Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Generation of Finite Inductive, Pseudo Random, Binary Sequences

  • Fisher, Paul (Dept. of Computer Science, Winston-Salem State University) ;
  • Aljohani, Nawaf (Institute of Public Administration) ;
  • Baek, Jinsuk (Dept. of Computer Science, Winston-Salem State University)
  • Received : 2015.05.19
  • Accepted : 2016.09.25
  • Published : 2017.12.31
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Abstract

This paper introduces a new type of determining factor for Pseudo Random Strings (PRS). This classification depends upon a mathematical property called Finite Induction (FI). FI is similar to a Markov Model in that it presents a model of the sequence under consideration and determines the generating rules for this sequence. If these rules obey certain criteria, then we call the sequence generating these rules FI a PRS. We also consider the relationship of these kinds of PRS's to Good/deBruijn graphs and Linear Feedback Shift Registers (LFSR). We show that binary sequences from these special graphs have the FI property. We also show how such FI PRS's can be generated without consideration of the Hamiltonian cycles of the Good/deBruijn graphs. The FI PRS's also have maximum Shannon entropy, while sequences from LFSR's do not, nor are such sequences FI random.

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