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Matlab program that shows Dominant Frequency detection using FFT method Use FT with zero-padding technique 10 times of FT resolution

matlab程序，通滤波器的例子,详细的看，适合初学matlab和图形图像处理的例子(matlab procedures pass filter example, detailed view, suitable for beginners and graphics image processing matlab example)

MATLAB CODE TO GET MFCC COEFFICIENTS

matlab模拟退火中源程序，基本例程都在压缩包中了。(simulated annealing matlab source code, the basic routines are in a compressed package. )

说明：  元胞自动机为使人员疏散离散模型更好描述行人微观特性，提出一种改进元胞自动机的人员疏散模型。(In order to make the discrete model of evacuation better describe the micro-characteristics of pedestrians, a new evacuation model based on improved cellular automata is proposed.)

说明：  计算非线性信号与5阶gold序列互相关程度大小的程序，含5阶gold序列的生成。并分析自互相关性能(Calculation of nonlinear signal and the 5-order serial correlation degree of gold the size of the program, including five gold bands generated sequences. And analyzed from the correlation properties)

maximumlikelihood (ML) channel estimation in time-hopping (TH) ultrawideband systems with pulse amplitude and position modulation, pulse amplitude modulation and pulse position modulation (PPM).

EKF_UKF_PF三种算法的比较粒子滤波matlab仿真程序(EKF UKF PF Comparison of the three algorithms particle filter matlab simulation program)

脉冲响应的相关分析算法并检验，多元数据分析的主分量分析投影，相控阵天线的方向图（切比雪夫加权）。( Related impulse response analysis algorithm and inspection, Principal component analysis of multivariate data analysis projection, Phased array antenna pattern (Chebyshev weights).)

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.)