code-for-dwt

单频网的链路级Matlab仿真程序,直接运行可得到误符号率。(Link level simulator for single carrier networks,The simulator outputs symbol error rate.)

说明：  matlab例子自适应滤波最陡下降法特征值扩散度仿真，变步长仿真(Matlab example of adaptive filtering steepest descent method for eigenvalue proliferation simulation, variable step simulation)

可以计算当发射端未知CSI时，MIMO信道在不同SNR下的遍历容量(ergodic capacity)

cacc matlab codes

复杂网络matlab样例（只要为sy003.m），包括求平均度，路径直径，平均路径长度，聚类系数，介数（需要加载bgl工具箱），核数，紧密度等(Complex network matlab sample (as long as it sy003.m), including the degree of averaging, the path diameter, the average path length, clustering coefficient, betweenness (bgl need to load the toolbox), auditing, tightness, etc.)

this program simulates 1D electromagnetic Maxwell equations "soft source", by using finite difference time domain method FDTD

基于信息熵和PCA的故障诊断方法，针对状态方程描述(The fault diagnosis method based on information entropy and PCA is described)

维特比译码算法的实现，以及卷积码译码的误码率性能分析(Viterbi decoding algorithm, as well as the BER performance of the convolutional code decoding)

How to use the ordinary Voronoi partition generator. The m-files also have their own help documentation that you can view in Matlab. This code creates a bounding polygon that is a square. Then it randomly creates 10 points and then creates and draws the partition. bounds = [0, 10, 10, 0 0, 0, 10, 10] points = 10 * rand(2, 10) regions = voronoi(bounds, points) drawRegions(bounds, regions) ================================ This is a brief set of instructions on how to use the Multiplicatively- weighted Voronoi partition generator. Each of the m-files has a help section, so in Matlab you can call: help mwvoronoi help drawRegions Here is an example that creates a bounding polygon, a square with side length 10. Then it randomly creates six points with random weights. Finally, the code draws the resulting partition. bounds = [0, 10, 10, 0 0, 0, 10, 10] points = 10 * rand(3, 6) regions = mwvoronoi(bounds, points) drawRegions(bounds, regions) Enjoy!

一种基于稀疏变量的欠定盲分离算法，很好用，大家试一下。(Underdetermined blind separation algorithm based on sparse variables)