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高斯过程是一种非参数化的学习方法，它可以很自然的用于regression，也可以用于classification。本程序用高斯过程实现分类！(Gaussian process is a non- parametric method of learning, it is very natural for regression. can also be used for classification. The procedures used to achieve classification Gaussian process!)

通过分析缸内热力过程，建立柴油机燃烧冲程微分方程。基于MATLAB工作软件实现柴油机燃烧过程仿真。其中，柴油机结构及运行参数可根据实际需要修改；传热系数的计算采用埃歇尔伯格公式；微分方程求解采用四阶龙格-库塔方法，计算步长为5度曲轴转角（可修改）；采用单韦伯曲线模拟燃烧放热规律；仿真结束后，绘制燃烧过程P-V图和P-phi图。(By analyzing the cylinder thermodynamic process, the establishment of diesel combustion stroke differential equations. Based on MATLAB working diesel engine combustion process simulation software. Among them, the diesel engine structure and operating parameters according to the actual needs modification heat transfer coefficient calculated using the formula 埃歇尔伯格 differential equations solved by Runge- Kutta method to calculate the step is 5 degrees crank angle (modifiable) single Webber curve simulation combustion Law after the simulation, drawing the combustion process PV diagram and P-phi diagram.)

说明：  数据统计描述 第一节 数据分析中常用的统计量及其分布 第二节 频数变量数据特征描述 第三节 综述变量数据特征的描述 第四节 数据分析过程的记录与重现(Data described in section I of data analysis commonly used in statistics and their distribution in section II, the frequency characteristics of variable data variables described in Section III Summary data on the characteristics of the process of data analysis described in section IV of the record and reproduce)

一个用matlab编写的一维FDTD算法程序，与大家共享(using Matlab to prepare a one-dimensional FDTD algorithm procedures, and share)

蒙特卡洛仿真。供个人学习研究的几个小程序。(Monte Carlo simulation. For individual learning research program.)

信号维数的估计，使用高阶累积量对MPSK信号进行调制识别，多抽样率信号处理。( Signal dimension estimates, Using high-order cumulants of MPSK signal modulation recognition, Multirate signal processing.)

利用Matlab语言开发的一种LSB最低有效位数字水印算法(Matlab language development using a least significant bit LSB watermarking algorithm)

对于粒子群算法和MATLAB学习者有很大帮助，非常好用的。(PSO algorithm and MATLAB for learners of great help, very useful.)

用MATLAB编程 ct图像重建，S-L头模型(ct image reconstruction)

Introduction to Stochastic Search and Optimization