AdaptiveequalizerofLMSalgorithmanditssimulation

三维锥束CT图像重建算法，利用Katsevich 算法。里面包含了两种分辩率的体数据。(Cone-Beam CT image reconstruction using the Katsevich Algorithm )

说明：  基于匹配滤波器输出的非线性判决反馈DF检测器(This paper introduees cause of formation of MAI in CDMA systems, and Points out that multi-user Deteetion(MUD) teehnology can improve eapability and performanee of CDMA through suppressing MAI)

the simulation of MG1 algo. for random numbers

离散随机系统的Bayes辨识法，matlab程序(Bayes discrete stochastic systems identification method, matlab program)

张贤达的现代信号处理课本第二版，清华出版社(Zhang Xian of modern signal processing second edition textbook, Tsinghua University Press)

本程序是莱斯信道的仿真程序，以sigma为参数，考虑了两节点之间距离的因素。(Is Rice channel simulation program, this program with sigma for parameters, considering the factors of the distance between two nodes.)

说明：  不同模态OAM的相位图，电场场强图，及用箭头表示相位变化。(Phase diagram, electric field strength diagram of OAM with different modes and phase change indicated by arrows.)

关于电力系统状态估计的MATLAB的源程序，敬请参考，不负侵权责任(On the MATLAB power system state estimation of the source, please refer to, live tort liability)

这是关于模型预测控制的动态矩阵算法的一个程序(This is a program about the dynamic matrix model predictive control algorithm)

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.)