fdtd

用Matlab写的FCM算法，包括GUI界面的设计，实现模糊聚类功能。(FCM algorithm is written using Matlab, including the GUI interface design, implementation, fuzzy clustering functionality.)

mean shift算法提取图像边缘和图像分割(mean shift algorithm for image edge extraction and image segmentation)

说明：  用于Monte Carlo仿真的二进制FSK系统(for the Monte Carlo simulation of the binary system FSK)

计算电磁学 二维PML程序 采用MATLAB编写(PML-dimensional computational electromagnetics programs written using MATLAB)

It can be applyed forthe Spectral Element Method conbined finite element and spectral method to solve the 2D wave equation.as a pecial case is SH. dynamic fault with slip weakening is considered either.

good for power analysis

信号功率谱分析。方法五、六，在此以详细编写。(Signal power spectral analysis. Methods 5, 6, in this case to the elaboration.)

This is a very simple and loose project manager.(This is a very simple and loose project manager. It saves only the following information for each project that is necessary to switch between projects : - Current working directory - Project specific Path - All opened m-files (order is not guaranteed in pm.) - Current active m-file. - List of run configurations you want to execute sequentially. )

Impementation code for CAC for NGMN ========================= This code simulates the operation of a wireless cellular system

说明：  粒子群算法（PSO）是一种基于群体的随机优化技术。与其它基于群体的进化算法相比，它们均初始化为一组随机解，通过迭代搜寻最优解。不同的是：进化计算遵循适者生存原则，而PSO模拟社会。将每个可能产生的解表述为群中的一个微粒，每个微粒都具有自己的位置向量和速度向量，以及一个由目标函数决定的适应度。所有微粒在搜索空间中以一定速度飞行，通过追随当前搜索到的最优值来寻找全局最优值。 PSO模拟社会采用了以下三条简单规则对粒子个体进行操作：①飞离最近的个体，以避免碰撞。②飞向目标。③飞向群体的中心。这是粒子群算法的基本概念之一。 粒子群算法其基本思想是受许多鸟类的群体行为进行建模与仿真研究结果的启发(Particle swarm optimization (PSO) is a population based stochastic optimization techniques. Based on evolutionary algorithms compared with other groups, they are initialized to a random solution, iterative search through optimal solution. The difference is: the principle of survival of the fittest evolutionary computation to follow, while PSO simulation community. The potential of each solution expressed as a group of particles, each particle has its own position vector and the velocity vector, and a fitness determined by the objective function. All particles in the search space at a constant speed flight, by following the current search to find the optimal values ​ ​ of the global optimum. PSO simulation community has adopted the following three simple rules for the operation of individual particles: ① recently departed individuals, in order to avoid collisions. ② to the target. ③ fly to center groups. This is one of the basic concepts of particle swarm algorithm. PSO algori)