RGB2YCbCr

MATLAB simulation of group velocity dispersion in an optical fiber

MLPG is a combination of collocation method and EFG

压缩感知常用程序oolbox_SparseMRS(CS Toolbox_SparseMRS)

Matrix of cofactors in matlab

程序实现ofdm系统中通过用选择性映射（SLM）来改善系统的峰均比（PAR）。减少较大功率峰值出现(Program ofdm system by using selective mapping (SLM) to improve the system of PAR (PAR). Reduce peak power greater)

image quality measurement

基于matlab仿真平台,快速匹配追踪OMP算法的图像稀疏分解与重构,并附带有OMP算法的详细讲解PPT(Based on the simulation platform of MATLAB, the fast matching pursuit (OMP) algorithm for image sparse decomposition and reconstruction. And with the OMP algorithm explained in detail PPT.)

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. )

说明：  使用Matlab软件来产生PN序列的程序(Using Matlab software program to generate PN sequences)

genetic algorithm program for power system