Rotate_sphere

MPCA在工业工程监控中程序。。。。。。。。。。。。(..............................................)

说明：  KF的一道例题，包括源程序代码以及具体的解题过程(KF' s an example, including source code and the specific problem-solving process)

基于加权l1范数的稀疏信号重建，稀疏信号是带噪声的(Sparse signal reconstruction based on the weighted l1 norm sparse signal with noise)

基于小波变换的ECG信号去噪，可以改小波基、阈值等参数完善实验效果，注释详细。尤其对这方面的初学者有参考价值。(ECG signal denoising based on wavelet transform.Wavelet basis , threshold and other parameters can be modified to improve the experimental results. detailed notes is especially valuable for beginners . )

maximal ratio combining-spatial diversiy

A matlab book which writing in Chinese.

高阶谱双谱程序，非常实用的双谱例程供你参考(Order spectrum bispectral procedures, very practical double spectral routines for your reference)

：压缩感知是近年来应用数学界提出的一套关于稀疏信号采集和重构的新理论，它突破了传统奈奎斯特采样定理 的限制，以远少于传统奈奎斯特采样定理所需的测量数据就能够精确恢复原信号或估计信号的相关参数。将压缩感知理论应 用到DOA估计，可以解决传统DOA估计中高采样率、以及较多辐射源信号条件下难以定位的限制。研究了基于压缩感知理 论的DOA估计方法，并利用MATLAB进行仿真，通过与传统MUSIC算法比较可知，基于压缩感知的DOA估计方法具有显著 的优势(The theory of Compressive Sensing(CS)is the application of mathematics in the field of a sparse signal acquisition and reconstruction of the new theory. It breaks through the traditional limitation of Nyquist sampling theorem. It can recover certain signals or estimate their parameters far fewer samples or measurements than traditional methods. The application of compressed sensing theory to DOA estimation can solve the traditional DOA estimation in high sampling rate, as well as more of the radiation source signal conditions is difficult to position limits. This paper studies the theory of DOA estimation method based on compressed sensing theory, and simulation uses MATLAB, compared with the traditional MUSIC algorithms, DOA estimation based on compressed sensing theory method has the obvious advantage)

用matlab实现的语音MFCC参数的提取，对语音此信号处理的朋友很重要，很好用(Matlab voice MFCC parameters extraction, friends are very important, the voice of the signal processing is very good)

基于标准的粒子群算法编写了一个简单通用的matlab程序(matlab code of standard particle swarm optimization)