FASTICA

MATLAB程序设计m文件讲解教程MATLAB m file programming tutorial explaining(MATLAB m file programming tutorial explaining MATLAB m file programming tutorial explaining)

这个代码是matlab仿真的在没有协作用户的情况下，进行的误码率性能仿真。(Matlab simulation of this code is the collaboration of users in the absence of circumstances, to carry out simulation of BER performance.)

Firefly Algorithm for Constrained Optimization (Other Files fa_mincon.m, firefly_simple.m, license.txt )

Rake Reciever explaination

对数据的分析，特别是线性回归其中有好几种方法来进行！(Analysis of the data, in particular linear regression in which there are several ways to carry out!)

多普勒频移下的RICIAN衰落 附加资料 (RICIAN)

图像的哈弗曼编码matlab实现，实现图像的无损压缩(Matlab coding huffman images achieved)

在删除信道下LDPC码的编译码研究与实现，可供参考(In the Delete Channel Encoding and Decoding of LDPC Codes and implementation, available for reference)

基于变桨距PID控制器的风力发电模型。仿真环境matlab->simulink。特点：随机风速模型；桨距恒功率控制；通俗易懂的PID控制器(Variable pitch wind model based PID controller. Simulation environment matlab-> simulink. Features: Random velocity model pitch constant power control straightaway PID controller)

EMD的Toolbox及使用方法 经验模态分解（Empirical Mode Decomposition, 简称EMD）是由美国NASA的黄锷博士提出的一种信号分析方法.它依据数据自身的时间尺度特征来进行信号分解, 无须预先设定任何基函数。这一点与建立在先验性的谐波基函数和小波基函数上的傅里叶分解与小波分解方法具有本质性的差别。正是由于这样的特点, EMD 方法在理论上可以应用于任何类型的信号的分解, 因而在处理非平稳及非线性数据上, 具有非常明显的优势。所以, EMD方法一经提出就在不同的工程领域得到了迅速有效的应用, 例如用在海洋、大气、天体观测资料与地震记录分析、机械故障诊断、密频动力系统的阻尼识别以及大型土木工程结构的模态参数识别方面。本工具书主要分以下几个部分：导论、非稳态信号介绍、一维时频信号分解、二维时频信号分解、时频影像信息提取等。 (Time frequency toolbox for use with MATLAB The Time-Frequency Toolbox has been mainly developed under the auspices of the French CNRS (Centre National de la Recherche Scientifique). It results from a research effort conducted within its Groupements de Recherche ”Traitement du Signal et Images” (O. Macchi) and ”Information, Signal et Images” (J.-M. Chassery). Parts of the Toolbox have also been developed at Rice University, when one of the authors (PG) was visiting the Department of Electrical and Computer Engineering, supported by NSF. Supporting institutions are gratefully acknowledged, as well as M. Guglielmi, M. Najim, R. Settineri, R.G. Baraniuk, M. Chausse, D. Roche, E. Chassande-Mottin, O. Michel and P. Abry for their help at different phases of the development.)