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Symbolic time series analysis (STSA) of complex systems for anomaly detection has been recently introduced in literature. An important feature of the STSA method is extraction of relevant information, imbedded in the measured time series data, to generate symbol sequences. This paper presents a wavelet-based partitioning approach for symbol generation,(More)
Probabilistic finite state machines have recently emerged as a viable tool for modelling and analysis of complex non-linear dynamical systems. This paper rigorously establishes such models as finite encodings of probability measure spaces defined over symbol strings. The well known Nerode equivalence relation is generalized in the probabilistic setting and(More)
Recent literature has reported a novel method for anomaly detection in complex dynamical systems, which relies on symbolic time series analysis and is built upon the principles of automata theory and pattern recognition. This paper compares the performance of this symbolic-dynamics-based method with that of other existing pattern recognition techniques from(More)
This paper formulates a signed real measure of sublanguages of a regular language based on the principles of automata theory and real analysis. The measure allows total ordering of any set of partially ordered sublanguages of a regular language for quantitative evaluation of the controlled behavior of a deterministic finite-state automaton (DFSA) plant(More)
Probabilistic finite state automata (PFSA) are constructed from symbol sequences for modeling the behavior of dynamical systems. This paper presents construction of finite history automata from symbol sequences; such automata, called D-Markov machines, are structurally simple and computationally efficient. The construction procedure is based on: (i) state(More)
This article presents an algorithm for adaptive sensor activity scheduling (A-SAS) in distributed sensor networks to enable detection and dynamic footprint tracking of spatial-temporal events. The sensor network is modeled as a Markov random field on a graph, where concepts of Statistical Mechanics are employed to stochastically activate the sensor nodes.(More)