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The emergence of data hiding schemes was to provide multimedia security besides the sharing of information in a confidential manner. Since then, various techniques have cropped up to meet the objective of secured data communication using multimedia documents. In this paper, a novel data hiding approach has been designed to exploit the superpixel of the image to identify the nature of the blocks for embedding data using spatial and transform domain techniques such as Cellular Automata (CA) and Discrete Cosine Transform (DCT) respectively. In addition, an Arnold transform is used to provide a blanket of security through a random selection of blocks via sharable keys. The proposed technique obtains YCbCr color model of an RGB image to use superpixel on only the Y color component. Thereafter, the acquired labeled image of superpixel is partitioned into ( 8 × 8 ) blocks which are to be categorised as homogeneous and heterogeneous blocks. Based on the categorization, DCT and CA are applied for the embedding of the secret data in the Cb and Cr color components. The experimental results and its comparison with state-of-the-art schemes uphold the effectiveness of the demonstrated scheme. Various analysis have been conducted to showcase the degree of robustness, imperceptibility, and visual quality attained by the proposed scheme.

Application of cellular automata (CA) to digital image processing has achieved considerable attention in last several years. CA are now employed in noise filtration of digital images; particularly many CA-based impulse noise filters have been proposed. Salt-and-pepper noise is a special kind of impulse noise and is introduced in an image during transmission over transmission media by external noise sources like atmospheric disturbances, or due to corrupted hardware memory locations or fault in camera sensors. In this paper, we present five salt-and-pepper noise filters based on modifications of outer totalistic cellular automata (OTCA) with the adaptive neighborhood. Use of OTCA model makes the proposed filters computationally simple on one hand and the use of adaptive neighborhood help the filters to provide efficient noise filtration at varying noise densities on the other. Comparative analysis of these filters followed by comparison with several standard and CA-based filters in terms of peak signal to noise ratio (PSNR) and structural similarity (SSIM) index is presented.

Glaucoma is the leading cause of blindness worldwide. Glaucoma being a chronic eye disease results in irreversible loss of vision. Everyone is at danger for glaucoma. But, certain groups are greater risk. Hence, accurate Classification of normal and glaucoma images is significant one. In the screening and early detection of glaucoma, Cup to Disc Ratio (CDR) method plays a significant role. The automatic segmentation for early detection from Optimal Coherence Tomography (OCT) images is a fundamental task. Several research works based on OCT were contributed for early detection of glacuoma. However, inherent feature extraction and over fitting issues while performing early glaucoma detection have been researched by the research community for long time. In this paper, we propose Artificial Intelligence architecture, called Phase Quantized Polar Transformed Cellular Automaton (PQPT-CA) method that use the displacement gradients and distance gradients for early glaucoma detection. The proposed PQPT-CA method comprises of inherent feature extraction, robust feature segmentation and early glaucoma detection based on OCT. To start with, First Order Phase Quantization model is applied to the raw input image for extracting inherent features. Next, with the extracted features, Differential Polar Transformation is applied to generate robust segmentation map. Finally, glaucoma detection is performed for three different state employing Cellular Automaton model. The experiments show that our PQPT-CA method achieves state-of-the-art early detection on ACRIMA database. Simultaneously, the proposed method also obtains the satisfactory glaucoma screening performances with calculated diagnosis accuracy on ACRIMA database. The PQPT-CA method increases the Diagnosis Accuracy by 24% and minimizes computational time and false positive rate by 21% and 54% respectively.

As urban traffic condition is diverse and complicated, maximizing the use of urban traffic signal control system to solve traffic issues becomes one of the hot and promising topics. Especially, how to control traffic signals at multiple intersections is a key challenge. To speed up urban traffic flow, this research presents a genetic timing scheduling model (GTSM) for urban traffic signal control. GTSM constructs cellular automata to update the timing cycles of traffic signals at multiple intersections, where state update functions are formulated to coordinate traffic signals. In addition, a proposed genetic optimization algorithm (GOA) in GTSM optimizes the timing cycles of traffic signals at multiple intersections in the dynamic timing optimization . The experimental results on urban road networks show that the performance of our model is excellent in various scenarios to control traffic signals to speed up urban traffic flow.

The graph model is a reliable propagation mechanism in saliency detection, saliency value propagation diffusion results between any two nodes are determined merely by defining an effect affinity matrix. Most existing methods generally calculamatrix using mean values of single or multiple feature vectors, not fully exploit the diversity and consistency of multi-view features, may produce poor foreground uniformity and completeness in the complex scene. Multi-views should share an affinity matrix as well as complement each other. In this paper, we propose a graph-based saliency detection with a learning joint affinity matrix. First, we capture multiple appearance features from the image and generate a learning joint affinity matrix based on low-rank representation. Then, for computing an effect affinity matrix, we linearly integrate the traditional affinity matrix and learning joint affinity matrix, helping to construct an affinity graph for diffusion-based compactness. Finally, to effectively optimize the initial saliency map, we diffuse the learning joint affinity matrix and traditional impact factor matrix via cross-view diffusion processing, which begets an approving advantage for single-layer cellular automata. Results on three benchmark datasets demonstrate that our proposed method shows the best performance against nine state-of-the-art models.

Nilpotent cellular automata have the simplest possible dynamics: all initial configurations lead in bounded time into the unique fixed point of the system. We investigate nilpotency in the setup of one-dimensional non-uniform cellular automata (NUCA) where different cells may use different local rules. There are infinitely many cells in NUCA but only a finite number of different local rules. Changing the distribution of the local rules in the system may drastically change the dynamics. We prove that if the available local rules are such that every periodic distribution of the rules leads to nilpotent behavior then so do also all eventually periodic distributions. However, in some cases there may be non-periodic distributions that are not nilpotent even if all periodic distributions are nilpotent. We demonstrate such a possibility using aperiodic Wang tile sets. We also investigate temporally periodic points in NUCA. In contrast to classical uniform cellular automata, there are NUCA—even reversible equicontinuous ones—that do not have any temporally periodic points. We prove the undecidability of this property: there is no algorithm to determine if a NUCA with a given finite distribution of local rules has a periodic point.

Image forgeries created using easy-to-use image editing tools like Adobe Photoshop have become prime sources of fake news, and are often used in malevolent ways in politics, courtrooms, and in scientific publishing as well. To reduce the problems caused by manipulated images in these domains, it is important to have reliable and fast mechanisms in place for verifying their authenticity before their consumption. Automated detection of image forgeries, is, therefore an ongoing challenge for the research community. One of a commonly used image forgeries is Copy-Move Forgery (CMF), where a region of an image is copied and pasted to some other location within the same image to alter its contents. To detect copy-move forgeries, several Copy Move Forgery Detection (CMFD) techniques have been proposed in the past. However, existing techniques are highly sensitive to post-processing attacks such as noise and compression. In this paper, we propose a new CMFD method in which Cellular Automata (CA) is used for feature extraction. The method works by dividing the input image into overlapping blocks and extracting features from these blocks using the CA inversion procedure. Then, the repeated regions are identified by matching the set of extracted feature vectors. The experimental results show that the detection under noise and compression attacks is accurate compared to many existing methods. As such, the proposed approach may be useful for a variety of CMFD scenarios and may have implications for researchers in related fields.

In cryptography, the previous research generally appears ambiguous and complex to the novice researcher due to the use of complex mathematical equations and rules of cryptography. Moreover, many research approaches lack flexibility in their key length or in their level of encryption. Consequently, combining simplicity, flexibility, and reliability is not easily obtainable in a cryptosystem, especially for larger and more complex data items. Therefore, a new system, called Flexible cryptosystem based on Cellular Automata (FcCA), is proposed here as a novel simplified flexible cryptosystem based on cellular automata (CA). FcCA presents simplified techniques for making CA reversible while creating a robust flexible cryptosystem that performs lossless encryption of three-dimensional (3D) objects and images of different types. It uses pure random CA as a diffusion technique, and it employs a modified existing confusion technique by substituting the static start point with proposed multi-dynamic intersected start points. In addition, FcCA novelty includes using a combination of aspects: random configuration with open boundary conditions, g-th order memory independent-cell technique, and classification of two parts of the encryption key into subkeys. The length and complexity of FcCA subkeys can be controlled easily because the subkeys depend on flexible parameters. Testing and validation of FcCA scrambling level were performed with several criteria including correlation, entropy, peak signal to noise ratio, and value difference degree. Experimental results showed that FcCA has high flexibility, a high level of scrambling, and higher robustness of keys compared to other methods of encryption. In addition, sensitivity analysis showed FcCA to be highly sensitive to changes in the encryption key and encrypted images and objects. Overall, the properties of FcCA demonstrated its effectiveness as a cryptosystem for images and 3D objects.

Natural and human-made features are not exclusive in settlements but interact across time and space, placing the context in constant evolution. The purpose of this paper is to search for the influ...

This study investigates the effects of sample size and sample prevalence on cellular automata (CA) simulation of urban growth. We take the CA models based on an artificial neural network (ANN), log...

Substantial studies have been conducted to simulate urban growth in the rapidly growing regions for planning and management. However, the difficulty remains in the establishment of urban growth mod...

Lots of emerging techniques can natively realize majority-of-three (MAJ) gates. Although enhanced gates such as exclusive-OR (XOR) gates can be implemented by several MAJ gates, there exists much efficient XOR design which directly derived by the physical attributes of emerging techniques. In this brief, we proposed multi-digit binary coded decimal (BCD) adder designs represented based on three-input exclusive-OR (XOR3) and MAJ gates. BCD adder is widely used in financial, commercial, and industrial computing. We implemented the designs using quantum-dot cellular automata (QCA) technology. The proposed logic representations are validated using different types of binary adders as well as QCA design strategies. The introduction of XOR3 gates is conducive to achieve compact logic representations, which positively affect both the area and delay of QCA layouts. Compared with the existing best designs, the proposed 1-digit BCD adder has 50% less area and 10% less delay, and the 8-digit BCD adder has a $3.42\times $ improvement of area-delay product.

Over the last decade, there has been an increase in air transportation, which has raised awareness to provide necessary procedures to respond to air transportation demands and reduce delay costs. One such procedure, “performance-based navigation (PBN),” currently studied, is expected to increase the airspace capacity based on aircraft's individual performances. This research describes an efficient and expandable model for allocating standard terminal arrival route during continuous descent approach (CDA) that effectively uses PBN requirements. As a case study, we have considered the “Atlanta International Airport” and verified the model's accuracy using real-time traffic data. The model is then used to study mixed traffic behavior concerning each aircraft's navigation capability. We show that our technique helps decrease time separation minima, reducing traffic delays by up to 50% during busy periods. Furthermore, we show that our approach could enhance target traffic flow by as much as 25% while maintaining proper separation minima. Finally, we show that near-optimal control advisory could also be obtained to minimize the target traffic flow's total delay.

A cellular automaton-finite element (CA-FE) model was used to predict the solidification grain structure of permanent mold cast A7086 alloy. In this model, a Gaussian distribution of nucleation sites was adopted, and the Kurz-Giovanola-Trivedi model was extended to a multicomponent Al–Zn–Mg–Cu alloy system to determine the growth kinetics of the dendrite tip. For describing the Gaussian distribution of nucleation sites on the mold surface, an empirical relationship between the initial cooling rate of the melt and the nucleation density was proposed. Under various casting conditions, the calculated grain structures agreed well with the experimental results. Subsequently, the model was applied to the direct-chill (DC) cast billets, and the simulated grain structures reproduced well the experimental results. This confirmed that the current CA-FE model can be used practically and effectively in DC casting processes.

The quantum-dot cellular automata (QCA) is considered to be one of the ground-breaking nanotechnologies developed over the last two decades. A layered T (LT) logic cell library is constructed herein, and the methodology is extended to generic adder and subtractor module designs. The two proposed algorithms lead to more efficient QCA layout designs for an n-bit ripple carry adder (RCA) and subtractor based on an effective clock zone assignment approach. The suggested one-, four-, and eight-bit RCAs and subtractors surpass most of their existing counterparts by offering lower effective area and cell complexity. A comparative analysis is presented regarding the complexity, irreversible power dissipation, and Costα of the proposed n-bit layouts from a cost estimation purview.

Brown Planthopper (BPH) is one of the most dangerous insects that cause damage to rice. Aphids infected rice fields with low productivity can be lost even. Dealing with this situation, the Plant Protection industry has invented the light trap - a device based on the specific activity of insects phototaxis. These measures are considered effective and less costly today. However, the current light traps are usually installed next to the home of the staff assigned to manage light traps for easy tracking without attention to the impact of environmental factors around. Currently, the plant protection industry wants more scientific basis in light traps arranged so they want to review and make the factors of climate and geography in the light traps installed but not yet performed. In this paper, we propose an approach to find appropriate positions to replace light traps based on a combination between weather factors and geographical factors with data on infected areas by BPH with various infection levels exhibited on the maps based on Cellular Automata method. We present the simulation results with 8 considered cases to determine positions for light traps in an area of more than 1400 square kilometres including 84 communes in Can Tho city, one of the largest rice granaries in Vietnam.

Quantum-dot cellular automata (QCA) has the capability to scale down beyond the range of CMOS. Besides wide acceptance of QCA, it suffers from different challenges, regular structure and configurability is one of them. The disparate design in QCA increases design complexity as well as cost. The disparity of design needs different clocking layout for the correct propagation of signals. Moreover, the interconnection of these non-symmetric designs also increases the routing difficulty in a specific, realistic clocking scheme. In this paper, configurable memory structures are investigated in QCA. First of all, a configurable level triggered flip flop (ConFF) is realized. The same ConFF is utilized to design configurable dual edge triggered flip-flop (EConFF) with some minor modification. Nine different logic functions can be produced using the same EConFF circuit which is not found in any existing QCA circuit.The proposed configurable structures are verified with QCADesigner 2.0.3.

In this paper, we have addressed a speed-area efficient VLSI implementation of a cellular automaton (CA) based random number generator (RNG) on Field Programmable Gate Arrays (FPGAs), in which each CA cell was proposed to be a multi-bit word in the original algorithm. This is in contrast to typical CA algorithms comprising one bit per CA cell. The original algorithm is shown favorable for FPGA implementations on adopting a fabric conscious approach involving instantiation of physical FPGA primitives. We have supplemented the original architecture with scan path and alternating logic to facilitate fault localization without area and delay overhead. The overheads have been carefully nullified by increasing the utilization ratio of the configured primitives, and exploiting the fast hardwired fabric of the FPGA. Generation of the hardware description of the RNG through Verilog has been automated. Our proposed designs outperform equivalent behavioral implementations expressed at higher levels of abstraction, both in speed and area.

Quantum-dot cellular automata (QCA) design forms a cellular alternative to the conventional CMOS design approach. In the present paper, a novel design of five input majority gate is proposed. The p...

Based on the characteristics of intelligent manufacturing and the theory of technology diffusion, this paper constructs a cellular automata model with government support policy, information exchange, technology maturity, diffusion intermediary, and market competition as the influencing factors and analyzes the influence mechanism of the first three main factors on the diffusion of intelligent manufacturing technology in industrial clusters using MATLAB. This paper also makes an empirical analysis of the diffusion of intelligent manufacturing technology in the bearing industry cluster in Xinchang County and finds that the results are basically consistent by comparing the simulation data with the fit degree of the real data. In this paper, the diffusion intermediary and government support policy have the greatest influence on the application of intelligent manufacturing in small- and medium-sized enterprises, and the model proposed in this paper is effective.

The paper concerns mathematical modeling and simulation of a continuous-flow stirred tank bioreactor (CSTBR) in which heterogeneous biofilm forms on its walls. The mathematical model couples a 2D cellular-automata biofilm model with differential equations describing the liquid phase in the bioreactor. Double-substrate aerobic phenol biodegradation by Pseudomonas putida bacteria was chosen as a process example. The influence of key process parameters on the dynamical characteristics of the bioreactor and the biofilm structure evolution during the process was investigated. Such an analysis has not been previously performed. It was shown that cell death strongly affects both biofilm heterogeneity and the technological properties of the bioreactor. Neglecting this phenomenon leads to improper design of the bioreactor. The influence of substrate load on the heterogeneity of the biofilm was evaluated in the process limited by two substrates. So far only a single substrate limitation has been evaluated in this regard. Even for large carbonaceous substrate concentrations, the biofilm growth and its structure are influenced by oxygen depletion. It was shown that an increased detachment rate increases bioreactor productivity.

Quantum-dot cellular automata (QCA) is an emerging nanotechnology to be a possible candidate for CMOS technology. There exist separate tools and algorithms for various kinds of design and simulation requirements of QCA circuits, but still lacks a unified software platform that can cover all the design stages. This brief describes a project that composes the state-of-the-art algorithms into a QCA circuit design tool suite named spars. The target of the project is establishing a fluent design flow that is similar to the CMOS circuit design process. Specifically, a new depth-oriented synthesis algorithm based on the majority inverter graph (MIG) is proposed. Besides, good software engineering ensures the easy extendability of spars, so that it provides a better starting point for the development of software design and circuit design in QCA.

Digital image communication over public networks requires a high level of security to protect picture elements that represent information. Security is an important and challenging issue that can be solved using cryptography techniques. Generally, image encryption techniques are based on multiple rounds and iterations. In this paper, a secured lightweight cryptosystem is designed based on lookup table operations that reduce computational overhead, resource requirement and power consumption compared to traditional security mechanisms. In this context, one-dimensional elementary cellular automaton has been combined with Henon chaotic map to design a cryptosystem, which can produce unprecedented results in cryptography. Initially, state attractors for rule space are investigated and analyzed in Wolfram’s cellular automata to extract the properties and functional abilities to perform cryptographic operations. A novel algorithm of keyed transposition cipher is applied to digital image in P-Box module to produce shuffled image. Then, the extracted properties of ECA are preserved in a tabular form and further used in the diffusion process. Based on the simulation and comparison with other existing mechanisms, it is evident that the proposed algorithm is promising and obstructive to all kinds of statistical attacks, and it yields security primacy in various areas of cryptography. Encryption/Decryption is based on indexed based lookup tables principal using ECA and can be easily implemented using logic gates. The proposed algorithm provides confidentiality and can be adopted in IoT networks that require lightweight cryptography modules. Experimental results of color and gray images demonstrate flourishing results in the real-time environment of cryptography.

Authors propose a hardware based approach for reliable encryption in wireless network using Cellular Automata (CA). Distinct layers of encryption have been utilized in proposed model for enhancing data security. Transmitter and receiver modules are designed for performing state transition based encrypted activities. Transmitter module is required for capturing environmental turbulences (noise) from a specific geographical location and send encrypted signal to receiver module through wireless network. Receiver module analyses received signal after decryption for desired activities based on real time situations. Different hardware components are selected and compared based on market study targeting efficient construction of proposed modules. Time complexity and code break complexity are measured using different key lengths, keysets, and data sizes for proposed CA based encryption scheme achieving unique results in hardware based cryptography system. Encryption key length, index, and keyset are required for computing code break complexity in design model. Large data size is effectively managed to calculate protection ratio and reliability factor for realizing efficient protectiveness and accuracy. Comparative study between existing encryption technique and proposed CA based encryption technique is established based on time complexity and code break complexity.

Quantum-dot cellular automata (QCA) based circuit designs are creating a surge in transistorless computational technologies. Due to its quasi-adiabatic switching resulting in extremely low leakage power dissipation as there is no continuous path. These circuits also enjoy extremely high packaging density of the order of 10 $$^{12}$$ devices/ $$\hbox {cm}^2$$ because of its extremely scaled area of 18 nm x 18 nm along with very high 100 GHz frequency of operation. Further the loss of bit information could be abolished by reversible logic computing. This thereby realizes an energy efficient logic operations owing to bijective relation between inputs and outputs in reversible logic. This work investigates the code converter circuits which converts 4-bit binary code to excess-3 code and vice versa based on reversible QCA logic gates for the first time. Moreover an area efficient design for 4-bit binary to gray and vice-versa code converters also designed here. All these four code converter circuits are designed using reversible logic gate Feynman and Peres gates by deploying the QCA Designer and Designer-E tool v2.0.3. Finally the in depth performance estimation of the proposed circuits in terms of circuit complexity, quantum cost and energy dissipation are also presented here. Moreover, these QCA based circuits provide a strong evidence that reversible logic based QCA circuits can be efficiently deployed for these code converter circuits.

Microstructure evolution of columnar dendrites during isothermal coarsening is simulated by a three-dimensional (3-D) cellular automaton (CA) model. The 3-D CA model incorporates the thermodynamics, and kinetics aspects influenced by the factors of temperature, composition, and interfacial weighted mean curvature. After quantitative validation through grid independence test and the liquid pool migration problem, the 3-D CA model is applied to simulate the dendrite coarsening phenomena caused by the simultaneous melting and solidification for a SCN-2.0 wt.% ACE alloy in an isothermal environment. Several typical features of dendrite coarsening occurring in the 3-D space are reproduced by the CA model, including melting of tertiary and small secondary branches, interdendritic groove base advancement, and coalescence of neighboring dendrite arms. The simulated dendritic microstructure compares well with the in situ experimental observation. Besides, the simulated specific surface area of the solid/liquid interface decreases with time following a power-law relation with an exponent of 1/3. The simulated distribution curve of the interfacial weighted mean curvatures becomes narrower with time, and the peak gradually shifts to zero, showing that the S/L interface is flattened during the isothermal dendrite coarsening. The average thickness of the primary dendrite arm and the secondary dendrite arm spacing (λ2) are found to increase with time, and the third power of λ2 shows a nearly linear relationship with time. The CA model not only visualizes morphological evolution of the dendrite arms, but also provides insight into the complex interaction among local melting/solidification, solute diffusion, and variation of weighted mean curvature in the 3-D space.

In order to study the influence of travelers’ self-adaptive adjustment behavior on transportation network under the assumption of bounded rationality, using cellular automaton to discretize the selection model under the analytic paradigm in the existing research, abstract each cell into a traveler, and describe the traveler characteristics with finite rationality characteristics through the travel risk attitude and travel generalized cost budget. Cellular automata and cumulative prospect theory is used to establish the travel route choice model, giving the dynamic evolution process of different reference points for travelers and taking the actual regional transportation network of Sichuan Tibet region in China as the study object, analyzes the impact of bounded rational travel behavior on route choice. The model and algorithm proposed in our study can not only guide the transportation organization of Sichuan Tibet region, but also provide theoretical support for the implementation of regional transportation planning and traffic control scheme in the future.

The complex behaviors in cellular automata have been widely developed in recent years to generate and analyze automata that produce space-moving patterns or gliders that interact in a periodic background. This type of automata has been frequently found either by conducting an exhaustive search or through a meticulous construction of the evolution rule. In this study, the specification of cellular automata with complex behaviors was obtained by utilizing randomly generated specimens. In particular, it was proposed that a cellular automaton of n states should be specified at random and then extended to another automaton with a higher number of states so that the original automaton operates as a periodic background where the additional states serve to define the gliders. Moreover, this study presents an explanation of this method. Furthermore, the random way of defining complex cellular automata was studied by using mean-field approximations for various states and local entropy measures. This specification was refined with a genetic algorithm to obtain specimens of a higher degree of complexity. By adopting this methodology, it was possible to generate complex automata with hundreds of states, demonstrating the fact that randomly defined local interactions with multiple states can construct complexity.

A Cellular Automata Finite Difference (CAFD) method was applied to analyze the effects of substrate grain size and texture on the microstructure and texture evolution during additive manufacturing. It was found that the grain size within the substrate is only effective at a short distance. Grains developed from the surrounding powders can lead to an almost similar microstructure away from the substrate. Texture evolution during build-up can be explained by the two main components of {001} and {011}. Development of the former and the latter components can be described by the competitive growth mechanism at the regions beneath and on the sides of the melt-pool, respectively. As a result, the former component is more pronounced at the higher ratios of the melt-pool width to the hatch distance. An almost similar fiber texture can develop from a substrate with the fiber texture of {001}||build-direction (BD). However, a substrate having the fiber {011}||BD evolves into the component {011}, implying that other components of this fiber are not favored. In either case, due to the highly elongated melt-pool, the orientation {001}||hatch-direction (HD) is preferred and {011}||HD is not likely to happen.

Although the neighborhood of the cellular automata (CA) model has been studied in detail, there is a contradiction in the selection of the neighborhood size that has not been revealed and addressed...

Signal machines form an abstract and idealized model of collision computing. Based on dimensionless signals moving on the real line, they model particle/signal dynamics in Cellular Automata. Each particle, or signal , moves at constant speed in continuous time and space. When signals meet, they get replaced by other signals. A signal machine defines the types of available signals, their speeds, and the rules for replacement in collision. A signal machine A simulates another one B if all the space-time diagrams of B can be generated from space-time diagrams of A by removing some signals and renaming other signals according to local information. Given any finite set of speeds S we construct a signal machine that is able to simulate any signal machine whose speeds belong to S . Each signal is simulated by a macro-signal , a ray of parallel signals. Each macro-signal has a main signal located exactly where the simulated signal would be, as well as auxiliary signals that encode its id and the collision rules of the simulated machine. The simulation of a collision, a macro-collision , consists of two phases. In the first phase, macro-signals are shrunk, and then the macro-signals involved in the collision are identified and it is ensured that no other macro-signal comes too close. If some do, the process is aborted and the macro-signals are shrunk, so that the correct macro-collision will eventually be restarted and successfully initiated. Otherwise, the second phase starts: the appropriate collision rule is found and new macro-signals are generated accordingly. Considering all finite sets of speeds S and their corresponding simulators provides an intrinsically universal family of signal machines.

Motion-Compensated Frame Interpolation (MCFI) is one of the common temporal-domain tamper operations, and it is used to produce faked video frames for improving the visual qualities of video sequences. The instability of temporal symmetry results in many incorrect Motion Vectors (MVs) for Bidirectional Motion Estimation (BME) in MCFI. The existing Motion Vector Smoothing (MVS) works often oversmooth or revise correct MVs as wrong ones. To overcome this problem, we propose a Cellular Automata-based MVS (CA-MVS) algorithm to smooth the Motion Vector Field (MVF) output by BME. In our work, a cellular automaton is constructed to deduce MV outliers according to a defined local evolution rule. By performing CA-based evolution in a loop iteration, we gradually expose MV outliers and reduce incorrect MVs resulting from oversmoothing as many as possible. Experimental results show the proposed algorithm can improve the accuracy of BME and provide better objective and subjective interpolation qualities when compared with the traditional MVS algorithms.

This paper attempts to propose a one-time pad block encryption using non-uniform cellular automata for the public cloud which satisfies avalanche diffusion and confusion properties. It is found tha...

Sensor nodes encompass the capability of controlling and monitoring the environment by measuring the factors, like temperature, pressure, and humidity. Ensuring secure routing in the network is an ...

The traveling salesman problem (TSP), a typical non-deterministic polynomial (NP) hard problem, has been used in many engineering applications. Genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. However, it has some issues for solving TSP, including quickly falling into the local optimum and an insufficient optimization precision. To address TSP effectively, this paper proposes a hybrid Cellular Genetic Algorithm with Simulated Annealing (SA) Algorithm (SCGA). Firstly, SCGA is an improved Genetic Algorithm (GA) based on the Cellular Automata (CA). The selection operation in SCGA is performed according to the state of the cell. Secondly, SCGA, combined with SA, introduces an elitist strategy to improve the speed of the convergence. Finally, the proposed algorithm is tested against 13 standard benchmark instances from the TSPLIB to confirm the performance of the three cellular automata rules. The experimental results show that, in most instances, the results obtained by SCGA using rule 2 are better and more stable than the results of using rule 1 and rule 3. At the same time, we compared the experimental results with GA, SA, and Cellular Genetic Algorithm (CGA) to verify the performance of SCGA. The comparison results show that the distance obtained by the proposed algorithm is shortened by a mean of 7% compared with the other three algorithms, which is closer to the theoretical optimal value and has good robustness.

In this paper, we propose a hyperchaotic image encryption system based on particle swarm optimization algorithm (PSO) and cellular automata (CA). Firstly, to improve the ability to resist plaintext attacks, the initial conditions of the hyperchaotic system are generated by the hash function value which is closely related to the plaintext image to be encrypted. In addition, the fitness of PSO is the correlation coefficient between adjacent pixels of the image. Moreover, On the basis of hyperchaotic system, cellular automata technology is adopted, which can enhance the randomness of population distribution and increase the complexity and diversity of the population so that the security of the encryption system can be improved and avoid falling into local optimum. The simulation results and security analysis of the proposed encryption system demonstrate that the hyperchaotic image encryption system has high resistance against plaintext attack and statistical attack.

The objective is to demonstrate that a probabilistic cellular automata rule can place reliably a maximal number of dominoes in different active area shapes, exemplarily evaluated for the square and diamond. The basic rule forms domino patterns, but the number of dominoes is not necessarily maximal and the patterns are not always stable. It works with templates derived from domino tiles. The first proposed enhancement (Rule Option 1) can form always stable patterns. The second enhancement (Rule Option 2) can maximize the number of dominoes, but the reached patterns are not always stable. All rules drive the evolution by specific noise injection.

People can post their comments on public events on the Internet, such as their ideas, emotions, and attitudes that can affect others. Online public opinions may affect the stability or security of the country because of the speed and convenience of information disseminating on the Internet. This article proposes the public opinion cellular automata for situation deduction to predict the possible trending of public events. In the cellular automata, online users are represented by cells, and their eigenvalues are calculated from the user’s historical comment data. The cell and their neighbors form a cellular space, whose topology is a directed graph. We set the state of each cell based on its attributes and initialize the cellular automata. The automata can deduce public opinion and predict the trend of public opinion by following the set evolutionary rules in advance. Experiments with the real data from Sina Weibo online users show that the cells in the cellular automata can accurately simulate users’ behavior, and the deduction results are close to the trend of real historical events. The cellular automata-based prediction of the number of participants and emotional trending are more accurate than other methods.

Negative effects of haze risk can easily spread faster and more widely, however, existing studies rarely investigate the whole period or cycle of diffusion events, which leads to lowered public knowledge, often resulting in exaggerated negative effects. In this paper, the diffusion simulation model based on cellular automata is used to evaluate the diffusion of haze risk information. Firstly, according to the whole-life cycle of emergencies, the public affected by haze risk information is classified by resembling the SEIR infectious disease model. Secondly, a diffusion rule from unknown to exposed individuals is developed based on the theory of cellular automata. Then, according to the individual state transformation at different stages, a whole-life cycle model regarding haze risk information diffusion and propagation model is constructed. Finally, appropriate parameters are selected to calculate the results without intervention. The results show that during the whole evolution process, the unknowns continue to decrease, and the lurkers continue to increase. Due to the existence of the immunization period, the immunized persons reach their maximum number before they are about to lose immunity, and the number of communicators reaches their minimum. Afterward, the number of immunized persons reduced to a stable level, and the number of communicators continues to increase toward an agglomeration benefit. Therefore, in order to achieve effective control of the spread of haze risk information, strong and weak control measures are taken for each type of individuals, and the immunity of unknowns and lurkers is increased for individual type control. For the entire information diffusion control, increase communicator immunity and reduce immunization conversion rate. The study of the spread of haze risk information helps to increase the public's sense of responsibility, helps to improve the government's credibility, and contributes to the establishment of a harmonious society.

Input data is one of the main sources of uncertainty in Land Use Cover Change (LUCC) modelling. Research has focussed on the sensitivity of LUCC models to the spatial resolution of Land Use Cover (...

SUMMARY The dynamic disturbance from explosion, earthquakes and stress impact may induce rock mass damage around the deep-buried tunnel. Here, we show the dynamic responses triggered by blasting load disturbance. In this paper, the theoretical formulae are first derived to evaluate the time-domain dynamic responses based on the wave function expansion method and the inversion of Laplace transform and then a self-developed numerical code, that is, elasto-plastic cellular automaton (EPCA), a module of CASRock, is subsequently employed to simulate the blasting induced elasto-plastic dynamic responses. It can be found from the analytical solutions that tensile stress concentration at two sidewalls occurs, while compressive counterpart at the roof and floor appears. Moreover, the radial velocity vibrations generate concentration at the two sidewalls, while the circumferential counterparts appear at the roof and floor. The numerical simulations indicate that compression-shear and tensile failure are the major mechanism for the rock mass damage around the rock tunnel. The analytical and numerical results help to understand the velocity vibrations and the major mechanism of rock mass damage and provide theoretical basis for the support of rock tunnel.

Quantum-dot cellular automata (QCA) technology has been known as an appropriate paradigm for implementing low power consumption digital circuits and different high-performance computations at nanoscale. Considering its nature, this technology has shallow energy losses. Besides, an adder is one of the main parts in the digital circuit designs. Moreover, full adder circuits are the basic units in the digital logic and arithmetic circuits. An emerging three-layer four-bit QCA-based carry-save adder (CSA) circuit is presented in this article. The proposed design offers good performance regarding the delay, area size, and cell number compared to existing ones. The suggested four-bit QCA-based CSA circuit has been depending on a new dedicated QCA full adder circuit. The proposed architectures are simulated with the utilization of the QCADesigner tool version 2.0.3. The outcomes of the simulation have proven that the improved circuit provides a significant development for cell number and area possession compared to previous single-layer and multilayer circuits, and the design leads to around 33.9% improvement in cell number in comparison with the best-presented QCA-based CSA design. Moreover, the proposed CSA architecture utilizes 347 QCA cells.

The traffic flow prediction using cellular automata (CA) is a trendy research domain that identified the potential of CA in modelling the traffic flow. CA is a technique, which utilizes the basic units for describing the overall behaviour of complicated systems. The CA model poses a benefit for defining the characteristics of traffic flow. This paper proposes a modified CA model to reveal the prediction of traffic flows at the signalised intersection. Based on the CA model, the traffic density and the average speed are computed for studying the characteristics and spatial evolution of traffic flow in signalised intersection. Moreover, a CA model with a self-organizing traffic signal system is devised by proposing a new optimization model for controlling the traffic rules. The Sunflower Cat Optimization (SCO) algorithm is employed for efficiently predicting traffic. The SCO is designed by integrating the Sunflower optimization algorithm (SFO) and Cat swarm optimization (CSO) algorithm. Also, the fitness function is devised, which helps to guide the control rules evaluated by traffic simulation using the CA model. Thus, the cellular automaton is optimized using the SCO algorithm for predicting the traffic flows. The proposed Sunflower Cat Optimization-based cellular automata (SCO-CA) outperformed other methods with minimal travel time, distance, average traffic density, and maximal average speed.

The feasibility of integrating urban biophysical descriptors into land surface temperature (LST) prediction in a rapidly urbanizing region, Jinan, China, was explored. We combined several models, including LST and an urban biophysical descriptor generation algorithm, through regression analysis and cellular automata-Markov modeling. First, the relationship between the indices, including the soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI), index-based built-up index (IBI), and the LST was established; then, the future LST was simulated and predicted. The results showed that LST has a negative correlation with SAVI (R2 = 0.905, P

Quantum-dot Cellular Automata (QCA) is a new model at Nano-scale for conceivable replacement of conventional Complementary Metal–Oxide–Semiconductor (CMOS) technology. It is the transistor less computation approach and feasible contestant for “Beyond CMOS” device technology. This paper presents the redesigning of arithmetic logic, in particularly the possible implementation of multiplier circuits using Quantum-dot Cellular Automata. The three-way majority gate and an inverter are the basic QCA elements. The mapping of logical circuits to their QCA equivalents via direct replacement of AND and OR gates with partially utilized majority gates (i.e., with a ‘0’ and ‘1’ input, respectively) leads to use of QCA basic components. In this paper, we propose a programmable QCA decimal multiplier that is composed of fully utilized majority gates. The proposed circuit designed and tested by QCA Designer.

In order to develop systems capable of artificial evolution, we need to identify which systems can produce complex behavior. We present a novel classification method applicable to any class of deterministic discrete space and time dynamical systems. The method is based on classifying the asymptotic behavior of the average computation time in a given system before entering a loop. We were able to identify a critical region of behavior that corresponds to a phase transition from ordered behavior to chaos across various classes of dynamical systems. To show that our approach can be applied to many different computational systems, we demonstrate the results of classifying cellular automata, Turing machines, and random Boolean networks. Further, we use this method to classify 2D cellular automata to automatically find those with interesting, complex dynamics. We believe that our work can be used to design systems in which complex structures emerge. Also, it can be used to compare various versions of existing attempts to model open-ended evolution (Ray (1991), Ofria et al. (2004), Channon (2006))., Comment: 15 pages. arXiv admin note: substantial text overlap with arXiv:2008.13503

The paper considers cellular automata and forms of reflection of their evolution. Forms of evolution of elementary cellular automata are known and widely used, which allowed specialists to model different dynamic processes and behavior of systems in different directions. In the context of the easy construction of the form of evolution of elementary cellular automata, difficulties arise in representing the form of evolution of two-dimensional cellular automata, both synchronous and asynchronous. The evolution of two-dimensional cellular automata is represented by a set of states of two-dimensional forms of cellular automata, which complicates the perception and determination of the dynamics of state change. The aim of this work is to solve the problem of a fixed mapping of the evolution of a two-dimensional cellular automaton in the form of a three-dimensional representation, which is displayed in different colors on a two-dimensional image The paper proposes the evolution of two-dimensional cellular automata in the form of arrays of binary codes for each cell of the field. Each time step of the state change is determined by the state of the logical "1" or "0". Moreover, each subsequent state is determined by increasing the binary digit by one. The resulting binary code identifies the color code that is assigned to the corresponding cell at each step of the evolution iteration. As a result of such coding, a two-dimensional color matrix (color image) is formed, which in its color structure indicates the evolution of a two-dimensional cellular automaton. To represent evolution, Wolfram coding was used, which increases the number of rules for a two-dimensional cellular automaton. The rules were used for the von Neumann neighborhood without taking into account the own state of the analyzed cell. In accordance with the obtained two-dimensional array of codes, a discrete color image is formed. The color of each pixel of such an image is encoded by the obtained evolution code of the corresponding cell of the two-dimensional cellular automaton with the same coordinates. The bitness of the code depends on the number of time steps of evolution. The proposed approach allows us to trace the behavior of the cellular automaton in time depending on its initial states. Experimental analysis of various rules for the von Neumann neighborhood made it possible to determine various rules that allow the shift of an image in different directions, as well as various affine transformations over images. Using this approach, it is possible to describe various dynamic processes and natural phenomena.

In this work a novel model to simulate advanced malware spreading is introduced and analyzed. It is an individual-based model such that the dynamics of the malware outbreak is governed by means of a cellular automaton. The network topologies considered are complex random networks and each device is endowed at every step of time with one of the following possible states: susceptible, infected, attacked and recovered. A study analyzing the influence of topology variability and the structural characteristics of initially infected devices is done.

The design and understanding of quantum-dot cellular automata (QCA) networks has been largely influenced by limitations in the approximation methods used in common design tools. In some cases, such limitations have led to unrealistic selections of clock zones which are not feasible for nanoscale QCA implementations given current fabrication constraints on clocking electrodes. A better understanding of the behaviour of larger QCA networks of perhaps tens to hundreds of QCA devices is needed. One approach is by investigating the low energy spectrum; however, diagonalization of the system Hamiltonian even in the 2-state approximation is impractical beyond 20 or so devices. In this work, we present a methodology for understanding the spectrum of the full network in terms of contributions from components of the network. We show that important features of the low energy spectrum can be attributed to specific critical components, and present one scheme for decomposing the network into these components. In addition, we address the question of computing the low energy spectrum of large QCA networks. A method based on basis reduction which naturally emerges from the component decomposition is successfully applied to a 49 cell XOR gate with results compared against a density matrix renormalization group implementation.

The use of smartphones has become an inherent part of daily human life. It allows users to keep personal information, emails, pictures, social media accounts, and financial data in one place. Consequently, smartphones are an attractive target for malware developers to spread malicious content, aiming at extracting information without the user’s knowledge. Therefore, understanding malware propagation characteristics could provide a means to evaluate how they behave in order to plan security solutions accordingly. Bluetooth antennas are a channel for spreading malware through smartphones, where the probability of infection, similar to biological viruses, depends mainly on the attacker’s physical proximity. This work presents a model based on cellular automata and epidemiological compartmental models for studying the spatial and temporal propagation of Bluetooth worms in smartphones. The proposed model incorporates the individual characteristics of each device, such as security settings, latency time, operating system, different classes of Bluetooth antennas (range and transfer rate), and different user mobility patterns. Several simulation scenarios are analyzed in order to study the spreading dynamics of Bluetooth-based worms, considering the location where the outbreak begins, and the different types of antennas integrated into the smart devices. Simulation results indicated that the proposed model is appropriate for studying how the users’ demographics affect the worm’s propagation dynamics in time and space. Moreover, the model permits an analysis of the impact of users’ awareness about the risks inherent in using smart devices in Bluetooth networks, based on the acceptance of incoming communication and the effects of recovery and immunity to threats. Finally, the proposed model preserves simplicity and computational efficiency, with the possibility of extending beyond Bluetooth in order to include other transmission media.