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在阵列信号处理中，一种很经典的信号波达角方向估计方法：MUSIC（多重信号分类）算法。该程序中有三个频率的信号，经过此算法可很好地分辨出这三个信号。(In array signal processing, a classic signal the direction of arrival angle estimation method: MUSIC (multiple signal classification) algorithm. The program has three frequency signals, by using this algorithm,and you can well distinguish these three signals.)

地震波格式转换 地震波格式转换 地震波格式转换(Earthquake Earthquake Pogue Pogue conversion conversion)

MATLAB实现的平移huaffman编码，(MATLAB implementation of the translation huaffman code)

说明：   灰度处理，可以直接运行，matlab实现 ( matlab implementation)

DSP编程,c语言和matlab常用算法 (DSP programming, c language and Matlab algorithms)

function [CellSpace_nextstep VehicleSpace] = CountingNextStepVehicleState(CellSpace_current,CellSpace_nextstep,VehicleSpace,TakenMat, TakenCarIDMat,VMAX,signalFlag,UseLaneChangingModelFlag) 计算下一时刻的元胞空间状态 获取当前时刻元胞空间中的车辆信息， [VehicleInCellSpaceMat]=GetVehicleInCellSpace(CellSpace_current,TakenMat,TakenCarIDMat) if isempty(VehicleInCellSpaceMat) == 1 如果为空 return end 对每个车辆进行遍历， 获取每个车辆的数据，以及邻居车辆的数据 计算每个车辆的下一时刻状态并更新到下一时刻的元胞空间中 [CellSpace_nextstep VehicleSpace] = UpdataCellSpaceWithVehicleInfo(CellSpace_current,CellSpace_nextstep,VehicleSpace,VehicleInCellSpaceMat,VMAX,signalFlag,UseLaneChangingModel(function [CellSpace_nextstep VehicleSpace] = CountingNextStepVehicleState(CellSpace_current,CellSpace_nextstep,VehicleSpace,TakenMat, TakenCarIDMat,VMAX,signalFlag,UseLaneChangingModelFlag) 计算下一时刻的元胞空间状态 获取当前时刻元胞空间中的车辆信息， [VehicleInCellSpaceMat]=GetVehicleInCellSpace(CellSpace_current,TakenMat,TakenCarIDMat) if isempty(VehicleInCellSpaceMat) == 1 如果为空 return end 对每个车辆进行遍历， 获取每个车辆的数据，以及邻居车辆的数据 计算每个车辆的下一时刻状态并更新到下一时刻的元胞空间中 [CellSpace_nextstep VehicleSpace] = UpdataCellSpaceWithVehicleInfo(CellSpace_current,CellSpace_nextstep,VehicleSpace,VehicleInCellSpaceMat,VMAX,signalFlag,UseLaneChangingModelFlag) )

关于合成孔径雷达聚束成像模式的一个仿真程序，效果比较理想(a code of matlab descre the spotlit imagnation of SAR)

海岸线插值与拟合过程，并画图显示其效果图(Coastline interpolation and fitting process, and drawing show)

MATLAB计算机视觉与深度学习实战-运行视频(As a biological technology, face is difficult to be stolen and infection-free. In addition to that, it enjoys the authentication concealment and is never forgotten. Therefore, authentication based on face recognition will become the mainstream of future development. It has important and extensive application in various fields, and is also a hot topic at present biological characteristics identification technology research. Under the non-restrictive condition, the face recognition rate is not high, and its accuracy needs to be further improved. In this paper, the following aspects is analyzed)

3 dimensin grafik simulator