source-code-file-part1

一， GUI界面，proj.m 图片选择窗口，testListBoxlj.m 二,最优化算法 PSO.m POWELL.m 三，互信息尺度 MI.m EMI.m RMI.m HiMI.m GMI.m FPMI.m 四，POWELL.m附属子程序 oneDimSearch.m 五，PSO.m附属子程序 myMI.m 六，配准结果检验程序 restore.m 七， gobad.m对图像进行“平移”和“旋转”(First, GUI interface, proj.m picture selection window, testListBoxlj.m II optimization algorithm PSO.mPOWELL.m three or four MI.mEMI.mRMI.mHiMI.mGMI.mFPMI.m-scale mutual information, POWELL.m subsidiary sub- oneDimSearch.m five procedures, PSO.m subsidiary Subroutine myMI.m six, matching the results of testing procedures restore.m 7, gobad.m images )

基于Henon映射的混沌伪随机序列发生器，用MATLAB7.1实现(Henon mapping based on Chaotic Pseudo-random sequence generator, to achieve with MATLAB7.1)

用于进行声压转换的matlab文件，声压数据来源于ansys软件的计算后导出来的。(Used for sound pressure to convert matlab file, sound pressure data from the calculation software ansys guide out.)

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)

3-Dimension FDTD代码PEC边界(3-Dimension FDTD code PEC boundary)

实现对地震数据的一维傅里叶变换 并对数据进行设定工业频率值的情况下滤波 (power frequency filtering)

说明：  matlab 程序，可用于地震正演模拟中的震源，数据处理等(Ricker wavelet,seismic modeling)

用kalman滤波算法来跟踪鼠标, 小有意思的程序, 有助于理解kalman算法(Using kalman filter algorithm to track the mouse, a small interesting programs, help to understand the kalman algorithm)

求网络图中各节点的聚类系数及整个网络的聚类系数 求解每个节点的聚类系数，找某节点的所有邻居，这些邻居节点构成一个子图(Seeking clustering coefficient network diagram and the entire network clustering coefficient of each node)

经典的使用Kalman filter的程序，快速掌握kalman的技术 (classic easy program for kalman filter)