dopplercenter

关于数字信号处理的几篇PPT文档(IIR/FIR滤波器设计等)，适合数字信号处理入门学习(Digital signal processing on several PPT files (IIR/FIR filter design, etc.), suitable for digital signal processing Introduction to learning)

MAP解码器 用于信道卷积码的编码译码 (MAP estimator for convolutif channel coding and decoding)

网络化控制系统，切换系统MATLAB源程序(The network control system, the switching system MATLAB source)

这本书针对MATLAB编程的浅显的讲解，比较适合初步开发者的思路。(This book for MATLAB programming facile explanations, more suitable for the initial developer of thinking.)

本文使用浅显易懂的文字简要介绍了ofdm的基本原理，可以作为入门的参考资料(In this paper, the text easy to understand ofdm outlined the basic principles of the reference materials can be used as entry)

tsp with genetic algorithm

VC++环境下调用MATLAB，其中路径的设置请根据自己电脑中的位置做相应修改(VC++ Environment called MATLAB, which set up the path, please according to their own computers make consequential amendments to the location of)

实例，简单的介绍了matlab的基本知识，适合初学者，希望对大家有帮助(Instance, a simple introduction to the basics of matlab, suitable for beginners, we want to help)

Kalman filter implementation

In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets. (In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar-xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets. )