MSE-PSNR

功能近红外光谱信号的极限漂移与其有用信号重叠，高通滤波会对有用信号产生影响，该方法可有效出去极限漂移(program for fNIR detrending)

(不错的一篇文章，已被EI收录)模型预测控制（model predictive control，MPC）路径规划算法适用于三维动态环境下的无人机（un-manned aerial vehicle，UAV）路径规划；动态贝叶斯网络（dynamic Bayesian network，DBN）能够有效推理战场态势，对无人机进行威胁评估。针对威胁尾随无人机时的路径规划问题，构建 DBN 威胁评估模型，将 UAV 在战场环境中的威胁态势用威胁等级概率表示，与 MPC 路径规划算法相结合，得到基于 DBN 威胁评估的 MPC UAV 路径规划算法。通过多组仿真分析表明，在三维动态环境下，特别是威胁尾随无人机时，基于 DBN 威胁评估的 MPC无人机路径规划算法可以得到有效的无人机路径。(The model predictive control (MPC)path planning algorithm can solve the problem of dynamic unmanned aerial vehicle (UAV)path planning.Dynamic Bayesian network (DBN)is an effective tool for rea-soning and threat assessment.Considering the problem of path planning when the dynamic threat tags the UAV,the MPC path planning algorithm combined with DBN threat assessment is presented,which used the threat lever probability to describe the threat situation.A group of simulations demonstrate the efficiency of MPC three-dimensional dynamic path planning algorithm for UAV based on DBN threat assessment especially when the dynamic threat tags the UAV.)

说明：  logistic 映射分叉图 用matlab 写的源程序(logistic mapping bifurcation and using Matlab was the source)

一维光子晶体波导的传输特性曲线代码！非常好(One-dimensional photonic crystal waveguide transmission characteristic curve of code! Very good)

This file contains the typedef s of toc data structure.

开发环境： Matlab Version 7.8.0.347(R2009a) Visual Studio 2005(.NET 2.0, C#) 演示如何在ASP.NET(C#)网页中调用MATLab函数的例子。(Development Environment: Matlab Version 7.8.0.347 (R2009a) Visual Studio 2005 (. NET 2.0, C#) to demonstrate how in ASP.NET (C#) page MATLab function call example.)

1. Transfer function to state space model transfer function to state space A=[1 1 1] B=[1 6 11 6] [A,B,C,D]=tf2ss(num,den)

this is a vehicle detection algo, all the files are well explained in the rar file

说明：  此程序是用vc做的喷泉模拟实验 产生数据在砸爱matlab中画图就可以得到了(This procedure is done fountain vc simulation generated data in matlab hit love in drawing can be a)

EEMD是针对EMD方法的不足,提出了一种噪声辅助数据分析方法。EEMD分解原理为:当附加的白噪声均匀分布在整个时频空间时,该时频空间就由滤波器组分割成的不同尺度成分组成。当信号加上均匀分布的白噪声背景时,不同尺度的信号区域将自动映射到与背景白噪声相关的适当尺度上去。当然,每个独立的测试都可能会产生非常嘈杂的结果,这是因为每个附加噪声的成分都包括了信号和附加的白噪声。既然在每个独立的测试中噪声是不同的,当使用足够测试的全体均值时,噪声将会被消除。全体的均值最后将会被认为是真正的结果,唯一持久稳固的部分是信号本身,所 加入的多次测试是为了消除附加的噪声。(The EEMD collection of experience decomposition is improved on the basis of the EMD more given a data processing method!)