VibrationSimulationUsingMatlabandANSYS

Matlab程序，用于比较线性均衡器、判决反馈均衡器(DFE)、盲最大似然序列估计均衡器(MLSE)等误码率性能。(Matlab procedures used to compare the linear equalizer, decision feedback equalizer (DFE), Blind maximum likelihood sequence estimation equalizer (MLSE), such as bit error rate performance.)

主要介绍了dvhop算法的源代码，先计算平均每跳的距离，再根据跳数求出锚节点与未知节点的距离，再用三边测量法求出未知节点坐标(Dvhop algorithm introduces the source code, to calculate the average distance of each jump, then jump a few calculated in accordance with anchor nodes and unknown distance from the node, and then calculated trilateration unknown node coordinates)

matlab cod efor 2013 ieee paper a two level fh cdma

This program shows the distributions of the co-channel interference in forward and reverse link in the cellular mobile system with cluster size N, when the mobile subscriber (MS) is at random positions of a cell. (This program shows the distributions of the co-channel interference in forward and reverse link in the cellular mobile system with cluster size N, when the mobile subscriber (MS) is at random positions of a cell.)

floyd warshall algorithm

利用qr算法进行机器人位姿正解，建立simulink模型(forward solution of robot)

This matlab code can be used to generate a parity check matrix for the LDPC code and further encoded and decoded in a complete communication system.

这是matlab中常用的快捷键，可以使你使用matlab更顺手。(This is the common shortcut keys of matlab.It can make you use matlab better.)

谱系聚类发 给定聚类点数可得到聚类中心 很好很强大(Hierarchical clustering distributed clustering fixed points can be obtained very powerful cluster centers)

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.)