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Abstract: A two-dimensional (2-D) cellular automata (CA) dynamic system constituted of cells-charges has been proposed for the simulation of the earthquake process. In this paper, the study is focused on the optimal parameterisation of the model introducing the use of genetic algorithm (GA). The optimisation of the CA model parameterisation, by applying a standard GA, extends its ability to study various hypotheses concerning the seismicity of the region under consideration. The GA evolves an initially random population of candidate solutions of model parameters, such that in time appropriate solutions to emerge. The quality criterion is realised by taking into account the extent that the simulation results match the Gutenberg–Richter (GR) law derived from recorded data of the area under test. The simulation results presented here regard regions of Greece with different seismic and geophysical characteristics. The results found are in good quantitative and qualitative agreement with the GR scaling relations. [Copyright &y& Elsevier]

Abstract: Creating collision-free trajectories for mobile robots, known as the path planning problem, is considered to be one of the basic problems in robotics. In case of multiple robotic systems, the complexity of such systems increases proportionally with the number of robots, due to the fact that all robots must act as one unit to complete one composite task, such as retaining a specific formation. The proposed path planner employs a combination of Cellular Automata (CA) and Ant Colony Optimization (ACO) techniques in order to create collision-free trajectories for every robot of a team while their formation is kept immutable. The method reacts with obstacle distribution changes and therefore can be used in dynamical or unknown environments, without the need of a priori knowledge of the space. The team is divided into subgroups and all the desired pathways are created with the combined use of a CA path planner and an ACO algorithm. In case of lack of pheromones, paths are created using the CA path planner. Compared to other methods, the proposed method can create accurate collision-free paths in real time with low complexity while the implemented system is completely autonomous. A simulation environment was created to test the effectiveness of the applied CA rules and ACO principles. Moreover, the proposed method was implemented in a system using a real world simulation environment, called Webots. The CA and ACO combined algorithm was applied to a team of multiple simulated robots without the interference of a central control. Simulation and experimental results indicate that accurate collision free paths could be created with low complexity, confirming the robustness of the method. [ABSTRACT FROM AUTHOR]

Abstract: Change of DNA sequence that fuels evolution is, to a certain extent, a deterministic process because mutagenesis does not occur in an absolutely random manner. So far, it has not been possible to decipher the rules that govern DNA sequence evolution due to the extreme complexity of the entire process. In our attempt to approach this issue we focus solely on the mechanisms of mutagenesis and deliberately disregard the role of natural selection. Hence, in this analysis, evolution refers to the accumulation of genetic alterations that originate from mutations and are transmitted through generations without being subjected to natural selection. We have developed a software tool that allows modelling of a DNA sequence as a one-dimensional cellular automaton (CA) with four states per cell which correspond to the four DNA bases, i.e. A, C, T and G. The four states are represented by numbers of the quaternary number system. Moreover, we have developed genetic algorithms (GAs) in order to determine the rules of CA evolution that simulate the DNA evolution process. Linear evolution rules were considered and square matrices were used to represent them. If DNA sequences of different evolution steps are available, our approach allows the determination of the underlying evolution rule(s). Conversely, once the evolution rules are deciphered, our tool may reconstruct the DNA sequence in any previous evolution step for which the exact sequence information was unknown. The developed tool may be used to test various parameters that could influence evolution. We describe a paradigm relying on the assumption that mutagenesis is governed by a near-neighbour-dependent mechanism. Based on the satisfactory performance of our system in the deliberately simplified example, we propose that our approach could offer a starting point for future attempts to understand the mechanisms that govern evolution. The developed software is open-source and has a user-friendly graphical input interface. [Copyright &y& Elsevier]

Seismicity is an extended geophysical characteristic of the Greek dominion. There are certain areas of high seismic activity, as well as, regions of low seismicity where strong earthquakes are rather rare events. Consequently, it is of great interest to present a methodology concerning the earthquake process in Greece even for areas considered to be of low seismicity. In this paper, a study of the earthquake activity of an area in Northeastern Greece, centred at Xanthi, Thrace, extending over a region of radius R =80km, during a certain time period is presented. A two-dimensional cellular automaton (CA) dynamic system consisting of cells representing charges is used for the simulation of the earthquake process. The model has been tested as well as calibrated using the recorded events of the above-mentioned region as initial conditions. The simulation results are found in good quantitative and qualitative agreement with the Gutenberg–Richter (GR) scaling relations. Finally, the CA model has a user-friendly interface and enables the user to change several of its parameters, in order to study various hypotheses concerning the seismicity of the region under consideration. [Copyright &y& Elsevier]

Abstract: Cellular automata (CA) are a powerful technique for modelling otherwise intractably complex systems. On the other hand, earthquake can be defined as a spatially extended dissipative dynamic system that naturally evolves into a critical state with no characteristic time or length scales. In this paper, a two-dimensional CA model capable of reproducing some prominent features of earthquake data is presented. The proposed model with continuous states and discrete time, comprises cell-charges and aims at simulating earthquake activity with the usage of potentials. Several measurements have been carried out at different critical states, leading to different paths to criticality, for various cascade (earthquake) sizes, various cell activities and different neighbourhood sizes. Most notably, the produced simulation results emulate the Gutenberg–Richter (GR) scaling law, in both quantitative and qualitative way. Furthermore, the CA model has been implemented with a user-friendly interface and the user can change several of its parameters, in order to study various hypotheses concerning the aforementioned earthquake activity features. [Copyright &y& Elsevier]

Abstract: Artificial intelligence techniques are widely used to solve complicated manufacturing and fabrication process problems in today''s industrial world. Cellular automata (CAs) have been applied numerous times as an evolvable technique to the solution of some of the aforementioned complicated problems with great success due to their inherent parallelism, structural locality, regularity and modularity. One of the most difficult problems that CAs dealt with in several cases (i.e. integrated circuit fabrication, pattern recognition and classification, computer aided design, machine vision, etc.) is the identification and the reproduction of circular fronts and shapes. In this paper a CA is used to propagate circular fronts. This CA has an extended Moore neighborhood and a relatively simple local rule based on Boolean operators. Simulation results of an integrated circuit fabrication process, namely chemical vapor deposition based on the proposed CA are also presented. These results were found to be in very good qualitative agreement with the experimental results published in the literature. Moreover, because of the CA''s binary states and its local rule simplicity, the VLSI implementation of the proposed CA is straightforward. [Copyright &y& Elsevier]

Abstract: A message passing algorithm for processor arrays that can tolerate any number of faulty blocks, which form any shape, is presented. Each message is delivered to its destination, provided that the destination processor is not surrounded by faults. In this case the message is returned to its source processor. Only local knowledge of faults is demanded. The hardware implementation of this algorithm leads to a message passing coprocessor which is allocated at each processor of the array. No need for high silicon overhead is required for the implementation of the message passing coprocessor. This coprocessor executes only the fault tolerant message passing algorithm presented here. The usage of the proposed coprocessor improves the general processing efficiency, as well as, the performance reliability under faulty conditions. [Copyright &y& Elsevier]

Recent studies of the quantum-mechanical processes in the DNA molecule have seriously challenged the principle that mutations occur randomly. The proton tunneling mechanism causes tautomeric transitions in base pairs resulting in mutations during DNA replication. The meticulous study of the quantum-mechanical phenomena in DNA may reveal that the process of mutagenesis is not completely random. We are still far away from a complete quantum-mechanical model of DNA sequence mutagenesis because of the complexity of the processes and the complex three-dimensional structure of the molecule. In this paper we have developed a quantum-mechanical description of DNA evolution and, following its outline, we have constructed a classical model for DNA evolution assuming that some aspects of the quantum-mechanical processes have influenced the determination of the genetic code. Conversely, our model assumes that the genetic code provides information about the quantum-mechanical mechanisms of mutagenesis, as the current code is the product of an evolutionary process that tries to minimize the spurious consequences of mutagenesis. Based on this model we develop an algorithm that can be used to study the accumulation of mutations in a DNA sequence. The algorithm has a user-friendly interface and the user can change key parameters in order to study relevant hypotheses. [Copyright &y& Elsevier]

Technology computer-aided design (TCAD) is essential for the design of modern integrated circuit fabrication processes. TCAD tools must not only model real processes accurately, to allow predictive simulation during technology research and development, but must work together as an integrated system to allow efficient exploration of new technology options and to perform numerical experiments. Cellular automata (CAs) have been applied successfully to the simulation of several physical systems and processes, and have been extensively used as VLSI architecture. This paper describes a TCAD system for the simulation of the two-dimensional (2-D) chemical vapor deposition (CVD) process. The TCAD system is fully automated and is also able to support, the hardware implementation of the corresponding CA algorithm, leading to its execution by dedicated parallel processor. The obtained simulation profiles of the CVD process are in very good qualitative agreement with experimental and simulation results found in the literature. The proposed system produces as output the corresponding VHDL code, which leads to the very large-scale integrated (VLSI) implementation of the CA algorithm. Furthermore, a user-friendly interface that enables easy and effective interaction between the user and the TCAD system has been developed. No prior knowledge of VHDL is required by the user. The produced VHDL code is synthesizable and can be used for the automated design of the corresponding VLSI system, using a commercial VLSI CAD system. [Copyright &y& Elsevier]

Cellular Automata (CAs) are models of physical systems where space and time are discrete and interactions are only local. CAs are very effective in simulating physical systems and solving scientific problems, because they can capture the essential features of systems in which global behaviour arises from the collective effect of simple components which interact locally. CAs have been applied successfully to the simulation of several physical systems and processes, and have been extensively used as a very large-scale integrated (VLSI) architecture.We present a CAD system that builds a bridge between CAs as models of physical systems and processes, and CAs as a VLSI architecture. The inputs to our CAD system are the CA dimensionality, lattice size, local rule, and the initial and boundary conditions imposed by the particular problem. Our system produces as output the corresponding VHDL code, which leads to VLSI implementation of the CA algorithm. No prior knowledge of VHDL is required by the user.We have tested our CAD system using well-known one- and two-dimensional CAs, namely the game of life and the rule 90 CAs. The VHDL code produced in all these cases has been used for the automated design of the corresponding VLSI system, using a commercial VLSI CAD system. Simulations of the operation of these VLSI systems showed that the corresponding CA has been successfully implemented into hardware. [Copyright &y& Elsevier]

Cellular automata are introduced as a model for DNA structure, function and evolution. DNA is modeled as a one-dimensional cellular automaton with four states per cell. These states are the four DNA bases A, C, T and G. The four states are represented by numbers of the quaternary number system. Linear evolution rules, represented by square matrices, are considered. Based on this model a simulator of DNA evolution is developed and simulation results are presented. This simulator has a user-friendly input interface and can be used for the study of DNA evolution. [Copyright &y& Elsevier]

Abstract: This paper presents an extensive simulation tool based on a Cellular Automata (CA) system that models fundamental seismic characteristics of a region. The CA-based dynamic model consists of cells-charges and it is used for the simulation of the earthquake process. The simulation tool has remarkably accelerated the response of the model by incorporating principles of the High Performance Computing (HPC). Extensive programming features of parallel computing have been applied, thus improving its processing effectiveness. The tool implements an enhanced (or hyper-) 2-dimensional version of the proposed CA model. Regional characteristics that depend on the seismic background of the area under study are assigned to the model with the application of a user-friendly software environment. The model is evaluated with real data that correspond to a circular region around Skyros Island, Greece, for different time periods, as for example one of 45years (1901–1945). The enhanced 2-dimensional version of the model incorporates all principal characteristics of the 2-dimensional one, also including groups of CA cells that interact with others, located to a considerable distance in an attempt to simulate long-range interaction. The advanced simulation tool has been thoroughly evaluated. Several measurements have been made for different critical states, as well as for various cascade (earthquake) sizes, cell activities and different neighbourhood sizes. Simulation results qualitatively approach the Gutenberg–Richter (GR) scaling law and reveal fundamental characteristics of the system. [Copyright &y& Elsevier]

In motion estimation, the sub-pixel matching technique involves the search of sub-sample positions as well as integer-sample positions between the image pairs, choosing the one that gives the best match. Based on this idea, this work proposes an estimation algorithm, which performs a 2-D correspondence search using a hierarchical search pattern. The intermediate results are refined by 3-D cellular automata (CA). The disparity value is then defined using the distance of the matching position. Therefore the proposed algorithm can process uncalibrated and non-rectified stereo image pairs, maintaining the computational load within reasonable levels. Additionally, a hardware architecture of the algorithm is deployed. Its performance has been evaluated on both synthetic and real self-captured image sets. Its attributes, make the proposed method suitable for autonomous outdoor robotic applications. [ABSTRACT FROM AUTHOR]

Abstract: This paper studies the on-chip realisation of a dynamic model proposed to simulate crowd behaviour, originated from electrostatic-induced potential fields. It is based on cellular automata (CA), thus taking advantage of their inherent ability to represent sufficiently phenomena of arbitrary complexity and, additionally, to be simulated precisely by digital computers. The model combines electrostatic-induced potential fields to incorporate flexibility in the movement of pedestrians. It primarily calculates distances in an obstacle filled space based on the Euclidean metric. Furthermore, it adopts a computationally fast and efficient method to overcome trouble-inducing obstacles by shifting the moving mechanism to a potential field method based on Manhattan-distance. The hardware implementation of the model is based on FPGA logic. Initialisation of the dedicated processor takes place in collaboration with a detecting and tracking algorithm supported by cameras. The instant response of the processor provides the location of pedestrians around exits. Hardware implementation exploits the prominent feature of parallelism that CA structures inherently possess in contrast to the serial computers, thus accelerating the response of the model. Furthermore, FPGA implementation of the model is advantageous in terms of low-cost, high-speed, compactness and portability features. Finally, the processor could be used as a part of an embedded, real-time, decision support system, aiming at the efficient guidance of crowd in cases of mass egress. [ABSTRACT FROM AUTHOR]

A novel memristor‐based circuit‐level cellular automata (CA)‐inspired approach to the solution of the classic sorting problem of nKeys in a linear array is presented. The presented system utilises the structural simplicity of CA combined with the threshold‐type switching behaviour of memristors and composite memristive components; the latter is used for both information encoding and computation. The focus is on a threshold‐type model for memristors for the implementation of the fundamental CA cell and the overall CA operation is verified via simulations. [ABSTRACT FROM AUTHOR]

In all the living organisms, the self-preservation behaviour is almost universal. Even the most simple of living organisms, like slime mould, is typically under intense selective pressure to evolve a response to ensure their evolution and safety in the best possible way. On the other hand, evacuation of a place can be easily characterized as one of the most stressful situations for the individuals taking part on it. Taking inspiration from the slime mould behaviour, we are introducing a computational bio-inspired model crowd evacuation model. Cellular Automata (CA) were selected as a fully parallel advanced computation tool able to mimic thePhysarum’s behaviour. In particular, the proposed CA model takes into account while mimicking thePhysarumforaging process, the food diffusion, the organism’s growth, the creation of tubes for each organism, the selection of optimum tube for each human in correspondence to the crowd evacuation under study and finally, the movement of all humans at each time step towards near exit. To test the model’s efficiency and robustness, several simulation scenarios were proposed both in virtual and real-life indoor environments (namely, the first floor of office building B of the Department of Electrical and Computer Engineering of Democritus University of Thrace). The proposed model is further evaluated in a purely quantitative way by comparing the simulation results with the corresponding ones from the bibliography taken by real data. The examined fundamental diagrams of velocity–density and flow–density are found in full agreement with many of the already published corresponding results proving the adequacy, the fitness and the resulting dynamics of the model. Finally, several realPhysarumexperiments were conducted in an archetype of the aforementioned real-life environment proving at last that the proposed model succeeded in reproducing sufficiently thePhysarum’s recorded behaviour derived from observation of the aforementioned biological laboratory experiments. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, a new methodology for deriving the velocity and the acceleration information of a digital encoder through processing its pulse train, is presented. The proposed method is based on accurate time measurement (with picosecond accuracy) as well as encoder pulse counting in adaptively changing time intervals, providing thus a wide-range velocity evaluation with very good accuracy. The method offers better response times at low speeds and very high-accuracy at the full range of measured velocities. By using the proposed method, the velocity measurement accuracy is improved compared to currently known methods, since high-resolution time-to-digital converters (TDC) are included in the design. The increased accuracy in velocity measurement allows the application of the simple arithmetic differentiation method on the velocity information in order to derive the acceleration, which in other cases would not be suggested due to accumulated quantization noise. A digital signal processor (DSP) also allows the implementation of numerous other methods to calculate acceleration. The proposed configuration has been implemented in specific hardware (FPGA), reserving thus the computational power of the system controlling DSP for high-level control tasks. [Copyright &y& Elsevier]

For the first time, the model of a physical nano‐scale memristor is integrated analytically. A closed‐form expression for the time evolution of the device memristance during the turn‐on process is mathematically derived. The complexity of the inverse imaginary error function‐based analytical formula clearly reflects the high degree of nonlinearity in the nano‐device switching kinetics, which may typically span several orders of magnitude and is critically dependent on input and initial condition. The excellent agreement between the analytical solution and numerical simulation results clearly demonstrates the correctness of the theoretical derivation. The introduction of this formula represents the first step towards a systematic approach to circuit design with memristors. [ABSTRACT FROM AUTHOR]