COPERTIVE

这是MATLAB中的三个很好的实验，通过这三个实验可以更好地理解数字图像处理的原理(This is the MATLAB three very good experiment, the adoption of these three experiments can be a better understanding of the principles of digital image processing)

说明：  matlab高级统计工具箱，做统计很方便，也很实用(Matlab Toolbox, do the statistics very convenient and practical)

dijkstra.m a tree examples of application of dijkstra algorithme with properties of the original algorithm

1d photonic crystal simulation

简单的奥村模型,其中程序描述了波束宽度与波达方向及阵元数的关系，以及最优权的傅里叶变换(OkumuraHata model,Array element spacing relations with wavelength,Beam width and doa and the number of array element relationship)

仿真实现均匀圆阵任意阵元工作时的方向图，工作参数可任意调节(Simulation of uniform circular array of arbitrary array work pattern)

Matlab源文件将矩阵m中所有a元素的替换为b元素。(Matlab source file will be a matrix m of all elements for the b element to replace.)

绘制3层正弦信号，对之进行小波分解（5层）重构5层逼近信号和细节信号。(Draw 3-layer sinusoidal signals, the wavelet decomposition (5 layer) Reconstruction of 5-layer approximation signal and detail signal.)

conjugate gradient tset file

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.)