PAM

说明：  Landau, Ioan D. Digital control systems : design, identification and implementation. - (Communications and control engineering)

伪随机序列与扩频通信方面的实验程序，虽然只有一个程序但是有通讯实现的全过程(Pseudo-random sequence spread spectrum communications with the experimental procedure, while only a program but the whole process of communication to achieve)

利用支持向量机完成的导航高度信息融合，需要用到lssvm toolbox。内的000是数据文件。(Support vector machine to complete a high degree of navigation information fusion, need to use lssvm toolbox. The 000 is the data file.)

A simple algorithm for Erythrocyte (RBC) segmentation peripheral blood smear images. Function takes the green component of RGB image as input. article: M. Jahanifar et al., Automatic Zone Identification in Blood Smear Images Using Optimal Set of Features , 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME)

说明：  产生m序列 画出其自相关函数和功率谱密度曲线 利用m序列模二产生gold序列 计算gold序列的互相关函数(Generating M-series Draw the autocorrelation function and power spectral density curve Generating gold sequence by m-sequence module two Calculating the cross correlation function of gold sequence)

MOPSO的算法，希望大家能有用处，呵呵呵呵呵(MOPSO algorithm, hope that we can be useful, Oh Oh Oh)

This is a matlab code for transfer of color from source image to target image. Details are in colormatch.m

Hybrid Simulated annealing for synthesis of fiber Bragg grating

说明：  这是关于《深入浅出MATLAB7_X混合编程》的源代码共有七章内容(The code is about the book"The mix program of MATLAB7_X")

说明：  SURROGATES工具箱是一个多维函数逼近和优化方法的通用MATLAB库。当前版本包括以下功能： 实验设计：中心复合设计，全因子设计，拉丁超立方体设计，D-optimal和maxmin设计。 代理：克里金法，多项式响应面，径向基神经网络和支持向量回归。 错误和交叉验证的分析：留一法和k折交叉验证，以及经典的错误分析（确定系数，标准误差;均方根误差等;）。 基于代理的优化：高效的全局优化（EGO）算法。 其他能力：通过安全裕度进行全局敏感性分析和保守替代。(SURROGATES Toolbox is a general-purpose MATLAB library of multidimensional function approximation and optimization methods. The current version includes the following capabilities: Design of experiments: central composite design, full factorial design, Latin hypercube design, D-optimal and maxmin designs. Surrogates: kriging, polynomial response surface, radial basis neural network, and support vector regression. Analysis of error and cross validation: leave-one-out and k-fold cross-validation, and classical error analysis (coefficient of determination, standard error; root mean square error; and others). Surrogate-based optimization: efficient global optimization (EGO) algorithm. Other capabilities: global sensitivity analysis and conservative surrogates via safety margin.)