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Introduction
Published in Vlad P. Shmerko, Svetlana N. Yanushkevich, Sergey Edward Lyshevski, Computer Arithmetics for Nanoelectronics, 2018
Vlad P. Shmerko, Svetlana N. Yanushkevich, Sergey Edward Lyshevski
Cellular automata can be defined as a class of computing arrays of spatial topology that are characterized by local interaction and inherently parallel processing. Cellular automata are models for complex systems and processing consisting a large number of identical, simple, and locally interconnected components called cells. Cellular automata can be described in terms of dimension (1D, 2D, and 3D lattice of cells), homogeneity (all cells perform the same function), state (each cell can take one of a finite number of possible states), interconnection (each cell can interact only with neighborhoods), and discrete dynamics (each cell updates its current state according to a transition rule at discrete time). Cellular automata are a suitable model of self-assembly.
Toward Smart Cities via the Smart Grid and Intelligent Transportation Systems
Published in Hussein T. Mouftah, Melike Erol-Kantarci, Smart Grid, 2017
Stephen W. Turner, Suleyman Uludag
Accordingly, the efforts highlighted here primarily examine decentralized approaches that employ self-organizing networks of traffic elements. Approaches have typically been modeled using cellular automata or agent-based modeling (ABM) methods. Cellular automata are defined as collections of cells that exist on a regular grid in one or more dimensions. Each cell is programmed with a series of deterministic rules to modify its behavior based on the status of its nearest neighbors [103]. Early efforts recognized the ability of cellular automata to model the behavior of deterministic networks of traffic lights on very regular street structures, such as perfect two-dimensional grids of roads.
Environmental Models in DEVS
Published in Gabriel A. Wainer, Pieter J. Mosterman, Discrete-Event Modeling and Simulation, 2018
Jean-Baptiste Filippi, Teruhisa Komatsu, David R.C. Hill
Cellular Automata (CA) are widely used to model different kinds of complex systems. A cellular automaton is usually represented by a matrix field of cells executing the same program simultaneously, receiving as input the state of their neighboring cells [7,8]. CA prove to be well suited to model systems that are spatially distributed, by defining the behavior of an atomic portion of the system (a cell) in relation to a distant or direct neighborhood such as presented in Figure 14.1.
Reconfigurable Architecture for Image Encryption Using a Three-Layer Artificial Neural Network
Published in IETE Journal of Research, 2022
M. Devipriya, M. Sreenivasan, M. Brindha
Using the neighborhood values, the current value of the active cell is changed in the proposed encryption algorithm. Sixteen valued array blocks are used to create a new diffused value of the specific pixel using its neighborhood values. In 1D cellular automata, the values prior and after the selected cell are used as neighborhood values. In 2D cellular automata, the neighboring cells are determined by Von Neumann or Moore model. The problem is considered as the grid of cells. Each cell is defined by the set of states and its neighboring cells. The next state of the current cell value is calculated by the function of the current cell and its neighboring cells. In this paper, two-dimensional elementary cellular automata using the Von Neumann model are applied. The image data are divided into 4 × 4 blocks and the function is applied to the current pixel and its neighbors.
Improving cellular automata scheduling through dynamics control
Published in International Journal of Parallel, Emergent and Distributed Systems, 2019
Tiago I. Carvalho, Murillo G. Carneiro, Gina M. B. Oliveira
Cellular automata are discrete dynamical systems composed of very simple components with local and limited communication. Despite having a simple definition, they present universal computability capacity [1] and as such are able to present a complex behaviour. A cellular automaton is formed by a collection of cells, in which their update is determined by a transition rule. In general, CA are employed as a modelling framework in many simulation studies of natural phenomena. There are also several studies that explore CA computability, such as the density classification problem (DCT) [2], cryptography [3] and scheduling [4]. The CA dynamical behaviour exhibited in the lattice update is important to many of these applications. For example, in simulation scenarios, the occurrence of chaotic rules can lead to instability. In addition, the ideal rules for cryptography are chaotic and for DCT, the most desired behaviour is null. Thus, strategies that control the dynamical behaviour are seen as desirable for many CA applications.
Localized algorithms for redundant readers elimination in RFID networks
Published in International Journal of Parallel, Emergent and Distributed Systems, 2019
Nafiul Rashid, Salimur Choudhury, Kai Salomaa
A cellular automaton consists of a collection of cells on a grid whose states evolve at discrete time steps according to rules based on the states of the neighboring cells. We can define a 2-dimensional cellular automaton as a quadruple, (C,S,, N) where C is the cellular grid, S is the set of states of the cells, is the transition rule of the automaton and N is the neighborhood of a cell. At time t, each cell of C is assigned a state of S. The state of a cell , at time , is determined via the transition function depending on the current state of c and the states of cells in the neighborhood of c at time t. If all the adjacent cells (maximum 8) of a cell are considered as the neighbors, then this is called a Moore Neighborhood. In a cellular automaton, all the cells synchronously verify the states of their neighbors and change their states accordingly. More details on cellular automaton can be found in [19]. In Figure 2, radius 1 and radius 2 neighborhoods are shown for a cell c. In a radius 1 neighborhood, one cell can have a maximum 8 neighbors (C,E,W, S,N,NW,NE,SW,SE). On the other hand, in a radius 2 neighborhood, one cell can have 24 neighbors. In Figure 2, the radius 2 neighborhood of the cell c consists of all cells that are within the radius 1 and the cells which are marked by 2.