huffman-image

M/M/1单服务台排队系统仿真（用事件调度法实现离散事件系统仿真） 顾客到达模到达时间间隔和顾客服务时间均服从负指数分布，单服务台系统，按照单队排队，按FIFO方式服务。考察服务n个顾客（n=1000，2000，3000，5000）后的顾客平均队长及平均排队等待时间。(M/M/1 single-server queuing system simulation (using Event Scheduling Method Discrete Event Simulation System) to customers Da-mo arrival time interval and customer service time are negative exponential distribution, single-desk system, in accordance with the single-team queue, by FIFO mode services. N inspection services customers (n = 1000,2000,3000. 5000) the average customer captain, and the average queue time.)

Bouc-Wen非线性滞回模型仿真程序，通过此程序可观察Bouc-Wen模型中各参数对其所描述的滞回曲线形状的影响(Bouc-Wen nonlinear hysteretic model simulation program, described by the Bouc-Wen model parameters on the hysteresis curve shape can be observed through this program)

胡广书 数字信号处理-理论,算法与实现的书中源程序 fortran版本(HU-book Digital Signal Processing- theory, algorithms and implementation fortran source code version of the book)

二维TE波的FDTD的实现，采用MUR吸收边界条件。(2D TE wave of FDTD the realization used MUR absorbing boundary conditions.)

说明：  采用粒子群算法对GARVER-6节点配电网络进行直流潮流计算(DC power flow calculation of GARVER-6 node distribution network using particle swarm optimization)

to help in computer vision ,pattern recognition and machine learning research for grad students in image analysis and computer science works of masters and doctorate

有源滤波器设计经典书籍，作者D.Lancaster(Active-Filter Cookbook by D.Lancaster)

关于Vega Prime的12篇期刊论文，涉及VP多场景切换、坐标系、多通道、飞行视景仿真等问题，有相当的参考价值。(About Vega Prime' s 12 journal articles, and more scenes involving VP switching coordinates, multi-channel, flight visual simulation and other issues, there is considerable reference value.)

Model 3232a2 - accelemeter sensor

标准压缩感知（CS）理论决定了可靠的信号恢复是可能给M= O（KLOG（N / K））的测量。我们证明了它可以通过利用超越简单的稀疏性和可压缩性由包括价值观和信号系数的位置之间的依赖关系更加逼真信号模型大大降低Mwithout牺牲的鲁棒性。(The standard compressive sensing (CS) theory dictates that robust signal recovery is  possible from  M=O(Klog(N/K))  measurements. We demonstrate that it is possible to substantially  decrease  Mwithout sacrificing robustness by leveraging more  realistic signal models that go beyond simple sparsity and  compressibility by including dependencies between values and  locations of the signal coefficients.   )