Vblast

Who to start working with C6713 Texas instrument

PID Controller Design and Tuning with MATLAB and Simulink(This demo file shows new capabilities for PID Controller design and tuning in MATLAB and Simullink)

说明：  利用自相关函数估计基音周期，函数jiyinzhouqi，输入参数是分析语音文件名和帧移，帧移在5~10ms(use of the correlation function is estimated Pitch, jiyinzhouqi function, input parameters to analyze voice file names and frame shift, frame shift in the 5-10ms)

CMOS集成电路基础知识，CMOS集成电路的接口电路，CMOS集成电路使用注意事项 (Basic knowledge of CMOS CMOS integrated circuit, integrated circuit interface circuit, CMOS integrated circuit use matters needing attention )

图像拼接的matlab代码，可以作为参考(Image mosaic matlab code, can be used as reference)

基于负熵最大化的fastICA，matlab程序。程序中的公式在所附带的pdf文件中都高亮标明(Based on negative entropy maximization fastICA, matlab program. The formula in the program attached pdf file are highlighted marked)

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. )

MATLAB教程.CHM2.9M.chm(MATLAB Tutorial. CHM2.9M.chm)

说明：  小波插值与小波基构造，实现均值插值和立方插值(Wavelet Interpolation and wavelet construction, the realization of the mean cubic interpolation and interpolation)

本文档包括，BA_net.m、WS_net.m和randomgraph.m是可以直接运行的文件，分别生成三种不同网络的网络模型和特性曲线等，以及一个说明文档(This document includes: BA_net.m, WS_net.m and randomgraph.m file can be run directly, namely generation network model and characteristics of three different networks and so on. )