EMD

基于Matlab/Simulink的定速风电机组的模型仿真，包括风速波动及电网故障时的输出特性仿真。(Model-based fixed-speed wind turbine Matlab/Simulink simulation, including fluctuations in wind speed and power output characteristics of a fault simulation.)

Cross Correlation function implemented in matlab

对固定结构的网络结构进行线路损耗的计算。简单方便(The line loss of fixed structure network is calculated. Simple and convenient)

一道程序编译顺序的考题，涉及到函数调用的先后顺序及运算符号的优先级等问题。下面我展开给你讲。 C的程序编译总是从main函数开始的，这道题的重点在“fun（（int）fun（a+c,b）,a-c）） ”语句。 系统首先要确定最外层 fun()函数的实参，第一个参数的确定需要递归调用fun()函数（不妨称其为内层函数）。内层函数的两个参数分别为x=a+b=2+8=10、y=b=5,执行函数体x+y=10+5=15,于是得外层函数的参数x=15。其另一个参数y=a-c=2-b=-6,再次执行函数体,得最终返回值x+y=15+(-6)=9。 (Compiling together the sequence of test procedures, involving the sequence of function calls and operator symbols, such as the priority problem. Now I give you to start speaking. Procedures for C compiler always start from the main function and at这道题the focus of " fun ((int) fun (a+ c, b), ac)) " statement. System must first determine the most outer layer of fun () function of real parameters, the first parameters of recursive calls required fun () function (may be called the inner function). Inner function separately for the two parameters x = a+ b = 2+8 = 10, y = b = 5, to execute the function body x+ y = 10+5 = 15, then the outer function parameters were x = 15 . Its another parameter y = ac = 2-b =- 6, once again to execute the function body may eventually return the value of x+ y = 15+ (-6) = 9.)

finding a root by position of falses

该程序在matlab环境下运行，完成BPSK调制解调仿真实现过程。可以看到经过滤波后的眼图。(err)

Error Vector Magnitude Calculation

direct torque control of induction machine with thre level inverter

Find edges of a color image

Start with the runlocalization track.m which is the entrance function to your lab. This function reads two les determined by simoutle and maple input arguments which contain information about sensor readings and the map of the environment respectively, runs a loop for all the sensor readings and calls the ekf localize.m to perform one iteration of EKF localization on the readings and plots the estimation(red)/ground truth(green) and odometry(blue) information.(This lab consists of two parts: 1. A preparatory case study with a standard Kalman lter where you learn more about the behavior of the Kalman lter. Very little extra code is needed. 2. The main lab 1 problem in which you need to complete an implementation of an Extended Kalman lter based robot localization.)