DCTSUANSHUIYIN

说明：  （1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象； （2）将所有子图象按列相接变成一个81维的行向量； （3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量； （4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。 （5）求出所有子块的特征向量。 ((1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-images according to out-phase into a 81-dimensional row vector (3) all 200 lines for KL transform vector, derived its corresponding covariance matrix of eigenvectors and eigenvalues, in descending order by eigenvalue and the corresponding eigenvector (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the PCA, the original image block to the 40 feature vectors on the projection, the projection coefficients obtained by this sub-block eigenvector. (5) calculated for all sub-block eigenvector.)

C-C算法应用关联积分能够同时估计出时间延迟和嵌入维数，是相空间重构的前提。 本程序通过C-C算法计算duffing方程产生的混沌时间序列的时间延迟和嵌入维数。(CC algorithm is used to simultaneously estimate the correlation integral time delay and embedding dimension, is a prerequisite for phase space reconstruction. This procedure CC algorithm duffing equation chaotic time series generated by the time delay and embedding dimension.)

线性约束最小方差波束形成 （仿真中在两个期望方向上形成波束指向，在两个干扰方向上形成零陷）(LCMV，linearly cinstrainted minimum variance)

说明：  开发的结合用C语言与matlab计算阶跃光纤中的模场分布、传播常数与功率分布情况。(The combination of development with C language and matlab calculation step in the fiber mode field distribution, propagation constant and power distribution.)

image mosaicing done in MATLAB

基于MATLAB/simulink的永磁同步电机仿真模型.空间失量磁场定向算法222(Based on MATLAB/simulink simulation model of permanent magnet synchronous motor. Spatial loss field oriented algorithms 222)

cat map function to scramble a one dimentson sequence

威布尔分布的参数估计，计算速度快，准确度高，值得深究(Parameter Weibull distribution estimates, calculation speed, high accuracy, worth studying)

System will automatically delete the directory of debug and release, so please do not put files on these two directory.

将粗糙集中的一系列概念引入到故障选线中，实现了高效可靠的选线判据，仿真证明算法正确可信(rough set based fault line detection)