gangjie_v27

利用matlab仿真GPS坐标转换，即知道一点的GPS坐标利用测出来的距离俯仰方位信息计算另外一点的GPS坐标。(GPS coordinates conversion using matlab simulation, that is to know that the GPS coordinates use the distance measured from the pitching position information to calculate the GPS coordinates of another point.)

this program generate 1D and 2D plane wave and apply Yee algorithm without dielectric

Gabor变换属于加窗傅立叶变换，Gabor函数可以在频域不同尺度、不同方向上提取相关的特征。另外Gabor函数与人眼的生物作用相仿，所以经常用作纹理识别上，并取得了较好的效果。(Gabor transform is windowed Fourier transform, Gabor functions in the frequency domain at different scales and different directions to extract relevant features. Gabor function and other biological effects similar to the human eye, so often used as a texture on the identification, and have achieved good results.)

说明：  smote算法，用于扩充对数据的采样，当某类样本量较小时，做神经网络对它的类型判断错误率就会较高，所以需要调整采样，语言matlab(smote Algorithms,which used to expand the sampling of data)

关于bp神经网络的源程序，对研究bp神经网络非常有帮助(bp on neural network source, the study bp neural network has been very helpful)

16元均匀圆阵的波束形成，目标信号位于0度，信噪比为0，效果不错(16 elements UCA beamforming, the bearing of target signal is 0, SNR is 0, good result)

小波在图像融合中的应用,还有自适应算法在图像融合中的应用。(Application of wavelet image fusion)

Simple model of FSK decoder

simple electrical engineering programs for matlab

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.)