Chirplets

说明：  408951混合SPMD 模拟退火算法及其应用(Hybrid SPMD simulated Annealing algorithm and its applicatio)

生成m序列和gold的matlab程序代码，可以作为扩频序列的伪随机序列。(M sequence and gold to generate the matlab program code can be used as spreading sequences of pseudo-random sequence.)

labview生产者消费者循环实例，适合初学者学习，非常简单实用的例子(Suitable for beginners to grasp the relationship between labview development process, producers and consumers)

说明：  Info about electronics

说明：  一篇关于大功率低谐波高频隔离的交流直流变换器，主要是应用与直流充电庄等设备(A 12Kw Three-phase low THD rectifier with high-frequency isolation and regulated DCoutput1)

51开发版仿真图用于仿真单片机极其使用明明哦嘻嘻咦咦咦XP明明哦栖息地一滴一滴名字(51danpianjszswo4zhinixinximingzid)

鼠标记录器，昨天刚写的，特点是简单实用！-Mouse Recorder

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：  获取CC2530内部电压与温度，通过串口向pc机上面发送数据(Obtain CC2530 internal voltage and temperature)

改进的粒子群算法，适用与多目标最优化项目，收敛速度快(The improved particle swarm optimization algorithm is applicable to multi objective optimization projects, and the convergence speed is fast)