On the decomposition of stochastic cellular automata

In this paper we present two interesting properties of stochastic cellular automata that can be helpful in analyzing the dynamical behavior of such automata. The first property allows for calculating cell-wise probability distributions over the state set of a stochastic cellular automaton, i.e. images that show the average state of each cell during the evolution of the stochastic cellular automaton. The second property shows that stochastic cellular automata are equivalent to so-called stochastic mixtures of deterministic cellular automata. Based on this property, any stochastic cellular automaton can be decomposed into a set of deterministic cellular automata, each of which contributes to the behavior of the stochastic cellular automaton.Comment: Submitted to Journal of Computation Science, Special Issue on Cellular Automata Application

Identification of cellular automata based on incomplete observations with bounded time gaps

In this paper, the problem of identifying the cellular automata (CAs) is considered. We frame and solve this problem in the context of incomplete observations, i.e., prerecorded, incomplete configurations of the system at certain, and unknown time stamps. We consider 1-D, deterministic, two-state CAs only. An identification method based on a genetic algorithm with individuals of variable length is proposed. The experimental results show that the proposed method is highly effective. In addition, connections between the dynamical properties of CAs (Lyapunov exponents and behavioral classes) and the performance of the identification algorithm are established and analyzed

Layered Cellular Automata

Layered Cellular Automata (LCA) extends the concept of traditional cellular automata (CA) to model complex systems and phenomena. In LCA, each cell's next state is determined by the interaction of two layers of computation, allowing for more dynamic and realistic simulations. This thesis explores the design, dynamics, and applications of LCA, with a focus on its potential in pattern recognition and classification. The research begins by introducing the limitations of traditional CA in capturing the complexity of real-world systems. It then presents the concept of LCA, where layer 0 corresponds to a predefined model, and layer 1 represents the proposed model with additional influence. The interlayer rules, denoted as f and g, enable interactions not only from adjacent neighboring cells but also from some far-away neighboring cells, capturing long-range dependencies. The thesis explores various LCA models, including those based on averaging, maximization, minimization, and modified ECA neighborhoods. Additionally, the implementation of LCA on the 2-D cellular automaton Game of Life is discussed, showcasing intriguing patterns and behaviors. Through extensive experiments, the dynamics of different LCA models are analyzed, revealing their sensitivity to rule changes and block size variations. Convergent LCAs, which converge to fixed points from any initial configuration, are identified and used to design a two-class pattern classifier. Comparative evaluations demonstrate the competitive performance of the LCA-based classifier against existing algorithms. Theoretical analysis of LCA properties contributes to a deeper understanding of its computational capabilities and behaviors. The research also suggests potential future directions, such as exploring advanced LCA models, higher-dimensional simulations, and hybrid approaches integrating LCA with other computational models.Comment: This thesis represents the culmination of my M.Tech research, conducted under the guidance of Dr. Sukanta Das, Associate Professor at the Department of Information Technology, Indian Institute of Engineering Science and Technology, Shibpur, West Bengal, India. arXiv admin note: substantial text overlap with arXiv:2210.13971 by other author

The design and implementation of fuzzy query processing on sensor networks

Sensor nodes and Wireless Sensor Networks (WSN) enable observation of the physical world in unprecedented levels of granularity. A growing number of environmental monitoring applications are being designed to leverage data collection features of WSN, increasing the need for efficient data management techniques and for comparative analysis of various data management techniques. My research leverages aspects of fuzzy database, specifically fuzzy data representation and fuzzy or flexible queries to improve upon the efficiency of existing data management techniques by exploiting the inherent uncertainty of the data collected by WSN. Herein I present my research contributions. I provide classification of WSN middleware to illustrate varying approaches to data management for WSN and identify a need to better handle the uncertainty inherent in data collected from physical environments and to take advantage of the imprecision of the data to increase the efficiency of WSN by requiring less information be transmitted to adequately answer queries posed by WSN monitoring applications. In this dissertation, I present a novel approach to querying WSN, in which semantic knowledge about sensor attributes is represented as fuzzy terms. I present an enhanced simulation environment that supports more flexible and realistic analysis by using cellular automata models to separately model the deployed WSN and the underlying physical environment. Simulation experiments are used to evaluate my fuzzy query approach for environmental monitoring applications. My analysis shows that using fuzzy queries improves upon other data management techniques by reducing the amount of data that needs to be collected to accurately satisfy application requests. This reduction in data transmission results in increased battery life within sensors, an important measure of cost and performance for WSN applications

Cellular Automata

Modelling and simulation are disciplines of major importance for science and engineering. There is no science without models, and simulation has nowadays become a very useful tool, sometimes unavoidable, for development of both science and engineering. The main attractive feature of cellular automata is that, in spite of their conceptual simplicity which allows an easiness of implementation for computer simulation, as a detailed and complete mathematical analysis in principle, they are able to exhibit a wide variety of amazingly complex behaviour. This feature of cellular automata has attracted the researchers' attention from a wide variety of divergent fields of the exact disciplines of science and engineering, but also of the social sciences, and sometimes beyond. The collective complex behaviour of numerous systems, which emerge from the interaction of a multitude of simple individuals, is being conveniently modelled and simulated with cellular automata for very different purposes. In this book, a number of innovative applications of cellular automata models in the fields of Quantum Computing, Materials Science, Cryptography and Coding, and Robotics and Image Processing are presented

Proceedings of AUTOMATA 2010: 16th International workshop on cellular automata and discrete complex systems

International audienceThese local proceedings hold the papers of two catgeories: (a) Short, non-reviewed papers (b) Full paper

Proceeding seminar ICABS di Malaysia

proceeding seminar internasiona

Methodology for predicting and/or compensating the behavior of optical frequency comb

RESUMEN: Optical frequency comb spectrum can change its behavior due to temperature fluctuations, normal dispersion, and mechanical vibrations. Such limitations can affect the peak power and wavelength separation of comb lines. In the propagation through single−mode fiber, the linear and non−linear phenomena can modify spectral shape, phase shifts and flatness of spectrum. To find a strategy of compensation, the PhD thesis is focused on a prediction methodology based on fuzzy cellular automata, intuitionistic fuzzy sets and fuzzy entropy measures. The research work proposes a predictor called intuitionistic fuzzy cellular automata based on mean vector and a validation measure called general intuitionistic fuzzy entropy based on adequacy and non−adequacy. In the accomplished experiments, the method was used in three experiments: mode−locked lasers, cascaded intensity modulators−Mach Zehnder modulators, and microresonator ring. The obtained results showed that the power and phase distortions were reduced by using a pulse shaper, where the method was programmed. In addition, the stability and/or instability of spectrum were found for the microresonator ring