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利用李亚普诺夫直接法和PID算法，分别仿真对差动驱动轮式机器人的控制，使其行走直线或者圆轨迹程序(Using Lyapunov direct method, the control differential drive wheeled mobile robot is walking a straight line or circle track program)

1． 对薄膜形成与生长中相关物理过程及现象进行分析和建模。 2． 利用蒙特卡罗（Monte Carlo）方法和分子动力学（Molecular Dynamics）方法对薄膜形成与生长过程进行计算机模拟。 3． 对多孔硅形成的模拟。 (1. The film formation and growth of relevant physical processes and phenomena analysis and modeling. 2. Monte Carlo (the Monte Carlo) methods and molecular dynamics (Molecular Dyna mics) method for thin film formation and growth process computer simulation. 3. On the formation of porous silicon simulation.)

上传Matlab源代码程序，希望对下载的人有所帮助(Upload Matlab source code procedures in the hope that the right people download help)

经典music算法，包括 详细的谱峰搜索函数，程序条例清楚，运行结果正确。(classic music, including the detailed function of spectrum peak searching. this pro is clear and works well ：）)

说明：  雷达信号处理 雷达作用距离 功率，距离，RCS(Pulse compression simulation MATLAB version, GUI procedures)

本程序用以仿真块状导频时不同信噪比条件下的误码率和均方误差，信道估计采用最小二乘法和改进的最小二乘法。 (The pilot program for the simulation block different SNR when the bit error rate and mean square error channel estimation using the least square method and the improved least square method.)

基于小波变换的图像去噪的matlab程序的源代码。(Image denoising based on wavelet transform of the matlab source code.)

金钱豹matlab测试设计方法 打开金钱豹 —》 设置—》交易模型设置（同时，打开安装目录中matlabfiles文件夹，如果不存在，就解压对应的压缩文件matlabfiles ）—》阅读其中的matlab程序(现有两个较为简单的示例策略） —》（假设我们要测试日内一分钟价差套利）—》选择接口类型matlab，添加模型名称，选择输入参数，选择输出参数，以及m文件路径—》以上步骤完成后按“修改",模型即转入—》打开测试品种的行情（IF1005，IF1006）——》转换成一分钟频率（按”分时/周期切换“或右键）——》选择模型—》对两个品种分别加装同一个模型matlab1 jiacha——》这个时候会自动生成当日价差图。(matlab1 jiacha)

CP-OFDM的MATLAB源代码，简洁明了，非常适合初学入门者(CP-OFDM MATLAB source code, concise, very suitable for beginners beginners)

AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files 1. ADABOOST_tr.m 2. ADABOOST_te.m to traing and test a user-coded learning (classification) algorithm with AdaBoost. A demo file (demo.m) is provided that demonstrates how these two files can be used with a classifier (basic threshold classifier) for two class classification problem.(AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files 1. ADABOOST_tr.m 2. ADABOOST_te.m to traing and test a user-coded learning (classification) algorithm with AdaBoost. A demo file (demo.m) is provided that demonstrates how these two files can be used with a classifier (basic threshold classifier) for two class classification problem.)