Characterization of the Attenuation due to Rain Cell Fields applying Cellular Automata to Weather Radar Images

2006 ◽  
Author(s):  
A. Nunez ◽  
V. Pastoriza ◽  
P. Marino ◽  
F. Fontan ◽  
U.-c. Fiebig
Keyword(s):  
Cellular Automata ◽  
Weather Radar ◽  
Radar Images

scholarly journals Generating infinite monoids of cellular automata

2021 ◽  
pp. 2250215
Author(s):  
Alonso Castillo-Ramirez
Keyword(s):  
Cellular Automata ◽  
Normal Subgroups ◽  
Finitely Generated ◽  
Residually Finite ◽  
Residually Finite Group ◽  
Group Of Units ◽  
Finite Dimensional ◽  
Index Condition ◽  
Indicable Group

For a group [Formula: see text] and a set [Formula: see text], let [Formula: see text] be the monoid of all cellular automata over [Formula: see text], and let [Formula: see text] be its group of units. By establishing a characterization of surjunctive groups in terms of the monoid [Formula: see text], we prove that the rank of [Formula: see text] (i.e. the smallest cardinality of a generating set) is equal to the rank of [Formula: see text] plus the relative rank of [Formula: see text] in [Formula: see text], and that the latter is infinite when [Formula: see text] has an infinite decreasing chain of normal subgroups of finite index, condition which is satisfied, for example, for any infinite residually finite group. Moreover, when [Formula: see text] is a vector space over a field [Formula: see text], we study the monoid [Formula: see text] of all linear cellular automata over [Formula: see text] and its group of units [Formula: see text]. We show that if [Formula: see text] is an indicable group and [Formula: see text] is finite-dimensional, then [Formula: see text] is not finitely generated; however, for any finitely generated indicable group [Formula: see text], the group [Formula: see text] is finitely generated if and only if [Formula: see text] is finite.

Recurrent Misconceptions in the Study of CA Reversibility on Triangular Grids

2021 ◽  
Vol 31 (01) ◽  
pp. 2150014
Author(s):  
Barbara Wolnik ◽  
Maciej Dziemiańczuk ◽  
Bernard De Baets
Keyword(s):  
Cellular Automata ◽  
Boundary Conditions ◽  
Periodic Boundary ◽  
Periodic Boundary Conditions ◽  
Triangular Grids

We present counterexamples illustrating that the characterization of the reversibility of linear cellular automata on finite triangular grids given by Uguz et al. [2017] and Uguz et al. [2019] is not valid, neither in the case of null boundary conditions nor in the case of periodic boundary conditions.

scholarly journals Sequentializing cellular automata

2019 ◽  
Vol 19 (4) ◽  
pp. 759-772 ◽  
Author(s):  
Jarkko Kari ◽  
Ville Salo ◽  
Thomas Worsch
Keyword(s):  
Cellular Automata ◽  
Cellular Automaton ◽  
Intermediate States ◽  
The Right

Abstract We study the problem of sequentializing a cellular automaton without introducing any intermediate states, and only performing reversible permutations on the tape. We give a decidable characterization of cellular automata which can be written as a single sweep of a bijective rule from left to right over an infinite tape. Such cellular automata are necessarily left-closing, and they move at least as much information to the left as they move information to the right.

scholarly journals Detecting and Tracking Communal Bird Roosts in Weather Radar Data

2020 ◽  
Vol 34 (01) ◽  
pp. 378-385
Author(s):  
Zezhou Cheng ◽  
Saadia Gabriel ◽  
Pankaj Bhambhani ◽  
Daniel Sheldon ◽  
Subhransu Maji ◽  
...  
Keyword(s):  
Latent Variable ◽  
Weather Radar ◽  
Radar Data ◽  
Learning System ◽  
Variable Model ◽  
Detection Model ◽  
Radar Images ◽  
Biological Phenomena ◽  
The Us ◽  
Spatio Temporal

The US weather radar archive holds detailed information about biological phenomena in the atmosphere over the last 20 years. Communally roosting birds congregate in large numbers at nighttime roosting locations, and their morning exodus from the roost is often visible as a distinctive pattern in radar images. This paper describes a machine learning system to detect and track roost signatures in weather radar data. A significant challenge is that labels were collected opportunistically from previous research studies and there are systematic differences in labeling style. We contribute a latent-variable model and EM algorithm to learn a detection model together with models of labeling styles for individual annotators. By properly accounting for these variations we learn a significantly more accurate detector. The resulting system detects previously unknown roosting locations and provides comprehensive spatio-temporal data about roosts across the US. This data will provide biologists important information about the poorly understood phenomena of broad-scale habitat use and movements of communally roosting birds during the non-breeding season.

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