PSNR

绝对极品！！美国数学建模2009年A篇优秀论文，附带MATLAB源程序，解决了于环岛问题的仿真问题！ 基于元胞自动机的交通系统（环岛）建模与模拟实例，MATLAB解决。 MCM-2009a题论文+答案(Absolutely superb! ! A 2009 American Mathematical modeling of excellent papers, with MATLAB source code to solve the problem of simulation of problems in the island! Cellular Automata Based Traffic System (roundabout) modeling and simulation examples, MATLAB solution. MCM-2009a question papers+ answers)

六种算法估计DOA 波达方向；智能天线；自适应滤波(Six algorithms estimate DOA Direction of Arrival smart antenna adaptive filtering,)

分析正弦稳态电路，建立网孔电流方程求解电路电流，采用GUI直观形象的显示电流向量图(Sinusoidal steady state circuit analysis, mesh current equations to establish the circuit current, using an intuitive GUI displays the current image of the vector map)

强跟踪滤波程序，其中包括st-ukf和st-ekf(code for st-ekf and st-ukf)

说明：  利用LDA算法，实现数据降维。输入样本数据、原维度数和目标维度数，输出降维后数据(Data Dimension Reduction Using LDA Algorithms)

in this image thresholding is done.image thresholding is getting by using seed polints concept

本书系统地介绍了生物医学数据分析的相关基础知识和主要的分析方法，并通过 MATLAB 对大量的 生物医学数据分析的实例进行实现。全书共分为 7 章：第 1 章绪论；第 2 章 MATLAB 应用基础；第 3 章 医用数学基础及其 MATLAB 实现；第 4 章多变量分析；第 5 章时间序列分析；第 6 章生物信息学基础； 第 7 章生物医学数据的检索方法。 (This book introduces the relevant basic knowledge and analysis of biomedical data analysis, and analysis of biomedical data through MATLAB instance implemented. The book is divided into seven chapters: Chapter 1 Introduction Chapter 2 based on MATLAB applications Chapter 3 Medical mathematical foundation and its MATLAB implementation Chapter 4 multivariate analysis Chapter 5, time series analysis Chapter 6 bioinformatics Chapter 7 of the foundation biomedical data retrieval methods.)

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.)

girachical cluster mat methid

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