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文章基本信息

  • 标题:Limit and Dynamics of Information Entropy in Transmission
  • 本地全文:下载
  • 作者:Bin Yan
  • 期刊名称:Network and Communication Technologies
  • 印刷版ISSN:1927-064X
  • 电子版ISSN:1927-0658
  • 出版年度:2012
  • 卷号:1
  • 期号:1
  • 页码:39
  • DOI:10.5539/nct.v1n1p39
  • 出版社:Canadian Center of Science and Education
  • 摘要:

    This paper presents a theoretical explanation and test results that an amount of information would not increase infinitely in transmission via the mathematical model of the matter for deciphering original works of information entropy and the principle of thermodynamic entropy. The rate formula with energy is derived via retrieval speed testing the vast amount of data in DBMS and variables in rate formula proved via other testing methods. We found that the actual value in the rate formula was quite different from the theoretical value that calculating the entropy per bit of information needed energy and the speed need energy. We used of testing data to illustrate these relations between bit and energy, 1 bit =3.92×10-4 J/K. We assumed that the energy of Brillouin’s information entropy was the surface entropy changes between information source and information channel rather than the information source itself.

  • 其他摘要:This paper presents a theoretical explanation and test results that an amount of information would not increase infinitely in transmission via the mathematical model of the matter for deciphering original works of information entropy and the principle of thermodynamic entropy. The rate formula with energy is derived via retrieval speed testing the vast amount of data in DBMS and variables in rate formula proved via other testing methods. We found that the actual value in the rate formula was quite different from the theoretical value that calculating the entropy per bit of information needed energy and the speed need energy. We used of testing data to illustrate these relations between bit and energy, 1 bit =3.92×10-4 J/K. We assumed that the energy of Brillouin’s information entropy was the surface entropy changes between information source and information channel rather than the information source itself.
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