mdof_modal_arbit_force_newmark

矢量算法模型 用于逆变器的空间矢量脉冲产生(vetor matlab)

MATLAB经典程序，内容由简单到困难。我也有相关视频(MATLAB classical procedures, content, from simple to difficult. I also have related videos)

说明：  dco-ofdm调制解调程序，非常实用的代码(case for DCO-OFDM system)

一个很好用的粒子群优化算法matlab源代码，用来解决单目标函数优化(A good use of particle swarm optimization algorithm matlab source code, used to solve single-objective function optimization)

As we know, the energy needed for sensors should be electrical. So the rst step before start to work is to convert environmental energy to electrical one. Inside the node there are dierent components for sensing the area, collecting data and processing the data. So the main dierent part of this kind of nodes, in compare of WSN nodes, is the power part. Once the stored energy reach to a certain amount, the power supply for micro controller and transceiver will start to work. They will continue to work until they have energy, as soon as the energy nish, they stops and energy storage device start to work to save energy again. The other fact should be consider about EH-WSN node is that the available energy can change in dierent nodes. So every node has its own harvesting rate.

对实际测得坐标实现圆拟合，前提是几何形状近似圆(The actual measure coordinates are fit to a circle, provided that that geometry approximates the circle)

根据《雷达数据处理及应用》书中的例题写的PDA数据处理算法(PDA algorithm Matlab simulation)

收集的一些CCP协议汽车标定相关的资料，希望能对从事这方面开发的朋友有些须帮助(Collection of some of the CCP protocol auto calibration of the relevant information, hoping to develop in this field to help some friends)

一个会动的五角星,应用matlab软件编程。(A moving star，using matlab program.)

Matlab functions implementing non-parametric local linear smoothing (LLS) for one-dimensional curve fitting: yi = f(xi) + ei for i = 1, 2, ..., n. The toolbox contains: (i) the usual LLS estimator, see e.g. [1] (ii) a jump-preserving (two-sided) LLS estimator proposed in [2] (iii) a modified version of the two-sided LLS proposed in [3].(Matlab functions implementing non-parametric local linear smoothing (LLS) for one-dimensional curve fitting: yi = f(xi)+ ei for i = 1, 2, ..., n. The toolbox contains: (i) the usual LLS estimator, see e.g. [1] (ii) a jump-preserving (two-sided) LLS estimator proposed in [2] (iii) a modified version of the two-sided LLS proposed in [3].)