ofdm-qpsk-mycreatechannel11

Shan and Chen type multiphase lattice Boltzmann study of viscous coupling effects for two-phase flow

归一化模型参考自适应控制的Matlab实例，仿真模拟闭环跟踪方波输入 关键字：归一化 模型参考自适应控制 Matlab 例程 MIT(A Matlab example of normalized model reference adaptive control.The closed-loop system is simulate to follow a square reference signal. Keywords: Normalized Model Reference Adaptive Control, Matlab, Simulink Model, MIT Rule)

EMPIRICAL MODE DECOMOPOSITION PAPER

说明：  本程序实现了通信原理中的平顶抽样与恢复，有代码与结果(This procedure implements the communication theory in the flat sample and recovery, with code and results)

弹奏青花瓷的matlab代码 (Blue and white porcelain matlab program play)

GetstartMATLAB

Simulink动态系统建模与仿真基础 李颖等编.pdf 精通simulink系统仿真与控制.pdf(A very useful tool SIMULINK simulation)

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. )

自相关法基音检测的MATLAB代码。自相关法计算速度较快的自相关函数法检测语音的基音频率，有效剔除了高频共振峰和噪音的影响，其估计基音频率准确性高，稳定性好，运算速度较快。(CAMDF)

this book speak to matlab