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说明：  标准label propagation的一个实现版本，实现为函数，可以直接调用即可。(As a standard implementation of label propagation algorihtm, you could integrate into your work freely.)

MATLAB实现HFC高光谱端元提取算法(MATLAB code of HFC algorithm)

利用中心差分法计算无阻尼结构动力响应，并作图(Using central difference method to calculate the structural dynamic response)

实现图像的降维处理,可以缩短大量的计算时间。(Dimensionality reduction of image processing, a lot of computation time can be shortened.)

最小费用最大流问题是经济学和管理学中的一类典型问题。在一个网络中每段路径都有“容量”和“费用”两个限制的条件下，此类问题的研究试图寻找出：流量从A到B，如何选择路径、分配经过路径的流量，可以达到所用的费用最小的要求。(Minimum cost maximum flow problem is economics and management in a class of typical problems. Each path in a network has the " capacity" and " costs" two limiting conditions, such problems of trying to find out: Flow from A to B, how to choose the path, the path of the flow through the distribution can be achieved The use of the minimum cost requirements.)

用于计算dft函数相关性的代码，包括自相关和互相关(To Calculate DFT correlation function)

这是模式识别中最小错误率Bayes分类器设计方案。 自行完善了在不同先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。 全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。 调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。 调用最小错误率贝叶斯分类器决策子函数时，根据先验概率数组，通过比较概率大小判断一个体重身高二维向量代表的人是男是女。 主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小错误率贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到不同先验概率条件下错误率的统计。 (This is the minimum error rate pattern recognition Bayes classifier design. Self- improvement prior probability in different conditions , male , female and total error rate error rate statistics , into which each array . All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions . Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector third step is to application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function . Call the minimum error rate decision Functions Bayesian)

一个基于PCA_Nicolas的matlab编程，可以实现人脸识别的功能(Based on PCA_Nicolas of matlab programming, face recognition function can be achieved)

This is code simulates maximal ratio combining

说明：  MATLAB实现的图像缩小程序，已通过调试(err)