openGI （几何图像）

高斯正算坐标程序计算高斯投影的正算问题克拉索夫斯基椭球-Gaussian count is calculated coordinates of the Gaussian projection is counting police used batons for dispersing ellipsoid

求串的长度算法的实现-Series for the length of the algorithm to achieve

Code in java for the Traveling salesman problem. This code is very simple because has only three pages. It s works same.

这是灰色预测代码,一般灰色预测的参数需要应用到矩阵的运算,本代码通过对其变形,使其运算简单化.-This is a gray code forecast, Gray forecast general parameters applied to the needs of the matrix computation, the code through its deformation, it Operational simplistic.

应用背景在世界坐标系中已知的矩形点，在摄像机图像中出现，得到了变换和逆变换。视觉工作室2010。m_homography相化学图谱学；的…m_homography。设定值（m_ptregion，350，600）；…图片上的双= 100；双五= 200，/ /或点对图像双×，，，和，/ / / /，世界坐标；m_homography。cam2world（U，V，X，Y）；…双U1，V1；m_homography。world2cam（x，y，U1，V1）；关键技术四点在世界坐标如下：（0,0），（一，0），（A，B），（0，B）四点在摄像机坐标如下：（u0，v0），（U1，V1），（U2，V2），（U3，V3）然后，得到的变换矩阵是简单的。W = HX，W =（U，V，1）T，X =（x，y，1）Tx = w-1w ； ； ； ；  H1 H2 H3； ；H =  ；H4 H5  ； ；H6 ； ； ； ；H7 H8  ； ；H9h2y + H3 = H1X + U（h7x + h8y + H9）h4x + h5y + H6 = V（h7x + h8y + H9）

CLM集合卡曼滤波数据同化算法，是转的别人的一个程序，很好的程序。做同化的同仁可以-Application of the case, a Chinese-based RC500 chip LCD attendance control procedures, and RS232 computer interface, the program is characterized by all the attendance data is stored in the computer, read and returned to the CPU to deal with liquid crystal display.

符号积分法，有中文注释，大家试试，供参考学习~-Symbolic integration, notes in Chinese, we try, for reference to learn ~

有限元的分析程序，对于初学有限元在结构中的应用很好用-Finite element analysis program for learning the structure of finite element in the application of good use

字符串近似匹配PEX算法（又称k+1分片算法），查找出模式串在目标串中所有近似匹配位置。算法中使用了一种多字符串匹配算法MultipleBNDM，代码也已提供。-PEX,String approximately matching

The Kalman filter30 is a minimum-variance filter in which time-series measurements are incorporated recursively into estimates of state variables it is the optimal, Bayesian least-squares estimator for linear dynamic systems.-The Kalman filter30 is a minimum-variance filter in which time-series measurements are incorporated recursively into estimates of state variables it is the optimal, Bayesian least-squares estimator for linear dynamic systems.