xiandaixinhao

matlab通过串口接收指定帧格式的数据的程序，和大家共享(through the serial port matlab designated to receive the data frame format of the program, and share)

用matlab进行图像滤波，图像滤波的效果在文档中有说明(The effect of image filtering, image filtering using matlab document)

image registration Voxel similarity based registration algorithms generally consist in the calculation of the geometrical transformation which, when applied to the source image, maximizes a similarity function. This similarity function estimates how similar the images are once the transformation has been applied. (image registration)

Heat equation with variable coeficients using Gauss-Seidel Method:

Monte Carlo Simulation On Estimating the probability of a flush

Extended kalman filter for tracking

lleach code under mathab for the simulation

Simulation of power calculation in fast fading channels

simulink for pid with state space

高速铁路路基沉降是一个复杂的系统过程,涉及不同时期、不同工况,选用单一的预测模型不足以反映路基整体的变形规律.Logistic模型及Verhulst模型的曲线均为S形,可以反映全过程的地基沉降与时间的关系.针对路基填筑期和降水期两个时段利用MATLAB软件编制程序计算模型参数,得到Logistic模型和灰色Verhulst模型,进而求得两者的最优加权组合模型.对比结果表明:在路基填筑期由两者的最优加权组合模型预测得到的结果与实测结果较吻合,拟合精度高于单一模型 而在降水期Verhulst模型的预测值更接近实测值,平均相对误差约为1.75％,拟合精度较高.(High-speed railway subgrade settlement process is a complex system involving different times and in different conditions, the choice of a single forecasting model does not adequately reflect the overall deformation of roadbed. Curve Logistic model and Verhulst models are S-shaped, the whole process can be reflected foundation settlement versus time. against roadbed construction period of two hours and precipitation using MATLAB software programming model parameters, the Logistic model and gray Verhulst model, and then get the optimal model of a weighted combination of the two. comparative results indicate : results and experimental results in the roadbed construction period by the weighted combination of the best of both models predicted than the anastomosis fitting accuracy than a single model precipitation predicted value of Verhulst model closer to the actual value of the average relative error approximately 1.75 fitting accuracy.)