ber_ldpc16_FFT_H

说明：  五阶CIC梳状积分滤波器，可以综合，非常有参考价值(Fifth-order CIC points comb filter, can be integrated and very useful)

Write twoMatlab functions L=clolesky_row(A) and L=clolesky_col(A) that compute the Cholesky factor L of a Symetric Positive De… nite (SPD) matrix A = LLT , acessing A by row and by column respectively.

隐马尔可夫模型前向算法matlab程序,其中 alpha为局部概率；pprob为最终的观察概率(a matlab code for Hidden Markov Model forward algorithm.)

这里面汇总了matlab局部放大的三种方法，可以对matlab中plot进行局部放大，带程序。(It mainly is concerned about the magnification of plot.The users can use it to magnify what they want.)

可对所求数据进行傅里叶变换，快速得到系统的频谱图。(Fourier transform can be used to obtain the spectrum of the system.)

Matlab, picture, example of color mix

this is a matlab program

混沌系统的李亚普洛夫指数的求解，步骤很详细，可以适用于不同的混沌系统。(Chaotic system Lyapulov Index solving steps in detail, can be applied to different chaotic systems.)

基于MATLAB的16QAM调制,模擬錯誤率(Based on MATLAB-16QAM modulation, analog error rate)

This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. (This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar-xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. )