matlab

有关彩色图像边沿提取的matlab程序效果不错可以试试(The color image edge extraction matlab program worked well try)

一个无刷电机仿真的模型，matlab环境，可以计算电机性能（注意：仿真的时候，对计算机硬件要求可能有点高）(a model of brushless DC motor (BLDC motor))

用matlab中gui做一个普通的科学计算器,可以参考一下(through Matlab gui，a common scientific calculator was made , you can consult it)

主要内容包括波束形成、DOA估计、相干信号的DOA估计、二维DOA估计、宽带阵列信号处理、阵列多参数估计等(The main contents include beamforming, DOA estimation, coherent signal DOA estimation, two-dimensional DOA estimation, wideband array signal processing, array multi parameter estimation, etc.)

用matlab显示图片的RGB各分量的灰度值(Matlab display images with the RGB values of the various components of the gray)

该文件是SVM神经网络的源程序，可以用于对数据进行分类预测(The file is SVM neural network source, can be used for data classification forecast)

详细阐述了波特沃斯低通滤波器的matlab仿真原理及对应的c语言实现过程，读完此资料后，对于巴特沃斯低通滤波器不管从c语言实现还是matlab实现，你都会有很好的理解(Matlab simulation principles elaborated Porter Worth low-pass filter and the corresponding c language implementation process, after reading this information, for Butterworth low-pass filter regardless of language or a c matlab implementation, you will have a very good understanding)

一个VSM分类器的具体例程，包含数据文件，调试通过。(VSM classifier a specific routines, including data files, debugging through.)

说明：  matlab图像高级处理，收录了高级处理函数，及其用法，并包含适当的例子。(matlab advanced image processing, a collection of high-level processing functions and their usage, and contains appropriate examples.)

1998年, Watts和Strogatz 提出了小世界网络这一概念,并建立了WS模型。 实证结果表明,大多数的真实网络都具有小世界特性(较小的最短路径) 和聚类特性(较大的聚类系数) 。 WS小世界模型构造算法 1、从规则图开始：考虑一个含有N个点的最近邻耦合网络，它们围成一个环，其中每个节点都与它左右相邻的各K/2节点相连，K是偶数。 2、随机化重连：以概率p随机地从新连接网络中的每个边，即将边的一个端点保持不变，而另一个端点取为网络中随机选择的一个节点。其中规定，任意两个不同的节点之间至多只能有一条边，并且每一个节点都不能有边与自身相连。 (In 1998, Watts and Strogatz proposed the concept of the small world network, and the establishment of the WS model. The empirical results show that most real networks have small world (the smaller the shortest path) and clustering properties (high clustering coefficient). WS small-world model construction algorithm 1, from the rules FIG: consider a nearest neighbor coupling network with N points, they are surrounded by a ring, wherein each node and its left and right adjacent to each of K/2 connected to the node, K is an even number. The 2 randomized reconnection: randomly with probability p from the new connection on each side of the network, coming section of one of the endpoints remain unchanged, while the other endpoint is taken as the random selection of a node in the network. Which states that between any two different nodes can have at most one edge, and each node can not be connected to its own side.)