imagePprocessingPtoolbox

一个利用matlab simulink仿真简化通信系统的模型。里面m文件为simulink模块参数设置，适合通信基础浅的人理解通信概念同时上手simulin学习(Matlab simulink simulation using a simplified model of communication system. M files inside simulink module parameters for setting suitable person communication shallow understanding of basic concepts of communication at the same time started studying simulin)

神经网络源代码，一个字符识别程序，分别实现了没有噪声和带有噪声的测试(Neural network source code, a character recognition program, were achieved with no noise and noise tests)

matlab纠错码编译原理之程序3 做实验时免去了手工输入的繁琐 和避免出错(matlab compiler theory of error-correcting code program 3)

说明：  一般线性多智能体系统，在有领导者时的仿真情况(Simulation of general linear multi-agent system wit leader)

说明：  基于电力系统场景集，采用智能算法对电力系统多场景可靠性进行分析(Based on the scene set of power system, intelligent algorithm is used to analyze the multi scene reliability of power system)

Wireless Communications over MIMO Channels - John Wiley and Sons

神经网络讲义，共八讲，主要讲述了神经网络发展史以及几个经典的人工神经网络(Neural network handouts, a total of eight speakers, mainly about the history of neural networks as well as several classic artificial neural network)

PCA与不用PCA进行人脸识别的比较，请先安装sltoolbox，人脸数据库采用orl(PCA and PCA for face recognition do not have the comparison, please install sltoolbox, face database used orl)

he files in this package comprise the Matlab implementation of a foreground segmentation algorithm based upon graph cuts, described in: Better Foreground Segmentation Through Graph Cuts, N. Howe & A. Deschamps. Tech report, http://arxiv.org/abs/cs.CV/0401017. The file extractForeground.m contains a sample function that will perform a complete foreground segmentation for static camera video. It uses a number of parameters, which are described in the documentation. An attempt was made to choose sensible default values, but they may need to be adjusted for some videos. More generally, the implementation in extractForeground.m is only a suggestion of how the graph-based segmentation might be used. The same approach can be applied with other (perhaps time-adaptive) background models. The key step is at line 113 of the code, once the deviation array has been created. The graph-cut segmentation can be applied similarly to a deviation matrix computed in any other way.

work in matlab, iris detection