RFCM

说明：  这是一些自己几个月的劳动成果，希望与大家共享，还需要大家多多交流(This is something that a few months of hard work, hope and share, but also to interact more)

这几段程序分别是matlab环境下图像逻辑操作、交换显示图像、矢量数据的显示、交换显示图像(These paragraphs procedures are logical operations matlab environment image, switching display images, vector data display, switching the display image)

BPSK SYSTEM SIMULATION We simulate the generation of random variables r0 and r1, which constitutes the input to detector.The detector output is compared with the binary transmitted sequence and an error counter is used to count the number of bit errors. (BPSK We simulate the gen Kearney of random variables r0 and r1. which constitutes the input to detector.The de tector output is compared with the binary trans mitted sequence and an error counter is used to c been the number of bit errors.)

基于偏微分方程(PDE)的热传导方程滤波的matlab实现(based on partial differential equations (PDE) of the heat conduction Filter Implementation of Matlab)

Software in matlab for image segmentation

本论文主要说明数字中频GPS信号的MATLAB仿真，有较详细的仿真图(This thesis describes the MATLAB simulation of the digital IF GPS signals, a more detailed simulation in Figure)

数字通信---基于matlab的仿真计算，PDF文本形式(Matlab Simulation Caculation)

格式：[huffcodes，氢，領 ，电子] = huffmancoding（磷，氮） ％输入：p是每个字母符号的概率 （如：对= [0.3 0.2 0.2 0.2 .06 .04]） n是一个整数，决定在每个帧的符号数量 （默认：n = 1时） ％输出：huffcodes：huffmancodes 答：源熵 甲肝：平均熵编码 电子邮件：科丁效率(format : [huffcodes,H,Hav,e]=huffmancoding(p,n) inputs: p is the probability of each alphabet symbol ( eg:p=[.3 .2 .2 .2 .06 .04] ) n is an integer that determines number of symbols at each frame (default:n=1) outputs: huffcodes: huffmancodes H: source entropy Hav: average entropy for coding e: codding efficiency )

1 通过实验，掌握多元正态分布的最大似然估计； 2 掌握多元正态分布下的最小错误率的贝叶斯分类； 3 对其他的参数估计有更深的认识。 (1 experiment, master multivariate normal distribution maximum likelihood estimation 2 multivariate normal distribution under the minimum control error rate Bayesian classifier 3 on the other have a better understanding of parameter estimation.)

this is canny edge detector to capture the edge of the imge.it is a good feature descriptor