MIMO-OFDM_Wireless_Com.matlab

用于网络拓扑的随机生成算法通用Matlab程序(For the randomly generated network topology algorithm generic Matlab procedures)

采用ALR修复ETM+ slc-off影像。(It use the method of ALR to repair ETM+ slc-off image.)

最新的标号学习分布matlab代码，是一种比较新的优化算法(The latest distribution matlab code label study, is a relatively new optimization algorithm)

APF仿真，关于matlab中APF的全面仿真，自己画的图，绝对正确，无任何错误(APF simulation model)

所给程序中，先给出一副原始图像，在其中添加椒盐噪声，然后用几种方法进行平滑滤波，包括邻域均值法，邻域中值滤波和K邻域均值滤波，其中的K邻域均值滤波所选掩膜为3*3，K值取5，图片选用board.ti。(Given program, the first is given an original image, in which the added salt & pepper noise, and then use several methods of smoothing, including the neighborhood average method, o-field value filtering and K neighborhood mean filter, where K o domain mean filter mask for the selected 3* 3, K value of taking five, picture selection board.ti.)

基于Matlab的循环码的编码与译码，内附源码的详细注释及其编译码的过程-Matlab code based on the cycle of encoding and decoding, containing detailed notes and source coding and decoding process(Matlab cyclic code encoding and decoding, enclosing the source detailed notes of the encoding and decoding process-Matlab code based on The Cycle of encoding and Decoding, containing detailed Notes and source Coding and Decoding Process)

图像锐化图像锐化图像锐化图像锐化图像锐化图像锐化图像锐化图像锐化图像锐化图像锐化图像锐化(imageimageimgeimageimageimageimagiii)

Figital Imagin process And Matlab E-book

Torque equation for human motion

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.)