Butter

说明：  matlab编写的粒子群优化算法，相当的好用，本人的压箱底的东西(matlab PSO prepared, very easy to use, what my bottom pressure)

study how to generate a symbol sequence, a pulse-shaping filter, and an eye-diagram.

说明：  SVDD 是一种重要的数据描述方法, 它能够对目标数据集进行超球形描述, 并可用于异类点检测或分类. 在现实生活中目标数据集通常包含多个样本类, 且需要同时对每一个样本类进行超球形描述。(SVDD is an important data description method, which can describe the hypersphere of the target data set, and can be used for the detection or classification of heterogeneous points. In real life, the target data set usually contains multiple sample classes, and needs to describe each sample class at the same time.)

matlab实现光纤中光孤子传输的数值仿真研究(ssft for soliton)

已知方差的高斯随机信号的序贯检测：给出了H0和H1假设下理论推导得到的序贯检测平均样本数的曲线，以及仿真得到的序贯检测平均样本数的曲线，理论结果和仿真结果较为吻合。(Sequential detection of known variance Gaussian random signals: H0 and H1 are given under the assumption that the theoretical derivation curve sequential detect the average number of samples obtained, and curve sequential simulation to detect the average number of samples obtained theoretical results and simulation The results are more consistent.)

说明：  SVM 的代码,工具箱的使用内部有详细的说明,请读者看看Matlab代码中参数使用,将训练数据以矩阵形式输入,即可使用(SVM code, the use of the toolbox there are detailed instructions, please take a look at Matlab code readers parameter use, will be a matrix of training data input, you can use)

说明：  伽辽金无网格法求解一维问题的MATLAB代码，内附2个子程序。(the method of glarkin meshless)

用MATLAB写的图像处理，包括绘制直方图，灰度拉伸，图像均衡化及频域平滑滤波代码，简洁实用，注释清晰(Written using MATLAB image processing, including the plot histogram, gray stretch, image equalization and frequency domain filtering code, simple and practical, clear notes)

基于Matlab，利用小波变换进行图像去噪(Image de-noising based on wavelet transform)

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.)