CLSPSO

The same staff as before.

Small signal stability

该实例主要是对离散时间系统建模与仿真。例子是以设计一个数字低通滤波器F（z），从受噪声干扰的多频率混合信号x（t）中获取10hz的信号。希望对研究电磁兼容以及学习MATLAB的学者有所帮助。(The examples are mainly discrete-time systems modeling and simulation. Example is the design of a digital low-pass filter F (z), acquiring a signal 10hz noise interference multi-frequency mixed signal x (t) in. I hope to study electromagnetic compatibility and scholars studying the MATLAB help.)

单快拍下的压缩感知（CS）应用于波达方向（DOA）估计的源程序，采用OMP算法(Single fast photographed compressed sensing (CS) is applied DOA (DOA) estimate of the source, using OMP algorithm)

scalablity and reliability in distributed IMS systems.

求二值图的算法园程序，在指定的目录下输入图像名称，就可以在Matlab的M文件中运行得到图像的二值图，并输入图像对应的矩阵。根据轮廓下可以生成一个二值图，并把它的图像输出。(Figure 2 value seeking algorithms garden program, in the specified directory name of the input image, we can run Matlab M-file to be binary image map and the input image corresponds to the matrix. Contour may be generated according to a binary image, and to its image output.)

voronoi图划分，用于点云的三角剖分，生成点云表面模型(voronoi graph partitioning triangulation for point cloud generated point cloud surface model)

精通GUI图形界面编程，接好matlab编程方法，很好的参考书， (Proficient in GUI graphical interface programming, then good matlab programming method, a very good reference books,)

创建一个n维的稀疏数组对象，n是任意值。 定义N可能是大于2的一类n维稀疏阵列。然而，它应该被认为是一种起动方式与普通的MATLAB稀疏矩阵和重塑它有N维度。换句话说，稀疏的数据，首先必须能够作为一个普通的2D MATLAB稀疏矩阵被前n维。事实上，如果目标数组的尺寸mxnxp……yxz，然后将它存储在内部类是一个普通的二维稀疏阵列的尺寸（M×N×P×……×Y）XZ。这导致了某些内存株时使用大量的尺寸。我发现有用的类主要用于中等尺寸像三维图像边缘检测，你经常要举行一个稀疏的3D的边缘地图。(Creates an N-dimensional sparse array object, for arbitrary N.This submission defines a class of N-dimensional sparse arrays for N possibly greater than 2. However, it should really be thought of as a way of starting with an ordinary MATLAB sparse matrix and reshaping it to have N dimensions. In other words, the sparse data must first be able to exist as an ordinary 2D MATLAB sparse matrix before being made N-dimensional. In fact, if the intended array has dimensions MxNxP...YxZ, then the class will store it internally as an ordinary 2D sparse array of dimensions (M*N*P*...*Y)xZ. This leads to certain memory strains when using large numbers of dimensions. I find the class useful mainly for moderate dimensional things like edge detection in 3D imaging, where you often want to hold a sparse 3D edge map. )

卡尔曼滤波原理程序手册，卡尔曼主要用于预测，轨迹跟踪(The Kalman filtering principle Procedures Manual, Kalman mainly used to predict the trajectory tracking)