FovNLM

读取avi视频呵呵呵呵 (Read avi video)

16V280柴油动力计算，活塞连杆作用力分析计算及侧向力计算(16V280diesel engine )

Relief算法中特征和类别的相关性是基于特征对近距离样本的区分能力。算法从训练集D中选择一个样本R，然后从和R同类的样本中寻找最近邻样本H，称为Near Hit，从和R不同类的样本中寻找最近样本M，称为Near Miss，根据以下规则更新每个特征的权重： 如果R和Near Hit在某个特征上的距离小于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻是有益的，则增加该特征的权重；反之，如果R和Near Hit在某个特征上的距离大于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻起负面作用，则降低该特征的权重。(The correlation between feature and category in Relief algorithm is based on distinguishing ability of feature to close sample. The algorithm selects a sample R from the training set D, and then searches for the nearest neighbor sample H from the samples of the same R, called Near Hit, and searches for the nearest sample M from the sample of the R dissimilar, called the Near Miss, and updates the weight of each feature according to the following rules: If the distance between R and Near Hit on a certain feature is less than the distance between R and Near Miss, it shows that the feature is beneficial to the nearest neighbor of the same kind and dissimilar, and increases the weight of the feature; conversely, if the distance between R and Near Hit is greater than the distance on R and Near Miss, the feature is the same. The negative effect of nearest neighbor between class and different kind reduces the weight of the feature.)

多目标遗传算法优化PID参数,是M文件。内容很详细，有需要的可以下载(this is matlab file ,it is for adpation GA for PID )

过程相关性检验，模型+脚本。matlab(correlation test, model+ scripts. matlab)

ωKA是实现SAR图像成像的一种方法，该程序实现了ωKA的近似和精确两种成像方式，仿真参数参考《合成孔径雷达成像——算法与实现》，利用Stolt插值实现了目标成像，最后在方位向和距离向进行切片，观察成像结果。(ωKA is one of the point target imaging algorithms of SAR . This program implements both exact and approximate ways of imaging. Simulation parameters refer to the book Synthetic Aperture Radar imaging- Algorithms and Implementation .)

基于四边单元速度传热问题程序,入门程序，简单易懂，适合初学者对该方法编程的一个初步认识(Based on four sides of the unit rate of heat transfer procedures, entry procedures, easy to understand for beginners to the method of programming a preliminary understanding)

用最速下降法求解多维函数的极值 function [x,minf]=minFD(f,x0,var,eps) 目标函数：f 初始点：x0 自变量向量：var 精度：eps 目标函数取最小值时的自变量值：x 目标函数的最小值：minf( With the steepest descent method for solving the multi-dimensional function of the extreme value function [x, minf] = minFD (f, x0, var, eps) target function: f Initial Point: x0 argument vector: var Accuracy: eps target function takes the minimum value of the independent variable values: x minimum target function: minf)

Desired beamformer， Beam patterns for broadband array(Desired beamformer, Beam patterns for broadband array)

Cooperative Diversity in Wireless Networks. show big benefit from results when the relay is located between the sender and the destination