Non-Inter-Symbol-Interference

实现目标形状的物体检测，该程序是从一对简单形状图形中提取出矩形(Goals shape object detection, the program is a pair of simple rectangular shape extracted graph)

对其它几种常用的FACTS器件也设计了阻尼控制器,并同样通过了PSASP下的仿真验证,阻尼效果很好。 ( Damping controllers of other kinds of FACTS including TCSC, STAT- COM and SSSC are also designed, which also have good effect to damping inter-area oscillation by simulation. The power oscillation between two areas is damped quickly while these FACTS are installed. )

首先对图像进行了边缘检测，设计相应的结构元素对图像进行腐蚀，再进行图像聚类和填充图像。(First, the image edge detection, the design of structural elements corresponding to the image etched image clustering and then fill the image.)

这些录音由Thorsten Zander，柏林，和蔼可亲地提供；如果你使用这些录音的出版物，请引用： 赞德，T. O.，Kothe，C.，Jatzev，S.＆Gettne，M（2010）。增强人机交互与主动和被动脑机接口的输入。在大脑-计算机接口中（181-199页）。斯普林格伦敦。(These recordings were kindly provided by Thorsten Zander, TU Berlin; if you use these recordings for a publication, please cite: Zander, T. O., Kothe, C., Jatzev, S., & Gaertner, M. (2010). Enhancing human-computer interaction with input from active and passive brain-computer interfaces. In Brain-Computer Interfaces (pp. 181-199). Springer London.)

We model the hunt as a game of three explicit stages: the stalk, the attack, and the subdual. We implemented this model in Matlab to simulate a velociraptor hunting a thescelosaurus and an African lion hunting a gazelle.

Sequentielle Quadratische Optimierung fuer f(x) = Min!, g(x) >= 0, h(x) = 0 unter Regularitaetsbedingung FUNKTIONEN: gp.m, gp_g.m, restor.m, sigini.m name_f.m: Zielfunktion/Gradient name_g.m: Ungleichunen/Gradienten name_h.m: Gleichungen/Gradienten INPUT: name Name des Problems x zulaessiger Punkt als Startvektor OUTPUT: (x,y,z) Kuhn-Tucker-Punkt f = f(x) Funktionswert errorcode = 1: Max. Schrittzahl in Iteration errorcode = 2: Max. Schrittzahl in Backtracking errorcode = 3: x nicht zulaessig

一个关于注水svd预编码的算法程序，一定可以用(Svd on the injection of a pre-coding algorithm program will be able to use)

从数据学习贝叶斯网络结构的蚁群优化实现 matlab平台下(Ant Colony Optimisation implementation for learning Bayesian Network structures from data)

基于粒子滤波的故障检测,使用似然函数作为检测函数(Particle filter-based fault detection, the use of likelihood function as a detection function)

This function calculates the curvature of a 2D line. Reference￡oS. Hermann and R. Klette. Multigrid analysis of curvature estimators. Proc. Image Vision Computing New Zealand, 108¨C112, 2003. by Jianfei Pang k = CurvatureEstimation(list,b,f) inputs, list : A M x 2 list of line points. fn: The number of forward points that were chosen to calcute. bn: The number of backward points that were chosen to calcute. outputs, Ei : M x 1 Curvature values thetai:M x 1 angle values Example [Ei,ti]=CurvatureEstimation(test,4,4)