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)


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.


Boolean Function Sampling Position Information Rate Regular Language Covert Channel 
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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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