number of complex wavelet transform of the A part of Matlab source code, all extract to a folder call the function directly.)

基于小波变换的电压暂降定位 利用模极大值原理实现暂降起止时刻的准确检测(voltage sag lacote based on wavelet transform Used to achieve maximum modulus principle sag beginning and ending time of the accurate detection)

脉搏信号分析 （1）设计滤波器，实现对脉搏信号的噪声抑制和基线纠漂； （2）时域分析：波形特征检测； （3）功率谱分析：对消噪后的信号进行功率谱分析。要求计算信号的功率谱，功率谱峰值，峰值频率。 (Pulse signal analysis (1) design the filter, to achieve the pulse signal noise suppression and baseline drift correction (2) time-domain analysis: waveform feature detection (3) power spectral analysis: noise cancellation signal after the power spectrum analysis . Require calculation of the signal power spectrum, peak power spectrum, peak frequency.)

通过设计Visual C程序源码对简单易懂的一维信号在加上了高斯白噪声之后进行Daubechies小波、Morlet小波与Haar小波变换，从而的到小波分解系数；再通过改变分解的到的各层高频系数数进行信号的小波重构达到消噪噪的目的。在这一程序源码实现的过程中能直观地理解信号小波分解重构的过程与在信号消噪中的重要作用，和在对各层高频系数进行权重处理时系数的选取对信号消噪效果的影响。 可直接 (Design Visual C program source code on a simple one-dimensional signal with a Gaussian white noise Daubechies wavelet, Morlet wavelet and Haar wavelet transform, and thus to the wavelet coefficients and then change the decomposition to each storey wavelet reconstruction frequency coefficient of the number of signal to noise canceling noise. In the process of realization of this program source code can be intuitively understood the signal wavelet decomposition and reconstruction process and an important role in signal denoising, and the selection coefficient in the high frequency coefficients of the layers of the weight of processing noise cancellation signal impact. Can be directly)