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说明：  广义S变换及其逆变换代码，可以直接进行属性分析(Generalized S-transform and its inverse transform code can be used for attribute analysis directly)

AR过程的线性建模的Burg算法参数估计m文件(Burg method for AR processing)

vc实现虚拟示波器，没有测试过不知道能用吧(vc virtual oscilloscope not tested do not know can)

个人编写的提升格式97小波去噪，采用硬阈值法，三层分解，包括小波变换，去噪，逆变换三步(Written format of 97 individuals to enhance the wavelet denoising using hard threshold method, three decomposition, including wavelet transforms, denoising, inverse transform three-step)

旨在用C语言程序和MATLAB程序通过MUSIC算法对“信号提取”进行实现，结果证明程序运行效果良好，分辨率较高(Designed with C language program and MATLAB program through the MUSIC algorithm " signal extraction" for implementation, the results show the program running well, higher resolution)

说明：  关于小波包分析的图像绘制，小波分析，三次分解，单支重构 故障信号(Image registration algorithms based on piecewise linear interpolators (spatiotemporal gradients or block matching) are analyzed to determine subpixel registration accuracy.)

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)

详细的基于离散小波变换的图像融合matlab程序,可以用于处理任何格式的灰度图像(Detailed based on discrete wavelet transform image fusion matlab procedures, can be used to deal with any format of the gray image)

同步压缩小波变换（synchrosqueezing wavelet transform）工具箱，用于信号处理时频分析(synchrosqueezing wavelet transform toolbox for time-frequency analysis in signal processing )

循环平稳信号是一类特殊的非平稳信号，其统计特征具有随时间呈周期变化的特点。旋转机械由于周期运行方式其振动信号具有循环平稳特性，因此利用循环平稳分析方法能够提取出在平稳假设下所不能得到的隐藏故障特征信息，为有效地分离和识别旋转机械早期微弱故障特征提供可能。(The cyclostationary signal is a special type of non-stationary signal whose statistical characteristics have a periodic variation with time. Due to the cyclical operation of the rotating machinery, the vibration signal has the characteristics of cyclostationary stability. Therefore, the cyclostationary analysis method can extract the hidden fault feature information that can not be obtained under the stationary assumption, which provides the possibility to effectively separate and identify the early weak fault features of the rotating machinery.)