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We propose a measure of complexity for symbolic sequences, which is based on conditional probabilities, and captures computational aspects of complexity without the explicit construction of minimal deterministic finite automata (DFA). Moreover, if the sequence is obtained from a dynamical system through a suitable encoding and its equations of motion are… (More)
We calculate block information versus size profiles for two-symbol strings generated by several dynamical processes: random, periodic, regular language, and substitutive. The profiles provide a good diagnostic of the complexity of the strings.
Simulating chaotic behavior with finite-state machines. The goal of radar space-time adaptive processing (STAP) is to detect slow moving targets from a moving platform, typically airborne or spaceborne. STAP generally requires the estimation and the inversion of an interference-plus-noise (I+N) covariance matrix. To reduce both the number of samples… (More)
Today's wireless sensor networks provide the possibility to monitor physical environments via small low-cost wireless devices. Given the large amount of sensed data, efficient and robust classification becomes a critical task in many applications. Typically, the devices must operate under stringent power and communication constraints and the transmission of… (More)
We show that applying a noise-reduction algorithm to a discretized time series increases its average error, compared to the original series. We find that adding external noise comparable to the discretization step before noise reduction limits the increase of the average error and improves the estimation of Lyapunov exponents.