Checking Whether an Automaton Is Monotonic Is NP-complete

An automaton is monotonic if its states can be arranged in a linear order that is preserved by the action of every letter. We prove that the problem of deciding whether a given automaton is monotonic is NP-complete. The same result is obtained for oriented automata, whose states can be arranged in a cyclic order. Moreover, both problems remain hard under the restriction to binary input alphabets.Comment: 13 pages, 4 figures. CIAA 2015. The final publication is available at http://link.springer.com/chapter/10.1007/978-3-319-22360-5_2

A Fast Algorithm Finding the Shortest Reset Words

In this paper we present a new fast algorithm finding minimal reset words for finite synchronizing automata. The problem is know to be computationally hard, and our algorithm is exponential. Yet, it is faster than the algorithms used so far and it works well in practice. The main idea is to use a bidirectional BFS and radix (Patricia) tries to store and compare resulted subsets. We give both theoretical and practical arguments showing that the branching factor is reduced efficiently. As a practical test we perform an experimental study of the length of the shortest reset word for random automata with $n$ states and 2 input letters. We follow Skvorsov and Tipikin, who have performed such a study using a SAT solver and considering automata up to $n=100$ states. With our algorithm we are able to consider much larger sample of automata with up to $n=300$ states. In particular, we obtain a new more precise estimation of the expected length of the shortest reset word $\approx 2.5\sqrt{n-5}$.Comment: COCOON 2013. The final publication is available at http://link.springer.com/chapter/10.1007%2F978-3-642-38768-5_1

Determining valuable ranges of handwritten signature using fuzzy approach and window method

The paper proposes possible improvements in signature recognition approach based on window method. The analysis focuses on a stage of window preprocessing using fuzzy sets in order to choose significant ranges of each signature. Proposed extension allows the solution to improve in two areas. First of all minimizing a number of processed windows significantly reduces computation time. Secondly, filtered signatures with valuable information about significant ranges allow the system to recognize signatures of a poor or good quality. Developed method of signature quality assessment can be used in any signature recognition system, regardless of used method of analysis. Merging the information about signature quality and choosing only important signature ranges should also improve the overall detection results, however, more examinations are needed to confirm this statement