ModulationAnalogique

计算机视觉，图像分割的matlab源程序(Computer vision, image segmentation matlab source)

本文根据对原始纯净图像进行的椒盐加噪处理，提出了利用中值滤波法消除图像噪声的实用方法。使用软件工具MATLAB快速地实现了图像的中值滤波。(According to the original purity of the salt and pepper plus noise image processing, proposed by using median filter to reduce image noise and practical method. MATLAB software tools used to achieve fast median filtering of images.)

GUI_tips MATLAB GUI Tips Collated advice on construction of user interfaces by R. S. Schestowitz Imaging Science and Biomedical Engineering

说明：  matlab程序模拟微分方程。各种流行的系统显示混沌运动特征。(This toolbox contains the functions which can be used to simulate some of the well-known fractional order chaotic systems, such as: - Chen s system, - Arneodo s system, - Genesio-Tesi s system, - Lorenz s system, - Newton-Leipnik s system, - Rossler s system, - Lotka-Volterra system, - Duffing s system, - Van der Pol s oscillator, - Volta s system, - Lu s system, - Liu s system, - Chua s systems, - Financial system, - 3 cells CNN. The functions numerically compute a solution of the fractional nonlinear differential equations, which describe the chaotic system. Each function returns the state trajectory (attractor) for total simulation time. )

SISTEMA DE Persistencia

用matlab编的神经网络程序，希望对大家有帮助(Matlab made by the neural network procedures, we hope to help)

说明：  卡尔曼滤波，从Andrew D. Straw s Python 移植过来的(Kalman filter algorithm，This M code is modified from Andrew D. Straw s Python )

模拟天气雷达接收到的含有噪声的降水信号，并提出了对收到的离散回波信号进行估计分析的改进的FFF法的仿真程序(The weather radar simulation received containing noises precipitation signal, and proposed to the discrete received echo signal analysis estimates of the improvement of the FFF method simulation program )

matlab 布拉格光栅仿真，包括各个类型的布拉格光栅(matlab simulation of Bragg grating, including various types of Bragg grating)

fit_mix_gaussian - fit parameters for a mixed-gaussian distribution using EM algorithm format: [u,sig,t,iter] = fit_mix_gaussian( X,M ) input: X - input samples, Nx1 vector M - number of gaussians which are assumed to compose the distribution output: u - fitted mean for each gaussian sig - fitted standard deviation for each gaussian t - probability of each gaussian in the complete distribution iter- number of iterations done by the function