Computer-Based+Exercises+for+Signal+Processing+Usi

说明：  多边形平板高频散射,采用物理光学发求解多边形平板的雷达散射截面(Polygonal planar high frequency scattering, Using physical optics to solve the radar cross section of a polygonal flat plate)

matlab例程，根轨迹，微分方程等 (Matlab routines, root-locus, differential equations, etc.)

说明：  可以对数量少、残缺、不完整的数据进行预测，如人口、缺水量等。也可以对灾害发生时间进行预测。(Can be a small number, mutilated, incomplete data to predict, such as population, water shortage and so on. You can also predict the time of occurrence of the disaster.)

航天器的飞行动力学计算，航空航天参数分析(Spacecraft flight dynamics calculations，Aerospace parametric analysis )

PAPR Reduction based on a Modified PTS with Interleaving and Pulse Shaping method for STBC MIMO-OFDM System. It is with GUI. Using GUI we can see comparison with number of sub carrier, sub-block and over sampling factor as well BER plot comparison of normal and with PTS technique. You need to run MIMO_OFDM_with_PTS.m Note: Do not delete or rename amy file and place all the files in one single folder because some are functions which are called in GUI code.

该程序主要实现了使用高斯脉冲成形脉冲对OFDM信号进行成形。并对其在AWGN信道下的误码性能进行了测试，对从事4G、5G研究的科研工作者很有好处。(This manusript uses the Gaussian pulse to shaping the OFDM system, ad test the BER performance in AWGN channel, its very helpful for who work in the 4G and 5G communication technology.)

方差分析法 应用统计学家Fisher在20世纪20年代最先提出了可同时比较多个正态总体均值是否相等的方差分析法。 本文通过几个案例来讲解方法分析法的应用(Analysis of variance application statistician Fisher first proposed in the 1920s, can simultaneously compare multiple normal population mean is equal to the analysis of variance. In this paper, several cases to explain the application of the method of analysis)

经典的双边滤波程序，可以设置2个参数的大小，保边去噪效果好 PS： 比一般改进型双边滤波器要好。(Classic bilateral filtering program, you can set the size of two parameters, edge-preserving denoising effect PS: better than generally improved bilateral filter.)

three level inverter using dc source

将所有Copula的例子Code分享出来。 其中包括时变Copula的。 大家可以参考例子进行处理。 得到的结果请看MATLAB右边的LL和Kappa(Share all the examples of Copula, Code. It includes the time-varying Copula. You can deal with the examples. For the results, look at the LL and Kappa on the right of MATLAB)