gst

A matlab version of object detector and tracker to be used as an auxiliary function for various computer vision applications

MATLAB数据文件的读写教程MATLAB data file read and write tutorial(MATLAB data files read-write tutorials MATLAB data file read and write tutorial)

Matlab for all simulation of modulation

Cancerdectionusing new algorithm

非线性振动必备工具箱，里面有详细的步奏讲解,频谱分析，poincare截面，分岔图，李雅普洛夫图(onlinear vibration necessary toolbox, There are detailed explanations of the steps for spectrum analysis, poincare section, bifurcation diagram and lyapunov figure )

Codigos Convolucionales

filter data in Matlab

根据对真空蒸发镀膜的分析，在40×40的范围内分别蒸 发：1000、5000、10000、20000、30000个粒子。  利用2维数组记录每个点的粒子数。  初始粒的横纵坐标为0～40的均匀分布。  能量为（15，3）的正态分布。  在基体表面自由行走时损失1的能量。  若该点有粒子，即与其他粒子相遇时损失3的能量。  由低处向高处扩散，每扩散一层，损失4的能量，由高处 向低处扩散，损失3的能量。  当粒子的能量小于等于0时停留下来，该点的粒子数加1。  临界核的尺寸为10个粒子。  根据每个点粒子个数的不同，以不同的灰度显示这些点。 点越亮，则表示该点的膜越厚。  以图形方式显示粒子初始能量的正态分布情况(Based on the analysis of the vacuum evaporation coating, respectively, in the range of from 4040 evaporated: 1000,5000,10000,20000,30000 particles.  each point using 2-dimensional array to record the number of particles. The  initial grains transverse and longitudinal coordinates of a uniform distribution of 0 to 40. The  energy as a normal distribution (15,3).  1 energy losses in the substrate surface to walk.  point particles that 3 energy loss in the encounter with the other particles.  by the lower diffusion tops every diffusion layer, a loss of 4 energy diffusion from high to low, the energy loss of 3.  When the energy of the particles is less than or equal to 0:00 stay down, add 1 to the number of particles that point.  critical nucleus size of 10 particles.  these points, depending on the number of particles of each point is displayed in a different shade. Point, the brighter, the thicker the point of the fi)

几何光学,振子在方形与圆形地板的辐射计算,matlab编写(Geometrical optics, oscillator in the square and circular floor radiation calculation, matlab write)

Particle swarm optimization has been used to solve many optimization problems since it was proposed by Kennedy and Eberhart in 1995 [4]. After that, they published one book [9] and several papers on this topic [5][7][13][15], one of which did a study on its performance using four nonlinear functions adopted as a benchmark by many researchers in this area [14]. In PSO, each particle moves in the search space with a velocity according to its own previous best solution and its group’s previous best solution. The dimension of the search space can be any positive integer.