PMSGWind

基于PCA的人脸特征抽取，首先将200幅人脸图像转换成向量形式，进而进行组成样本矩阵。(The PCA-based facial feature extraction, first 200 face images into a vector format, then the composition of the sample matrix.)

混沌时序多步预测函数，采用matlab编程，简单易懂(Chaotic time series multi-step prediction function, using the Matlab programming, easy to understand)

说明：   有一个长度为整数L(1<=L<=10000)的马路，可以想象成数轴上长度为L的一个线段，起点是坐标原点，在每个整数坐标点有一棵树，即在0,1,2，...，L共L+1个位置上有L+1棵树。 现在要移走一些树，移走的树的区间用一对数字表示，如 100 200表示移走从100到200之间（包括端点）所有的树。 可能有M(1<=M<=100)个区间，区间之间可能有重叠。现在要求移走所有区间的树之后剩下的树的个数。 输入： 两个整数L(1<=L<=10000)和M(1<=M<=100)。 接下来有M组整数，每组有一对数字。 输出： 可能有多组输入数据，对于每组输入数据，输出一个数，表示移走所有区间的树之后剩下的树的个数。(Has a length of integer L (1 &lt = L &lt = 10000) of the road, one can imagine a number line segment of length L, the starting point is the coordinate origin, each integer coordinate points in a tree, i.e. 0,1 , 2, ..., L total has L+1 L+1 positions on the tree. Now to remove some trees, tree removed by a range of digitally represented, such as 100 200 represents removed (inclusive) between 100 and 200 all the trees. There may be M (1 &lt = M &lt = 100) intervals, there may be overlap between the intervals. Now, after the number of trees require the removal of all remaining sections of the tree. Input: two integer L (1 &lt = L &lt = 10000) and M (1 &lt = M &lt = 100). Then there is an integer M groups, each with a pair of numbers. Output: there may be multiple sets of input data, for each set of input data, the output of a number indicating the number of the removal of all remaining sections of the tree after tree.)

利用预测的方法实现图像的压缩，可以应用于光谱图像谱间去相关(The use of projected images of the compression method can be applied to spectral-spectral images to the relevant)

说明：  simulation of ballistic target trajectiry

matlab的数据分析的能力非常强，利用matlab软件进行数据分析，在数学建模中有非常重要的应用(matlab data analysis, data analysis using matlab software has very important applications in the mathematical modeling)

FDTD(时域有限差分法)算法的Matlab源程序(FDTD (finite difference time domain method) algorithm of Matlab source code)

Using Cuk Converter model

除氧器计算,主要用于除氧器蒸汽耗量计算，简单准确(pipe design)

Edge detection result should be enhanced using linear method like Median filter to remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the centre of the pupil by counting the number of black pixels (zero value) of each column and row. Then get each row and column that has the maximum number of these black pixels. Then determine the center by simple calculation according to the image coordinate to set it correct on the image, consequently we can determine the radius of the pupil. Thus we can find the pupillary boundary (inner). A similar procedure is extended by using a coarse scale to locate the outer boundary (limbus) which can be apparent by using the mid-point algorithms of circle and ellipse.