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This work is an extension of research done by the Evolving Cellular Automata (EvCA) group at the Santa Fe Institute. Specifically, it is an extension of their application of genetic algorithms (GAs) to design cellular automata (CAs) that synchronize globally while using only local information.
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The necessity of catastrophe modeling is stipulated both by the essential increase of losses due to recent natural and anthropogenic hazards and by a lack of reliable real observation data. This paper focuses on risks of earthquakes. The region of Italy is considered as an example. A brief overview of different approaches in mathematical modelling of the lithosphere dynamics is presented.
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This paper discusses an extension on the Inverse Perspective Mapping geometrical transform to the processing of stereo images and present the calibration method used on the ARGO autonomous vehicle. The article features also an example of application in the automotive field, in which the stereo Inverse Perspective Mapping helps to speed up the process.
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With the advent and popularization of digital computers, it has been realized that simple, rule-based discrete models can be implemented with greater ease in numerical simulations and still generate non-trivial results that are in qualitative agreement with empirical observations. These discrete models fall under the general category models known as Cellular Automaton (CA) models.
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Given an explicit dynamical system and given ε>0, is it possible in principle to compute the associated entropy, either topological or measure-theoretic, with a maximum error of ε? In practice, is there an effective procedure to carry out this computation in a resonable length of time? In the most general case, the answer to both questions is certainly no: Cellular automaton mappings from a Cantor set to itself have an explicit finite description.
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The goal of this project was to introduce NKS to engineering design problems and estimate a true potential of this approach. It was an initial step in exploring the world of simple programs for engineering design applications as well as introducing a novel methodology presented in Wolfram’s A New Kind of Science. The motivation for this research was based on the fact that even designers of complex and sophisticated engineering systems.
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Spatially structured evolutionary algorithms (EAs) have shown to be endowed ith useful features for global optimization. Distributed EAs (dEA) and cellular EAs (cEA) are two of the most widely known types of structured algorithms. In this paper we deal with cellular EAs. Two important parameters guiding the search in a cEA are the population topology and the neighborhood defined on it.
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In this article, we present a diffusion-generated approach for evolving volume-preserving motion by mean curvature. Our algorithm alternately diffuses and sharpens characteristic functions to produce a normal velocity which equals the mean curvature minus the average mean curvature. This simple algorithm naturally treats topological mergings and breakings and can be made very fast even when then the volume constraint is enforced to double precision.
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In part, the oal of the Ultra Program is to extract useful computation from nanometer-scale effects. To accomplish this goal, those of us who are computer scientists must communicate clearly to those of youwho are chemists and communicate clearly to those of you who are chemists and device physicists precisely what kinds of “computational primitives”.
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In this paper we present improved bounds on the complexity of solutions to the firing synchronization problem. In the firing synchronization problem we consider a one-dimensional array of n identical finite automata. Initially all automata are in the same state except for one automaton which is designated as the initiator for the synchronization.
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