SLMPAPR

Image histogram equalization

说明：  matlab 基于形状的图像检索算法 关于七个不变矩的主要算法 中 主要体现出 输入输出 的步骤！ ！！！！！！(matlab image retrieval algorithm based on the shape of seven invariant moments on the main algorithm is mainly reflected the input and output of the steps! ! ! ! ! ! !)

Matlab about Interpolating and Fitting

当一个节点需要给网络中的其他节点传送信息时，如果没有到达目标节点的路由，则必须先以多播的形式发出RREQ(路由请求)报文(AODV(Ad hoc On-demand Distance Vector Routing))

this is a Demand response programing. Offer cost minimization with Emergency demand response programming. OCM+EDRP

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.)

说明：  用matlab读取uff格式的数据文件。(load the data type of *.uff using matlab.)

普林斯顿开发的快速球面调和变换算法，我把它移植到windows下，并做成链接库，可以直接调用(Princeton rapid development of the spherical harmonic transform algorithm, I transpose it to the windows, and causing link library, and can directly call)

这个是自己编写的MOD算法的程序，用于字典学习，具体的应用有图像融合，图像去噪，丢失点补充等。(this is a dictionary learning method, and the reader can use this algorithm to do image fusion and image denosing.)

Its a IEEE Pesgm 08 paper.