mymultisvm

matlab的netcdf插件安装程序，适用于R14版本(Matlab netcdf plug-in installation procedures, apply to R14 version )

电磁场时域有限差分法FDTD吸收边界二维TE波的计算。(Electromagnetic Field Finite Difference Time Domain method FDTD absorbing boundary two-dimensional calculation of the TE wave)

说明：  基于MATLAB的HMM算法的实现（Machine Learning Toolbox）包含Baum-Welch 算法的应用(MATLAB-based HMM Algorithm)

此程序相关卡尔曼滤波，用matlab描述了卡尔曼滤波的过程。(ocedure-related Kalman filtering, Kalman filtering with matlab describes the process.)

关于信道仿真的源代码，内含信道模型的ppt，希望对研究信道的朋友有帮助(Channel simulation on the source code, including channel model ppt, hope that the study of channel friends have helped)

在matlab上的FDTD程序关于一维时域有限差分(Matlab program on the one-dimensional FDTD finite difference time domain)

与理论分析结果相比，小波包分析提取振动信号中的特征频率，最小均方误差等算法的MSE的计算。( Compared with the results of theoretical analysis, Wavelet packet analysis to extract vibration signal characteristic frequency, Minimum mean square error MSE calculation algorithm.)

统的联合概率数据关联算法(IPDA)是在密集杂波环境下的一种良好的多目标跟踪算法， 但它是针对单传感嚣对多目标跟踪的情况下使用，不能直接用于多传感器对多目标的跟踪。针对这 一问题，文中提出了一种适用于多传感器多目标跟踪的JPDA算法，它以极大似然估计完成对来自 多传感器的测量集合进行同源最优分划，然后采用JPDA方法对多目标进行跟踪。经过理论分析和 仿真试验，证明了该方法能有效地进行多传感器多目标的跟踪，且具有算法简单、跟踪精度高、附 加的计算量小等优点。(Abstract)m JointProbabilisticDataAssociation(JPDA)solves single sensormultitatgcttrackingin ClUtter,butitc锄not bc used directly in mnitisensor multitarget tracking．喇8 paper presents a method to implement multi—sensor multi—target tracking by combining maximum likelihood estimation wim JPDA The maximum Iikelihood estimation is used to classify the same source observations at one time into the same set,and血en JPDA implements multi—target uacking after the position fitsed．The theory analysis and computer simulation show that this algorithm may achieve multi—sensor multi—target tracking perfectly with low calculation load added and higher precision 。)

This is a PDF about gabor filter

百个实用的matlab范例 1-32是：图形篇 33-66是：界面篇 67-84是：形理 (100 matlab practical examples 1-32 are: graphic papers is 33-66: 67-84 Part interface is: form the rationale for)