wsn-dijkstra

说明：  阵列信号处理中常用的LS算法,用于估计目标的方位角等位置参数.(err)

这是之前上传的Extract_test_IMFCC程序里面调用的子函数，作配合使用，请看清楚再下载(This is one subfunction of Extract_test_IMFCC uploaded before.Since pudn.com cannot upload several functions, they must divided.)

cHANNEL ESTIMATION mimo

频率选择性信道下空时分组码直接盲信号检测算法.(Frequency-selective channel under the direct space-time block codes blind signal detection algorithm.)

The MATLAB code is used to match two iris templates.

用于时间序列的去噪以及预测，基于独立成分分析的最小二乘时间序列方法。(Noising for time series and forecasting, time series least squares method based on independent component analysis.)

local binery (lbp), feature extraction for target tracking applications

CrossOver code in matlab

说明：  永磁同步电机的弱磁控制，使电机进入弱磁区域，达到提高转速的效果。(Flux weakening control of permanent magnet synchronous motor, available)

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.)