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This program in Matlab code simulates the wave propagation in a 1D dispersive medium by using FDTD method

DCT变换：变换域系数选取采用阈值编码，：阈值越小，图像恢复效果越好，但压缩效果差，一般取在5-10 即可很好的获得恢复图像信息。(DCT transform: transform domain coefficients selected using the threshold coding: the smaller the threshold value, the better the image restoration, but the compression is poor, and generally in the 5-10 to get a good recovery image information.)

混合编程matlab与C#,帮助你学习混合编程(matlab and C# hybrid programming)

This exchange file creates a clickable image map for a figure with links to multiple data sources

用matlab编制的，用GP算法计算关联维数，希望有用(Using matlab prepared, calculated correlation dimension algorithm with GP, hope useful)

Principal Components Analysis (PCA) is used to compress data in such a way that the least information is lost. It does so by truncating data and thereby leaving out the data which is of the least importance to the information stored in the data. This PCA process is called dimensionality reduction,

PSO滤波，我没试过，请高手用了之后传一个有中文解释的(PSO filter, I never tried, after you pass a master with a Chinese interpretation)

QoS组播路由问题的蚁群算法通用MATLAB源码(Ant Colony Algorithm for QoS Multicast Routing)

Learning_random.m : 随机选择样例，从（90）pool里随机选择样本，删除版本空间树的数量 activeLearning.m:根据最大熵原则,从pool里选择样本，删除版本空间树的数量 getlabel.m:根据树和测试样例，得到根据该树得到的类标 getTrees.m:从提供的大量树结构(随机生成的)中，随机挑选一定数量的树(如果该树的叶子节点无类标随机添加) RandomCreateTree_d_unbalance:根据样本点割点中的非平衡割点建造树， RandomCreateTree_d_all.m:根据所有样本点的割点建造树 randomdata.m：给定属性取值，造数据 randomselect.m:从数据中随机选择一部分作为 showTree.m:显示树的结构 test.m:给出树，测试样例，得到正确率(Learning_random.m: randomly selected sample, randomly selected sample from (90) pool the The deleted version space tree quantity activeLearning.m: selecting a sample from the pool based on the principle of maximum entropy, delete the number of version space tree getlabel.m: According to the tree and the test sample obtained according to the class standard getTrees.m the tree: from the tree structure (randomly generated), randomly selected a certain number of trees (the leaves of the tree node class marked randomly adding ) RandomCreateTree_d_unbalance: According to the sample point cut point unbalanced cut point construction tree, RandomCreateTree_d_all.m: construction of the tree randomdata.m all sample points cut point: given the value of the property, manufacturing data randomselect.m: random data Select as part showTree.m: tree structure test.m: tree, the test sample is given to get the correct rate)

n_gram algoritm for language identification