[1]翟其清,王友国,郑克. 噪声改善码元传输[J].计算机技术与发展,2014,24(11):152-154.
　ZHAI Qi-qing,WANG You-guo,ZHENG Ke. Noise Improving Code Elements Transmission[J].,2014,24(11):152-154.
点击复制

 噪声改善码元传输()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
24
期数:
2014年11期
页码:
152-154
栏目:
智能、算法、系统工程
出版日期:
2014-11-10

文章信息/Info

Title:
 Noise Improving Code Elements Transmission
文章编号:
1673-629X（2014）11-0152-03
作者:
 翟其清; 王友国; 郑克
 南京邮电大学 理学院
Author(s):
 ZHAI Qi-qing; WANG You-guo; ZHENG Ke
关键词:
 ; 随机共振; 编码; 加性噪声; 乘性噪声
Keywords:
 ; stochastic resonance; coding; additive noise; multiplicative noise
分类号:
TP31
文献标志码:
A
摘要:
 文中运用随机共振来改善码元的传输。对于由字符构成的文本，通过编码生成一系列的码元作为系统输入信号，经过带有噪声的系统传输后，进行译码得到接收文本。文中使用的噪声为高斯型的加性与乘性噪声，逐渐增加噪声强度，接收文本中出错字符比例先降低再增高，从而存在最佳噪声强度，此时出错比例最小，系统性能最好。另外，乘性噪声在改善信号传输时，表现出了一定的鲁棒性。最后，讨论了阈值单元数目与系统阈值的变化对系统性能的影响。
Abstract:
 In this paper,stochastic resonance is applied to improve code elements transmission. A series of code elements are generated by encoding characters in a text,and they act as the input signals of system. And then,get the translated text by decoding these received code words. The system is subject to additive and multiplicative noises which are both Gaussian. Increasing noise intensity gradually,the ratio of error characters in the translated text will decrease first,and increase later. There exists an optimal noise intensity,which reduces the ra-tio of error to the least level,and the performance of the system is at the best. In addition,in the process of improving signals transmis-sion,multiplicative noise shows its robustness. At last,discuss the influence on varying of threshold units number and system threshold for the system performance.

相似文献/References:

[1]邱勤伟 焦贤发 朱良燕.对称二值噪声下线性系统的随机共振[J].计算机技术与发展,2010,(08):239.
　QIU Qin-wei,JIAO Xian-fa,ZHU Liang-yan.Stochastic Resonance in Linear System with Dichotomous Noise[J].,2010,(11):239.
[2]王友国 刘沁雨.多阈值系统中高斯混合噪声改善信息的传输[J].计算机技术与发展,2011,(04):120.
　WANG You-guo,LIU Qin-yu.Gaussian Mixture Noise to Improve Information Transmission in Multi-threshold System[J].,2011,(11):120.
[3]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(11):1.
[4]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(11):5.
[5]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(11):13.
[6]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(11):21.
[7]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(11):25.
[8]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(11):29.
[9]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(11):34.
[10]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(11):38.
[11]王友国[][],潘慧[],刘健[]. 加性乘性噪声改善多元信号检测[J].计算机技术与发展,2016,26(10):160.
　WANG You-guo[][],PAN Hui[],LIU Jian[]. Improvement of Multiple Signal Detection by Additive and Multiplicative Noise[J].,2016,26(11):160.

更新日期/Last Update: 2015-04-14