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Electricity consumption pattern recognition is the foundation of intelligent electricity distribution data analysis. However, as the scale of electricity consumption data increases, traditional clustering analysis methods encounter bottlenecks such as low computation speed and processing efficiency. To meet the efficient mining needs of massive electricity consumption data, in this paper a parallel processing method of the density-based k-means clustering is presented. First, an initial cluster center selection method based on data sample density is proposed to avoid inaccurate initial cluster center point selection, leading to clustering falling into local optima. The dispersion degree of the data samples within the cluster is also used as an important reference for determining the number of clusters. Subsequently, parallelization of density calculation and clustering for data samples were achieved based on the MapReduce model. Through experiments conducted on Hadoop clusters, it has been shown that the proposed parallel processing method is efficient and feasible, and can provide favorable support for intelligent power allocation decisions. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have shown great performance in computer vision tasks, from image classification to pattern recognition. However, CNNs′${\rm CNNs}^{\prime }$ superior performance arises at the expense of high computational costs, which restricts their employment in real‐time decision‐making applications. Computationally intensive convolutions can be offloaded to optical metasurfaces, enabling sub‐picosecond latency and nearly zero energy consumption, but the currently reported approaches require additional bulk optics and can only process polarized light, which limits their practical usages in integrated lightweight systems. To solve these challenges, a novel design of the metasurface‐based optical convolutional accelerator is experimentally demonstrated, offering an ultra‐compact volume of 0.016 mm3${\rm mm}^{3}$, a low cross‐talk of ‐20 dB, polarization insensitivity, and is capable of implementing multiple convolution operations and extracting simultaneously various features from light‐encoded images. The ultra‐compact metasurface‐based optical accelerator can be compactly integrated with a digital imaging system to constitute an optical‐electronic hybrid CNN, which experimentally achieves a consistent accuracy of 96 % in arbitrarily polarized MNIST digits classification. The proposed ultra‐compact metasurface‐based optical convolutional accelerator paves the way for power‐efficient edge‐computing platforms for a range of machine vision applications. [ABSTRACT FROM AUTHOR]

Recognizing patterns associated with particular events enables the detection of specific critical changes in the events. Due to the resource constraints inherent in WSNs, pattern recognition is highly dependent on the complexity of the computation, the number of iterations, and the requirements for node training. Iterative learning is frequently used in computer-based computer vision. As a result, these methods are in conflict with the perfectly alright architecture of the WSN. The proposed technique, Optimal Cellular Microscopic Pattern Recognizer (OCMPR), enables the detection of macroscale events in WSN. Using the distributed system computational resources of WSNs, the approach reduces calculations for conserving energy and improves recognition. The method generates promising results by combining a well-known optimization technique (the genetic algorithm) with CMPR. This approach addresses the resource-constrained WSN's real-time mission-critical application needs. Global and quick recognition is achieved by dispersing processing over a network's nodes, allowing for loosely connected communication. The results demonstrate the suggested scheme's versatility. [ABSTRACT FROM AUTHOR]

The string matching problem is considered one of the substantial problems in the fields of computer science like speech and pattern recognition, signal and image processing, and artificial intelligence (AI). The increase in the speedup of performance is considered an important factor in meeting the growth rate of databases, Subsequently, one of the determinations to address this issue is the parallelization for exact string matching algorithms. In this study, the E-Abdulrazzaq string matching algorithm is chosen to be executed with the multi-core environment utilizing the OpenMP paradigm which can be utilized to decrease the execution time and increase the speedup of the algorithm. The parallelization algorithm got positive results within the parallel execution time, and excellent speeding-up capabilities, in comparison to the successive result. The Protein database showed optimal results in parallel execution time, and when utilizing short and long pattern lengths. The DNA database showed optimal speedup execution when utilizing short and long pattern lengths, while no specific database obtained the worst results. [ABSTRACT FROM AUTHOR]

This study designs and implements a scoring system for open-spoken English using NN technology. The system scores the oral recording from the phonetic level and the text level, respectively, and can comprehensively evaluate its oral level. The system will separately score the spoken speech and the spoken content through different scoring models and add the scoring results as the final score, in which the spoken content is obtained by text transcription of the recording by an external speech recognition engine. An acoustic sensor is adopted to collect pronunciation signals of spoken English. Modern signal processing and automatic pattern recognition technology are used to distinguish the quality of spoken pronunciation. Similar semantic units are marked between acoustic feature sequences, which make use of the parallel algorithm processing mode of multi-computing cores of modern GPU and allow multiple units to independently execute the comparison algorithm at the same time. Experiments show that the model in this study achieves better comprehensive scoring performance. The scoring model is of great significance to the development of educational informatization and intelligence, and it also provides a reference for the construction of intelligent oral scoring system. [ABSTRACT FROM AUTHOR]

The weak spots in an integrated energy system that may jeopardize the overall reliability call for timely and efficient Inspection and Maintenance (I&M). One core step is the reasonable allocation and deployment of limited I&M personnel or apparatus to the regions or periods with higher event risks, which requires a pinpoint spatiotemporal distribution forecast of future vulnerabilities. This paper presents a hybrid forecast methodology, the Saliency-Rough Fuzzy Utility Pattern recognition ensemble, in light of space-air-ground multi-source-heterogeneous input data. A parallel learning architecture is established and identifies the critical components with higher yields to enhance efficiency. Accordingly, more reasonable quantitative and qualitative evaluations can be carried out concurrently. Potential imprecise and uncertain data scenes are handled in quantitative assessments, both the failure hazard path sets and survival function likelihood boxes are incorporated in the designed relative path-Fussell Vesely Saliency (rp-FVS) model; and in qualitative analyses, the underlying perilous components can be distinguished via a combination of the variable precision-rough model. The rp-FVS-based fuzzy inference logic configures all membership functions identically according to components' impacts. These two parts are integrated into the rough-fuzzy Utility Measure to discover concealed component-vulnerability interconnection patterns. Finally, an empirical case study is conducted for validation. • A forecast methodology of IES vulnerability spatiotemporal distribution is designed. • A parallel identification workflow for high-utility patterns is built. • Likelihood box of survival functions is deployed in the rp-FVS quantitative model. • Variable precision rough and saliency fuzzy are combined for a more reasonable FIL. • An empirical case study validates its feasibility during applications. [ABSTRACT FROM AUTHOR]

We propose a new rotation invariant correlator using dimensionality reduction. A diffractive phase element is used to focus image data into a line which serves as input for a conventional correlator. The diffractive element sums information over each radius of the scene image and projects the result onto one point of a line located at a certain distance behind the image. The method is flexible, to a large extent, and might include parallel pattern recognition and classification as well as further geometrical invariance. Although the new technique is inspired from circular harmonic decomposition, it does not suffer from energy loss. A theoretical analysis, as well as examples, are given. [ABSTRACT FROM AUTHOR]

The partition dimension is a variant of metric dimension in graphs. It has arising applications in the fields of network designing, robot navigation, pattern recognition and image processing. Let G (V (G) , E (G)) be a connected graph and Γ = {P1, P2, ..., Pm} be an ordered m-partition of V (G). The partition representation of vertex v with respect to Γ is an m-vector r (v|Γ) = (d (v, P1) , d (v, P2) , ..., d (v, Pm)), where d (v, P) = min {d (v, x) |x ∈ P} is the distance between v and P. If the m-vectors r (v|Γ) differ in at least 2 positions for all v ∈ V (G), then the m-partition is called a 2-partition generator of G. A 2-partition generator of G with minimum cardinality is called a 2-partition basis of G and its cardinality is known as the 2-partition dimension of G. Circulant graphs outperform other network topologies due to their low message delay, high connectivity and survivability, therefore are widely used in telecommunication networks, computer networks, parallel processing systems and social networks. In this paper, we computed partition dimension of circulant graphs Cn (1, 2) for n ≡ 2 (mod 4), n ≥ 18 and hence corrected the result given by Salman et al. [Acta Math. Sin. Engl. Ser. 2012, 28, 1851-1864]. We further computed the 2-partition dimension of Cn (1, 2) for n ≥ 6. [ABSTRACT FROM AUTHOR]

This paper introduces a cycle-based clustering technique using the cyclic spaces of reversible cellular automata (CAs). Traditionally, a cluster consists of close objects, which in the case of CAs necessarily means that the objects belong to the same cycle; that is, they are reachable from each other. Each of the cyclic spaces of a cellular automaton (CA) forms a unique cluster. This paper identifies CA properties based on "reachability" that make the clustering effective. To do that, we first figure out which CA rules contribute to maintaining the minimum intracluster distance. Our CA is then designed with such rules to ensure that a limited number of cycles exist in the configuration space. An iterative strategy is also introduced that can generate a desired number of clusters by merging objects of closely reachable clusters from a previous level in the present level using a unique auxiliary CA. Finally, the performance of our algorithm is measured using some standard benchmark validation indices and compared with existing well-known clustering techniques. It is found that our algorithm is at least on a par with the best algorithms existing today on the metric of these standard validation indices. [ABSTRACT FROM AUTHOR]

This paper develops a formal logic, named LCA, targeting modeling of one-dimensional binary cellular automata. We first develop the syntax of LCA, then give semantics to LCA in the domain of all binary strings. Then the elementary cellular automata and four-neighborhood binary cellular automata are shown as models of the logic. These instances point out that there are other models of LCA. Finally it is proved that any one-dimensional binary cellular automaton is a model of the proposed logic. [ABSTRACT FROM AUTHOR]

Inspired by the information processing mechanism of the human brain, the artificial neural network (ANN) is a classic data mining method and a powerful soft computing technique. The ANN provides a valuable tool for information processing and pattern recognition, thanks to its advantages in distributed storage, parallel processing, fast problem-solving and adaptive learning. The constructive neural network (CNN) is a popular emerging neural network model suitable for processing largescale data. In this paper, a novel neural network classification model was established based on the covering algorithm (CA) and the immune clustering algorithm (ICA). The CA is highly comprehensible, and enjoys fast computing speed, and high recognition rate. However, the learning effect of this algorithm is rather poor, because the training set is randomly selected from the original data, and the number of nodes (covering number) and area being covered are greatly affected by the learning sequence. To solve the problem, the ICA was introduced to preprocess the data samples, and calculate the cluster centers based on the antibody-antigen affinity. The CA and the ICA work together to determine the covering center and radius automatically, and convert them into the weights and thresholds of the hidden layer of neural network. The number of hidden layer neurons equals the number of covering. In addition, the McCulloch-Pitts (M-P) neurons were adopted for the output layer. Based on the input feature of the hidden layer, the output feature completes the mapping from input to output, creating the final classes of the original data. The introduction of the ICA fully solves the defect of the CA. Finally, our neural network classification model was verified through experiments on real-world datasets. [ABSTRACT FROM AUTHOR]

This paper describes a roadmap for the development of the SP Machine , based on the SP Theory of Intelligence and its realization in the SP Computer Model. The SP Machine will be developed initially as a software virtual machine with high levels of parallel processing, hosted on a high-performance computer. The system should help users visualize knowledge structures and processing. Research is needed into how the system may discover low-level features in speech and in images. Strengths of the SP System in the processing of natural language may be augmented, in conjunction with the further development of the SP System's strengths in unsupervised learning. Strengths of the SP System in pattern recognition may be developed for computer vision. Work is needed on the representation of numbers and the performance of arithmetic processes. A computer model is needed of SP-Neural , the version of the SP Theory expressed in terms of neurons and their interconnections. The SP Machine has potential in many areas of application, several of which may be realized on short-to-medium timescales. [ABSTRACT FROM AUTHOR]

In this study, we detail the conditions that result in the generation of self-assembled quantum fortresses (QFs), in SiGe/Si. A QF consists of four quantum dots (QDs) clustered around a central square pit, one QD per side. This structure strongly resembles the proposed quantum cellular automata (QCA) unit cell—the basis for a computer architecture. We map the growth conditions (epilayer thickness and Ge concentration) under which self-assembly of strain-stabilized QFs and their precursors occur. Additionally, we characterize how QFs change in height, width, and internal size scales within this parameter space. From this information, we develop a phenomenological model for why QFs form based upon changes in lattice spacing. We then calculate how QFs of the observed shapes and sizes would function as QCAs based on a Hubbard-type Hamiltonian model. This analysis reveals that self-assembled QFs grown at 550 °C, a rate of 1 Å/s, a SiGe alloy composition of 37–40%, and a thickness of 15–35 nm could be used as QCAs. [ABSTRACT FROM AUTHOR]

We discuss two ways in which information theory can be used to assess complexity in a system of interacting agents. In the first part, we adopt a global viewpoint and propose a characterization of complexity based on successive maximum entropy estimations of the probability density describing the system, thereby quantifying the respective role played by low and high orders of interaction. In the second part we reconsider the question from a local perspective, focussing on the statistical dependencies between neighbouring agents. These tools are tried on simple cellular automata in order to put them in perspective with other notions of complexity usually employed for such systems. We show that these approaches are hardly comparable, despite some overlap in simple cases. However this allows to interpret complexity in terms of interactions at work in a system (instead of making reference to any particular realization of this dynamics), and to shed some light on the role of initial conditions in complex systems. Clustering of the 88 non-equivalent Elementary Cellular Automata according to their position in the space of information processing features. Rules are coloured according to their Wolfram class. ECA in class I are shown in black, class II in red, chaotic automata (class III) in green and automata displaying complex behaviour (class IV) in blue. In spite of some important important differences, information features and Wolfram class are seen to overlap to a certain extent. [ABSTRACT FROM AUTHOR]

Affinity Propagation (AP) clustering is an algorithm based on message passing between data points, which mainly achieves clustering through the similarity between data. Compared with traditional clustering methods, the AP clustering algorithm can implement clustering without giving a predetermined number of clusters. Therefore, it has the advantages of fast and high efficiency. However, it has certain limitations in dealing with high-dimensional complex datasets. In order to improve the efficiency and accuracy of the AP clustering algorithm, a coarse-grained parallel AP clustering algorithm based on intra-class and inter-class distances is proposed: IOCAP. Firstly, the idea of granularity is introduced to divide the initial dataset into multiple subsets. Secondly, the similarity matrix is improved by combining the intra-class and inter-class distances for each subset. Finally, the improved parallel AP clustering is implemented based on the MapReduce model. Experiments on the Iris dataset, the Diabetes dataset, and the MNIST dataset show that the IOCAP algorithm has good adaptability on large datasets and can effectively improve the accuracy of the algorithm while maintaining the AP clustering effect. [ABSTRACT FROM AUTHOR]

A new Multi-Stage Multi-Secret Image Sharing (MSMSIS) scheme, based on polynomial sharing and cellular automata, is proposed in this paper. The proposed scheme shares multiple secret images among a number of players. The shares and the authentication strings, produced by a cryptographic hash function, are then embedded in cover images by an XOR-based operation in such a way that visual and statistical quality of covers are preserved as much as possible. A particular type of cellular automata is used to guarantee that the secrets are only recovered according to a pre-specified order. The scheme is multi-use, since some parameters are distributed only once and used multiple times. The scheme is verifiable as well, because a cryptographic hash function is used in each stage for each secret image separately. Generality of our scheme also implies that it can be applied on a heterogeneous set of different secret formats, where each secret has its own access structure. Based on our experiments, the PSNR of the proposed method is improved almost 11 percent compared to the average PSNR of the previous similar schemes. [ABSTRACT FROM AUTHOR]

At signalized intersection areas, bicycle traffic presents a dispersion feature which may influence the movements of vehicles during peak period. The primary objective of this study is to simulate the dispersion effect in through-movement bicycle traffic at intersection areas and evaluate its influence on through-movement traffic. A cellular automata (CA) model is developed and validated to simulate the operations of through-movement bicycle traffic departing from two types of intersection approaches. Simulation results show that bicycles benefit from the dispersion effect when they depart from the approach with an exclusive right-turn vehicle lane. But when bicycles travel from the approach with a shared right-turn and through vehicle lane, the dispersion effect will result in friction interference and block interference on through-movement vehicles. Bicycle interferences reduce the vehicle speed and increase the delay of through-movement vehicles. The policy implications in regard to the dispersion effect from two types of approaches are discussed to improve the performance of through-movement traffic operations at signalized intersections. [ABSTRACT FROM AUTHOR]

Multimodal signal analysis based on sophisticated sensors, efficient communication systems and fast parallel processing methods has a rapidly increasing range of multidisciplinary applications. The present paper is devoted to pattern recognition, machine learning, and the analysis of sleep stages in the detection of sleep disorders using polysomnography (PSG) data, including electroencephalography (EEG), breathing (Flow), and electro-oculogram (EOG) signals. The proposed method is based on the classification of selected features by a neural network system with sigmoidal and softmax transfer functions using Bayesian methods for the evaluation of the probabilities of the separate classes. The application is devoted to the analysis of the sleep stages of 184 individuals with different diagnoses, using EEG and further PSG signals. Data analysis points to an average increase of the length of the Wake stage by 2.7% per 10 years and a decrease of the length of the Rapid Eye Movement (REM) stages by 0.8% per 10 years. The mean classification accuracy for given sets of records and single EEG and multimodal features is 88.7% ( standard deviation, STD: 2.1) and 89.6% (STD:1.9), respectively. The proposed methods enable the use of adaptive learning processes for the detection and classification of health disorders based on prior specialist experience and man–machine interaction. [ABSTRACT FROM AUTHOR]

When fish that have been trained to school toward a blue or a yellow target are mixed together with only a small difference in population size they rarely change their target preference. When untrained fish are introduced, however, often the whole mixed population will school toward the target preferred by the initial majority. This is a macroscopic, biological example of the "majority identification problem" studied in the cellular automaton literature. By closely adhering to the empirical conditions of these experiments we have developed a "directed majority" cellular automaton that: (a) solves the identification problem well even for small initial majorities; (b) explains why untrained fish help persuade minority fish to join the majority; and (c) predicts that untrained fish achieve this feat by collaborating in spatially structured cliques that might be observable in experiments with larger populations. The directed majority automaton provides insight into the consensus forming process and holds promise for being able to predict when systems (similar to the fish) will successfully perform majority identification. [ABSTRACT FROM AUTHOR]

Urban cellular automata (CA) models propagate and accumulate errors during the modeling process due to the model structure or stochastic processes involved. It is feasible to assimilate real-time observations into an urban CA model to reduce model uncertainties. However, the assimilation performance is sensitive to the spatio-temporal units in the assimilation algorithm, that is, spatial block size and window length (temporal interval). In this study, we coupled an assimilation model, an ensemble Kalman filter (EnKF) and a Logistic-CA model to simulate the urban dynamic in Beijing over a period of two decades. Our results indicate that the coupled EnKF-CA model outperforms the CA-alone counterpart by about 10% in terms of the figure of merit, which reflects the agreement of modeled pixels. We also find that the assimilation performance using a finer block (1 km) is better than that using a coarser block (5 km and 10 km) because of the better depiction of spatial heterogeneity using a finer block. Moreover, the improvement of intermediate outputs using the coupled EnKF-CA model is effective for a certain period (e.g. 5 years). This implies that a high-frequency assimilation may not significantly improve the model performance. The sensitivity analyses of spatio-temporal assimilation in the EnKF-CA model provide a better understanding of the assimilation mechanism that couples with land-use change models. [ABSTRACT FROM PUBLISHER]

Interactive segmentation of images has become an integral part of image processing applications. Several graph based segmentation techniques have been developed, which depend upon global minimization of the energy cost function. An adequate scheme of interactive segmentation still needs a skilled initialization of regions with user-defined seeds pixels distributed over the entire image. We propose an iterative segmentation technique based on Cellular Automaton which focuses to reduce the user efforts required to provide initialization. The existing algorithms based on Cellular Automaton only use local smoothness term in label propagation making them highly sensitive to user-defined seeds pixels. To reduce the sensitivity towards initial user definition of regions, global constraints are introduced along with local information to propagate labels. The results obtained are comparable to the state-of-the-art interactive segmentation techniques on a standard dataset. [ABSTRACT FROM AUTHOR]

Quantum dot cellular automata (QCA) is an upcoming nano-technology for its high speed and low power operation in the field of nano-science and nano-electronics. As QCA overcomes the drawbacks of CMOS technology, it has appreciable applications in quantum computation. There are thousands of designs of different logical circuits using QCA but there is no hazard free design of the logical circuits in the field of QCA. In a circuit, hazards always produce an unpredictable output which can be avoided. In this paper, both hazardous and hazard-free asynchronous sequential circuits are considered and compared in terms of kink energy. It is shown that hazard free asynchronous circuit performs better in terms of kink energy in the field of QCA. [ABSTRACT FROM AUTHOR]

For nano scale logic circuit, device area and power consumption are the major concerns. In this paper, the design of an optimized SISO shift register is explored based on quantum-dot cellular automata (QCA) device. The design is achieved in a single layer. To design the shift register circuit, a new QCA D flip-flop has been proposed. These new D flip-flop has minimum cell count and lower device area over existing designs. The proposed shift register also outperforms the existing designs by reducing cell count and area. The power consumption by the proposed design is carried out that shows the low energy consumption nature of the circuit. Logic gate, QCA cell and device density have been considered to evaluate the circuit. Simulation timing diagram and truth table of the proposed circuits are compared which shows that all the designs are functioning efficiently. Stability under thermal randomness for all the designs are analyzed which shows the steadiness of the designs. The impacts of control input, i.e., clock on the designs are explored. Besides the defects in proposed QCA layouts are also identified and excelled for fault free implementation. [ABSTRACT FROM AUTHOR]

Cellular automata locally define dynamical systems, discrete in space, time and in the state variables, capable of displaying arbitrarily complex global emergent behavior. One core question in the study of cellular automata refers to their limit behavior, that is, to the global dynamical features in an infinite time evolution. Previous works have shown that for finite time evolutions, the dynamics of one-dimensional cellular automata can be described by regular languages and, therefore, by finite automata. Such studies have shown the existence of growth patterns in the evolution of such finite automata for some elementary cellular automata rules and also inferred the limit behavior of such rules based upon the growth patterns; however, the results on the limit behavior were obtained manually, by direct inspection of the structures that arise during the time evolution. Here we present the formalization of an automatic method to compute such structures. Based on this, the rules of the elementary cellular automata space were classified according to the existence of a growth pattern in their finite automata. Also, we present a method to infer the limit graph of some elementary cellular automata rules, derived from the analysis of the regular expressions that describe their behavior in finite time. Finally, we analyze some attractors of two rules for which we could not compute the whole limit set. [ABSTRACT FROM AUTHOR]

The updating scheme is very important for cellular automata, which costs the vast majority of CPU time of the whole calculating process and dramatically influences the convergence rate, and the traditional node sequence scheme is often ineffective. Aimed at this issue, based on the unbalanced nodal force, an adaptive updating scheme for continuous–discontinuous cellular automaton method is proposed in this paper. The cell whose unbalanced force is maximum is the first updating cell, and the next one is its neighbors whose unbalanced force is also maximum in all remain neighbors, and do the updating operation according to this scheme until the updating for all cells is finished. Theory and convergence of the present scheme is studied, then we can get the maximum calculating efficiency, and the computational expanse can be greatly improved. Some examples by different iteration schemes are shown to demonstrate that the present scheme is efficient and time saving. [ABSTRACT FROM AUTHOR]

Based on the thermal activation theory and curvature-driven mechanism, a 2D cellular automaton model with different state transition rules was built. Validity of the model was proved by the shrinking of circular grains. Grain growth of high temperature austenite was simulated by this model; the morphology, grain size distribution, topological aspects, and local kinetics of austenite grain growth were analyzed under different simulation time. Among the grains with different sides, the 6-sided grains are the most common and 5-sided grains are the second most common. The grains with more than six sides will grow and grains with less than six sides will shrink, while the 6-sided grains will neither grow nor shrink. The kinetics of normal grain growth follows the Burke equation and the growth exponent at different temperatures and activation energies has been researched. [ABSTRACT FROM AUTHOR]

This article is devoted to the application of the cellular automata mathematical apparatus to the problem of continuous optimization. The cellular automaton with an objective function is introduced as a new modification of the classic cellular automaton. The algorithm of continuous optimization, which is based on dynamics of the cellular automaton having the property of geometric symmetry, is obtained. The results of the simulation experiments with the obtained algorithm on standard test functions are provided, and a comparison between the analogs is shown. [ABSTRACT FROM AUTHOR]

Clustering aims to classify different patterns into groups called clusters . Many algorithms for both hard and fuzzy clustering have been developed to deal with exploratory data analysis in many contexts such as image processing, pattern recognition, etc. However, we are witnessing the era of big data computing where computing resources are becoming the main bottleneck to deal with those large datasets. In this context, sequential algorithms need to be redesigned and even rethought to fully leverage the emergent massively parallel architectures. In this paper, we propose a parallel implementation of the fuzzy minimals clustering algorithm called Parallel Fuzzy Minimal (PFM). Our experimental results reveal linear speed-up of PFM when compared to the sequential counterpart version, keeping very good classification quality. [ABSTRACT FROM AUTHOR]

The density classification task is a famous problem in the theory of cellular automata. It is unsolvable for deterministic automata, but recently solutions for stochastic cellular automata have been found. One of them is a set of stochastic transition rules depending on a parameter η, the traffic-majority rules. Here I derive a simplified model for these cellular automata. It is valid for a subset of the initial configurations and uses random walks and generating functions. I compare its prediction with computer simulations and show that it expresses recognition quality and time correctly for a large range of η values. [ABSTRACT FROM AUTHOR]

It is a great challenge to evaluate the network performance of cellular mobile communication systems. In this paper, we propose new spatial spectrum and energy efficiency models for Poisson-Voronoi tessellation (PVT) random cellular networks. To evaluate the user access to the network, a Markov chain based wireless channel access model is first proposed for PVT random cellular networks. On that basis, the outage probability and blocking probability of PVT random cellular networks are derived, which can be computed numerically. Furthermore, taking into account the call arrival rate, the path loss exponent and the base station (BS) density in random cellular networks, spatial spectrum and energy efficiency models are proposed and analyzed for PVT random cellular networks. Numerical simulations are conducted to evaluate the network spectrum and energy efficiency in PVT random cellular networks. [ABSTRACT FROM PUBLISHER]

This paper extends a recently proposed single-lane cellular automata (CA) model to simulate asymmetric two-lane traffic flow. The model incorporates drivers' individual characteristics and acceleration constraints of vehicles in the definition of lane changing decision process. The aim is to make the lane change process more in line with real features. Thus, the drivers' representation in the model is more like human response by reconstructing the local behavior on a microscopic level. Simulation results on a system with periodic conditions show that lane usage inversion, lane change rate versus density and different traffic states are reproduced by the model. Besides, in this model the flow is not dominated by the introduction of larger vehicles with smaller maximum velocity, even if their lane change is not prohibited. Moreover, ping-pong lane changes are reduced. The model preserves the computational simplicity of CA models. [ABSTRACT FROM AUTHOR]

Leader election plays a crucial role in numerous distributed protocols and biological societies. Yet, there is a fundamental gap in understanding its simplest variant employing components that are fully uniform, deterministic, and anonymous, such as in one-dimensional cellular automata (CA). In our previous work we found several binary CAs that elect a leader by using spatial-temporal patterns, domains and particles, which carry and exchange information across distance. The best strategy reached performance of 94-99% with a modulo 6 limitation for the number of cells. As opposed to state-of-the-art distributed algorithms, a CA consists just of binary components without any extra memory or communication capabilities, and therefore operates with minimal resources possible. In this paper we identify fundamental limitations of leader election for one-dimensional CAs and formulate an upper bound on performance. We show that configurations that are symmetric or loosely-coupled are insolvable. The proportion of configurations that are insolvable decreases dramatically with the system size. Our findings could help to design more effective distributed protocols and also to model biological processes such as morphogenesis of cell differentiation, where leader election breaks symmetry in a newly formed organism. [ABSTRACT FROM AUTHOR]

Research on asynchronous cellular automata has received a great amount of attention these last years and has turned to a thriving field. We present a state of the art that covers the various approaches that deal with asynchronism in cellular automata and closely related models. [ABSTRACT FROM AUTHOR]

The standard cellular automata (CA) model is based on three main features: locality, uniformity and synchronicity. Recently, some variants have been introduced, most of them consist in relaxing one of those three properties. In this paper, we study the dynamical behavior of m-ACA (using fair measures), a variant of cellular automata in which the synchronicity property has been relaxed. Inspired by literature about α-asynchronous CA (a special case of m-ACA), the paper focuses on doubly quiescent elementary rules i.e., rules with radius 1, boolean alphabet and such that homogeneuous configurations are fixed points. We show that for many of these rules, the limit behavior is fully characterized by a subshift of finite type. [ABSTRACT FROM AUTHOR]

Leakage squeezing is a new proposal for preventing side channel attacks mainly power analysis attacks of cryptographic implementations. It is reasoned that codes are important for leakage squeezing. But it is necessary to have certain cryptographic properties in leakage squeezing functions as argued in this paper. In this paper we analyze certain Cellular Automata (CA) configurations towards leakage squeezing. We show that certain nonlinear CA configurations have those cryptographic properties and thus are suitable in leakage squeezing applications. We also propose a new leakage squeezing scheme that uses encryption. This encryption based leakage squeezing generates masks using some symmetric key encryption process. It is demonstrated in this paper as well that scan attack on AES can be resisted using leakage squeezing. [ABSTRACT FROM AUTHOR]

Two fine-grained models of artificial biological cells are presented. They can be used as elements of computing devices that mimic properties of living organisms: growth, self-reproduction, and self-healing. The models are based on the parallel substitution algorithm that is a spatial model for representing fine-grained parallel algorithms and architectures. An artificial biological cell is constructed based on a genome fed to the input tape. The result is a model of artificial biological cell that contains the phenotype as a set of fixed data and the genotype as a set of mobile data. [ABSTRACT FROM AUTHOR]