Transductions Computed by One-Dimensional Cellular Automata

Cellular automata are investigated towards their ability to compute transductions, that is, to transform inputs into outputs. The families of transductions computed are classified with regard to the time allowed to process the input and to compute the output. Since there is a particular interest in fast transductions, we mainly focus on the time complexities real time and linear time. We first investigate the computational capabilities of cellular automaton transducers by comparing them to iterative array transducers, that is, we compare parallel input/output mode to sequential input/output mode of massively parallel machines. By direct simulations, it turns out that the parallel mode is not weaker than the sequential one. Moreover, with regard to certain time complexities cellular automaton transducers are even more powerful than iterative arrays. In the second part of the paper, the model in question is compared with the sequential devices single-valued finite state transducers and deterministic pushdown transducers. It turns out that both models can be simulated by cellular automaton transducers faster than by iterative array transducers.Comment: In Proceedings AUTOMATA&JAC 2012, arXiv:1208.249

A Quasi-Linear Time Algorithm Deciding Whether Weak B\"uchi Automata Reading Vectors of Reals Recognize Saturated Languages

This work considers weak deterministic B\"uchi automata reading encodings of non-negative $d$-vectors of reals in a fixed base. A saturated language is a language which contains all encoding of elements belonging to a set of $d$-vectors of reals. A Real Vector Automaton is an automaton which recognizes a saturated language. It is explained how to decide in quasi-linear time whether a minimal weak deterministic B\"uchi automaton is a Real Vector Automaton. The problem is solved both for the two standard encodings of vectors of numbers: the sequential encoding and the parallel encoding. This algorithm runs in linear time for minimal weak B\"uchi automata accepting set of reals. Finally, the same problem is also solved for parallel encoding of automata reading vectors of relative reals

A Signal Distribution Network for Sequential Quantum-dot Cellular Automata Systems

The authors describe a signal distribution network for sequential systems constructed using the Quantum-dot Cellular Automata (QCA) computing paradigm. This network promises to enable the construction of arbitrarily complex QCA sequential systems in which all wire crossings are performed using nearest neighbor interactions, which will improve the thermal behavior of QCA systems as well as their resistance to stray charge and fabrication imperfections. The new sequential signal distribution network is demonstrated by the complete design and simulation of a two-bit counter, a three-bit counter, and a pattern detection circuit