switch--circuit

CDMA的matlab仿真CDMA的matlab仿真CDMA的matlab仿真(simulation of CDMAsimulation of CDMAsimulation of CDMA)

提供了集中语音去噪的matlab代码，并提供了相应的声音文件(Provides a centralized voice denoising matlab code, and provide the corresponding sound files)

说明：  偶图最大匹配完全照算法说明利用集合运算实现的matlab代码。这里需要大家讨论的是增广路的选取的算法，希望朋友们多多讨论，争取找一种更好的增广路方法，有想法的一定告诉我，谢谢，jxrhhu@hhu.edu.cn(Bipartite graph maximum matching algorithm description completely according to the use of computing to achieve a collection of matlab code. Here we need to discuss is the way to broaden the selection of the algorithm, a lot of friends who wish to discussions in order to find a better way to broaden the way, there is a certain mind to tell me, thank you, jxrhhu@hhu.edu.cn)

基本的调制解调程序集(包括BPSK,QPSK,QAM,GMSK等)(Basic modulation and demodulation assembly (including BPSK, QPSK, QAM, GMSK, etc.))

CRC码的加入 LTE编码过程中验证码。。。。。。。(Join LTE encoding process in the CRC verification code)

these codes are about simulation of vcsel laser diod

Compare<T> implements IArgumentMatcher.

TD—SCDMA物理层的调制解调仿真，本程序忽略了很多细节，只把最重要的调制解调过程仿真。(TD_SCDMA physics level simulation)

用matlab编的科斯塔斯环，希望有帮助(Costas loop using matlab helpful)

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.)