Strategies for Parallel Execution of Cellular Automata in Distributed Memory Architectures

This paper presents a new framework for modelling tumour-induced angiogenesis. Classical Cellular Automata approach is employed to model cellular and intracellular processes that occur in cancer tissue and neighbourhood. Vascular system is modelled by using Graph of Cellular Automata, which combines graph theory with Cellular Automata paradigm. A new model is proposed as a starting point for further investigations on multiscale model covering wide range of spatio-temporal scales including blood flow processes. The basis of the model with the algorithms are presented. Preliminary results with short discussion are also included.KeywordsCellular AutomatonVascular NetworkTransportation NetworkCapillary VesselVessel NetworkThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Environmental processes are usually conceptualized as complex systems whose dynamics are best understood by examining the relationships and interactions of their constituent parts. The cellular automata paradigm, as a bottom‐up modeling approach, has been widely used to study the macroscopic behavior of these complex natural processes. However, the cellular automata models are largely restricted to the two‐dimensional spatial perspective even though the process dynamics they represent evolve in the three spatial dimensions. The objective of this study is to develop a voxel‐based automata approach for modeling the propagation of airborne pollutants in three‐dimensional space over time. The GIS‐based geo‐atom theory was used to manage the data within the automaton. The simulation results indicate the model has the capability to generate effective four‐dimensional (4D) simulations from simple transition rules that describe the processes of particle advection and diffusion. The application of voxel‐based automata and the geo‐atom concepts allows for a detailed 4D analysis and tracking of the changes in the voxel space at every time‐step. The proposed modeling approach provides new means to examine the relationships between pattern and process in 4D.

Understanding the deposition processes in submarine canyons and channels has significant implications for petroleum exploration. A numerical model has been established to simulate the sedimentary fill of canyons and channel-levee complexes present in turbiditic environments. The model is based on a Cellular Automata (CA) approach. Depositional processes which control the evolution of the system are described by the rules established either empirically from observations or from simplified physical laws. Sedimentary systems are described by a meshed domain and are built with a series of unitary events. Two versions have been developed and couple the impact of flow effects on the nature and architecture of deposits. The first model allows a dynamic description of seabed evolution under the activity of gravity flows. For a single event simulation, both the morphology of the seabed and sediment distribution is estimated at each time step. The model was calibrated by the application to the turbulent surge that occurred during the 1999 storm in Capbreton canyon (France) using core control. The second model simulates deposit architecture accumulated by a succession of steady state flows. A possible way to average physical processes over time is to then consider geological events such as successive quasi-steady state flows for which sediment transport has permanent values. The CA paradigm is an efficient approach to quickly obtain such stationary states. Results obtained for the first sequence of the Pab channel complex (Indo-Pakistani margin) provides the first step towards the quantitative understanding of external parameters on catastrophic gravity flow dynamics and on the organisation of subsequent deposits.

Cellular automata algorithm for simulation of surface flows in large plains

Unconventional modelling of complex system via cellular automata and differential evolution

SCIARA γ2: An improved cellular automata model for lava flows and applications to the 2002 Etnean crisis

Modeling of the 2001 lava flow at Etna volcano by a Cellular Automata approach

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.

Forecasting lava flow hazards during the 2006 Etna eruption: Using the MAGFLOW cellular automata model

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.

A multi-agent based cellular automata model for intersection traffic control simulation

Feature Extraction is a long active topic in image processing, and it attracts lots of attention over the last five decades due to its uses in real-life applications. Edge detection method allows users to discover the features of an image for a notable change in the gray level. This texture indicates an ending of one region and the beginning of another in the image. Likewise, over the last few decades, researchers have been exploring to exploit the simple computing model of Cellular Automata (CA) with local neighborhood structure in image processing techniques. The usage of CA in applications of medical image-processing is identified by Kendall Preston [1] in 1979. It is found that edge detection using CA has a potential advantage over traditional approaches for its lightweight nature and computational efficiency. This paper introduces a new technique for image edge detection using two-dimensional CA. The method can use specific rule sets of CA using the Von Neumann neighborhood for edge detection with null boundary conditions. The performance analysis of the scheme is done and compared with some existing standard edge detection techniques. The results obtained from the proposed technique is very promising for feature extraction.

Cellular automata (CA) are fully parallel computational models and are widely applied to numerical modelling for many complex systems or nonlinear systems, such as fluid dynamics. Those systems are often governed by nonlinear partial differential equations which are hard to solve by using traditional numerical methods. In this paper, based on CA, a general model for a kind of evolutionary physics systems is proposed. As an example, a CA-like model for nonlinear parabolic equation is built by using multi-scalar analysis. The model is applied to several typical problems and satisfactory results are achieved.

From classical infinite space–time CA to a hybrid CA model for natural sciences modeling

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.