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In this paper, a new 2D Arnold-sin-coupling (2D-ASC) hyperchaotic map is established and a new image encryption algorithm based on bit-level operations and the ASC hyperchaotic system is proposed. The algorithm first decomposes a normal image into bit-level images by rows, incorporates a sequence of bits generated by a chaotic system to the algorithm to increase randomness and security, and then performs a circular shift of the rows and columns of the bit-level image to obtain a confused intermediate image. Finally, the intermediate image is diffused by our constructed S-box according to the size of each pixel value to obtain a stable and robust encrypted image. Moreover, the initial condition of the chaotic mapping used is calculated from the SHA-256 hash value of the original image, enhancing the correlation of the encryption process with the plaintext image. Through simulation experiments, the image encryption scheme is analyzed in terms of key space, histogram, information entropy, robustness and key sensitivity. The theoretical analysis and experimental findings demonstrate that the algorithm has good performance and sufficient level of security. The information entropy of the encrypted image is above 7.9993, the average NPCR is 99.6440 and the average UACI is 33.4996. [ABSTRACT FROM AUTHOR]

3D image is a kind of image closer to life, and 3D image has a stronger visual impact. In the past, researchers mostly protected flat images, but ignored the protection of 3D images. This paper combines the 2D chaotic system and uses random cross diffusion to propose a new encryption algorithm for 3D images which are STL format. Different from the traditional scrambling and diffusion operations, this paper uses only one iterative operation to achieve the effects of scrambling and diffusion, making the algorithm more secure. In the encryption algorithm, each value of the ciphertext is XORed with the value of the plaintext on the same coordinate axis and different from the coordinate where the ciphertext is located, the chaotic sequence, the ciphertext of different coordinate axes significantly reduces the correlation between the adjacent coordinate values. In addition, the proposed 3D image encryption algorithm is tested by statistical analysis. The experimental results show that this 3D image encryption algorithm has high security. [ABSTRACT FROM AUTHOR]

In this paper, based on multi-arbitrators and the product states, a new quantum signature scheme (QSS) is proposed. In our scheme, two arbitrators are used to share private keys with the signer. The signer encodes the bit message into orthogonal product states, which are locally indistinguishable. Then, to produce the signature, the signer signs the product states with the key-controlled Hadamard operator and Y operator. Our scheme has the good virtues as follows. First, our signature not only has the security property of unforgeability, but also has the property of strong non-repudiation. Second, when signing a message and verifying a signature, the partners do not need to prepare or transmit any entangled particle. Third, the partners do not need to perform the state comparison algorithm. Fourth, the signer does not need to share any private key with the signature verifier. For our scheme, the signer only shares two private keys with the arbitrators. At last, in our scheme, the repudiation can be flexibly solved. Compared with the other schemes with semi-trusted arbitrator, ours has better performances in security, practicability and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, for the (3+1)-dimensional extended Jimbo–Miwa equation, by the direct algebraic method, together with the inheritance solving strategy, we construct its interaction solutions among solitons, rational waves, and periodic waves. Meanwhile, we construct its interaction solutions among solitons, breathers, and lumps of any higher orders by an N -soliton decomposition algorithm, together with the parameters conjugated assignment and long-wave limit techniques. The highlight of the paper is that by applying new algorithms and new techniques, we obtained different types of new multiwave interaction solutions for the (3+1)-dimensional extended Jimbo–Miwa equation. [ABSTRACT FROM AUTHOR]

This paper focuses on the study of dynamic boundary flow problems based on the Element-Free Galerkin method. First, Navier–Stokes equation is discretized with the Galerkin method. The inertial term in the equation is discretized with the method of the speed term and direct deduction, respectively. The penalty function method is used to deal with the pressure and the essential boundary condition in the equation, and the discretization of two-dimensional N–S equation based on the EFG method is established. However, irregular changes in boundary conditions are often encountered in practical fluid problems. For example, the motion of flapping-foil is not uniform relative to the flow. In this paper, numerical experiments are carried out for the flow problems with non-uniform boundary motions. The problem which a plate with non-uniform drag movement above a rectangular tank filled with water is studied with the EFG method. The feasibility of the proposed algorithm is verified by comparison with the FEM method. Then, the procession of the water in the tank is stimulated. In the end, the influence of different calculation time steps on the accuracy of the solution is discussed. [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]

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]

Ranking of sports teams has always been significant to sponsors, coaches, as well as audiences. Prevailing prediction methods investigate probabilities by taking into account of different kinds of attributes (e.g. field goals, fields goal attempts) in order to establish a detail-based mechanism for analyzing the capability among competing teams. The different types of activation and inhibition actions between athletes provide a considerable challenge in the framework of network analysis. Moreover, these attributes interactions might add up the substantial redundancy to network frame as well. This paper proposes a weighted PageRank algorithm based on the normalized basketball match scores from a macroscopic point of view. Taking Chinese Basketball Association and Chinese University Basketball Association as examples, the developed approach takes into account the win/lose nature of interactions between each pair of competing teams in the framework of PageRank network. We also evolve a weighted network model for the network matrix which highlights the capability difference of teams whose PageRank probabilities are most sensitive with respect to the scores of the two competing teams. The chance of championship of teams can be better demonstrated by the PageRank probabilities. The results show that our method achieves more precise predicting result than that of original PageRank algorithm and Hypertext-Induced Topic Search algorithm. [ABSTRACT FROM AUTHOR]

The consensus dynamics with additive stochastic disturbances are characterized by the network coherence, which is the robustness of consensus algorithms when the nodes are subject to external perturbations. In this paper, the research goal is to obtain the first- and second-order network coherence quantifying as the sum and square sum of reciprocals of all nonzero Laplacian eigenvalues. One innovation point of this paper is the structure of a family of the weighted recursive trees with weight factor. We mainly obtain the exact expressions and scalings of network coherence on the family of weighted recursive trees. The scalings of first-order network coherence with network size obey three laws along with the range of the weight factor, while the scalings of second-order network coherence obey four laws along with the range of the weight factor. In addition, the scalings of first- and second-order network coherence on our studied networks are smaller than those performed on other studied networks when 1 m + 1 < r ≤ 1. The obtained results indicate that the efficiency of network coherence on the weighted network has close relation to the weight distribution, and we can design a better weight distribution to make the coherence of network more efficient. [ABSTRACT FROM AUTHOR]

Community detection is important for understanding the structure and function of networks. Resistance distance is a kind of distance function inherent in the network itself, which has important applications in many fields. In this paper, we propose a novel community detection algorithm based on resistance distance and similarity. First, we propose the node similarity, which is based on the common nodes and resistance distance. Then, we define the distance function between nodes by similarity. Furthermore, we calculate the distance between communities by using the distance between nodes. Finally, we detect the community structure in the network according to the nearest-neighbor nodes being in the same community. Experimental results on artificial networks and real-world networks show that the proposed algorithm can effectively detect the community structures in complex networks. [ABSTRACT FROM AUTHOR]

In today's world scenario, many of the real-life problems and application data can be represented with the help of the graphs. Nowadays technology grows day by day at a very fast rate; applications generate a vast amount of valuable data, due to which the size of their representation graphs is increased. How to get meaningful information from these data become a hot research topic. Methodical algorithms are required to extract useful information from these raw data. These unstructured graphs are not scattered in nature, but these show some relationships between their basic entities. Identifying communities based on these relationships improves the understanding of the applications represented by graphs. Community detection algorithms are one of the solutions which divide the graph into small size clusters where nodes are densely connected within the cluster and sparsely connected across. During the last decade, there are lots of algorithms proposed which can be categorized into mainly two broad categories; non-overlapping and overlapping community detection algorithm. The goal of this paper is to offer a comparative analysis of the various community detection algorithms. We bring together all the state of art community detection algorithms related to these two classes into a single article with their accessible benchmark data sets. Finally, we represent a comparison of these algorithms concerning two parameters: one is time efficiency, and the other is how accurately the communities are detected. [ABSTRACT FROM AUTHOR]

In this paper, the first-passage behavior of under-damped asymmetric bistable system driven by Lévy noise is studied. The two aspects considered are the mean first-passage time (MFPT) and the distribution of first-passage time in two opposite directions. To begin with, using the Janicki–Weron algorithm to generate Lévy noise, the system driven by Lévy noise is simulated through the fourth-order Runge–Kutta algorithm. Then the first-passage time of 2 × 1 0 4 response tracks is calculated, and the MFPT and the distribution of first-passage time are obtained. Finally, the influence of Lévy noise and system parameters on MFPT and the distribution of first-passage time are analyzed. Moreover, the noise enhanced stability (NES) effect is found. [ABSTRACT FROM AUTHOR]

Path planning has always been a hot topic in the field of mobile robot research. At present, the mainstream issues of the mobile robot path planning are combined with the swarm intelligence algorithms. Among them, the firefly algorithm is more typical. The firefly algorithm has the advantages of simple concepts and easy implementation, but it also has the disadvantages of being easily trapped into a local optimal solution, with slow convergence speed and low accuracy. To better combine the path planning of mobile robot with firefly algorithm, this paper studies the optimization firefly algorithm for the path planning of mobile robot. By using the strategies of optimizing the adaptive parameters in the firefly algorithm, an adaptive firefly algorithm is designed to solve the problem that the firefly algorithm is easy to get into the local optimal solution and improves the performance of firefly algorithm. The optimized algorithm with high performance can improve the computing ability and reaction speed of the mobile robot in the path planning. Finally, the theoretical and experimental results have verified the effectiveness and superiority of the proposed algorithm, which can meet the requirements of the mobile robot path planning. [ABSTRACT FROM AUTHOR]

This paper studies an inverse problem of reconstructing a sound-soft obstacle from a homogeneous medium. We deal with it in the framework of statistical inversion and adopt an iterative ensemble Kalman algorithm to reconstruct the boundary. Some numerical examples show that the algorithm is effective and it can recover the shape of the boundary using one or several of the incident waves. [ABSTRACT FROM AUTHOR]

In order to rationally formulate a customized bus route plan to improve the operation efficiency of customized buses, in view of the problem of ignoring the distance between the boarding area and the alighting area, and setting it as a fixed value when performing custom bus route optimization modeling, as well as solving the problem of multiple custom bus parking lots. This paper proposes a method based on the NSGA-II algorithm using a three-stage hybrid coding method to solve. First, according to real life, the entire operation process of customized buses is divided into four phases. Second, based on the four stages, a multi-objective optimization model of customized bus routes that satisfies multiple parking lots, multiple cars, and multiple boarding and alighting stations is constructed to pursue the minimum total travel time of passengers and minimizes the operating costs of customized bus companies. Third, the NSGA-II algorithm is employed to solve the model, including three-stage hybrid coding, segmented crossover, and segmented mutation operators. Finally, the local road network in Lanzhou City is adopted for simulation research. The results show that it is feasible to use NSGA-II algorithm to solve the problem of customizing bus routes for multiple parking lots, multiple vehicles, and multiple boarding and alighting stations. The research results are of great value for exploring the optimization methods of customized bus routes and improving the operating efficiency of custom buses. [ABSTRACT FROM AUTHOR]

Self-similarity is a significant property for complex networks. Box coverage method and dimension calculation are vital tools to study the characteristic of complex networks. In this paper, we propose an outside-in (OSI) box covering method for the Sierpinski networks, and it is attested that this coverage algorithm is superior to CBB algorithm. In addition, we deduce the optimal box recurrence formula of weighted and unweighted networks theoretically, and the result is the same as that of the algorithm value. We also obtain the information dimension of weighted network, which verifies the validity and feasibility of our method. [ABSTRACT FROM AUTHOR]

This paper designs a novel classification hardware framework based on neural network (NN). It utilizes COordinate Rotation DIgital Computer (CORDIC) algorithm to implement the activation function of NNs. The training was performed through software using an error back-propagation algorithm (EBPA) implemented in C++, then the final weights were loaded to the implemented hardware framework to perform classification. The hardware framework is developed in Xilinx 9.2i environment using VHDL as programming languages. Classification tests are performed on benchmark datasets obtained from UCI machine learning data repository. The results are compared with competitive classification approaches by considering the same datasets. Extensive analysis reveals that the proposed hardware framework provides more efficient results as compared to the existing classifiers. [ABSTRACT FROM AUTHOR]

This paper provides a Priority-based Optimized-Hybrid Asynchronous Centralized and Decentralized (POHACD) algorithm for multi-robot path planning (MRPP) issue, where multiple robots communicate with each other, as well as through collision check makes their decision. The priority-based approach provides priority to each robot thus bypassing another robot in conflict situation. Optimization lies in terms of minimal path length. The proposed hybrid asynchronous algorithm leverages the centralized approach to improve the overall system efficiency and distributed approach to reduce communication overhead, which automatically reduces execution time. This algorithm prevents unnecessary communication by transmitting global information and in the meantime limits specific information that is only in the interest of certain robots. By carrying out the simulations, it has been demonstrated that the ant lion optimization for prioritized planning performs better in all the roadmaps. [ABSTRACT FROM AUTHOR]

Overlapping community detection is a hot topic in research of complex networks. Link community detection is a popular approach to discover overlapping communities. Line graph is a widely used model in link community detection. In this paper, we propose an overlapping community detection algorithm based on node distance of line graph. Considering topological structure of links in graphs, we use line graph to transform links of graph into nodes of line graph. Then, we calculate node distance of line graph according to their dissimilarity. After getting distance matrix, we proposed a new ρ measure based on nodes of line graph and combine it with clustering algorithm by fast search and density peak to identify node communities of line graph. Finally, we acquire overlapping node communities after transforming node communities of line graph back to graph. The experimental results show that our algorithm achieves a higher performance on normalized mutual information metric. [ABSTRACT FROM AUTHOR]

In this paper, a new chaotic image encryption algorithm based on pseudo-random bit sequence and DNA plane is proposed. The coupled map lattice (CML) is applied to design a pseudo-random bit sequence generation (PBSG) system and use the system to generate the random sequence needed in the encryption process. The initial values and parameters of the system are generated by the SHA-256 hash algorithm combined with given keys. Firstly, the plane image is decomposed into four DNA planes in combination with the DNA encoding rules, and then the four DNA planes are subjected to row circular permutation and column circular permutation. After that, the diffusion operation on each DNA plane is performed. Finally, the four DNA planes are decoded and then combined into a pixel matrix, that is, the final cipher image is obtained. Throughout the encryption process, the choice of DNA encoding and decoding rules is determined by the PBSG system. Simulation results and security analysis show that the algorithm not only has good encryption effect, but also can resist various classic attacks, and has excellent security performance. [ABSTRACT FROM AUTHOR]

The sample size based on the Linex loss function and Blinex loss function is studied in this paper, and the analytical solution of the optimal sample size is deduced on the assumption that the Linex loss function and the normal distribution exist. For the Blinex loss function, an accurate algorithm was presented to obtain the optimal sample size. Furthermore, the optimal sample size is obtained, respectively, by taking Poisson distribution and normal distribution as examples. Due to the wide application of Blinex function in reality, the algorithm presented in this paper has immediate applications. [ABSTRACT FROM AUTHOR]

This paper studies the dynamics of solitons to the nonlinear Schrödinger's equation (NLSE) with spatio-temporal dispersion (STD). The integration algorithm that is employed in this paper is the Riccati-Bernoulli sub-ODE method. This leads to dark and singular soliton solutions that are important in the field of optoelectronics and fiber optics. The soliton solutions appear with all necessary constraint conditions that are necessary for them to exist. There are four types of nonlinear media studied in this paper. They are Kerr law, power law, parabolic law and dual law. The conservation laws (Cls) for the Kerr law and parabolic law nonlinear media are constructed using the conservation theorem presented by Ibragimov. [ABSTRACT FROM AUTHOR]

Coarse-graining of complex networks is one of the important algorithms to study large-scale networks, which is committed to reducing the size of networks while preserving some topological information or dynamic properties of the original networks. Spectral coarse-graining (SCG) is one of the typical coarse-graining algorithms, which can keep the synchronization ability of the original network well. However, the calculation of SCG is large, which limits its real-world applications. And it is difficult to accurately control the scale of the coarse-grained network. In this paper, a new SCG algorithm based on K-means clustering (KCSCG) is proposed, which cannot only reduce the amount of calculation, but also accurately control the size of coarse-grained network. At the same time, KCSCG algorithm has better effect in keeping the network synchronization ability than SCG algorithm. A large number of numerical simulations and Kuramoto-model example on several typical networks verify the feasibility and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Modern space vehicles face the challenges to obtain more and more accurate attitudes in order to complete the demanding tasks. Onboard star sensors which identify the observed stars in the field of view according to the loaded guide star catalog and output accurate attitude have attracted most interests. Guide stars are usually required to distribute uniformly on the celestial sphere to improve the performance of the star pattern identification. An optimal selection algorithm is proposed to achieve an even distribution of guide stars in this paper. Constellation features are discussed. The mean shift algorithm is analyzed. The idea that distributes stars in the local field of view to constellations is proposed by using the star pair angular separations according to the star positions in the inertial coordinate system. The optimal selection algorithm of guide stars based on star clustering is developed. Its detailed implement procedures are introduced completely. The guide star optimal selection experiment in visible band by using SAO star catalog as the original star data is implemented. It proves that the proposed algorithm has the virtue of simple calculation and easy realization. The obtained guide star distribution is superior to the regression selection algorithm and the magnitude weighted method. [ABSTRACT FROM AUTHOR]

Most of the existing image encryption algorithms had two basic properties: confusion and diffusion in a pixel-level plane based on various chaotic systems. Actually, permutation in a pixel-level plane could not change the statistical characteristics of an image, and many of the existing color image encryption schemes utilized the same method to encrypt R, G and B components, which means that the three color components of a color image are processed three times independently. Additionally, dynamical performance of a single chaotic system degrades greatly with finite precisions in computer simulations. In this paper, a novel coupled map lattice with time-varying delay therefore is applied in color images bit-level plane encryption to solve the above issues. Spatiotemporal chaotic system with both much longer period in digitalization and much excellent performances in cryptography is recommended. Time-varying delay embedded in coupled map lattice enhances dynamical behaviors of the system. Bit-level plane image encryption algorithm has greatly reduced the statistical characteristics of an image through the scrambling processing. The R, G and B components cross and mix with one another, which reduces the correlation among the three components. Finally, simulations are carried out and all the experimental results illustrate that the proposed image encryption algorithm is highly secure, and at the same time, also demonstrates superior performance. [ABSTRACT FROM AUTHOR]

Inspired by the missing (new) link prediction and the spurious existing link identification in link prediction theory, this paper establishes an auxiliary optimization method for public transit route network (PTRN) based on link prediction. First, link prediction applied to PTRN is described, and based on reviewing the previous studies, the summary indices set and its algorithms set are collected for the link prediction experiment. Second, through analyzing the topological properties of Jinan's PTRN established by the Space R method, we found that this is a typical small-world network with a relatively large average clustering coefficient. This phenomenon indicates that the structural similarity-based link prediction will show a good performance in this network. Then, based on the link prediction experiment of the summary indices set, three indices with maximum accuracy are selected for auxiliary optimization of Jinan's PTRN. Furthermore, these link prediction results show that the overall layout of Jinan's PTRN is stable and orderly, except for a partial area that requires optimization and reconstruction. The above pattern conforms to the general pattern of the optimal development stage of PTRN in China. Finally, based on the missing (new) link prediction and the spurious existing link identification, we propose optimization schemes that can be used not only to optimize current PTRN but also to evaluate PTRN planning. [ABSTRACT FROM AUTHOR]

Currently, community detection is a hot topic. This paper, based on the self-organizing map (SOM) algorithm, introduced the idea of self-adaptation (SA) that the number of communities can be identified automatically, a novel algorithm SA-SOM of detecting communities in complex networks is proposed. Several representative real-world networks and a set of computer-generated networks by LFR-benchmark are utilized to verify the accuracy and the efficiency of this algorithm. The experimental findings demonstrate that this algorithm can identify the communities automatically, accurately and efficiently. Furthermore, this algorithm can also acquire higher values of modularity, NMI and density than the SOM algorithm does. [ABSTRACT FROM AUTHOR]

How to identify influential nodes in complex networks continues to be an open issue. A number of centrality measures have been presented to address this problem. However, these studies focus only on a centrality measure and each centrality measure has its own shortcomings and limitations. To solve the above problems, in this paper, a novel method is proposed to identify influential nodes based on combining of the existing centrality measures. Because information flow spreads in different ways in different networks, in the specific network, the appropriate centrality measures should be selected to calculate the ranking of nodes. Then, an interval can be generated for the ranking of each node, which includes the upper limit and lower limit obtained from different centrality measures. Next, the final ranking of each node can be determined based on the median of the interval. In order to illustrate the effectiveness of the proposed method, four experiments are conducted to identify vital nodes simulations on four real networks, and the superiority of the method can be demonstrated by the results of comparison experiments. [ABSTRACT FROM AUTHOR]

Understanding the structural characteristics of urban traffic network comprehensively can provide references for improving road utilization rate and alleviating traffic congestion. This paper focuses on the spatial-temporal correlations between different pairs of traffic series and proposes a complex network-based method of constructing the urban traffic network. In the network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding spatial-temporal correlation. Further, a modified PageRank algorithm, named the geographical weight-based PageRank algorithm (GWPA), is proposed to analyze the spatial distribution of important segments in the road network. Finally, experiments are conducted by using three kinds of traffic series collected from the urban road network in Beijing. Experimental results show that the urban traffic networks constructed by three traffic variables all indicate both small-world and scale-free characteristics. Compared with the results of PageRank algorithm, GWPA is proved to be valid in evaluating the importance of segments and identifying the important segments with small degree. [ABSTRACT FROM AUTHOR]

Because of the shortage of rule gap measurement for light guide plate (LGP) warpage degree, image definition criterion of quality indexing is present based on single CCD camera. The detection paper and LGP are respectively arranged on the camera detection device platform, located by X- and Y-axes driving unit and focused by Z-axis driving unit firstly, different image definition criterion peaks of edge detection evaluation function are obtained in the middle surface of LGP bottom and surface. Finally, different edge detection algorithms are compared under different situations. Results show that the algorithm has good repetition and can meet the online, untouched detection needs. [ABSTRACT FROM AUTHOR]

Azimuth electromagnetic wave is a new type of electromagnetic prospecting technology. It can detect weak electromagnetic wave signal and realize real-time formation conductivity imaging. For effectively optimizing measurement accuracy of azimuth electromagnetic wave imaging tool, the efficient numerical simulation algorithm is required. In this paper, self-adaptive finite element method (FEM) has been used to investigate the azimuth electromagnetic wave logging tool response by adjusting antenna array system in different geological conditions. Numerical simulation examples show the accuracy and efficiency of the method, and provide physical interpretation of amplitude attenuation and phase shift of electromagnetic wave signal. Meanwhile, the high-accuracy numerical simulation results have great value to azimuth electromagnetic wave imaging tool calibration and data interpretation. [ABSTRACT FROM AUTHOR]

Although the linear minimum mean square error (LMMSE) filter removes speckle in polarimetric synthetic aperture radar (PolSAR) images, it has the disadvantage of losing edge detail. In this paper, we propose a new filter based on robust independent component analysis (RICA) and LMMSE. This approach describes edge features in a span image by selecting the adaptive direction window and calculating the edge weight value of the spatial domain, and improves the objective function by using a step polynomial to extract the estimate of the source image with minimum noise. This technique preserves not only the edge information in the images, but also the polarimetric information. Experiments were conducted on the NASA/JPL AIRSAR L-band of the San Francisco area, and evaluated by means of the speckle reduction index and the edge preservation index. The experimental results show that the proposed method effectively reduces speckle, retains edges, and preserves the polarimetric scattering mechanisms. [ABSTRACT FROM AUTHOR]

The infrared traffic image acquired by the intelligent traffic surveillance equipment has low contrast, little hierarchical differences in perceptions of image and the blurred vision effect. Therefore, infrared traffic image enhancement, being an indispensable key step, is applied to nearly all infrared imaging based traffic engineering applications. In this paper, we propose an infrared traffic image enhancement algorithm that is based on dark channel prior and gamma correction. In existing research dark channel prior, known as a famous image dehazing method, here is used to do infrared image enhancement for the first time. Initially, in the proposed algorithm, the original degraded infrared traffic image is transformed with dark channel prior as the initial enhanced result. A further adjustment based on the gamma curve is needed because initial enhanced result has lower brightness. Comprehensive validation experiments reveal that the proposed algorithm outperforms the current state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Efficient task scheduling is critical to achieve high performance in a heterogeneous multi-core computing environment. The paper focuses on the heterogeneous multi-core directed acyclic graph (DAG) task model and proposes a novel task scheduling method based on an improved chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm. A task priority scheduling list was built. A processor with minimum cumulative earliest finish time (EFT) was acted as the object of the first task assignment. The task precedence relationships were satisfied and the total execution time of all tasks was minimized. The experimental results show that the proposed algorithm has the advantage of optimization abilities, simple and feasible, fast convergence, and can be applied to the task scheduling optimization for other heterogeneous and distributed environment. [ABSTRACT FROM AUTHOR]

This paper considered the parameter identification problem of Hammerstein finite impulse response models and a novel stochastic gradient identification algorithm is derived for the Hammerstein system modeling. By using the gradient search principle and minimizing the quadratic criterion functions, the presented stochastic gradient identification algorithm has a better computational efficiency. The given simulation validates that the proposed algorithm can identify the wind power characteristic curve accurately and contributes to calculate the wind power curtailment prediction. [ABSTRACT FROM AUTHOR]

The high mounting precision of the fiber underwater acoustic array leads to an array manifold without perturbation. Besides, the targets are either static or slowly moving in azimuth in underwater acoustic array signal processing. Therefore, the covariance matrix can be estimated accurately by prolonging the observation time. However, this processing is limited to poor bearing resolution due to small aperture, low SNR and strong interferences. In this paper, diagonal rejection (DR) technology for Minimum Variance Distortionless Response (MVDR) was developed to enhance the resolution performance. The core idea of DR is rejecting the main diagonal elements of the covariance matrix to improve the output signal to interference and noise ratio (SINR). The definition of SINR here implicitly assumes independence between the spatial filter and the received observations at which the SINR is measured. The power of noise converges on the diagonal line in the covariance matrix and then it is integrated into the output beams. With the diagonal noise rejected by a factor smaller than 1, the array weights of MVDR will concentrate on interference suppression, leading to a better resolution capability. The algorithm was theoretically proved with optimal rejecting coefficient derived under both infinite and finite snapshots scenarios. Numerical simulations were conducted with an example of a linear array with eight elements half-wavelength spaced. Both resolution and Direction-of-Arrival (DOA) performances of MVDR and DR-based MVDR (DR-MVDR) were compared under different SNR and snapshot numbers. A conclusion can be drawn that with the covariance matrix accurately estimated, DR-MVDR can provide a lower sidelobe output level and a better bearing resolution capacity than MVDR without harming the DOA performance. [ABSTRACT FROM AUTHOR]

Currently, the majority of the Network-on-Chip (NoC) researches are based on 2D algorithm or simple 3D structure. However, the congestion and faulty links in the topology can increase the latency and power consumption. In this paper, the authors try to build a novel 3D topology based on hierarchical structure and TSV links which can reduce the latency and power consumption by decreasing the hops during the process of passing the packets. We employ the C++ tool to test our method, and the results show that the performance can be improved about 21%-36% in throughput, also 3%-11% in latency. [ABSTRACT FROM AUTHOR]

Multi-objective optimization problems (MOPs) need to be solved in real world recently. In this paper, a multi-objective particle swarm optimization based on Pareto set and aggregation approach was proposed to deal with MOPs. Firstly, velocities and positions were updated similar to PSO. Then, global-best set was defined in particle swarm optimizer to preserve Pareto-based set obtained by the population. Specifically, a hybrid updating strategy based on Pareto set and aggregation approach was introduced to update the global-best set and local search was carried on global-best set. Thirdly, personal-best positions were updated in decomposition way, and global-best position was selected from global-best set. Finally, ZDT instances and DTLZ instances were selected to evaluate the performance of MULPSO and the results show validity of the proposed algorithm for MOPs. [ABSTRACT FROM AUTHOR]

This paper presents an attitude solution algorithm based on the Micro-Electro-Mechanical System and quaternion method. We completed the numerical calculation and engineering practice by adopting fourth-order Runge-Kutta algorithm in the digital signal processor. The state space mathematical model of initial alignment in static base was established, and the initial alignment method based on Kalman filter was proposed. Based on the hardware in the loop simulation platform, the short-range flight simulation test and the actual flight test were carried out. The results show that the error of pitch, yaw and roll angle is fast convergent, and the fitting rate between flight simulation and flight test is more than 85%. [ABSTRACT FROM AUTHOR]

In order to improve the reliability of the spectrum feature extracted by wavelet transform, a method combining wavelet transform (WT) with bacterial colony chemotaxis algorithm and support vector machine (BCC-SVM) algorithm (WT-BCC-SVM) was proposed in this paper. Besides, we aimed to identify different kinds of pesticide residues on lettuce leaves in a novel and rapid non-destructive way by using fluorescence spectra technology. The fluorescence spectral data of 150 lettuce leaf samples of five different kinds of pesticide residues on the surface of lettuce were obtained using Cary Eclipse fluorescence spectrometer. Standard normalized variable detrending (SNV detrending), Savitzky-Golay coupled with Standard normalized variable detrending (SG-SNV detrending) were used to preprocess the raw spectra, respectively. Bacterial colony chemotaxis combined with support vector machine (BCC-SVM) and support vector machine (SVM) classification models were established based on full spectra (FS) and wavelet transform characteristics (WTC), respectively. Moreover, WTC were selected by WT. The results showed that the accuracy of training set, calibration set and the prediction set of the best optimal classification model (SG-SNV detrending-WT-BCC-SVM) were 100%, 98% and 93.33%, respectively. In addition, the results indicated that it was feasible to use WT-BCC-SVM to establish diagnostic model of different kinds of pesticide residues on lettuce leaves. [ABSTRACT FROM AUTHOR]

In this paper, a speech emotion recognition (SER) algorithm was proposed to improve the emotional perception of hearing-impaired people. The algorithm utilizes multiple kernel technology to overcome the drawback of SVM: slow training speed. Firstly, in order to improve the adaptive performance of Gaussian Radial Basis Function (RBF), the parameter determining the nonlinear mapping was optimized on the basis of Kernel target alignment. Then, the obtained Kernel Function was used as the basis kernel of Multiple Kernel Learning (MKL) with slack variable that could solve the over-fitting problem. However, the slack variable also brings the error into the result. Therefore, a soft-margin MKL was proposed to balance the margin against the error. Moreover, the relatively iterative algorithm was used to solve the combination coefficients and hyper-plane equations. Experimental results show that the proposed algorithm can acquire an accuracy of 90% for five kinds of emotions including happiness, sadness, anger, fear and neutral. Compared with KPCA+CCA and PIM-FSVM, the proposed algorithm has the highest accuracy. [ABSTRACT FROM AUTHOR]

The formality of signed relationships has been recently adopted in a lot of complicated systems. The relations among these entities are complicated and multifarious. We cannot indicate these relationships only by positive links, and signed networks have been becoming more and more universal in the study of social networks when community is being significant. In this paper, to identify communities in signed networks, we exploit a new greedy algorithm, taking signs and the density of these links into account. The main idea of the algorithm is the initial procedure of signed modularity and the corresponding update rules. Specially, we employ the 'Asymmetric and Constrained Belief Evolution' procedure to evaluate the optimal number of communities. According to the experimental results, the algorithm is proved to be able to run well. More specifically, the proposed algorithm is very efficient for these networks with medium size, both dense and sparse. [ABSTRACT FROM AUTHOR]

Currently, with the rapid development of information technology, the electronic media for social communication is becoming more and more popular. Discovery of communities is a very effective way to understand the properties of complex networks. However, traditional community detection algorithms consider the structural characteristics of a social organization only, with more information about nodes and edges wasted. In the meanwhile, these algorithms do not consider each node on its merits. Label propagation algorithm (LPA) is a near linear time algorithm which aims to find the community in the network. It attracts many scholars owing to its high efficiency. In recent years, there are more improved algorithms that were put forward based on LPA. In this paper, an improved LPA based on random walk and node importance (NILPA) is proposed. Firstly, a list of node importance is obtained through calculation. The nodes in the network are sorted in descending order of importance. On the basis of random walk, a matrix is constructed to measure the similarity of nodes and it avoids the random choice in the LPA. Secondly, a new metric IAS (importance and similarity) is calculated by node importance and similarity matrix, which we can use to avoid the random selection in the original LPA and improve the algorithm stability. Finally, a test in real-world and synthetic networks is given. The result shows that this algorithm has better performance than existing methods in finding community structure. [ABSTRACT FROM AUTHOR]

A numerical method for the simulation of parachute inflation process is presented in this paper. The unsteady compressible N-S equations are fully coupled with MSD (Mass Spring Damper) structure model and integrated forward in time. The CFD solver is based on an unstructured finite volume algorithm and the preconditioning technique is applied to alleviate the stiffness caused by low Mach number. The Spalart-Allmaras one-equation turbulence model is implemented to evaluate the turbulent viscosity. The whole system (fluid equations and structural model equations) is marched implicitly in time using a dual time stepping method. An overset deforming grids method is adopted in this paper to deal with the very large domain deformation during the parachute inflation process. Finally numerical test is performed to validate the robustness of this method. [ABSTRACT FROM AUTHOR]

This paper presents the application of an adaptive stencil diffuse interface method to the simulation of dam break problem. The adaptive stencil diffuse interface method is the combination of the diffuse interface method and a stencil adaptive algorithm, where the diffuse interface method is used as the solver, and the adaptive stencil refinement scheme is applied to improve the resolution around the interface so that the fine-scale interface behavior can be captured. In this paper, we use this method to simulate the dam break problem, study the dam height and leading edge position, and compare our results with the experiment data available in the literature. It is shown that the results using the adaptive stencil diffuse interface method agree very well with the experimental results. [ABSTRACT FROM AUTHOR]

The text content of Aviation Safety Confidential Reports contains a large number of valuable information. Term frequency-inverse document frequency algorithm is commonly used in text analysis, but it does not take into account the sequential relationship of the words in the text and its role in semantic expression. According to the seven category labels of civil aviation unsafe incidents, aiming at solving the problems of TF-IDF algorithm, this paper improved TF-IDF algorithm based on co-occurrence network; established feature words extraction and words sequential relations for classified incidents. Aviation domain lexicon was used to improve the accuracy rate of classification. Feature words network model was designed for multi-documents unsafe incidents classification, and it was used in the experiment. Finally, the classification accuracy of improved algorithm was verified by the experiments. [ABSTRACT FROM AUTHOR]

In this paper, a novel optical absorber that is flexible in size for fabrication and unique properties is studied numerically by aid of finite difference in time domain (FDTD) algorithm. The structure consists of three layers based on the metal-dielectric-metal design scheme. The middle layer is a dielectric in which the electronic and magnetic resonance energy dissipates. The bottom side is a metallic plane and the top layer is a hexagonal prism array. We also investigate the influence of the structure parameters on the absorbance and the absorption wavelength. The results show that a perfect absorber can be designed, and the number of the absorption peaks varies from two to three for the different radius of circumcircle of the hexagonal prism ranging from 280 nm to 400 nm. Meanwhile, position of the first absorption peak is nearly unchanged. This unique feature may be a significant advantage and possesses great potential applications such as biosensing and photovoltaic. Additionally, position and amplitude of the first absorption peak is constant for broad incident angles. The absorption is insensitive to the polarization of the incident beam due to the highly symmetric structure. [ABSTRACT FROM AUTHOR]

In complex network analysis, the local community detection problem is getting more and more attention. Because of the difficulty to get complete information of the network, such as the World Wide Web, the local community detection has been proposed by researcher. That is, we can detect a community from a certain source vertex with limited knowledge of an entire graph. The previous methods of local community detection now are more or less inadequate in some places. In this paper, we have proposed a new local modularity metric and based on it, a two-phase algorithm is proposed. The method we have taken is a greedy addition algorithm which means adding vertices into the community until does not increase. Compared with the previous methods, when our method is calculating the modularity metric, the range of vertices what we considered may affect the quality of the community detection wider. The results of experiments show that whether in computer-generated random graph or in the real networks, our method can effectively solve the problem of the local community detection. [ABSTRACT FROM AUTHOR]

In this paper, we propose a localization simulator based on the random walk/waypoint mobility model and a hybrid-type location–compensation algorithm using the Mean kShift/Kalman filter (MSKF) to enhance the precision of the estimated location value of mobile modules. From an analysis of our experimental results, the proposed algorithm using the MSKF can better compensate for the error rates, the average error rate per estimated distance moved by the mobile node (Err_ RateDV) and the error rate per estimated trace value of the mobile node (Err_RateTV) than the Mean shift or Kalman filter up to a maximum of 29% in a random mobility environment for the three scenarios. [ABSTRACT FROM AUTHOR]

The evaluation of node importance has great significance to complex network, so it is important to seek and protect important nodes to ensure the security and stability of the entire network. At present, most evaluation algorithms of node importance adopt the single-index methods, which are incomplete and limited, and cannot fully reflect the complex situation of network. In this paper, after synthesizing multi-index factors of node importance, including eigenvector centrality, betweenness centrality, closeness centrality, degree centrality, mutual-information, etc., the authors are proposing a new multi-index evaluation algorithm of identifying important nodes in complex networks based on linear discriminant analysis (LDA). In order to verify the validity of this algorithm, a series of simulation experiments have been done. Through comprehensive analysis, the simulation results show that the new algorithm is more rational, effective, integral and accurate. [ABSTRACT FROM AUTHOR]