spectrogram_example

用户手册是一本关于MATLAB的参考书，其中有大量的实例和相关应用介绍，值得大家收藏，可以作为学习教材和开发的参考书. English version(User)

Low pass filter desinged using matlab

关联向量机，很好的工具箱，有用于分类和回归的例子(Relevance vector machine, a good toolbox, there are examples for classification and regression)

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.)

信道模型讲解程序(Channel model to explain the procedures)

MATLAB入门,有利于快速熟悉MATLAB(MATLAB portal is conducive to rapid familiar with MATLAB)

用于计算分析无源定位中TDOA的定位精度，画出了GDOP图(Used to calculate and analyze the positioning accuracy of TDOA in passive location, and draw the GDOP graph )

关于经过多径信道后，对于接收到的信息进行符号速率的估计，代码简洁易懂，有完整注释(After about multipath channel, the estimate for the received information symbol rate, the code is simple and easy to understand, complete comment)

说明：  此代码是svm的源码，可利用此程序进行支持向量机的分类处理(This code is the source code of svm, which can be used to classify support vector machines.)

diffraction grattings and optical devises