ACO-TSP

DAta Encryption standard implemented IN MATLAB

建立分数阶的matlab的类，定义了这个类后，对象就可以用分数阶的传递函数定义出来，适合初学者(After establishing fractional matlab class defines this class, you can use the object transfer function defined Fractional out, suitable for beginners)

EM 算法是Dempster，Laind，Rubin于1977年提出的求参数极大似然估计的一种方法，它可以从非完整数据集中对参数进行 MLE估计，是一种非常简单实用的学习算法。这种方法可以广泛地应用于处理缺损数据、截尾数据以及带有噪声等所谓的不完全数据，可以具体来说，我们可以利用EM算法来填充样本中的缺失数据、发现隐藏变量的值、估计HMM中的参数、估计有限混合分布中的参数以及可以进行无监督聚类等等。(EM algorithm is a method for maximum likelihood estimation of parameters proposed by Dempster, Laind and Rubin in 1977. It can estimate parameters from incomplete data sets by MLE. It is a very simple and practical learning algorithm. This method can be widely used in dealing with the so-called incomplete data such as defective data, censored data and noisy data. Specifically, we can use EM algorithm to fill missing data in samples, find hidden variable values, estimate parameters in HMM, estimate parameters in finite mixed distribution, and so on. Unsupervised clustering and so on.)

说明：  该程序用于在实验中所测量的振动、噪声、水声以及数值模拟监测所得到的信号，实现信号的频谱分析。(The program is used to measure the vibration, noise, underwater sound and the signal obtained by numerical simulation monitoring in the experiment to realize the spectrum analysis of the signal.)

说明：  MATLAB s symbolic features to derive Simpson s 1/3 integration rule

提供一元函数插值,样条插值,多项式插值或拟合,曲线拟合等数据拟合源码(Provide one yuan function interpolation, spline interpolation, polynomial interpolation or fitting, curve fitting, etc. Data fitting source)

本文对变步长(VSS) 自适应滤波算法进行了分析,针对输入信号高度相关时算法收敛速度下降导致性能下降 的问题,提出了一种解相关变步长归一化LMS 自适应算法,引入相关性原理,使得算法保持良好的收敛性能。计算机仿真 结果与理论分析相一致(In this paper , we discuss and analyze the variable step2size adaptive algorithm(VSSLMS) ,in which the descent of it s convergence speed leads to the reduction of performance when input signals are heavily correlated . As a result , we propose an uncorrelated variable step2size normalized LMS adaptive algorithm. Due to the principle of correlation ,the algo2 rithm can achieve good performance. The simulations agree to the theoretical analysis.)

Compute the PSNR of two gray scale image

he files in this package comprise the Matlab implementation of a foreground segmentation algorithm based upon graph cuts, described in: Better Foreground Segmentation Through Graph Cuts, N. Howe & A. Deschamps. Tech report, http://arxiv.org/abs/cs.CV/0401017. The file extractForeground.m contains a sample function that will perform a complete foreground segmentation for static camera video. It uses a number of parameters, which are described in the documentation. An attempt was made to choose sensible default values, but they may need to be adjusted for some videos. More generally, the implementation in extractForeground.m is only a suggestion of how the graph-based segmentation might be used. The same approach can be applied with other (perhaps time-adaptive) background models. The key step is at line 113 of the code, once the deviation array has been created. The graph-cut segmentation can be applied similarly to a deviation matrix computed in any other way.

R语言学习资料,R语言与统计分析-汤银才(R language)