matalab

说明：  一个关于考虑铁损的感应电机用M语言写的模型，输出为电流等十六个参数。(1 on the induction motor considering iron loss of the written language with the M model, the output current of 16 parameters.)

Good instructions for the beginners of MPI programming. It holds all the basics.

calculate the clutter signal in radar application

应用分布傅里叶分析法解广义非线性薛定谔方程(Applying SSFM to solve generalized schodinger equation)

93lc56a/b的datasheet 參考看看(93lc56a/b The datasheet for reference to see)

matlab实现图上需找最短路径的迪杰斯特拉算法，寻找任一点和其他点之间的最短距离，最终结果为最短路径度量矩阵和正序路由矩阵(matlab implementation on the map to find the shortest path Dijkstra algorithm to find the shortest distance between any point and other points, the final result is the shortest path metric matrix and positive sequence routing matrix)

An implementation of Unscented Kalman Filter for nonlinear state estimation. (Nonlinear state estimation is a challenge problem. The well-known Kalman Filter is only suitable for linear systems. The Extended Kalman Filter (EKF) has become a standarded formulation for nonlinear state estimation. However, it may cause significant error for highly nonlinear systems because of the propagation of uncertainty through the nonlinear system. The Unscented Kalman Filter (UKF) is a novel development in the field. The idea is to produce several sampling points (Sigma points) around the current state estimate based on its covariance. Then, propagating these points through the nonlinear map to get more accurate estimation of the mean and covariance of the mapping results. In this way, it avoids the need to calculate the Jacobian, hence incurs only the similar computation load as the EKF. For tutorial purpose, this code implements a simplified version of UKF formulation, where we assume both the process and measurement noises are additive to avoid augment of state and a)

newmark gravity newton rapton method

模拟二维可压缩超音速风动内的流动参数随风动形状的变化(Simulate the two-dimensional compressible supersonic wind-driven flow parameters in the wind dynamic changes in shape)

说明：  贝叶斯方法将所有参数看作随机变量，将未知参数的先验信息与观测数据综合，再由贝叶斯定理求算出后验概率分布，进而根据后验分布去推断未知参数。(Bayesian method regards all parameters as random variables, synthesizes the prior information of unknown parameters with observation data, calculates the posterior probability distribution by Bayesian theorem, and infers unknown parameters according to the posterior distribution.)