EM_GM

Hidden Markov Model (HMM) Toolbox written by Kevin Murphy A problem of fundamental interest is characterizing such real-world signals in terms of signal models. HMM is referred to as Markov sources or probabilistic functions of Markov chains in the communications literature.

its a nice toolbox for compressive sensing

The code is of Normalized Cross Correlation technique for object detection

matlab调用c程序经典实例,详细注释了接口函数和参数的意义功能，并以实例的形式完成matlab对c程序的调用(a classic example of matlab calling C source codes)

该程序包包含五个代码，均用matlab编写，内容分别是混沌动力系统中，logistics函数随参数a变化周期收敛图像；以及seidel，jacobi两种迭代法求解线性方程组；以及常微分方程求解乒乓球运动轨迹；以及用newton向后插值法估计lnx函数值的源文件。 (This package contains five codes are written in matlab, the contents are chaotic dynamical systems, logistics function with parameters a change cycle convergence image and seidel, jacobi two iterative method for solving linear equations and ordinary differential equations Pong ball trajectory and an interpolation method to estimate with newton backward lnx function value source files. )

扩展INFOMAX算法是盲源分离算法中极大似然估计的一种扩展，功能更强大。(The extended INFOMAX algorithm is an extension of the maximum likelihood estimation in the blind source separation algorithm, and the function is more powerful.)

电力线通信系统信道估计，LS、MMSE、SVD三种算法(Chaannel estimation for power line communication, using LS、MMSE and SVD)

说明：  Modulation/Demodulation AM

MMSE Detection 2x2 with BER Plot

本算法为用遗传算法改进粒子群GA-PSO算法，附带含有程序使用说明。(This algorithm uses genetic algorithm to improve particle swarm optimization GA-PSO algorithm, with instructions for the use of the program.)