lengsou

说明：  Simulation distribution1

MATLAB语言在Java里的使用：详细介绍了相关的环境配置，MATLAB打包步骤，以及在Eclipse里使用Java调用资源包的全过程。含测试实例(MATLAB language used in the Java：details of the environment configuration, MATLAB package, and the use of Java resource bundles to call the whole process in the Eclipse. Containing the test case)

特征空间波束形成图 稳健 改进算法 比较了lcmv特征空间 改进的特征空间 (The feature space beamforming Figure steady improvement algorithm)

数字通信原理基于matlab的仿真计算，希望对大家有用(Principles of digital communication matlab simulation based on the hope of useful)

心电数据的滤波代码，已经调试通过，可以滤出工频干扰，基线飘移。谢谢合作(ECG data filtering code debugging has been passed, and labor-frequency interference filters, baseline drift. Thank you for your cooperation)

自适应滤波器是一种参数可自适应调整的有限冲激响应(FIR)数字滤波器，具有非递归结构形式。介绍了自适应滤波器的基本原理，用LMS自适应算法来仿真，得到LMS算法在自适应滤波中的收敛特性。(Adaptive filter is an adaptive adjustment of parameters can be finite impulse response (FIR) digital filter, with a non-recursive structure. Describes the basic principles of adaptive filter with LMS adaptive algorithm simulation, LMS algorithm in adaptive filtering convergence characteristics.)

能够很准确的计算单输入单输出系统的信噪比。(Very accurate calculation of the SNR of the single-input single-output system.)

数学交互界面，可以实现求解线性方程组，三维绘图，二重积分等(Math interactive interface for solving linear equations, three-dimensional drawing, the double integral)

number of simulations to run

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.)