IEEE80211b_PHY_DBPSK

sparsebayes matlab RVM(relevant vector machine)

基于Matlab的改进人工势场法实现路径规划与避障(Improved artificial potential field method based on Matlab)

很好的关于风力发电的资料，希望对大家有所帮组。(good material)

可在Matlab上阅读地震数据SEGY格式，并进行编辑(reading on the seismic data in Matlab SEGY format, and edit)

hamming编码和RS码，在AWGN下性能(hamming codes and RS codes in AWGN performance)

说明：  基于Matlab的MIMO通信系统仿真”，收获良多，特将其记录下来，与诸位分享。此主题分为上下两部分，第一部分主要涉及系统架构，第二部分侧重程序优化。若能帮助读者一二，实属万幸。(Multi-input multi-output (MIMO) communication system based on Matlab simulation ", so, will its record, share with you. This topic is divided into two parts, the first part mainly involves the system architecture, the second part focuses on process optimization. If can help readers just a little, is lucky.)

a gain calculator about semiconductor optical amplifier gain and ode method

UTV分解工具包,常用的相关matlab程序(tools of UTV)

基于comsol server的matlab代码，用于模拟二维等离子体辉光放电。(matlab code based on comsol server to simulate 2D glow discharge.)

蕞优分组周游路线问题是一类物流智能调度问题，是众多的计算难度极大的组合爆炸问题之一。自从运输问题最早是由Hitchcock在1941年提出的[2][4]后，人们对这一类问题给予了极大的关注并进行了大量的研究，提出了有效解决问题的优化算法——单纯形法的变形[2]。但由于问题规模的扩大，纯数学方法在允许的时间和空间上都很难找到问题的最优解，于是人们开始转向寻找问题的近似最优解，遗传算法以其在求解最优化问题中的独特的自组织性、自适应性很快进入人们的视野。 经典的遗传算法对于求解多目标的NP完全性问题非常有效，但对于有多个限制条件的多目标最优化问题缺显得有点力不从心，很难得到稳定度较高、收敛较快的解。本文提出的双层遗传算法模型给这一类问题提供了一个很好的解决方法，虽然建模条件限制了描述的问题的通用性，有待推广，但它能有效利用问题的领域知识，提高了求解效率，保证了解的可行性和最优性，算法思想极具推广潜力，可在其它物流智能调度问题求解算法设计中借鉴。(The most optimal packet travel route problem is a kind of intelligent logistic scheduling problem is one of combinatorial calculation difficult problem many. Since the transportation problem was first proposed by Hitchcock in 1941 after the [2][4], the people of this class of problems given great attention and made a lot of research, the deformation of [2] optimization, simplex method algorithm is effective to solve the problem of. But due to the expansion of the scale of the problem, the pure mathematical methods in the allowed time and space are very difficult to find the optimal solution of the problem, so people begin to turn to the approximate optimal solution for the problem, genetic algorithm in solving optimization problems with its unique in the self-organizing, adaptive soon entered people s field of vision. Very effective classical genetic algorithm for solving multi-objective NP complete problems, but for the multi-objective optimization problem with multiple constraints. I)