SPMSM_1

数据关联是多目标跟踪的一项关键技术。JPDA是大家公认的多目标跟踪中性能较好的数据关联算法，它 认为量测和目标是一一对应的关联关系，但在许多实际情况中，量测和目标是多一多对应的关系。针对上述情况，该文提 出了广义概率数据关联算法(Generalized Probability Data Association，GPDA)。文中从理论上对这两种算法的性能进行了 详细分析，并利用Monte Carlo技术对其性能进行了仿真比较。(Data association is one of the key technologies in multi—target tracking．And JPDA is considered as the best da· ta association method．JPDA considers the association of measurements with targets is simply one-to-one problem．But in many practical cases，the association of measurements with targets will be multiple—to—multiple problem．For this case，a Generalized Probability Data Association(GPDA)algorithm is proposed in this paper．Furthermore，this paper analyzes the performance of these two algorithms theoretically．And we give the comparative analysis of those performances by using Monte Carlo method．)

退火算法matlab源码，并带有详细设计说明，执行效率较高(Annealing algorithm matlab source, with the detailed design shows that the implementation of more efficient)

一个很好用的粒子群优化算法matlab源代码，用来解决单目标函数优化(A good use of particle swarm optimization algorithm matlab source code, used to solve single-objective function optimization)

MPC coding for CSTR

205-GHz (Al,In)N/GaN HEMTs.more than 15 years of development, AlGaN/GaN high-electron-mobility transistors (HEMTs) have become the most technologically mature form of GaNbased HEMTs. There is, however, growing evidence that the total strain (i.e., the lattice-mismatch and piezoelectric contributions) limits the reliability of conventional AlGaN/GaN HEMTs

产生速率160Gbit/s,脉宽1.25ps的一阶高斯脉冲模拟序列（101101）(Production rate of 160 Gbit/s, pulse width 1.25ps first order Gaussian pulse simulation sequence (101101))

is suitable for renewable energy conversion applications.

该代码仅计算了一个101×101尺寸的Gabor函数变换，得到均值和方差。代码采用两种卷积计算方式，从结果中可以看出，快速傅立叶变换卷积的效率是离散二维叠加和卷积的近50倍。 (The code is only the calculation of a 101 × 101 size Gabor function transformation, the mean and variance. Convolutional code using two methods of calculation can be seen from the results, fast Fourier transform convolution efficiency are two-dimensional discrete convolution superposition and nearly 50 times.)

用遗传算法估计伪随机码（PN序列），包含初始化，选择，交叉，变异(Is estimated by using genetic algorithms pseudo-random code (PN sequence), including initialization, selection, crossover and mutation)

说明：  本代码内容用遗传算法求解极值，包括种群的选择，遗传，变异等。(he code content using genetic algorithms to solve the extreme value, including populations of selection, genetic variation and so on.)