OFDM_64QAM

用蚂蚁算法智能的解决大规模TSP问题，有大量测试用例，图形化的展示运行过程。(Intelligent use of ant algorithm to solve large-scale TSP problem, a large number of test cases, graphical display of running.)

microstrip antenna design

gcc-phat frequency domain (gcc-phat frequency domain)

这个是用matlab现实薇薇安尼曲线的源程序，非常的方便，解压后放入matlab中即可运行出结果。(This is matlab practical source program called vvanni， very convenient, after decompression can run results in matlab.)

基于MATLAB软件的陷波器及另一种LMS算法程序(Notch filter based on MATLAB software and another LMS algorithm)

说明：  拓扑优化常用优化算法，移动渐进线方法，内含详细的程序说明，指出使用改算法的具体操作(Topology optimization commonly used optimization algorithm, method of moving asymptotes, contains detailed program description, and points out the specific operation of the modified algorithm)

三维数据矩阵的中值滤波方法，采用快速实现算法(Three-dimensional data matrix median filtering method, using fast algorithm)

TDOA/AOA定位的扩展卡尔曼滤波定位跟踪算法Matlab源码(TDOA/AOA positioning of the extended Calman filter positioning tracking algorithm, Matlab source code)

用matlab程序编写的，用于计算机视觉或摄影测量中图片的特征点提取。(Using matlab programming, for computer vision or photogrammetry image feature extraction.)

标准粒子群算法的实现思想基本按照粒子群算法(2) 标准的粒子群算法的讲述实现。主要分为3个函数。第一个函数为粒子群初始化函数 InitSwarm(SwarmSize......AdaptFunc)其主要作用是初始化粒子群的粒子，并设定粒子的速度、位置在一定的范围内。本函数所采用的数据结构如下所示： 表ParSwarm记录的是粒子的位置、速度与当前的适应度值，我们用W来表示位置，用V来代表速度，用F来代表当前的适应度值。在这里我们假设粒子个数为N，每个粒子的维数为D。(His thought of the implementation of the standard particle swarm algorithm according to the particle swarm algorithm (2)- the standard particle swarm algorithm about implementation. Mainly divided into three functions. The first function of particle swarm initialization function InitSwarm (SwarmSize... AdaptFunc), its main function is to initialize particle swarm particle, and set the particle s velocity, position in a certain range. The function of data structure is as follows: ParSwarm record table is the particle s position, speed and the current fitness value, we use W to represent the position, represented speed with a V, to represent the current fitness value with F. Here we assume that the number of particles N, each particle dimension for D.)