Ball_Tracker_Using_Covariance_Tracking

压缩感知重构算法在“鬼”成像中的应用研究.pdf(Compressed sensing reconstruction algorithm in the "ghost" image of applied research. Pdf)

MATLAB课程报告，关于菲涅尔全息过程的计算机仿真(MATLAB program reports on the Fresnel hologram Computer Simulation)

天文导航方向，导航信号转化为星历表信息Matlab程序。(The direction of celestial navigation, navigation signal into Matlab ephemeris information procedures.)

基于matlab的蚁群算法LF解决聚类问题(matlab ants clusting)

CCA的实现，用于两个数据集合上的典型相关分析，需要确定维数等(kjasd asdgf kl dsf)

Segmentation of images

vc2008 dos 程序 已知AB两点坐标，半径R。 求2个圆心。 公式使用MATLAb推导的，做了一点简化，和小的改动。 都在函数 double jisuanYanxin(double Ax,double Ay,double Bx,double By,double R) 里面(vc2008 dos program known AB two coordinates, radius R. Seeking two center of the circle. Formula derived using MATLAb do a little simplification, and small changes. In the function double jisuanYanxin (double Ax, double Ay, double Bx, double By, double R) which)

不同频率正弦信号产生，可用于生成DDS中的ROM(Sinusoidal signals of different frequencies generated can be used to generate the ROM DDS)

说明：  RSOFT软件资料，可以解决光子带隙和光纤光栅。(RSOFT software information, can solve the photonic band gap and the fiber grating.)

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.