li5-39

应用Matlab进行小波包对时变谐波分析的代码(Matlab Applications for wavelet packet on time-varying harmonic analysis)

基于matlab小波变换的轴承故障外圈点蚀分析。(Based on wavelet transform matlab punctuate eclipse outer bearing failure analysis.)

这个程序是基于小波变换的信号奇异性检测及去噪 的一些例程(using wavelet method and SVD to process the signal and denoise)

《MATAB小波分析（第2版）》张德丰 书籍配套所有源代码("MATAB wavelet analysis (Second Edition)" - Zhang Defeng, books supporting all the source code)

Gabor小波变换的matlab实现，可以完成对图像的纹理特征提取，广泛应用于图像处理，模式识别等方向(Gabor wavelet transform matlab implementation can complete the image texture feature extraction is widely used in image processing, pattern recognition and so the direction of)

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)

说明：  提出了一种基于复解析小波变换的相位诊断齿轮故障的方法。复解析小波变换将Hilbert变换与小波分析紧密结合在一起,具有自适应分析能力。利用复解析小波变换得到齿轮振动信号的相位,其功率谱图上可提取调制边频带结构来识别故障模式。试验结果表明这种方法可有效应用于齿轮局部故障诊断中。(A new method using the phase based on complex analytical wavelet transform for fault diagnosis of gears was proposed. The phase of vibration signals of gears is got by complex analytical wavelet transform, which combines Hilbert transform with wavelet transform tightly and possesses adaptive analysis ability. By making spectrum analysis of the phase, the sideband structure in power spectrum can be protruded, and the fault pattern can be identified. Experimental results show that the proposed method is effective in local defect detection of gears.)

使用wavelet 將影像分解 再利用threshold方法去雜訊(use wavelet to denoised in a picture)

说明：  傅立叶功率谱法求纹理特征 可解压缩后直接执行(Fourier power spectrum method for texture features can be extracted directly after implementation)

说明：  有小波分解求小波熵，还有小波包技术求解信号特征，适应与分析脑电信号特征(Seeking a wavelet decomposition wavelet entropy, and wavelet packets technique to solve the signal characteristics, and analysis of EEG characteristics of adaptation)