多重信号分类music

这个m代码实现快速图像匹配，而且还有示例图片方便学习，程序设计简单快速。(Image matching method using template matching.)

MATLAB GUI 实例教程，简单易学，设计GUI必看(MATLAB GUI examples guides, easy to design GUI watched every game)

matlab程序代码，三维杆单元有限元结构刚度和质量矩阵的生成(Matlab program code, 3 d finite element structure stiffness and stem the generation of mass matrix)

Matlab实现界面友好，用MATLAB编写的遗传算法路径规划，仿真图是速度、距离、幅度三维图像。( Matlab to achieve user-friendly, Genetic algorithms using MATLAB path planning, FIG simulation speed, distance, amplitude three-dimensional image.)

使用FIR进行滤波器设计，应用kaiser窗函数进行的50阶设计的，适合初学者。(Conducted using FIR filter design, application kaiser window function 50 bands designed for beginners.)

说明：  适合新手的matlab教程，方便新手学习了解matlab的基础知识。(The matlab tutorial suitable for novices is convenient for novices to learn and understand the basic knowledge of matlab.)

16QAM仿真系统，学通信实验必备，绝对有用。(16QAM simulation system, learning communication experiment must absolutely useful.)

This demo nstrates 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. (This demo nstrates 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. )

认知无线电主用户信号为BPSK信号在不同信噪比下的检测概率和虚警概率(Cognitive radio primary user signal is BPSK signals in different SNR detection probability and false alarm probability)

SVM算法在 matlab实现, 包含说明书，源代码等(SVM experiment on matlab)