hsmmInitialize

for face detection using web cam

k均值算法是一种迭代求解的聚类分析算法，其步骤是随机选取K个对象作为初始的聚类中心，然后计算每个对象与各个种子聚类中心之间的距离，把每个对象分配给距离它最近的聚类中心。聚类中心以及分配给它们的对象就代表一个聚类。(kmeans algorithm is an iterative solution clustering analysis algorithm. First, the initial clustering centers are randomly selected. Then, distance between between each point ans others are calculated, and each data point is designed to its nearest clustering center.)

说明：  OFDM系统功能仿真，很好很强大，嗯嗯嗯(OFDM system, functional simulation, very good very strong, er er er)

二次型的仿真，利用matlab实现，很好的例子啊(The second type of simulation using matlab to achieve a good example)

用于一维信号的小波包去噪，选取特定的阈值，采用最优小波基(For one-dimensional signal denoising using wavelet packet , select a specific threshold, the optimal wavelet)

在MATLAB下对LDPC的编译码。比较基础，用以理解。(In the encoding and decoding of the LDPC MATLAB. Basis of comparison for understanding.)

Gaussian Elimination with Pivoting Method by Cheuk Function uses Gauss elimination with pivoting to solve a linear system in standard format. (linear, algebra, gaussian)

利用校验多项式h(x)编码 即k级 编码(Use check polynomial h (x) encoding the k-level coding)

基于matlab/simulink的无刷直流电机控制系统仿真，设计了速度电流双闭环。(Based on matlab/simulink for brushless DC motor control system simulation, design speed current double closed loop.)

蕞优分组周游路线问题是一类物流智能调度问题，是众多的计算难度极大的组合爆炸问题之一。自从运输问题最早是由Hitchcock在1941年提出的[2][4]后，人们对这一类问题给予了极大的关注并进行了大量的研究，提出了有效解决问题的优化算法——单纯形法的变形[2]。但由于问题规模的扩大，纯数学方法在允许的时间和空间上都很难找到问题的最优解，于是人们开始转向寻找问题的近似最优解，遗传算法以其在求解最优化问题中的独特的自组织性、自适应性很快进入人们的视野。 经典的遗传算法对于求解多目标的NP完全性问题非常有效，但对于有多个限制条件的多目标最优化问题缺显得有点力不从心，很难得到稳定度较高、收敛较快的解。本文提出的双层遗传算法模型给这一类问题提供了一个很好的解决方法，虽然建模条件限制了描述的问题的通用性，有待推广，但它能有效利用问题的领域知识，提高了求解效率，保证了解的可行性和最优性，算法思想极具推广潜力，可在其它物流智能调度问题求解算法设计中借鉴。(The most optimal packet travel route problem is a kind of intelligent logistic scheduling problem is one of combinatorial calculation difficult problem many. Since the transportation problem was first proposed by Hitchcock in 1941 after the [2][4], the people of this class of problems given great attention and made a lot of research, the deformation of [2] optimization, simplex method algorithm is effective to solve the problem of. But due to the expansion of the scale of the problem, the pure mathematical methods in the allowed time and space are very difficult to find the optimal solution of the problem, so people begin to turn to the approximate optimal solution for the problem, genetic algorithm in solving optimization problems with its unique in the self-organizing, adaptive soon entered people s field of vision. Very effective classical genetic algorithm for solving multi-objective NP complete problems, but for the multi-objective optimization problem with multiple constraints. I)