SEI

This code is in geophysics field.

利用偏直接一致（PDC）指数分析同步采集的脑电信号和肌电信号之间的信息传递方向 (Analysis the information transfer direction btween synchronous EEG and EMG )

聚类方法在雷达信号分选中的应用，聚类方法在雷达信号分选中的应用(Clustering method selected in the application of radar signal, clustering method selected in the application of radar signal)

EM算法的详细介绍（含PDF文件）及其matlab实现(EM algorithm is a detailed description (including PDF files) and its implementation matlab)

基于短时能量的基音短点检测，有基于短时能量的计算方法、短时过零率，有matlab程序，同事对应word讲解，算法原理图，和算法详细介绍。(Short pitch detection based on short-term energy, based on the calculation method of the short-time energy, short-time zero-crossing rate, matlab program, colleagues corresponding word on the schematic diagram of the algorithm, and the algorithm described in detail.)

说明：  三角形，四边形 空间体单元等的刚度矩阵的组装，应力求解，有限元分析，该m文件可直接被matlab调用(Assemble the stiffness matrix of triangle, quadrilateral space element, stress solution, finite element analysis, the M file can be directly called by MATLAB)

QPSK SIMULINK调制解调仿真绝对能运行(Modulation and Demodulation QPSK SIMULINK)

Tracking control of a mobile robot with kinematic and dynamic constraints

eps的东西，做得挺好的 eps的东西，做得挺好的(eps thing, doing something very good eps, doing very good)

说明：  多目标遗传算法是NSGA-II[1]（改进的非支配排序算法）,该遗传算法相比于其它的多目标遗传算法有如下优点：传统的非支配排序算法的复杂度为 ，而NSGA-II的复杂度为 ，其中M为目标函数的个数，N为种群中的个体数。引进精英策略，保证某些优良的种群个体在进化过程中不会被丢弃，从而提高了优化结果的精度。采用拥挤度和拥挤度比较算子，不但克服了NSGA中需要人为指定共享参数的缺陷，而且将其作为种群中个体间的比较标准，使得准Pareto域中的个体能均匀地扩展到整个Pareto域，保证了种群的多样性。（消除了共享参数）。(Multi-objective genetic algorithm is nsga-ii [1] (improved non-dominant sorting algorithm), which has the following advantages compared with other multi-objective genetic algorithms: the complexity of the traditional non-dominant sorting algorithm is, while the complexity of nsga-ii is, where M is the number of objective functions and N is the number of individuals in the population.The introduction of elite strategy to ensure that some good individuals in the evolutionary process will not be discarded, thus improving the accuracy of the optimization results.The comparison operator of crowding degree and crowding degree not only overcomes the defect that NSGA needs to specify the Shared parameter artificially, but also takes it as the comparison standard between individuals in the population, so that individuals in the quasi-pareto domain can uniformly expand to the whole Pareto domain, ensuring the diversity of the population.(eliminating Shared parameters).)