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Multilevel thresholding is a basic method in image segmentation. The conventional image multilevel thresholding algorithms are computationally expensive when the number of decomposed segments is high. In this paper, a novel and powerful technique is suggested for Crow Search Algorithm (CSA) devoted to segmentation applications. The main contribution of our work is to adapt Darwinian evolutionary theory with heuristic CSA. First, the population is divided into specified groups and each group tries to find better location in the search space. A policy of encouragement and punishment is set on searching agents to avoid being trapped in the local optimum and premature solutions. Moreover, to increase the convergence rate of the proposed method, a gray-scale map is applied to out-boundary agents. Ten test images are selected to measure the ability of our algorithm, compared with the famous procedure, energy curve method. Two popular entropies i.e. Otsu and Kapur are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are implemented and compared to the introduced method. The obtained results show that our method, compared with the original CSA, and other heuristic search methods, can extract multi-level thresholding more efficiently. [ABSTRACT FROM AUTHOR]

Inspite of technological advancement, inherent processing capability of current age sensors limits the desired details in the acquired image for variety of remote sensing applications. Pan-sharpening is a prominent scheme to integrate the essential spatial details inferred from panchromatic (PAN) image and the desired spectral information of multispectral (MS) image. This paper presents an effective two-stage pan-sharpening method to produce high resolution multispectral (HRMS) image. The proposed method is based on the premise that the HRMS image can be formulated as an amalgam of spectral and spatial components. The spectral components are estimated by processing the interpolated MS image with a filter approximated with modulation transfer function (MTF) of the sensor. Sparse representation theory is adapted to construct the spatial components. The high-frequency details extracted from the PAN image and its low resolution variant are utilized to construct dual dictionaries. The dictionaries are jointly learned by an efficient training algorithm to enhance the adaptability. The hypothesis of sparse coefficients invariance over scales is also incorporated to reckon the appropriate spatial information. Further, an iterative filtering mechanism is developed to enhance the quality of fused image. Four distinct datasets generated from QuickBird, IKONOS, Pléiades and WorldView-2 sensors are used for experimentation. The comprehensive assessment at reduced-scale and full-scale persuade the effectiveness of proposed method in the retention of spectral information and intensification of the spatial details. [ABSTRACT FROM AUTHOR]

The size and shape of the foveal avascular zone (FAZ) have a strong positive correlation with several vision-threatening retinovascular diseases. The identification, segmentation and analysis of FAZ are of great significance to clinical diagnosis and treatment. We presented an adaptive watershed algorithm to automatically extract FAZ from retinal optical coherence tomography angiography (OCTA) images. For the traditional watershed algorithm, "over-segmentation" is the most common problem. FAZ is often incorrectly divided into multiple regions by redundant "dams". This paper analyzed the relationship between the "dams" length and the maximum inscribed circle radius of FAZ, and proposed an adaptive watershed algorithm to solve the problem of "over-segmentation". Here, 132 healthy retinal images and 50 diabetic retinopathy (DR) images were used to verify the accuracy and stability of the algorithm. Three ophthalmologists were invited to make quantitative and qualitative evaluations on the segmentation results of this algorithm. The quantitative evaluation results show that the correlation coefficients between the automatic and manual segmentation results are 0.945 (in healthy subjects) and 0.927 (in DR patients), respectively. For qualitative evaluation, the percentages of "perfect segmentation" (score of 3) and "good segmentation" (score of 2) are 99.4% (in healthy subjects) and 98.7% (in DR patients), respectively. This work promotes the application of watershed algorithm in FAZ segmentation, making it a useful tool for analyzing and diagnosing eye diseases. [ABSTRACT FROM AUTHOR]

In this paper, a secure and low energy dynamic slicing algorithm, namely, Improved D-SMART (IM-D-SMART) based on the Data Aggregation Protocol on Slice Mix Aggregate (D-SMART) is proposed to improve the security and confidentiality of wireless sensor networks and reduce the energy consumption of nodes in data collection and transmission in wireless sensor networks. According to the importance of data, the residual energy of nodes and the relative density of nodes, the data are dynamically partitioned to improve the D-SMART algorithm. Simultaneously, sending negative number splicing is used to compensate for the loss caused by the collision of data transmission between nodes. The simulation results show that IM-D-SMART outperforms D-SMART in terms of computation, privacy and communication cost. [ABSTRACT FROM AUTHOR]

Chaotic maps have been widely used in image encryption due to their complexity, pseudorandomness and high sensitivity to initial values. In this paper, an image encryption algorithm based on a 4D chaotic map and steganography is proposed. The algorithm consists of two rounds of encryption and one operation of embedding hash. In the first round of encryption, the hash value of plaintext image is used to control the generation of chaotic sequences, which makes the encryption algorithm highly relevant to the content of the image. Then, the hash value is embedded into the intermediate image according to the idea of steganography. In the second round of encryption, the image is encrypted under the control of another set of chaotic sequences to hide the hash value and further enhance the security of the algorithm. Different from other algorithms, our algorithm does not need to additionally transmit the hash value to the decryptor through a special channel. It has good availability and adaptability. Experimental results and security analysis demonstrate that the algorithm has high security performance and can resist various attacks. [ABSTRACT FROM AUTHOR]

The three-level inverter is extensively preferred in medium-voltage industrial applications over the conventional inverter due to its benefits. The performance of inverter is better when the space vector modulation (SVM) approach is preferred. However, the implementation of conventional SVM approach is more complex. In this paper, a decomposition-based new SVM algorithm for three-level inverter is proposed to reduce computational complexity and implementation time using a low-cost digital controller. Initially, the performance of the proposed SVM algorithm is studied through MATLAB simulation using the conventional switching sequence 0127. Further enhancing the performance, the proposed algorithm is implemented with various advanced discontinuous SVPWM strategies using the advanced switching sequences of 1012–2721 and 0121–7212. The no-load motor current THD and switching loss analysis for ADSVPWM and conventional SVPWM strategies at different modulation indices are presented is this paper for a comparison point of view. The no load motor current THD results obtained through simulation are validated experimentally by implementing the proposed algorithm on real-time interface tool through dSPACE, which is used to control constant V/F-controlled three-level inverter-fed induction motor. Further to show the effectiveness of the proposed technique, the experimental investigation is done on induction motor at loaded condition and the results are presented. [ABSTRACT FROM AUTHOR]

In shallow-water waveguide, the normal mode information of acoustic signals plays an important role in localization research. In this paper, an extraction algorithm of modal intensity distribution based on horizontal circular array is proposed. By using the horizontal wavenumber of each mode, the modal domain beamforming is used to estimate the modal intensity distribution of the acoustic signal. The theoretical derivation, data simulation and experimental data are used to verify the feasibility of the algorithm based on circular array and the superiority compared with the line array extraction algorithm. At the last part of the paper, an application based on modal intensity distribution is proposed: water surface interference suppression, and the effectiveness of the interference suppression algorithm is verified from theoretical derivation and simulation experiments. [ABSTRACT FROM AUTHOR]

Skeleton is a new notion designed for constructing space-filling curves of self-similar sets. In a previous paper by Dai and the authors [Space-filling curves of self-similar sets (II): Edge-to-trail substitution rule, Nonlinearity32(5) (2019) 1772–1809] it was shown that for all the connected self-similar sets with a skeleton satisfying the open set condition, space-filling curves can be constructed. In this paper, we give a criterion of existence of skeletons by using the so-called neighbor graph of a self-similar set. In particular, we show that a connected self-similar set satisfying the finite-type condition always possesses skeletons: an algorithm is obtained here. [ABSTRACT FROM AUTHOR]

Generally, the blades of wind turbine installed in cold regions often encounter the situation of icing on their surface in winter, which affects the performance of wind turbine greatly and reduces power generation. Blade icing of wind turbine is a typical alarm related to multivariates, such as environment temperature, humidity, blade rotation and so on. To successfully achieve a supervised classification, it is necessary to extract the correlated features with blade icing. This paper proposes an extraction algorithm using reward extremum which can extract a group of variables correlated to blade icing from many monitoring variables of wind turbine, and they affect each other. This algorithm, using reward function which can measure the reward or value of alarm problem, can effectively distinguish the variables correlated to blade icing from the uncorrelated ones without knowing the mean of variables and the relations between them. Whether the blade of wind turbine is iced or not is a binary logic, so logistic regression algorithm is better choice to detect blade icing. Hence, this paper proposes a hybrid model using logistic regression algorithm and extraction algorithm using reward extremum, which can be built by machine learning with historic monitoring data. This model can real-time detect blade icing by real-time monitoring data. A correlation variable set can be obtained by the application of extraction algorithm using reward extremum on the historic monitoring data; and then the weights of the correlation variables in the set can be gained from the historic data by using logistic regression algorithm, so a blade icing detection pattern can be found using logistic regression algorithm; finally, whether the blade of wind turbine is iced in delay time or not can be real-time detected with the help of the hybrid model by real-time monitoring data. It is proved by wind turbine data that this hybrid model can work well not only to get correlation variable set but also to improve the generalization performance of logistic regression and achieve better prediction results. The performance of the model on blade icing alarm of wind turbine provides a new way to find out multivariate correlation to alarm from the mass historic monitoring data. [ABSTRACT FROM AUTHOR]

Sorting networks are a class of parallel oblivious sorting algorithms. Not only do they have interesting theoretical properties but they can be fabricated. A sorting network is a sequence of parallel compare-exchange operations using comparators which are grouped into stages. This underlying graph defines the topology of the network. The majority of results on sorting networks concern the unrestricted case where the underlying graph is the complete graph. Prior results are also known for paths, hypercubes, and meshes. In this paper we introduce a sorting network whose underlying topology is a tree and formalize the concept of sorting networks on a restricted graph topology by introducing a new parameter for graphs called its sorting number. The main result of the paper is a description of an O (min (n Δ 2 , n 2)) depth sorting network on a tree with maximum degree Δ. [ABSTRACT FROM AUTHOR]

With the increase of emergencies in large public places, emergency evacuation research has become an important and urgent issue. This paper first proposes a tree hierarchical evacuation network. According to the hierarchical path selection strategy, the evacuation routes are obtained and sorted by the length of route. This hierarchical path selection strategy is more realistic than using the straight line distance. An evacuation model based on hierarchical directed evacuation network is presented in this paper, and a hierarchical directed artificial fish swarm algorithm is proposed to solve the evacuation problem. The model simulates the movements of pedestrians by means of preying, swarming, following and waiting behaviors of artificial fish swarm algorithm. During the evacuation process, the effects of congestion, retrograde and blocking time on evacuation speed and route selection are considered. The simulation results show that the proposed model and algorithm can effectively improve the evacuation efficiency in a stadium, and provide scientific and reasonable path guidance. [ABSTRACT FROM AUTHOR]

How to choose a chaotic sequence to construct a chaotic S-box is a key factor to determine the performance of the S-box. In this paper, a construction method for chaotic S-box based on fireworks algorithm is proposed, in which the fireworks algorithm is used to search for the optimal solution in the solution space of the Lorenz chaotic system. The performances of S-box are tested through the nonlinearity, differential uniformity, strict avalanche criterion, output bits independence criterion and bijectivity, and compared with the existing classical S-boxes based on the Lorenz chaotic system. The results indicate that the S-box in this paper has more excellent cryptographic characteristics, which will provide a new idea for the efficient generation of chaotic S-box. [ABSTRACT FROM AUTHOR]

In this paper, the artificial intelligence control algorithm for steering robot of steering wheel is studied. The steering movement of wheeled soccer robot is controlled by artificial intelligence control algorithm, and the steering movement is modeled and simulated. Firstly, the characteristics of artificial neurons are simulated and a similar control model is constructed to complete the simulation of football. The artificial intelligence control algorithm has a dynamic feedback item compared with the traditional intelligent model, which has a better effect on the steering control of the wheeled soccer robot. In this paper, artificial intelligence control algorithm is used to optimize the parameters of artificial intelligence control algorithm, and the output of control signal of each steering part of wheeled soccer robot is simulated in the experiment, and the control of the steering action of wheeled soccer robot by artificial intelligence control algorithm is verified by experiments. Then the artificial intelligence control algorithm forms the connection structure. This method provides a good reference for steering control of wheeled soccer robots. [ABSTRACT FROM AUTHOR]

Continuous wavelet transform (CWT) is a linear convolution of signal and wavelet function for a fixed scale. This paper studies the algorithm of CWT with Morlet wavelet as mother wavelet by using nonzero-padded linear convolution. The time domain filter, which is a non-causal filter, is the sample of wavelet function. By making generalized discrete Fourier transform (GDFT) and inverse transform for this filter, we can get a geometrically weighted periodic extension of the filter when evaluated outside its original support. From this extension of the time domain filter, we can get a causal filter. In this paper, GDFT-based algorithm for CWT, which has a more concise form than that of linear convolution proposed by Jorge Martinez, is constructed by using this causal filter. The analytic expression of the GDFT of this filter, which is essential for GDFT-based algorithm for CWT, is deduced in this paper. The numerical experiments show that the calculation results of GDFT-based algorithm are stable and reliable; the running speed of GDFT-based algorithm is faster than that of the other two algorithms studied in our previous work. [ABSTRACT FROM AUTHOR]

The density peaks clustering (DPC) is known as an excellent approach to detect some complicated-shaped clusters with high-dimensionality. However, it is not able to detect outliers, hub nodes and boundary nodes, or form low-density clusters. Therefore, halo is adopted to improve the performance of DPC in processing low-density nodes. This paper explores the potential reasons for adopting halos instead of low-density nodes, and proposes an improved recognition method on Halo node for Density Peak Clustering algorithm (HaloDPC). The proposed HaloDPC has improved the ability to deal with varying densities, irregular shapes, the number of clusters, outlier and hub node detection. This paper presents the advantages of the HaloDPC algorithm on several test cases. [ABSTRACT FROM AUTHOR]

Swarm intelligent algorithms can effectively tackle optimization problems that are difficult to solve by using traditional optimization algorithms. However, with the huge increase in the time and space cost for solving optimization problems, the use of swarm intelligent algorithms suffer from the limitation of overly long computation time. Based on Spark, which is the most popular open-source distributed computing framework, this paper studies specifically using swarm intelligent algorithms to solve combinatorial optimization problems. Based on the characteristics of typical swarm intelligent algorithms, we develop Spark-based parallel implementation of these algorithms to accelerate the population updating and parameter tuning procedures involved in swarm intelligence. Specifically, we first initialize the swarm and generate the initial solution, then perform the distributed iterative evolution procedure, and finally obtain the optimal solution. In addition, in order to improve solution quality, we rely on the Spark platform to perform distributed parameter tuning. The tuning strategy first generates different parameter combinations according to a given parameter list, then execute swarm intelligent algorithms with different parameter combinations in a distributed and parallel manner, and finally determine the optimal parameter combination by comparing the solutions of all algorithms. Experimental results on benchmark datasets show that the distributed algorithms can significantly enhance the computational efficiency without affecting the solution quality. [ABSTRACT FROM AUTHOR]

In order to propose a more realistic epidemic dynamics model and effective medical resource allocation strategy, this paper constructs an improved SEIR model combined with a dynamical medical resource allocation model and individual behavior sharing medical resources. Simultaneously, a genetic algorithm to solve the medical resource allocation model is proposed to obtain the optimum resource allocation strategy. In this SEIR model, there is an important critical value of the stored medical resources, when the number of stored medical resources is more than the critical value, the inhibition of epidemic can be continuously enhanced until it reaches a minimum threshold, and then stabilizes near a minimum value, but when the resource surplus is below the critical value, the inhibitory effect on epidemic will weaken. The results demonstrate that the number of patients in the proposed method decreased more than 40% compared with the conventional control experiment. Moreover, the algorithm can automatically make decisions according to individual behavior in sharing preferences and the epidemic development trend. [ABSTRACT FROM AUTHOR]

The implication of firefly and fuzzy firefly optimization algorithms has been greatly witnessed in clustering techniques and extensively used in applications such as Image segmentation. Parameters such as step factor and attractiveness have been kept constant in these algorithms, which affect the convergence rate and accuracy of the clustering process. Though fuzzy adaptive firefly algorithm tackled this problem by making those parameters an adaptive one, issues such as low convergence rate, and provision of non-optimal solutions are still there. To tackle these issues, this paper proposed a novel fuzzy adaptive fuzzy firefly algorithm that significantly improves the accuracy and convergence rate while comparing with the existing optimization algorithms. Further, the proposed algorithm fused with existing hybrid clustering algorithms involving fuzzy set, intuitionistic fuzzy set, and rough set resulted in eight novel hybrid clustering algorithms which lead to better performance in optimizing the selection of initial centroids. To validate the proposal, experimental studies have been conducted on datasets found in bench-marking data repositories such as UCI, and Kaggle. The performance and accuracy evaluation of proposed algorithms have been carried out with the aid of seven accuracy measures. Results clearly indicate the improved accuracy and convergence rate of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The end-to-end learning approaches possess the advantages of high efficiency, rapidity and superior solving precision for combinatorial optimization problems, while exploring generalization to instances different from training scale is an open question. In this paper, we focus on the knapsack problem (KP) and employ an end-to-end data-driven approach based on attention model incorporated with different forms of baseline of policy gradient algorithm to solve KP. We first investigate the generalization performance of the proposed approach for KP on various problem scales with different capacities. The experimental results show that the end-to-end model possesses certain learning and generalization abilities to discover the intrinsic characteristics between instances, then guides to solve other instances of different scales. [ABSTRACT FROM AUTHOR]

Based on the presentation of the Kauffman bracket skein algebra of the thickened torus given by the third author in previous work [4], Frohman and Gelca established a complete description of the multiplicative operation leading to a famous product-to-sum formula. In this paper, we study the multiplicative structure of the Kauffman bracket skein algebra of the thickened four-holed sphere. We present an algorithm to compute the product of any two elements of the algebra, and give an explicit formula for some families of curves. We surmise that the algorithm has quasi-polynomial growth with respect to the number of crossings of a pair of curves. Further, we conjecture the existence of a positive basis for the algebra. [ABSTRACT FROM AUTHOR]

The computing power of mobile devices is too limited to execute computation tasks fast. Mobile edge computing (MEC) allows mobile devices to offload tasks to near servers to reduce the completion time of the tasks (a.k.a makespan). The input data of some critical tasks should be encrypted before the offloading. Aiming at the security critical tasks in the MEC composed of multiple servers, this paper addresses to minimize the makespan by scheduling security-critical tasks. We provide the formulation of the problem which is generally an integer programming problem, and three effective composite heuristics CH1–CH3 are proposed to solve the problem. Task permutations are considered as solutions. We construct a greedy heuristic algorithm to calculate the value of the objective. These three composite heuristics consist of two phases: solution initialization and solution improvement. In the first phase, the solutions of all the proposed algorithms are generated by a task arrangement rule called Biggest data Task First (BTF), and then in the second phase, improved by three searching methods based on different neighborhoods including a insertion neighborhood, a swap neighborhood and a hybrid neighborhood, respectively. Experimental results show that CH1–CH3 outperform the well-known RoundRobin algorithm. Particularly, BTF is demonstrated to initialize highly qualified solutions, making contributions to the high effectiveness. Meanwhile, all the improvement methods are justified to be effective and the method based on the hybrid neighborhood achieves the best effectiveness. [ABSTRACT FROM AUTHOR]

In this survey we overview known results on the strong subgraph k -connectivity and strong subgraph k -arc-connectivity of digraphs. After an introductory section, the paper is divided into four sections: basic results, algorithms and complexity, sharp bounds for strong subgraph k -(arc-)connectivity, minimally strong subgraph (k , ℓ) -(arc-) connected digraphs. This survey contains several conjectures and open problems for further study. [ABSTRACT FROM AUTHOR]

In the present paper, we propose a novel generalization of the celebrated MMP algorithm in order to find the wavefront propagation and the cut-locus on a convex polyhedron with an emphasis on actual implementation for instantaneous visualization and numerical computation. [ABSTRACT FROM AUTHOR]

This paper proposes the matrix representation of formal polynomials over max-plus algebra and obtains the maximum and minimum canonical forms of a polynomial function by standardizing this representation into a canonical form. A necessary and sufficient condition for two formal polynomials corresponding to the same polynomial function is derived. Such a matrix method is constructive and intuitive, and leads to a polynomial algorithm for factorization of polynomial functions. Some illustrative examples are presented to demonstrate the results. [ABSTRACT FROM AUTHOR]

Community structure detection is one of the fundamental problems in complex network analysis towards understanding the topology structure and function of the network. Modularity is a criterion to evaluate the quality of community structures, and optimization of this quality function over the possible divisions of a network is a sensitive detection method for community structure. However, the direct application of this method is computationally costly. Nonnegative matrix factorization (NMF) is a widely used method for community detection. In this paper, we show that modularity maximization can be approximately reformulated under the framework of NMF with Frobenius norm, especially when n is large. A new algorithm for detecting community structure is proposed based on the above finding. The new method is compared with four state-of-the-art methods on both synthetic and real-world networks, showing its higher clustering quality over the existing methods. [ABSTRACT FROM AUTHOR]

Exploration algorithms based on the Boustrophedon path seldom consider the impacts of a robot turning at corners on the exploration time. This paper proposes the Forecast-Island and Bidding A*-Euclidean Selecting Boustrophedon Coordination (FIBA*ESBC) algorithm to calculate the turning time at corners in the overall exploration time and introduces a method to estimate the walking time in the Boustrophedon paths in order to determine the directions for path execution. Typically, in bidding-based exploration tasks, the cost is the Euclidean distance between the current position of the robot and the target point. When there is an obstacle between two points, the cost is set to infinity. Therefore, the selected target point is sometimes not optimal. The FIBA*ESBC algorithm is based on the exploration cost of a combination of the Euclidean distance and A* algorithm walking path, which can effectively solve this problem. Because the bidding is based on a greedy algorithm, the robot has a small unexplored island in the later exploration stage; therefore, full exploration is not possible or requires a long time with several repeated paths. The FIBA*ESBC algorithm prioritizes the exploration and estimation of hidden and existing unexplored islands. It can realize complete exploration and decrease the exploration time. Through simulation experiments conducted using Gazebo and RViz, the feasibility of the FIBA*ESBC algorithm is verified. Moreover, a simulation experiment is conducted in MATLAB for comparison with other algorithms. The analysis of the experimental data shows that the proposed algorithm has a relatively short exploration time. [ABSTRACT FROM AUTHOR]

Approximation space can be said to play a critical role in the accuracy of the set's approximations. The idea of "approximation space" was introduced by Pawlak in 1982 as a core to describe information or knowledge induced from the relationships between objects of the universe. The main objective of this paper is to create new types of rough set models through the use of different neighborhoods generated by a binary relation. New approximations are proposed representing an extension of Pawlak's rough sets and some of their generalizations, where the precision of these approximations is substantially improved. To elucidate the effectiveness of our approaches, we provide some comparisons between the proposed methods and the previous ones. Finally, we give a medical application of lung cancer disease as well as provide an algorithm which is tested on the basis of hypothetical data in order to compare it with current methods. [ABSTRACT FROM AUTHOR]

In this paper, a novel fractional quantum logistic map (FQLM) based on fractional difference is proposed and image encryption algorithm based on it are exploited. First, the FQLM is proposed and its dynamical behaviors are observed with bifurcation diagram, phase portraits and largest Lyapunov exponent plots. After that, the ECC is utilized to cover up the encryption keys and the FQLM is used for the permutation and diffusion process of image encryption. Finally, the cryptosystem is used in image encryption and the algorithm is analyzed in four respects, showing a distinct advantage of the proposed cryptosystem over some of existing ones. [ABSTRACT FROM AUTHOR]

In this paper, we have adopted a new approach to study the Killing vector fields of locally rotationally symmetric and general Bianchi type I spacetimes. Instead of directly integrating the set of Killing's equations, an algorithm is developed in Maple which converts these equations to the reduced involutive form (Rif) and consequently it imposes some restrictions on the metric functions in the form of a tree, known as Rif tree. The set of Killing's equations is then solved for each branch of the Rif tree, giving the explicit form of the Killing vector fields. The structure of Lie algebra is presented for each set of the obtained Killing vector fields and some physical implications of the obtained metrics are discussed. [ABSTRACT FROM AUTHOR]

This paper proposed a pulse-coupled synchronization protocol for wireless sensor networks. The time information of the node can be encoded at the Mac layer and then sent and received in the form of pulse waves. During the exchange of time information between a pair of nodes, one node will adjust its own clock information according to certain rules after receiving the pulse time signal of the other node. This process is repeated in the entire wireless sensor network. Under certain environmental and estimated parameter conditions, all nodes in the network can finally converge to a coherent frequency and phase, thus realizing time synchronization. The relationship between time synchronization and coupling coefficient was proved theoretically, and the optimal coupling coefficient was derived. An optimized algorithm was proposed after the optimization of the protocol. Finally, the correctness of the proposed protocol and its optimized algorithm was verified by data simulation. [ABSTRACT FROM AUTHOR]

As intelligence technology develops, there is a higher requirement for computing speed and accuracy of water injection system simulation. In this paper, aiming at the tree-shaped water injection pipe network system of large-scale oilfields, based on the energy equation for calculating the pressure drop Δ H of pipe section, a mathematical model of the pipeline unit and the node unit is established, and finally, a mathematical model of pipe network for the entire water injection system is established; then, the improved iterative algorithm is used to solve the simulation model of water injection system. In this way, we determine the boundary calculation conditions, take the water injection station as reference node, and use the maximum pressure of water injection well as the initial value of the reference node for calculation, which reduces the number of iterations in model calculation; by comparing the simulation results of different iteration steps, 0.01 is selected as the iteration step size due to its higher calculation accuracy; and the calculation process has also been optimized. The process of solving the characteristic matrix K is combined with the process of calculating the pressure drop Δ H of pipe section, and placed outside the algorithm loop, thereby shortening the calculation time of a single cycle and reducing the calculation amount of the algorithm. The application cases show that the proposed optimization algorithm for water injection system pipe network simulation can be used as an effective method to improve the solution speed and calculation accuracy of the simulation algorithm of tree-shaped water injection system in large-scale oilfields. [ABSTRACT FROM AUTHOR]

Earthquake prediction based on extreme imbalanced precursor data is a challenging task for standard algorithms. Since even if an area is in an earthquake-prone zone, the proportion of days with earthquakes per year is still a minority. The general method is to generate more artificial data for the minority class that is the earthquake occurrence data. But the most popular oversampling methods generate synthetic samples along line segments that join minority class instances, which is not suitable for earthquake precursor data. In this paper, we propose a Safe Zone Synthetic Minority Oversampling Technique (SZ-SMOTE) oversampling method as an enhancement of the SMOTE data generation mechanism. SZ-SMOTE generates synthetic samples with a concentration mechanism in the hyper-sphere area around each selected minority instances. The performance of SZ-SMOTE is compared against no oversampling, SMOTE and its popular modifications adaptive synthetic sampling (ADASYN) and borderline SMOTE (B-SMOTE) on six different classifiers. The experiment results show that the quality of earthquake prediction using SZ-SMOTE as oversampling algorithm significantly outperforms that of using the other oversampling algorithms. [ABSTRACT FROM AUTHOR]

In graph theory, one of the most important problems is graph labeling. Labeling of graph has a very wide range of applications in x -ray crystallography, missile guidance, coding theory, signal processing, radar, data base management, astronomy, communication network addressing, circuit design, etc. An extension of L (2 , 1) -labeling (L 2 1 -labeling) is L (3 , 2 , 1) -labeling (L 3 2 1 -labeling), which is now becoming a well-studied problem due to its application in real life situations. L 2 1 -labeling problem and the importance of L 3 2 1 -labeling problem motivates us to consider L 3 2 1 -labeling problem for trapezoid graph. The L 3 2 1 -labeling of a graph G = (V , E) is an assignment ζ from V to the set { 0 , 1 , 2 , ... } such that if d (p 1 , p 2) = 1 then | ζ (p 1) − ζ (p 2) | ≥ 3 , if d (p 1 , p 2) = 2 then | ζ (p 1) − ζ (p 2) | ≥ 2 and if d (p 1 , p 2) = 3 then | ζ (p 1) − ζ (p 2) | ≥ 1 , where d (p 1 , p 2) is the distance between the nodes p 1 and p 2 . The L 3 2 1 -labeling number of a graph is denoted by λ 3 , 2 , 1 (G) , it is the highest label used to label the graph G. In this paper, for trapezoid graph G , it is proved that λ 3 , 2 , 1 (G) ≤ max { 1 1 Δ − 8 , 1 3 Δ − 1 8 } , where Δ is the degree of the graph G. This upper bound is the first upper bound for L 3 2 1 -labeling problem on trapezoid graph. Also, to label any trapezoid graph, we have designed an algorithm which maintains the upper bound of the label. [ABSTRACT FROM AUTHOR]

For a simple, undirected, connected graph G , a function h : V (G) → { 0 , 1 , 2 , 3 } which satisfies the following conditions is called a total Roman {3}-dominating function (TR3DF) of G with weight h (V) = ∑ p ∈ V h (p) : (C1) For every vertex u ∈ V if h (u) = 0 , then u has m (m ≥ 1) neighbors such that whose sum is at least 3, and if h (u) = 1 , then u has n (n ≥ 1) neighbors such that whose sum is at least 2. (C2) The subgraph induced by the set of vertices labeled one, two or three has no isolated vertices. For a graph G , the smallest possible weight of a TR3DF of G denoted γ t { R 3 } (G) is known as the total Roman { 3 } -domination number of G. The problem of determining γ t { R 3 } (G) of a graph G is called minimum total Roman {3}-domination problem (MTR3DP). In this paper, we show that the problem of deciding if G has a TR3DF of weight at most l for chordal graphs is NP-complete. We also show that MTR3DP is polynomial time solvable for bounded treewidth graphs, chain graphs and threshold graphs. We design a 3 (ln (Δ − 0. 5) + 1. 5) -approximation algorithm for the MTR3DP and show that the same cannot have (1 − δ) ln | V | ratio approximation algorithm for any δ > 0 unless NP ⊆ DTIME (| V | O (log log | V |)). Next, we show that MTR3DP is APX-complete for graphs with Δ = 4. We also show that the domination and total Roman {3}-domination problems are not equivalent in computational complexity aspects. Finally, we present an integer linear programming formulation for MTR3DP. [ABSTRACT FROM AUTHOR]

Let G = (V , E) be a graph. A subset D ⊆ V is a dominating set of G if for each v ∈ V ∖ D there is a vertex u ∈ D adjacent to v. A dominating set D of G is a secure dominating set of G if for each v ∈ V ∖ D there is a vertex u ∈ D adjacent to v such that (D ∖ { u }) ∪ { v } is also a dominating set of G. The minimum cardinality of a secure dominating set of G is called the secure domination number of G. Burger et al. [A linear algorithm for secure domination in trees, Discrete Appl. Math. 171 (2014) 15–27] proposed a nontrivial algorithm for computing a minimum secure dominating set of a given tree in linear time and space. In this paper, we give a dynamic programming algorithm to compute the secure domination number of a given tree T = (V , E) in (| V |) time and space and then using a backtracking search algorithm we can find a minimum secure dominating set of T in (| V |) time and space that its implementation is much simpler than the implementation of the algorithm proposed by Burger et al. [ABSTRACT FROM AUTHOR]

Image segmentation is a prime operation to understand the content of images. Multilevel thresholding is applied in image segmentation because of its speed and accuracy. In this paper, a novel multilevel thresholding algorithm based on Cuckoo search (CS) is introduced. One of the major drawbacks of metaheuristic algorithms is the stagnation phenomenon which leads to a fall into local optimums and premature convergence. To overcome this shortcoming, the idea of Darwinian theory is incorporated with CS algorithm to increase the diversity and quality of the individuals without decreasing the convergence speed of CS algorithm. A policy of encouragement and punishment is considered to lead searching agents in the search space and reduce the computational time. The algorithm is implemented based on dividing the population into specified groups and each group tries to find a better location. Ten test images are selected to verify the ability of our algorithm using the famous energy curve method. Two popular entropies criteria, Otsu and Kapur, are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are also implemented and compared with our method. Experimental results manifest that DCS is a powerful tool for multilevel thresholding and the obtained results outperform the CS algorithm and other heuristic search methods. [ABSTRACT FROM AUTHOR]

In recent times, there has been a tremendous progress in image dehazing for computer vision applications, while the sky region processed by these algorithms tends to degrade by noise and color distortion. In this paper, an improved dark channel prior algorithm is proposed which detects the sky region first and divides the image into sky region and non-sky region and then estimates the transmission of two parts separately, followed by combining with refining step. The proposed algorithm also accurately corrects the transmission of the sky region to avoid noise and color distortion. Experimental results show a greater quality improvement in the output images than the existing strategies. [ABSTRACT FROM AUTHOR]

A long range observing systems can be sturdily affected by scintillations. These scintillations are caused by changes in atmospheric conditions. In recent years, various turbulence mitigation approaches for turbulence mitigation have been exhibiting a promising nature. In this paper, we propose an effectual method to alleviate the effects of atmospheric distortion on observed images and video sequences. These sequences are mainly affected through floating air turbulence which can severely degrade the image quality. The existing algorithms primarily focus on the removal of turbulence and provides a solution only for static scenes, where there is no moving entity (real motion). As in the traditional SGL algorithm, the updated frame is iteratively used to correct the turbulence. This approach reduces the turbulence effect. However, it imposes some artifacts on the real motion that blurs the object. The proposed method is an alteration of the existing Sobolev Gradient and Laplacian (SGL) algorithm to eliminate turbulence. It eliminates the ghost artifact formed on moving object in the existing approach. The proposed method alleviates turbulence without harming the moving objects in the scene. The method is demonstrated on significantly distorted sequences provided by OTIS and compared with the SGL technique. The information conveyed in the scene becomes clearly visible through the method on exclusion of turbulence. The proposed approach is evaluated using standard performance measures such as MSE, PSNR and SSIM. The evaluation results depict that the proposed method outperforms the existing state-of-the-art approaches for all three standard performance measures. [ABSTRACT FROM AUTHOR]

Computer vision-based recognition of Bangle vehicle license plates (LPs) is an arduous task in dirty and muddy situations. This paper proposes an efficient method for real-time computer vision-based recognition of Bangla vehicle LPs using contour analysis and prediction algorithms. The method initially applies gray scaling the input RGB images, histogram equalization to improve the grayscale image quality, edge detection using Sobel edge detector, and adaptive thresholding to convert it to a binary image. The system localizes the vehicle LP based on the maximum rectangular contour area and converts it into a predefined size. Noise removal technique using morphological dilation and erosion operation is used, followed by Gaussian filtering on binary image to improve the image quality further. The system clusters the two-lined LP into seven clusters. The sub-clustering is applied on specific clusters and makes 68 individual sub-clusters. The system extracts vector contour (VC) from each 68 individual classes. After VC extraction, the system normalizes it into a q predefined length. The system applies inter co-relation function (ICF) to categorize each sub-cluster to its previously defined individual class. For that, it calculates the maximum similarity between test and previously trained VCs. The system applies the dependency prediction algorithm in parallel to predict the whole string (district name) in the cluster-1 based on previously categorized class or classes (starting character or characters of the district part). (Metro) or (null) from cluster-2, "-" (hyphen) from cluster-3 and 6 are predicted automatically as their positions are fixed. The system is trained using 68 classes in which each class contains 100 samples generated by the augmentation technique. The system is tested using another set of 68 classes with a total of 6 8 × 1 0 0 = 6 8 0 0 images acquiring the recognition accuracy of 96.62% with the mean computational cost of 8.363 ms/f. The system is also tested using 500 vehicle whole Bangla LPs achieving the mean whole LP recognition accuracy of 95.41% with a mean computational cost of 35.803 ms/f. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to accelerate parameter extraction process of surface-potential-based (PSP) MOSFET model is presented for the submicron MOS transistor. To reduce the computational time, Field Programmable Gate Array (FPGA) implementation of PSP model library — SiMKit is demonstrated using Xilinx's Zynq Ultrascale + MPSoC (Multi-processor System-on-Chip) platform. Parameter extraction is carried out using Particle Swarm Optimization (PSO) algorithm for 65 nm technology nMOS devices. With the available measurement data, 32 various PSP model parameters are extracted. Experimental results validate the performance and accuracy of parameter extraction by achieving Root Mean Square Error below 10% for various current–voltage characteristics of nMOS device. 41.57% acceleration in execution time for extraction process is achieved by Zynq Ultrascale + MPSoC platform compared to the conventional computer-based software approach. In addition, various design optimization directives are explored, and their performances are compared as a part of RTL generation of SiMKit. [ABSTRACT FROM AUTHOR]

The Cox proportional hazards model has been widely used in cancer genomic research that aims to identify genes from high-dimensional gene expression space associated with the survival time of patients. With the increase in expertly curated biological pathways, it is challenging to incorporate such complex networks in fitting a high-dimensional Cox model. This paper considers a Bayesian framework that employs the Ising prior to capturing relations among genes represented by graphs. A spike-and-slab prior is also assigned to each of the coefficients for the purpose of variable selection. The iterated conditional modes/medians (ICM/M) algorithm is proposed for the implementation for Cox models. The ICM/M estimates hyperparameters using conditional modes and obtains coefficients through conditional medians. This procedure produces some coefficients that are exactly zero, making the model more interpretable. Comparisons of the ICM/M and other regularized Cox models were carried out with both simulated and real data. Compared to lasso, adaptive lasso, elastic net, and DegreeCox, the ICM/M yielded more parsimonious models with consistent variable selection. The ICM/M model also provided a smaller number of false positives than the other methods and showed promising results in terms of predictive accuracy. In terms of computing times among the network-aware methods, the ICM/M algorithm is substantially faster than DegreeCox even when incorporating a large complex network. The implementation of the ICM/M algorithm for Cox regression model is provided in R package icmm, available on the Comprehensive R Archive Network (CRAN). [ABSTRACT FROM AUTHOR]

In this work, a generalized (G ′ / G) -expansion method has been used for solving the nonlinear Chen–Lee–Liu equation. This method is a more common, general, and powerful mathematical algorithm for finding the exact solutions of nonlinear partial differential equations (NPDEs), where G = G (τ) follows the Jacobi elliptic equation [ G ′ (τ) ] 2 = H (G) , and we let H (G) be a fourth-order polynomial. Many new exact solutions such as the hyperbolic, rational, and trigonometric solutions with different parameters in terms of the Jacobi elliptic functions are obtained. The distinct solutions obtained in this paper clearly explain the importance of some physical structures in the field of nonlinear phenomena. Also, this method deals very well with higher-order nonlinear equations in the field of science. The numerical results described in the plots were obtained by using Maple. [ABSTRACT FROM AUTHOR]

The volatility is one of the essential characteristics of financial time series, which is vital for the knowledge acquisition from financial data. However, since the high noise and nonsteady features, the volatility identification of financial time series is still a challenging problem. In this paper, from a perspective of granule complex network, a novel approach is proposed to study this problem. First, numeric time series is structured into fuzzy information granules (FIGs), where the segments of time series in each granule would own similar volatility features. Second, by using the transfer relations among granules, granule complex network is to be constructed, which intuitively describes the transfer processes among the different volatility patterns. Third, a novel community detection algorithm is applied to divide the granule complex networks, where granules with frequent mutual transfers would belong to the same granule community. Finally, Markov chain model is carried out to analyze the higher level of transfer processes among different granule communities, which would further describe the larger-scale transitions of volatility in overall financial time series. An empirical study of the proposed system is applied in the Shanghai stock index market, where volatility patterns of financial data can be effectively acquired and the corresponding transfer processes can be analyzed by means of the granule communities. [ABSTRACT FROM AUTHOR]

With the continuous improvement of science and technology, the substation remote control system has been constantly improved, which provides the possibility for the complete realization of intelligent and unmanned substation. However, due to the special substation environment, it is easy to cause interference, coupled with the low accuracy of today's video image processing algorithm, which leads to the frequent occurrence of false alarms and missing alarms. Manual intervention is needed to deal with this, which inhibits the display of automatic intelligent substation processing functions. Therefore, in this paper, the most rapidly developed machine learning algorithm — deep learning is applied to the substation instrument equipment identification processing, in order to improve the accuracy and efficiency of instrument equipment identification, and make due contributions to the full realization of unattended substation. [ABSTRACT FROM AUTHOR]

Adaptive diagnosis is an approach in which tests can be scheduled dynamically during the diagnosis process based on the previous test outcomes. Naturally, reducing the number of test rounds as well as the total number of tests is a major goal of an efficient adaptive diagnosis algorithm. The adaptive diagnosis of multiprocessor systems under the PMC model has been widely investigated, while adaptive diagnosis using comparison model has been independently discussed only for three networks, including hypercube, torus, and completely connected networks. In addition, adaptive diagnosis of general Hamiltonian networks is more meaningful than that of special graph. In this paper, the problem of adaptive fault diagnosis in Hamiltonian networks under the comparison model is explored. First, we propose an adaptive diagnostic scheme which takes five to six test rounds. Second, we derive a dynamic upper bound of the number of fault nodes instead of setting a value like normal. Finally, we present an algorithm such that at least one sequence obtained from cycle partition can be picked out and all nodes in this sequence can be identified based on the previous upper bound. [ABSTRACT FROM AUTHOR]

In this paper, we aim to investigate the security of multiple images based on affine hill cipher (AHC), fractional discrete cosine transform (FrDCT) and Arnold transform (AT). Three original R G B images are encoded as a single R G B image by first converting three R G B images into three indexed images by extracting their color maps. These three indexed image are treated as R , G and B channel of a single R G B image. Each channel is first encrypted using cryptographic encryption scheme AHC in time domain. The partially encoded image is transformed to frequency domain using FrDCT. In frequency domain, the partially encoded image is scrambled with the help of AT. The proposed scheme assures security in time domain, frequency domain as well as in coordinate domain. The original images can be recovered correctly by using proper arrangements of secret keys. The storage and transmission of the encrypted image is easy due to single encrypted image. The robustness of the proposed approach is proved by conducting computer simulations on standard examples. [ABSTRACT FROM AUTHOR]

In this paper, the ancient Chinese algorithm is applied to derive fractal microgravity forced oscillator's frequency–amplitude response curves using the two-scale fractal transform derivative. Depending on the fractal parameter values, it is shown that the system restoring forces exhibit hardening or softening behavior. Furthermore, the proposed solution approach is simple, efficient, and accurate for obtaining the approximate steady-state solution of a fractal microgravity nonlinear oscillator with a purely nonlinear power-form restoring force. [ABSTRACT FROM AUTHOR]

In this paper, the steady-state solution of Duffing-type oscillators with fractal-order derivative is obtained. First, the two-scale fractal-order derivative transform is used to write the fractal differential equation of motion as an ordinary differential non-homogenous equation of motion. Then the ancient Chinese algorithm and He's formulation are used to find the approximate frequency-amplitude response curve. The results show that the steady-state response diagrams exhibit hardening or softening behavior depending on the fractal parameter value. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is a graphical visualization of the electrical activity of the human heart that is recorded by placing a surface electrode at standardized position on a person's chest. ECG signals suffer from artifacts/noises due to baseline wander (BW), electrode artifacts, muscle artifacts, power-line interference and channel noises during acquisition and transmission of the ECG signals. Reduction of these artifacts is crucial for efficient diagnosis and interpretation of the human heart condition. In this paper, an effective adaptive noise canceller (ANC) based on empirical mode decomposition (EMD)-Jaya algorithm is proposed for denoising electrocardiogram. In this approach, intrinsic mode functions (IMFs) produced by EMD are used as reference and Jaya algorithm is used to calculate optimum weights of finite impulse response (FIR) filter. This scheme is compared with EMD, wavelet transform (WT) thresholding, and hybrid EMD-least mean square (LMS) approaches through extensive simulation on noise corrupted ECG besides verifying the robustness with real ECG signals. The performance of the proposed technique is assessed using standard metric signal-to-noise ratio (SNR) with different contamination levels. The results obtained demonstrate the superiority of the hybrid when compared to other competing approaches. [ABSTRACT FROM AUTHOR]

In this paper, we show how the quasinormal modes (QNMs) arise from the perturbations of massive scalar fields propagating in the curved background by using the artificial neural networks. To this end, we architect a special algorithm for the feedforward neural network method (FNNM) to compute the QNMs complying with the certain types of boundary conditions. To test the reliability of the method, we consider two black hole spacetimes whose QNMs are well known: 4 D pure de Sitter (dS) and five-dimensional Schwarzschild anti-de Sitter (AdS) black holes. Using the FNNM, the QNMs of are computed numerically. It is shown that the obtained QNMs via the FNNM are in good agreement with their former QNM results resulting from the other methods. Therefore, our method of finding the QNMs can be used for other curved spacetimes that obey the same boundary conditions. [ABSTRACT FROM AUTHOR]