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Sampling a Two-Way Finite Automaton

  • Zhe Dang
  • Oscar H. Ibarra
  • Qin Lin
Part of the Emergence, Complexity and Computation book series (ECC, volume 12)

Abstract

We study position sampling in a 2-way nondeterministic finite automaton (2NFA) to measure the information dependency and information flow between state variables, based on the information-theoretic sampling technique proposed in [16]. We prove that for a 2NFA, the language generated by position sampling is regular. We also show that for a 2NFA, we can effectively find a vector of sampling positions that maximizes dependency and information flow in a run of the 2NFA. Finally, we give some language properties of sampled runs of 2NFAs augmented with restricted unbounded storage.

Keywords

Boolean Function Sampling Position Information Rate Regular Language Covert Channel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Zhe Dang
    • 1
    • 2
  • Oscar H. Ibarra
    • 3
  • Qin Lin
    • 1
  1. 1.School of Computer Science and TechnologyAnhui University of TechnologyMa’anshanChina
  2. 2.School of Electrical Engineering and Computer ScienceWashington State UniversityPullmanUSA
  3. 3.Department of Computer ScienceUniversity of CaliforniaSanta BarbaraUSA

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