app (2)

An demo of view base iPhone tennis game

基于DTW与K-MEDOIDS的时间序列聚类的算法(Computing DTW distance code)

labview实现模糊PID控制的例程，控制温度的调节，并且实时显示图像(LabVIEW implementation of fuzzy PID control routines.Control temperature adjustment and display images in real time.)

for ks0108 LCD Controller

说明：  plot two dimensional three dimensional

使用vhdl编写的一段程序。 主要功能是声音周期计算，以区别声音信号与噪音信号。-vhdl prepared by the use of a procedure. Its main function is voice cycle, in order to distinguish voice signal and the noise signal.

说明：  粒子群算法，也称粒子群优化算法或鸟群觅食算法（Particle Swarm Optimization），缩写为 PSO， 是由J. Kennedy和R. C. Eberhart等开发的一种新的进化算法(Evolutionary Algorithm - EA)。PSO 算法属于进化算法的一种，和模拟退火算法相似，它也是从随机解出发，通过迭代寻找最优解，它也是通过适应度来评价解的品质，但它比遗传算法规则更为简单，它没有遗传算法的“交叉”(Crossover) 和“变异”(Mutation) 操作，它通过追随当前搜索到的最优值来寻找全局最优。这种算法以其实现容易、精度高、收敛快等优点引起了学术界的重视，并且在解决实际问题中展示了其优越性。粒子群算法是一种并行算法(Particle Swarm Optimization (PSO), also known as Particle Swarm Optimization (PSO), is a new evolutionary algorithm developed by J. Kennedy and R. C. Eberhart. PSO is a kind of evolutionary algorithm, which is similar to simulated annealing algorithm. It starts from random solution and searches for the best solution by iteration. It also evaluates the quality of solution by fitness. But it is simpler than genetic algorithm rules. It has no Crossover and Mutation operation of genetic algorithm. As a result, it searches for global optimum by following the optimal value currently searched. This algorithm has attracted the attention of academia for its advantages of easy implementation, high accuracy and fast convergence, and has shown its superiority in solving practical problems. Particle swarm optimization is a parallel algorithm)

说明：  基于arduino的灯带程序，可以实现RGB灯带的控制，使用fastLED库(Based on Arduino, the control of RGB light band can be realized, and fastled library is used)

本程序比较有趣，可以实现不用文件操作输出自己的代码-this program more interesting, can not achieve their document output operation code

说明：  遥感图像的语义分割，分别使用Deeplab V3+(Xception 和mobilenet V2 backbone)和unet模型(Semantic segmentation of remote sensing images using Deeplab V3+ (Xception and Mobilenet V2 backbone) and UNET models)