FRUC_ARPS

关于MATLAB的经典教程，百看不厌，十分经典。(classic tutorial about matlab,and it is suitable fot the fresh man.)

16QAM is good use for students

此为SVM支持向量机，下载后放入matlab工具箱里，即可使用！(This is the SVM support vector machine, after download in matlab toolbox, can use!)

different types of signals matlab code

Otto结构反射率代码，用于搜索各个介质参数和厚度(Otto structure reflectivity code, used to search each of the dielectric parameters and thickness)

Wrapps Excel在处理一类表的MATLAB 支持： *启动一个Excel在前台或在背景板 *保存工作簿 *运行宏 *指定一个范围 *刷新工作簿 *出口表为PDF *选择一个给定表 *重新安装一个外接 *获取数据表给出了一个数据集，作为第一列名 *在给定的表（横向或字段的值 垂直） 例如： 包装= ExcelWrapper（ wbname ） tableDataset = wrapper.GetXLSDataset（ sheetname ， firstcolumnname ） (Wrapps handling an excel sheet in a MATLAB Class Supports: * Launching an Excel Sheet in Foreground or in Background * Save the workbook * Run a macro * Assign a range * Refresh workbook * Export Sheet to PDF * Select a given sheet * Reinstall an Add-in * Get a data table given the name of the first column as a dataset * Get the value of field in a given sheet (Horizontally or vertically) Example: wrapper = ExcelWrapper( wbname ) tableDataset = wrapper.GetXLSDataset( sheetname , firstcolumnname ) )

读入ORL人脸库的指定数目的人脸前前五张(训练) 输入：nFacesPerPerson --- 每个人需要读入的样本数，默认值为 5 nPerson --- 需要读入的人数，默认为全部 40 个人 bTest --- bool型的参数。默认为0，表示读入训练样本（前5张）；如果为1，表示读入测试样本（后5张）(Read into the ORL face database face before the specified number of five sheets (Training) Input: nFacesPerPerson--- everyone needs to read the sample size, the default value is 5 nPerson--- the number of people need to read , the default for all 40 individual bTest--- bool type parameters. The default is 0, read the training samples (top 5) If 1, read that the test samples (after 5))

二次规划法用拉格朗日方法求解等式约束二次规划和用有效集方法解一般约束二次规划问题(Quadratic Programming Lagrangian method for solving equality constrained quadratic programming and the use of efficient collection method for solving general constrained quadratic programming problems)

模糊控制仿真模型,采用MATLAB进行模糊控制系统仿真(FUZZY CONTROL SIMULINK)

Traditional optical flow technique that developed by Lucas and Kanade was improved with the optimized variable setting. It is quite simple and also helpful.