Q-learning

编写了一种代数重建算法的投影系数计算程序，可用于物体的三维恢复计算。(The projection coefficient of the algebraic reconstruction technique was developed.)

this is a Cyclic Hamming code Simulation in matlab.

基于matlab开发的手势识别,里面有测试代码和图片 还有一个可以运行的.fig文件,可以直接运行的.并且同时分享了matlab的源码,效果很不错,可以学习和参考.相比C++开发的要简单易懂，可以通过代码了解手势识别的方法和原理，可以先学习matlab版的手势识别，再尝试编写C++和opencv版的。(Gesture recognition based on MATLAB development, there are test code and pictures and one can run the.Fig file, can be directly run. And at the same time sharing matlab source code, the effect is very good, you can learn and reference. Compared to the development of C++ is simple and easy to understand, can understand the principle and method of gesture recognition through the code, gesture recognition can learn the matlab version, and then try to write C++ and opencv version.)

说明：  编程分别用jocabi迭代法求解方程。在一般情况下，Guass-Seide迭代法比Jacobi迭代法收敛要快一些，但这也不是绝对的，在木些情况下，Jacobi迭代法收敛，Guass-Seide迭代却可能不收敛(use matlab to deal with the equation )

相控阵雷达的最优分区搜索算法 (Search algorithm for optimal partition of phased array radar)

此代码为《雷达系统分析与设计》的书中的代码(This code is " radar system analysis and design," the code book)

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 ) )

code for maximum likelyhood in matlab

空时编码的一个matlab实现源程序，适合初学者。(Space-time coding to achieve a matlab source code, suitable for beginners.)

说明：  运动载体的定位与导航一般应用Kalman滤波算法。可 靠的Kalman滤波算法要求有可靠的函数模型、随机模型以 及合理的估计方法。实际情况中运动载体一般难以确保规 则运动，因而精确的函数模型的构造十分困难。近年来， 有不少学者对机动载体运动模型进行了讨论，所属内容各 具特点，建立的各种模型包括微分多项式模型、常速度模 型、常加速度模型、时间相关模型，半马尔柯夫模型、 Noval统计模型及机动载体的“当前”统计模型(Moving vehicle positioning and navigation general application of the Kalman filter algorithm. Kalman filter algorithm requires a reliable function model, a stochastic model, as well as a reasonable estimation method. Moving carrier in the actual situation is generally difficult to ensure a regular motion, and thus the the precise function model structure is very difficult. In recent years, many scholars have discussed the movement of the motor carrier model, each with distinct characteristics of their respective content, establish various models including the differential polynomial model, constant velocity model, constant acceleration model, time-dependent model, semi-Markov model, Noval statistical model and motor carrier &quot current&quot statistical model)