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四自由度的简易机器人（转载），通过输入四个关节的旋转角度，显示出实体在X、Y、Z三轴上的投影，并可计算出位置坐标(Simple robot four degrees of freedom (reprint), by entering the four joint rotation angle, showing that the entity in the X, Y, Z axis on the projection, and calculate the location coordinates)

Matlab code for bacterial foraging

利用MATLAB对语音进行端点检测，检测起始端和终点段(Using MATLAB speech endpoint detection, detecting the start and end segment ends)

m序列产生m序列产生m序列产生m序列产生m序列产生m序列产生(m-sequence generator m sequence generation m-sequence generator m-sequence generator m sequence generation m-sequence generator)

Aperture Radar Imaging reference material.

蒙塔卡洛方法求pi值的计算方法，通过分布在立方体和其内切球内的随机点数计算得出(Monte Carlo computation of pi)

编码教常用的哈弗曼编码的MATLAB仿真的源程序(MATLAB source to huffman coding)

说明：  时域有限差分法，计算一维的光子晶体能量的变化(FDTD method to calculate one-dimensional photonic crystal energy changes)

说明：  是自己上模式识别讨论课时黄庆明老师布置的大作业，是关于基于消失点的道路侦测算法，对视觉导航研究很有帮助(Pattern recognition is discussed on their own class teacher arranged a big job Huang Qingming, is based on the vanishing point on the road detection algorithm is useful for visual navigation of)

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.