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一束光通过一个透明圆柱体，圆柱后有一个屏幕，程序最终反映屏幕上的 图象随圆柱半径的变化而变化。其中光强分布是采用相干叠加原理，因为其中采 样点数目不足，所以光强分布的结果和真实的有一定差距。增加采样点数目和改 变采样点范围能够改善输出光强的结果逼真程度。(一束光through a transparent cylinder, a cylindrical post-screen process will ultimately reflect the on-screen image with the cylindrical radius change. Light intensity distribution of which is the principle of coherent superposition, because the insufficient number of sampling points, so light intensity distribution of the results and have some real gaps. Increase in the number of sampling points and to change the scope of sampling points can improve the outcome of the output light intensity the degree of realism.)

扩展EKF 序列最大估计，经典原码，有注释(Sequence of the largest expansion of EKF estimates, the classic original code, annotated)

WRK实验所需要软件的参考说明（Windbg）(inactivated laboratory reference to the need for software (Windbg))

协作通信中的AF，DF转发的matlab代码，其中还有一些是编码的代码。(Collaborative communication of AF, DF forwarding of matlab code, and some of them are encoded code. )

说明：  matlab 多元线性回归模型求解 实现对多影响因素的分析(matlab Linear_regression)

实验7+图形句柄的应用、可视化界面的设计-答案(Handle applications the experimental visual interface design- the answer)

matlab book for engineering and sicentfic

散射工具箱BSC-plane 用于计算米散射的散射系数，入射场系数，计算Tmatrix(calculating scattering coefficiency)

在雷达成像中脉冲压缩技术使非常重要的一部分，去斜处理的方法是最常使用的(Pulse compression radar imaging technology enables a very important part, to ramp handling method is the most commonly used)

1. 按照式计算相似性矩阵 2. 排除自身的相似度 3. 按照式计算归一化矩阵 4. 按照式计算归一化拉普拉斯图矩阵L 5. 计算L的特征向量，将前个特征值最大的向量按列放置成一个矩阵，即依次为前个特征值最大的特征向量 6. 归一化X形成矩阵Y 7. 对矩阵Y按每行为一个数据点，进行k-means聚类，第行所属的类就是原来所属的类。(1. similarity matrix based on formula computation 2. rule out your own similarity. 3. normalized matrix based on formula computation 4. normalized formula Laplasse matrix L according to formula. 5. Calculate the eigenvectors of L. Place the vectors with the largest eigenvalues in columns into a matrix, that is, the eigenvectors with the largest eigenvalues in turn. 6. normalized X formation matrix Y 7. K-means clustering is applied to matrix Y according to one data point per behavior. The first row belongs to the original class.)