MOSGA3

my_seg_for_post_filtering_it is used for segmentation

Lab1 sur le filtrage pour la commande prédictive

SUSAN算法 角点提取算法 matlab(SUSAN code matlab)

说明：  Another matlab file for transporter seller progrramming, as before

use alamouti for communication system.

说明：  matlab对轨道角动量的仿真，可以更改波长和模态值以适应光学和电磁应用(Matlab simulation of orbit angular momentum can change the wavelength and mode value to adapt to optical and electromagnetic applications)

数字移动:图中的九个点上,空出中间的点,其余的点上任意填入数字1到8.1的位置固定不动,然后移动其余的数字,使1到8顺时针从小到大排列.移动的规律是:只能将数字沿线移向空白的点. (Digital Mobile: Figure in the nine points, empty out the middle point, the rest of the points fill any position of the numbers 1 to 8.1 fixed and then move the rest of the figures, and make 1-8 clockwise from small to large array . The law of movement is: can only move along the number of blank spots.)

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.)

基于小波多尺度分析的彩色图像检索方法的研究(Color image retri method based on wavelet multiscale analysis)

lssvm相关教程，工具箱的使用指南包括中文版，对于初学者lssvm的使用说明(lssvm tutorials, tool use guide includes Chinese version, for starters lssvm instructions)