fft

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.)

主要仿真风力发电系统下matlab pwm整流及波形分析(Main simulation wind power systems matlab rectifier )

基于多尺度奇异值分解的图像融合程序及一组待融合图像，以及一篇文献。(Image fusion program based on multiscale singular value decomposition and a set of images to be fused, and a document.)

此为提供初学计算电磁学应用於色散介质的人，一维色散介质-Lorentz的程序(It is fortran code to provide a chance to learn the computational EM wave application of dispersive media for Lorentz model)

语音信号去噪，在MATLAB开发环境下，对含噪声的语音信号进行小波变换，实现语音增强的功能。(The speech signal denoising, wavelet transform, the noisy speech signal in the MATLAB development environment, Speech enhancement functions.)

分别用Pisarenko谐波估计法、MUSIC算法和ESPRIT算法对三正弦叠加高斯白噪声信号源进行频率估计，附件中有代码说明，编译环境为matlab。(Respectively Pisarenko harmonic estimation, MUSIC algorithm and the ESPRIT algorithm superimposed on the three sine Gaussian white noise signal source for frequency estimation, in the annex to the code description, the compiler environment matlab.)

小波神经网络实现短期电力负荷预测，神经网络模型及其BP学习算法。 (Wavelet neural network short term load forecasting, neural network model and BP learning algorithm.)

双树复小波程序，具有平移不变心，改进了小波变换(The DTCWT program with shift-invariant heart improve wavelet transform)

Block-based random image sampling is coupled with a projectiondriven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously with a smooth reconstructed image. Both contourlets as well as complex-valued dual-tree wavelets are considered for their highly directional representation, while bivariate shrinkage is adapted to their multiscale decomposition structure to provide the requisite sparsity constraint. Smoothing is achieved via a Wiener filter incorporated into iterative projected Landweber compressed-sensing recovery, yielding fast reconstruction. The proposed approach yields images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation. Additionally, reconstruction quality is substantially superior to that from several prominent pursuits-based algorithms that do not include any smoothing

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.)