A fine-grain asynchronous VLSI cellular array processor architecture

The checkerboard update Glauber model for the one-dimensional Ising model is mapped onto a cellular automata model. This cellular automata model is found to be a special case of the well known (two-state) triangular cellular automata model. The author shows, therefore, that there exists a subspace of the full parameter space of the triangular cellular automata that obeys detailed balance. Using this result the shows that there are no phase transitions within the interior of the cubical parameter space of the triangular cellular automata model. He identifies a new line of critical points for the triangular cellular automata. This critical line is associated with the zero-temperature critical line of the one-dimensional Ising antiferromagnet in an external field. The critical properties of the checkerboard Glauber model are studied and are found to be identical to those of the single-spin update model. In addition, the correlations in the checkerboard Glauber model are found to exhibit a null-cone structure, similar to that found in relativity theory. This null-cone, which originates in the constraints imposed by the checkerboard update procedure, is found to be a general property of the triangular cellular automata model.

Diabetic retinopathy is one of the most important causes of visual impairment. In this paper, a supervised automatic lesion detection in digital retina images for diagnosis and screening purposes. The aim of this study is to present a supervised approach for exudate detection in fundus images and also to analyze the method to find the optimum structure. Cellular automata model is used as the base for this task. To improve the adaptability and efficiency of the cellular automata, the rules are updating by a learning process to produce the cellular learning automata. Then, the algorithm is transferred to fuzzy domain for the task of digital retina image analysis. Automaton is created with simple and extended Moore neighborhood. Rule selection and rule updating are performed automatically and the score and penalty assignments are applied to the cells toward a segmentation process. To evaluate the proposed method, statistical parameters of sensitivity, specificity and accuracy are used. A comprehensive experiment is then executed comprising two main phases. First all structural parameters of the automaton are optimized in an investigation study and then a comparison is made between the proposed method with six other well-known methods to verify the results. In the best structure the statistical parameters of sensitivity, specificity and accuracy are computed as 96.3%, 98.7% and 96.1% for STARE retina image dataset.

As an integrated kind of railway signal-control pattern, the four-aspect fixed autoblock system has been used in many train control systems. This paper takes the four-aspect fixed autoblock system as the research object and proposes the cellular automata model of the fixed autoblock system based on the existing theoretical researches on different train systems and traffic systems by means of the analytical study of the classical cellular automata model. The CA (cellular automata) models combine complexity of passenger railway line with the theory of cellular automata and introduce some new CA models into the existing control systems. After analyzing the relevant simulation results, we study thoroughly and obtain efficiently the needed data for the variation of the section carrying capacity, the average train delay and the train speed which have been affected by redundant time on the operation of passenger trains with different speeds.

Over the last decades Cellular Automata (CA) have become more and more present in solving general-purpose problems, but the main issue is how to map a problem to a Cellular Automata model. Special languages were developed for programming such models, but learning a new programming language is very time consuming. Furthermore software developers have to keep in mind the specific structure of Cellular Automata when designing a new algorithm. In this paper we present a method to generate Cellular Automata models from standard C code. The code is transcoded by mapping the complete algorithm written in C to a Cellular Automata model that may be compiled for CPU, GPU and even FPGA without further user interaction.

The evolution of fluvial-deltaic system has been one of the important topics in global change research. With the development of spatial science and technology, Remote Sensing(RS), Geographic Information System(GIS) and Global Positioning System(GPS) have been extensively applied to observing, interpreting and analyzing complex processes in the fluvial-deltaic system. However, modeling and predicting has proved very difficult because such system shows a wide range of different types of non-linear responses. Cellular automata (CA) are discrete, dynamical systems that are divided up into small cells with each cell taking a certain state. The basic idea of cellular automata is to reduce a complex system using certain rules into something simpler. In this paper, we present a cellular automata model for coast erosion simulation at large spatial scale. Of the factors which control the coast erosion, wind is most important because it influences both the power and direction of marine dynamics. In the CA model presented here, coast cells can be eroded by their neighboring marine cells. Due to wind effects, the erosion rate at some direction is larger than that at other direction. Such anisotropy erosion was represented by adding different probability weighting for each direction in CA rules. The model was used to simulate the erosion process of the Qingshuigou spit in the Yellow River Delta. The simulation results resemble the actual coastlines extracted from multitemporal TM images to great degree.

Strategies for landscape ecology: An application using cellular automata models

Mountain snowpacks are key components of surface water resources, as snowmelt significantly contributes to streamflow and serves as an essential indicator of climate change and variability. Accurate estimation of snowmelt-induced streamflow and assessment of its potential response to climate change are therefore essential for effective water resources planning and management. This study presents a new coupled modeling framework that integrates a cellular automata model (CAM) to simulate snow cover area (SCA) with the Snowmelt Runoff Model (SRM) to simulate daily streamflow. The model was applied to assess the impacts of climate change and associated uncertainty on SCA and streamflow in a Mediterranean mountainous catchment. Four local climate scenarios were developed from an ensemble of nine downscaled Regional Climate Models (RCMs), assuming two warming levels for Spain (1.5 °C and 3.0 °C). The CAM–SRM framework was calibrated and validated using precipitation and temperature data from the AEMET 5 km gridded product for the period 2000–2020. Under future climate conditions, the model projected a substantial decrease in SCA and a two-month shorter snow season in warmer scenarios. An earlier onset of snowmelt peak flows is also expected, along with a projected decline in mean annual streamflow of 19.4–32.9%. These results indicate significant changes in the hydrological regime driven by climate change. The proposed modeling framework proves effective for reproducing snowmelt-driven streamflow dynamics in snow-dominated catchments and offers valuable insights for future water resources planning and management in mountainous regions. Graphical Abstract Graphical abstract description : This study presents a novel coupled modelling framework for assessing climate change impacts on streamflow using the Snowmelt-Runoff Model (SRM). The new framework integrates a cellular automata model with the SRM, enabling streamflow simulations under non-historical conditions even in the absence of snow cover observations, which are required as input along with climate variables (precipitation and temperature) in the SRM formulation. The cellular automata model provides daily future snow cover simulations, which are incorporated into the SRM to generate streamflow projections. We evaluated the impacts of climate change on climate variables under four climate scenarios considering two warming levels for Spain (1.5 °C and 3.0 °C). These scenarios were derived from an ensemble of nine Regional Climate Models (RCMs) and downscaled using two different approaches: bias correction and delta change. This study identified the significant impacts of climate change in a Mediterranean snow-dominated basin (Canales basin, southern Spain). The results revealed increased temperatures and reduced precipitation, which led to substantial declines in snow cover and streamflow. This framework offers a valuable tool for water resource management and the development of adaptive climate strategies in snow-dominated mountain regions.

Modeling dynamic urban growth using cellular automata and particle swarm optimization rules

Cellular automata represent a computational model that is based on updating the states of the cells, that are arranged in a regular structure, by means of local interactions between the cells. Cellular automata have often been utilized as a developmental model in engineering areas to solve many complex problems. In the area of the evolutionary algorithms, cellular automata can be applied as an indirect mapping between genotypes and phenotypes. In the recent years, this approach has successfully been applied on the evolutionary development of digital circuits at the gate level. Combinational multipliers represent a class of circuits that is usually considered as hard task for the design using the evolutionary techniques. In our previous research regarding the cellular automata-based development, 2times2-bit multipliers were successfully evolved using this approach. Combinational multipliers have been chosen in this paper to demonstrate capabilities of an advanced developmental system that allows to apply cellular automata of different sizes in order to design larger instances of this kind of circuits. In the experiments presented herein, the 2times3-bit and 3times3-bit multipliers will be considered which represent the first case when such instances of multipliers have been successfully developed at the gate level using cellular automata. The proposed developmental model is investigated in detail with respect to the success rate of the evolutionary experiments for different experimental setups (such as the cellular automata size, the number of cell states and developmental steps). Moreover, it will be demonstrated that different ways of connections of the circuit outputs can be utilized without a significant influence on the successfulness of the evolutionary process.

The diversity of human immunodeficiency virus (HIV) in vivo has been reported. In this article, we propose a cellular automata (CA) model describing the interactions between the immune system and HIV, and examine the effect of the diversity of these interactions. The novel aspects of our CA model are that it not only considers four states (HIV, virgin, dead, infect) but also the diversity exhibited by both HIV and T cells. We simulated maximum diversities for these states by simulating CA on a computer. The model revealed that increased diversity had the effect of increasing the HIV population and simulation steps. In addition, we observed that the CA model accurately reflects the occurrence of infection, incubation period, and the development of AIDS. The CA model demonstrated that the diversity of the virus is the major factor affecting the success rate of the escape of HIV from the immune response.

The Cellular Automata paradigm is an efficient tool to model and study complex systems such as traffic simulation, lava flows and swarm based behaviour. In addition, cellular automata can be profitably used in many mathematical problems such as differential equations and chaos theory. Due to their inherent parallel nature, cellular automata can be efficiently parallelized among a set of computing nodes in order to scale and speed up their execution. This paper presents a preliminary study on different parallelizzation techniques for structured grid models such as cellular automata on distributed memory architectures. In particular, three strategies are presented and compared in order to evaluate their efficiency in terms of speedup. An experimental section shows the performance achieved by the three strategies when a real-life application, namely the SciddicaT cellular automata model for debris-flows simulation, is adopted.

A cellular automaton model for the effects of population movement and vaccination on epidemic propagation

Modelling the spread of covid-19 in the capital of Brazil using numerical solution and cellular automata

Three-dimensional physics-based cellular automata model for granular shear flow

Traffic congestion caused by traffic accidents has seriously affected daily life. The cellular automata model can predict traffic congestion after the traffic accident by simulating the characteristics of vehicle movement. However, the prediction accuracy is poor. Aiming at the shortcomings of the cellular automata model, we studied the characteristics of urban traffic flow, integrated the passenger car unit and random traffic flow. We also improved the probability optimization design in the traditional cellular automata model. Thus, an improved cellular automata model was put forward. The prediction accuracy of the improved model was higher and more stable than that of the traditional model. The model provided technical references for traffic congestion.