nsga2--original--test

说明：  practical work shop in C/C++

circle fitting to find damping

天线圣经，每个学习天线的人必看经典。其中对天线的原理性描述非常深刻。(Antenna Bible study each antenna, a person must classic. The principle on which the description of the antenna is very profound.)

global minimization of the active contour model

基于simulinck的坐标变换详解，包括所有park变换，clack变化等所有模块(Detailed simulinck coordinate transformation based, including all modules all park transform, clack changes)

Cheby 四种基本滤波器(Cheby four basic filter)

标准粒子群算法，应用领域广阔，收敛快计算精度高(Standard particle swarm algorithm, broader applications, fast convergence and high precision )

几个有关通信系统的matlab程序,有关信号，包含：a,对抽样频率为90kHz的信号进行下抽样抗混叠滤波器设计的程序，b,信号抽样率转换的程序，c,通过补零内插对信号进行下抽样，分析抽样对信号频谱的影响的程序(Some matlab programs about communiction system,mainly about signal,a,program about filter to signal sampling without aliasing ,b,transfering signal sampling rate,c,zeros-added sampling,with its influence of phase-frequency。)

通过在语音信号中嵌入鲁棒性水印和脆弱性水印，实现对数据的版权保护。(Embedded in the speech signal through robust watermarking and fragile watermarking, copyright protection to achieve the data.)

说明：  决策树(Decision Tree）是在已知各种情况发生概率的基础上，通过构成决策树来求取净现值的期望值大于等于零的概率，评价项目风险，判断其可行性的决策分析方法，是直观运用概率分析的一种图解法。由于这种决策分支画成图形很像一棵树的枝干，故称决策树。在机器学习中，决策树是一个预测模型，他代表的是对象属性与对象值之间的一种映射关系。Entropy = 系统的凌乱程度，使用算法ID3, C4.5和C5.0生成树算法使用熵。这一度量是基于信息学理论中熵的概念。 决策树是一种树形结构，其中每个内部节点表示一个属性上的测试，每个分支代表一个测试输出，每个叶节点代表一种类别。 分类树（决策树）是一种十分常用的分类方法。它是一种监督学习，所谓监督学习就是给定一堆样本，每个样本都有一组属性和一个类别，这些类别是事先确定的，那么通过学习得到一个分类器，这个分类器能够对新出现的对象给出正确的分类。这样的机器学习就被称之为监督学习。(Decision tree is a decision analysis method based on the known probability of occurrence of various situations, which can calculate the probability that the expected value of net present value is greater than or equal to zero, evaluate the project risk and judge its feasibility by constructing a decision tree. It is a graphic method of intuitively using probability analysis. Because this kind of decision branch is drawn as a graph, it is very similar to the branch of a tree, so it is called decision tree. In machine learning, decision tree is a prediction model, which represents a mapping relationship between object attributes and object values. Entropy = the disorder degree of the system, using algorithms ID3, C4.5 and C5.0, spanning tree algorithm using entropy. This measure is based on the concept of entropy in information theory.)