ned

对要发送的序列进行上变频，上变频到21MHz后进行发送。(change the frequence uop to the one that suit for transmission,which here is 21MHz.)

SVPWM MODEL BASED ON MATLAB SIMULINK(SVPWM MODEL)

2nd one, this is the next file for code

matlab解偏微分方程，里面详细介绍了偏微分方程怎么离散化通过Matlab求解。(The MATLAB solution of partial differential equations)

说明：  一个MATLAB的应用程序，很方便的！使用时需要转换为M文件。(a MATLAB applications, a convenient! Use need to change for the M documents.)

差分格式中的一种类型，matlab编程实现的，能看到明显效果。(difference format of a type, Matlab programming, and be able to see tangible results.)

机器人运动学正解求解过程，计算出机器人各个关节角的角度(Robot kinematics solving process to calculate the angle of each joint angle of the robot)

implementation ols sparse algorithm

判断信号质量好坏，主要用于人体生理信号的质量判断(Judgments The signal quality good or bad, mainly used for the the the quality of the judgment of of the the human body physiological signal)

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.)