STDM

常见的Matlab命令的实用例子，例子详细清楚明白，(failed to translate)

Excel Link is a software add-in that integrates Microsoft Excel and MATLAB in a Microsoft Windows-based computing environment. By connecting Excel and MATLAB, you can access the numerical, computational, and graphical power of MATLAB from Excel worksheet and macro programming tools. Excel Link lets you exchange and synchronize data between the two environments.

说明：  运用de Casteljau递归求值法编写三角形域上的n次Bezier曲面计算程序，给定 控制顶点(de Casteljau using recursive method for the preparation of the triangle domain n Bezier surface calculation procedures for determining control vertices)

All about rough set based fuzzy algorithm for image segmentation

this file is simulink of DC motor and PID controller

一个瑞利衰落信道，很有用，下来看看吧!!!(this is raleigh channel,it is very usefull,go to downloading)

基于多小波变换的阈值降噪技术的相邻系数法降噪算法(Based on multi-threshold wavelet transform coefficients of adjacent noise reduction noise reduction algorithm)

omp 算法 你可以通过这个算法完成图像恢复，效果好，恢复时间短，误码率低，解决凸优化算法复杂度高的缺点(omp algorithm you can accomplish by this algorithm for image restoration, good effect, shorter recovery time, error rate, to solve the shortcomings of convex optimization algorithm with high complexity)

Proportional-Integral-Derivative (PID) control is the most common control algorithm used in industry and has been universally accepted in industrial control. The popularity of PID controllers can be attributed partly to their robust performance in a wide range of operating conditions and partly to their functional simplicity, which allows engineers to operate them in a simple, straightforward manner (exp. ref:national instruments). This file analyse different conditions and methods for tuning the PID controllers.

说明：  选择“BreastCancer”数据集，使用支持向量机(SVM)对其进行分类。作为对比，第一次对特征集直接进行支持向量机分类，第二次对特征集进行主成分分析法的特征提取后，再对特征提取后的特征集进行支持向量机分类。并且对比和分析了两次分类的结果。(The BreastCancer data set is selected and classified by Support Vector Machine (SVM). For comparison, the first time the feature set is classified directly by support vector machine, the second time the feature set is extracted by principal component analysis, and then the feature set is classified by support vector machine. The results of the two classifications are compared and analyzed.)