wangmppt

matlab numerical analysis

这是几个粒子群算法的经典例子，思路清晰，编程易懂，注释也非常详细。这里主要是介绍matlab中的粒子群工具箱。(This is a classic example of the particle swarm optimization, clear, easy to understand programming ideas, comments, very detailed. Here mainly to the introduction matlab Particle Swarm toolbox.)

intersection_lines(A, B) between two lines

实时录音去噪,来自足球场上的呜呜组啦噪声，有GUI，可以自己选择噪声类型，使用FIR滤波器滤波(real-time denoising)

MIMO-OFDM channel estimation 。是基于mmse准则的，欢迎大家下载使用。(MIMO-OFDM channel estimation. Is based on the MMSE criteria, welcomed everyone download.)

enframe这个就是matlab里面可能用到的(enframe)

卡尔曼和粒子滤波程序 看起来是比较好的 我也用了 谢谢(Kalman and particle filtering procedures is a better look, I have also used the thank you)

该程序实现多特征红线报警算法，两个matlab文件，一个为函数，另一个为主函数，调用该函数文件；该程序将4路信号，每一路信号的特征量值及其固定阈值求出来，并一起画出图来。(The program features multi-red alarm algorithm, two matlab files, one for function, the other main function calls the function file the program seek out four signals characteristics of each road and fixed threshold value ,then draw them out .)

可逆跳跃马尔科夫蒙特卡洛贝叶斯模型选择，主要用于神经网络(Reversible Jump MCMC Bayesian Model Selection This demo demonstrates 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.)

基于学习的图像超分辨率重建，可以进一步了解基于学习的方法。(Learning-based super-resolution image reconstruction, we can further understand the learning-based approach.)