Cross-factor-with-pso

用一般的最小二乘方法和SVD—TLS（总体最小二成）方法估计观测数据的ARMA模型的AR参数。(Least squares method and with the general SVD-TLS (Twenty-general minimum) to estimate the ARMA model of observed data AR parameters.)

说明：  粗糙度计算程序，可以用来计算声学上的尖锐度，当然要先导入音频。。。。(Roughness calculation program, can be used to calculate the acoustic sharpness, of course, first to import audio....)

对要发送的序列进行上变频，上变频到21MHz后进行发送。(change the frequence uop to the one that suit for transmission,which here is 21MHz.)

ipv4过渡到ipv6的一些技术分析，希望对大家有用(ipv4 transition to ipv6 some of the technical analysis, hope for all of us)

无线传感器最短路径算法，MATLAb代码，可以直接在MATLAb上运行。(Wireless sensor the shortest path algorithm, MATLAb code, can be directly in the MATLAb operation. )

光伏电池阵列输出功率受光照强度和温度变化的影响，因此最大功率点跟踪（MPPT）技术广泛应用于光伏系统中。在所有最大功率点(MPP)控制策略中，扰动观察（P&O）MPPT算法因易实现被广泛应用，然而它的缺点是在稳定工作状态下工作点通过MPP 时会导致能量振荡损耗，并且在光照强度或温度发生突变时表现较差的动态响应。在本文中，提出一种改进型变步长扰动观察MPPT 算法，此方法依据工作点动态调整步长变化，与传统固定步长方法比较，本文提出的方法能有效地提高MPPT 速度和转换效率，通过仿真和实验结果分析，验证了此改进算法的可行性。(PV array output power by light intensity and temperature changes, so the maximum power point tracking (MPPT) technology is widely used in photovoltaic systems. In all the maximum power point (MPP) control strategy, the perturbation and observation (P & O) MPPT algorithm because easy to implement is widely used, but its disadvantage is the steady-state operating point by the MPP causes energy oscillations loss, and in the light intensity or temperature mutated poor performance dynamic response. In this paper, we propose a modified variable step size perturbation and observation MPPT algorithm, this method is based on dynamically adjusting the operating point changes in steps, with the traditional fixed-step methods, the proposed method can effectively improve the speed and efficiency MPPT, The simulation and experimental results verify the feasibility of this algorithm.)

detect cars numbers with matlab

Weighted maximum matching ，图论中的计算。 matlab与C++ 混合编程，如果运行出现问题，请将所有Weighted maximum matching 函数换成 mWM(Weighted maximum matching for graph)

This MATLAB code is an example of how to train the GCRF model described in "Learning Gaussian Conditional Random Fields for Low-Level Vision" by M.F. Tappen, C. Liu, E.H. Adelson, and W.T. Freeman in CVPR 2007. If you use this code in your research, please cite this paper

用MATLab仿真了基于矩量法的V形阵子天线的方向图和天线上的电流分布图(With MATLab simulation time around based on the method of moments of the V-shaped antenna pattern and antenna current distribution)