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关于如何产生一个GOLD码的程序，对于在MATLAB中的应用有一定的好处，用户可以根据自己的情况加以改正达到自己的目的(on how to produce a code GOLD procedures for the application of MATLAB there are certain advantages, users can according to its own circumstances be corrected to achieve its goal)

The VLFeat open source library implements popular computer vision algorithms specializing in image understanding and local featurexs extraction and matching. Algorithms incldue Fisher Vector, VLAD, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixes, quick shift superpixels, large scale SVM training, and many others. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux. The latest version of VLFeat is 0.9.19.

关于非线性离散系统辨识，调试通过可以使用，各种kalman滤波器的设计。( Nonlinear discrete system identification, Debugging can be used, Various kalman filter design.)

Kalman Example for robotics Visual Servoing(Kalman Example for robotics Visual Servoi Vi)

using MonteCarlo Method

PMSM双闭环转矩磁链控制的simulink模型(Simlink Model of PMSM Double Closed Loop Torque Flux Control)

APF svpwm方式的matla simulink模型(Matla simulink model APF svpwm way)

数学建模样本程序，可供联系使用，matlab程序(Code code code code code code program)

filtre active parallèle pour la qualité d énergie

This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. Before getting to a description of PCA, this tutorial first introduces mathematical concepts that will be used in PCA. It covers standard deviation, covariance, eigenvectors and eigenvalues. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.