sanciyangtiao-sanwanju

这是一个matlab代码，实现mfsk信号源的产生。(This is a matlab program ,it is a m file to create mfsk source.)

Algorithm for multi robot

MATLAB 7.x数字信号处理源代码,包括: 1 常用函数列表 2 第一章到第九章源代码 3 程序说明(MATLAB 7.x digital signal processing source code, including: a list of commonly used functions to the Chapter 2 Chapter 3 source code description of the procedures)

说明：  我编写的基于颜色分量的汽车牌照定位识别。主要有以下几步：1 基于颜色分量的灰度值识别出车牌（基于蓝色车牌）2 从蓝色区域中识别白色的号码(prepared by the color components based on the vehicle license location identification. The following are the main steps : a component based on the color of gray values identification plates (based on blue plates) 2 from the blue and white identification numbers)

An encoder/decoder for binary BCH codes Error correction using the BERLEKAMP-MASSEY ALGORITHM.Any (valid) code length can be input(An encoder/decoder for binary BCH codes Er Ryan correction using the BERLEKAMP- MASSEY ALG ORITHM.Any (valid) code length can be input)

霍夫曼编码的具体方法：先按出现的概率大小排队，把两个最小的概率相加，作为新的概率 和剩余的概率重新排队，再把最小的两个概率相加，再重新排队，直到最后变成1。每次相 加时都将“0”和“1”赋与相加的两个概率，读出时由该符号开始一直走到最后的“1”， 将路线上所遇到的“0”和“1”按最低位到最高位的顺序排好，就是该符号的霍夫曼编码。(Specific methods Huffman coding: the probability of emergence of the size press line, the two smallest sum probability, as a new probability and the probability of re-queue the remaining, then the minimum of two probabilities are added, and then re-queue, until finally become one. Each time the sum will be 0 and 1 Fu added two probabilities, when read by the symbol has come to begin the final 1 , the route will encounter a 0 and 1 at the lowest to the highest bit of the right order, that is the symbol of the Huffman coding.)

采用RGB模型进行阴影的检测与去除。能够显示前景、背景、去除阴影后的运动目标(RGB model with shadow detection and removal. To show foreground, background, after the removal of shadows moving target)

说明：  太阳光聚焦光线偏移的一个小程序，方便大家学习与应用。(Focus sunlight light offset a small program to facilitate learning and application.)

元胞自动机程序模型，用来研究和分析随时间节点变化，元宝状态的程序！(cells automatic，)

基于BP神经网络的数据分类-语音特征信号分类(Data Classification Based on BP Neural Network-Voice Feature Signal Classification)