parameter_classic

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通过MATLAB来实现的一个对于简单背景下小球的一个滤波模拟过程(MATLAB to achieve through a simple background for a ball filter simulation)

需要进行特征分解的MUSIC法的计算了很大，通过改进，不需要特征分解就可以测向。(Necessary characteristic decomposition of MUSIC method of calculating a lot, through the improved features of decomposition can be measured without the.)

说明：  这是用模拟退火算法解决0-1背包问题，下载后可直接运行，可以帮助有需要对模拟退火算法进行了解的朋友。(This is a simulated annealing algorithm to solve the 0-1 knapsack problem, after downloading, can be directly run, you can help those in need of the simulated annealing algorithm to find out about friends.)

自己按J David的方法做的7个Hu时刻，用于边缘检测(7 HU moments，similitude Moments，matlab)

通信原理实验 数字信号2ask2psk2fsk调制与解调(Communication Theory Digital Signal 2ask2psk2fsk experimental modulation and demodulation)

this file contains basic amplify and forward technique with physical layer network concept

Improved differential evolution algorithm to optimize the hybrid particle swarm optimization intelligent optimization algorithm source code

说明：  自己写的一个计算空间时频分布的程序，可知指定Cohen类分布类型WVD，CWD，PWVD，SPWVD，BJD(himself in a room when calculating the frequency distribution procedures, we can see Cohen category designated distribution type WVD, CWD, Wigner, SPWVD, BJD etc.)

说明：  采用栅格对机器人的工作空间进行划分，再利用优化算法对机器人路径优化，是采用智能算法求最优路径的一个经典问题。目前，采用蚁群算法在栅格地图上进行路径优化取得比较好的效果，而利用遗传算法在栅格地图上进行路径优化在算法显得更加难以实现。 利用遗传算法处理栅格地图的机器人路径规划的难点主要包括：1保证路径不间断，2保证路径不穿过障碍。 用遗传算法解决优化问题时的步骤是固定的，就是种群初始化，选择，交叉，变异，适应度计算这样，那么下面我就说一下遗传算法求栅格地图中机器人路径规划在每个步骤的问题、难点以及解决办法。(It is a classical problem to divide the workspace of the robot by grids and optimize the path of the robot by using optimization algorithm. At present, the ant colony algorithm is used to optimize the path on the grid map, and the genetic algorithm is used to optimize the path on the grid map, which is more difficult to achieve. The difficulties of using genetic algorithm to deal with the path planning of robot on raster map mainly include: 1. guaranteeing that the path is uninterrupted, 2. guaranteeing that the path does not cross obstacles. The steps of genetic algorithm in solving optimization problems are fixed, that is, population initialization, selection, crossover, mutation, fitness calculation. Then I will talk about the problems, difficulties and solutions of genetic algorithm in each step of robot path planning in raster map.)