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一些非常有用的MATLAB源码,对于提高编程水平和学习通信理论知识还有帮助.(Some very useful MATLAB source code for programming improve the level of theoretical knowledge and learning of communication also help.)

rastaplp m-file calculates RASTA-PLP coefficients, defaults to 1 if 0, just calculate PLP coefficients.

Voting simulation in Matlab

里查得森(Richardson)法解偏微分方程的程序。解的是定步长的抛物型偏微分方程： du/dx - a * d2u/dx2 = 0 在程序中可以更改 a 的值以实现不同系数的解。由于它是一种显式格式，解的结果不稳定 在Matlab7下面运行发现竟然有一些值达到十的几十次方,这是算法本身决定的。不是程序有错误(Richard Anderson (Richardson) Solving partial differential equations procedures. Solution is scheduled to step parabolic partial differential equation: du/dx- a* d2u/dx2 = 0 in the procedure can be changed in order to realize the value of a different coefficient of the solution. Because it is an explicit format, the result of instability in the solution of the following Matlab7 running even found some value reached ten dozens of parties, this is the decision algorithm itself. Not a procedural error)

It is used for understanding the phenomenon of pendulum and chaos. It s good for beginners.

这是一本简明易学的编码理论方面的书籍，适合于通信电子或其他专业的人自学。(This is a condensed learn coding theory books, suitable for electronic communications or other professional person self-study.)

相似度算法的实现， 在利用支持向量机进行模式分类的时候，有时需要考虑到实时性，为了提高实时性，则利用相似度算法减少样本集个数，从而减少训练时间和支持向量的个数，使得建立起的向量机实时性提高。(Similarity algorithm, support vector machine in pattern classification, it is sometimes need to take into account the real-time, in order to improve real-time, the use of similarity algorithm to reduce the number of sample collection, thus reducing training time and the number of support vectors， improve the real-time of established Vector Machines.)

非平稳信号分析与处理，对非平稳信号的处理进行了详细的论述(Non-stationary signal analysis and processing of non-stationary signal analysis and processing to give a detailed exposition)

在带限信道信号传输的仿真。仿真4PAM调制信号在AWGN信道下的性能，画出误比特率和误符号率。 (In the band-limited signal transmission channel in the simulation. Simulation 4PAM modulated signal in AWGN channel performance.)

convex optimizationconvex optimization algo new mit dimtri bertskas lesson 1