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Articoli di riviste sul tema "Cellular automata"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 9 giugno 2025

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1

Mardiris, Vassilios A., Georgios Ch Sirakoulis, and Ioannis G. Karafyllidis. "Automated Design Architecture for 1-D Cellular Automata Using Quantum Cellular Automata." IEEE Transactions on Computers 64, no. 9 (September 1, 2015): 2476–89. http://dx.doi.org/10.1109/tc.2014.2366745.

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2

Jung, Goeun, and Youngho Kim. "Modeling of Spatio-temporal changes of Urban Sprawl in Jeju-island: Using CA (Cellular Automata) and ARD (Automatic Rule Detection)." Journal of the Association of Korean Geographers 10, no. 1 (April 30, 2021): 139–52. http://dx.doi.org/10.25202/jakg.10.1.9.

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3

Hasanzadeh Mofrad, Mohammad, Sana Sadeghi, Alireza Rezvanian, and Mohammad Reza Meybodi. "Cellular edge detection: Combining cellular automata and cellular learning automata." AEU - International Journal of Electronics and Communications 69, no. 9 (September 2015): 1282–90. http://dx.doi.org/10.1016/j.aeue.2015.05.010.

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4

Bhardwaj, Rupali, and Anil Upadhyay. "Cellular Automata." Journal of Organizational and End User Computing 29, no. 1 (January 2017): 42–50. http://dx.doi.org/10.4018/joeuc.2017010103.

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Abstract (sommario):

Cellular automata (CA) are discrete dynamical systems consist of a regular finite grid of cell; each cell encapsulating an equal portion of the state, and arranged spatially in a regular fashion to form an n-dimensional lattice. A cellular automata is like computers, data represented by initial configurations which is processed by time evolution to produce output. This paper is an empirical study of elementary cellular automata which includes concepts of rule equivalence, evolution of cellular automata and classification of cellular automata. In addition, explanation of behaviour of cellular automata is revealed through example.

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5

Bandini, S. "Cellular automata." Future Generation Computer Systems 18, no. 7 (August 2002): v—vi. http://dx.doi.org/10.1016/s0167-739x(02)00067-5.

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6

Kutrib, Martin, Roland Vollmar, and Thomas Worsch. "Cellular automata." Parallel Computing 23, no. 11 (November 1997): 1565. http://dx.doi.org/10.1016/s0167-8191(97)82081-9.

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7

Schöfisch, B., and K. P. Hadeler. "Dimer automata and cellular automata." Physica D: Nonlinear Phenomena 94, no. 4 (July 1996): 188–204. http://dx.doi.org/10.1016/0167-2789(96)00039-5.

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8

Dennunzio, Alberto, Pierre Guillon, and Benoît Masson. "Sand automata as cellular automata." Theoretical Computer Science 410, no. 38-40 (September 2009): 3962–74. http://dx.doi.org/10.1016/j.tcs.2009.06.016.

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9

Allouche, J. P., F. V. Haeseler, E. Lange, A. Petersen, and G. Skordev. "Linear cellular automata and automatic sequences." Parallel Computing 23, no. 11 (November 1997): 1577–92. http://dx.doi.org/10.1016/s0167-8191(97)00074-4.

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10

Sutner, Klaus. "Linear cellular automata and Fischer automata." Parallel Computing 23, no. 11 (November 1997): 1613–34. http://dx.doi.org/10.1016/s0167-8191(97)00080-x.

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11

Pighizzini, Giovanni. "Asynchronous automata versus asynchronous cellular automata." Theoretical Computer Science 132, no. 1-2 (September 1994): 179–207. http://dx.doi.org/10.1016/0304-3975(94)90232-1.

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12

OKAYAMA, Tsuyoshi, and Haruhiko MURASE. "Leaf Cellular Automata." Shokubutsu Kojo Gakkaishi 14, no. 3 (2002): 152–56. http://dx.doi.org/10.2525/jshita.14.152.

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13

Beros, Achilles, Monique Chyba, and Oleksandr Markovichenko. "Controlled cellular automata." Networks & Heterogeneous Media 14, no. 1 (2019): 1–22. http://dx.doi.org/10.3934/nhm.2019001.

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14

Bolognesi, Tommaso, and Vincenzo Ciancia. "Nominal Cellular Automata." Electronic Proceedings in Theoretical Computer Science 223 (August 10, 2016): 24–35. http://dx.doi.org/10.4204/eptcs.223.2.

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15

BOCCARA, NINO. "RANDOMIZED CELLULAR AUTOMATA." International Journal of Modern Physics C 18, no. 08 (August 2007): 1303–12. http://dx.doi.org/10.1142/s0129183107011339.

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Abstract (sommario):

We define and study a few properties of a class of random automata networks. While regular finite one-dimensional cellular automata are defined on periodic lattices, these automata networks, called randomized cellular automata, are defined on random directed graphs with constant out-degrees and evolve according to cellular automaton rules. For some families of rules, a few typical a priori unexpected results are presented.

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16

Nobe, Atsushi, and Fumitaka Yura. "Linearizable cellular automata." Journal of Physics A: Mathematical and Theoretical 40, no. 26 (June 12, 2007): 7159–74. http://dx.doi.org/10.1088/1751-8113/40/26/004.

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17

Agapie, Alexandru, Anca Andreica, and Marius Giuclea. "Probabilistic Cellular Automata." Journal of Computational Biology 21, no. 9 (September 2014): 699–708. http://dx.doi.org/10.1089/cmb.2014.0074.

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18

Mayer, Gary R., and Hessam S. Sarjoughian. "Composable Cellular Automata." SIMULATION 85, no. 11-12 (July 17, 2009): 735–49. http://dx.doi.org/10.1177/0037549709106341.

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19

Stauffer, Dietrich. "Programming Cellular Automata." Computers in Physics 5, no. 1 (1991): 62. http://dx.doi.org/10.1063/1.4822970.

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20

Maddox, John. "Mechanizing cellular automata." Nature 321, no. 6066 (May 1986): 107. http://dx.doi.org/10.1038/321107a0.

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21

Lent, C. S., P. D. Tougaw, W. Porod, and G. H. Bernstein. "Quantum cellular automata." Nanotechnology 4, no. 1 (January 1, 1993): 49–57. http://dx.doi.org/10.1088/0957-4484/4/1/004.

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22

KOKOLAKIS, I., I. ANDREADIS, and PH TSALIDES. "PROGRAMMABLE CELLULAR AUTOMATA." Journal of Circuits, Systems and Computers 09, no. 05n06 (October 1999): 255–60. http://dx.doi.org/10.1142/s0218126699000219.

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Abstract (sommario):

A new architecture of the binary cellular automata (CA), called programmable CA (PCA), is presented for the first time in this letter. The basic properties of the PCA are also studied. The proposed architecture is simple and fast and aims at providing efficient solutions to a variety of CA applications.

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23

Kornyak, V. V. "Symmetric cellular automata." Programming and Computer Software 33, no. 2 (March 2007): 87–93. http://dx.doi.org/10.1134/s0361768807020065.

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24

Moore, Cristopher. "Quasilinear cellular automata." Physica D: Nonlinear Phenomena 103, no. 1-4 (April 1997): 100–132. http://dx.doi.org/10.1016/s0167-2789(96)00255-2.

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25

Fricke, Thomas. "Stochastic cellular automata." Nonlinear Analysis: Theory, Methods & Applications 30, no. 3 (December 1997): 1847–58. http://dx.doi.org/10.1016/s0362-546x(96)00378-1.

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26

Kutrib, Martin. "Pushdown cellular automata." Theoretical Computer Science 215, no. 1-2 (February 1999): 239–61. http://dx.doi.org/10.1016/s0304-3975(97)00187-4.

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27

Bandini, Stefania, and Giancarlo Mauri. "Multilayered cellular automata." Theoretical Computer Science 217, no. 1 (March 1999): 99–113. http://dx.doi.org/10.1016/s0304-3975(98)00152-2.

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28

Di Lena, Pietro, and Luciano Margara. "Nondeterministic Cellular Automata." Information Sciences 287 (December 2014): 13–25. http://dx.doi.org/10.1016/j.ins.2014.07.007.

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29

Jadur, Camilo, Masakazu Nasu, and Jorge Yazlle. "Permutation Cellular Automata." Acta Applicandae Mathematicae 126, no. 1 (April 4, 2013): 203–43. http://dx.doi.org/10.1007/s10440-013-9814-7.

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30

Fokas, A. S., E. P. Papadopoulou, and Y. G. Saridakis. "Soliton cellular automata." Physica D: Nonlinear Phenomena 41, no. 3 (April 1990): 297–321. http://dx.doi.org/10.1016/0167-2789(90)90001-6.

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31

Montgomery, David, and Gary D. Doolen. "Magnetohydrodynamic cellular automata." Physics Letters A 120, no. 5 (February 1987): 229–31. http://dx.doi.org/10.1016/0375-9601(87)90214-3.

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32

Kaneko, Kunihiko. "Symplectic cellular automata." Physics Letters A 129, no. 1 (May 1988): 9–16. http://dx.doi.org/10.1016/0375-9601(88)90464-1.

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33

Toole, Jameson, and Scott E. Page. "Predicting Cellular Automata." Complex Systems 19, no. 4 (December 15, 2010): 343–62. http://dx.doi.org/10.25088/complexsystems.19.4.343.

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34

Phan, Victor Duy. "Commutative Cellular Automata." Complex Systems 25, no. 1 (March 15, 2016): 23–38. http://dx.doi.org/10.25088/complexsystems.25.1.23.

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35

Milošević, M. V., G. R. Berdiyorov, and F. M. Peeters. "Fluxonic cellular automata." Applied Physics Letters 91, no. 21 (November 19, 2007): 212501. http://dx.doi.org/10.1063/1.2813047.

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36

Hatori, Tadatsugu. "Magnetohydrodynamic Cellular Automata." Progress of Theoretical Physics Supplement 99 (1989): 229–43. http://dx.doi.org/10.1143/ptps.99.229.

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37

Bruschi, M., P. M. Santini, and O. Ragnisco. "Integrable cellular automata." Physics Letters A 169, no. 3 (September 1992): 151–60. http://dx.doi.org/10.1016/0375-9601(92)90585-a.

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38

Jen, Erica. "Cylindrical cellular automata." Communications in Mathematical Physics 118, no. 4 (December 1988): 569–90. http://dx.doi.org/10.1007/bf01221109.

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39

Kari, Jarkko, Ville Salo, and Thomas Worsch. "Sequentializing cellular automata." Natural Computing 19, no. 4 (June 1, 2019): 759–72. http://dx.doi.org/10.1007/s11047-019-09745-7.

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Abstract (sommario):

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.

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40

Takahashi, Satoshi. "Cellular automata and multifractals: Dimension spectra of linear cellular automata." Physica D: Nonlinear Phenomena 45, no. 1-3 (September 1990): 36–48. http://dx.doi.org/10.1016/0167-2789(90)90172-l.

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41

Adamatzky, Andrew. "Automatic programming of cellular automata: identification approach." Kybernetes 26, no. 2 (March 1997): 126–35. http://dx.doi.org/10.1108/03684929710163074.

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42

Calidonna, C. R., S. Di Gregorio, and M. Mango Furnari. "Mapping applications of cellular automata into applications of cellular automata networks." Computer Physics Communications 147, no. 1-2 (August 2002): 724–28. http://dx.doi.org/10.1016/s0010-4655(02)00385-5.

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43

Ruivo, Eurico L. P., and Pedro P. B. de Oliveira. "Inferring the Limit Behavior of Some Elementary Cellular Automata." International Journal of Bifurcation and Chaos 27, no. 08 (July 2017): 1730028. http://dx.doi.org/10.1142/s0218127417300282.

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Abstract (sommario):

Cellular automata locally define dynamical systems, discrete in space, time and in the state variables, capable of displaying arbitrarily complex global emergent behavior. One core question in the study of cellular automata refers to their limit behavior, that is, to the global dynamical features in an infinite time evolution. Previous works have shown that for finite time evolutions, the dynamics of one-dimensional cellular automata can be described by regular languages and, therefore, by finite automata. Such studies have shown the existence of growth patterns in the evolution of such finite automata for some elementary cellular automata rules and also inferred the limit behavior of such rules based upon the growth patterns; however, the results on the limit behavior were obtained manually, by direct inspection of the structures that arise during the time evolution. Here we present the formalization of an automatic method to compute such structures. Based on this, the rules of the elementary cellular automata space were classified according to the existence of a growth pattern in their finite automata. Also, we present a method to infer the limit graph of some elementary cellular automata rules, derived from the analysis of the regular expressions that describe their behavior in finite time. Finally, we analyze some attractors of two rules for which we could not compute the whole limit set.

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44

BEIGY, HAMID, and M. R. MEYBODI. "OPEN SYNCHRONOUS CELLULAR LEARNING AUTOMATA." Advances in Complex Systems 10, no. 04 (December 2007): 527–56. http://dx.doi.org/10.1142/s0219525907001264.

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Abstract (sommario):

Cellular learning automata is a combination of learning automata and cellular automata. This model is superior to cellular learning automata because of its ability to learn and also is superior to single learning automaton because it is a collection of learning automata which can interact together. In some applications such as image processing, a type of cellular learning automata in which the action of each cell in the next stage of its evolution not only depends on the local environment (actions of its neighbors) but it also depends on the external environments. We call such a cellular learning automata as open cellular learning automata. In this paper, we introduce open cellular learning automata and then study its steady state behavior. It is shown that for a class of rules called commutative rules, the open cellular learning automata in stationary external environments converges to a stable and compatible configuration. Then the application of this new model to image segmentation has been presented.

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45

Gavrilov, S. V., I. V. Matyushkin, and A. L. Stempkovsky. "Computability via Cellular Automata." Scientific and Technical Information Processing 44, no. 5 (December 2017): 314–28. http://dx.doi.org/10.3103/s0147688217050057.

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46

D’Ambrosio, Donato, Giuseppe Filippone, Rocco Rongo, William Spataro, and Giuseppe A. Trunfio. "Cellular Automata and GPGPU." International Journal of Grid and High Performance Computing 4, no. 3 (July 2012): 30–47. http://dx.doi.org/10.4018/jghpc.2012070102.

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Abstract (sommario):

This paper presents an efficient implementation of the SCIARA Cellular Automata computational model for simulating lava flows using the Compute Unified Device Architecture (CUDA) interface developed by NVIDIA and carried out on Graphical Processing Units (GPU). GPUs are specifically designated for efficiently processing graphic data sets. However, they are also recently being exploited for achieving excellent computational results for applications non-directly connected with Computer Graphics. The authors show an implementation of SCIARA and present results referred to a Tesla GPU computing processor, a NVIDIA device specifically designed for High Performance Computing, and a Geforce GT 330M commodity graphic card. Their carried out experiments show that significant performance improvements are achieved, over a factor of 100, depending on the problem size and type of performed memory optimization. Experiments have confirmed the effectiveness and validity of adopting graphics hardware as an alternative to expensive hardware solutions, such as cluster or multi-core machines, for the implementation of Cellular Automata models.

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47

Lobanov, Alexey I. "Model of cellular automata." Computer Research and Modeling 2, no. 3 (September 2010): 273–93. http://dx.doi.org/10.20537/2076-7633-2010-2-3-273-293.

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48

Bashkin, Vladimir A., and Irina A. Lomazova. "Cellular Resource-Driven Automata." Fundamenta Informaticae 120, no. 3-4 (2012): 243–57. http://dx.doi.org/10.3233/fi-2012-760.

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49

Tošic, Predrag T. "Cellular Automata Communication Models." International Journal of Natural Computing Research 1, no. 3 (July 2010): 66–84. http://dx.doi.org/10.4018/jncr.2010070105.

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Abstract (sommario):

In this paper, cellular automata (CA) are viewed as an abstract model for distributed computing. The author argues that the classical CA model must be modified in several important respects to become a relevant model for large-scale MAS. The paper first proposes sequential cellular automata (SCA) and formalizes deterministic and nondeterministic versions of SCA. The author then analyzes differences in possible dynamics between classical parallel CA and various SCA models. The analysis in this paper focuses on one-dimensional parallel and sequential CA with node update rules restricted to simple threshold functions, as arguably the simplest totalistic, yet non-linear (and non-affine) update rules. The author identifies properties of asymptotic dynamics that can be proven to be entirely due to the assumption of perfect synchrony in classical, parallel CA. Finally, the paper discusses what an appropriate CA-based abstraction would be for large-scale distributed computing, insofar as the inter-agent communication models. In that context, the author proposes genuinely asynchronous CA and discusses main differences between genuinely asynchronous CA and various weakly asynchronous sequential CA models found in the literature.

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50

Ollinger, Nicolas. "Intrinsically Universal Cellular Automata." Electronic Proceedings in Theoretical Computer Science 1 (June 25, 2009): 199–204. http://dx.doi.org/10.4204/eptcs.1.19.

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