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This paper presents the implementation of two digital noise-cancellation circuits (DNCCs) using a proposed algorithm and a Karnaugh map for a 2 + 1 multistage noise-shaping (MASH) delta-sigma modulator (DSM). The MASH architecture inherits a superior signal-to-noise-and-distortion ratio (SNDR) with the aid of an efficient noise-cancellation technique either in the analogue or digital domain. The key motivation of this study was to design an area-efficient DNCC. The first approach employed a proposed algorithm (Algorithm-based DNCC) to implement the DNCC and to construct a delay block with an inverter and transmission gate. The second approach involved a Karnaugh map (K-map DNCC) and a delay block with a pair of D flip-flops. A maximum simulated signal-to-noise ratio of 135 dB was completed with optimal analogue scaling coefficients for the proposed 2 + 1 MASH DSM with DNCC. The simulated SNDRs of the Algorithm-based DNCC and K-map DNCC were 91.04 dB and 91.16 dB, respectively. Measured results show that the SNDR of the Algorithm-based DNCC, the SNDR of the K-map DNCC, power consumption and core area are approximately 58.7 dB, 62.1 dB, 0.26 μ W and 2275 μ m2, respectively, for the designed DNCCs with an operating frequency of 10.24 MHz and supply voltage of 1.8 V. The transistor counts of the Algorithm-based DNCC are 74 transistors, while they are 106 transistors for the K-map DNCC. The proposed Algorithm-based DNCC saves 32 transistors and approximately reduces its chip area to 69.8% of the K-map DNCC. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the properties of well-rounded twists of a given ideal lattice of an imaginary quadratic field K. We show that every ideal lattice I of K has at least one well-rounded twist lattice. Moreover, we provide an explicit algorithm to compute all well-rounded twists of I. [ABSTRACT FROM AUTHOR]

In recent years, the study of social-based recommender systems has become an active research topic. We incorporate a combination of static and dynamic interest characteristics to predict users' real-time dynamic interests, which has rarely been considered in previous studies. In this paper, we propose a graph neural network social recommendation model that integrates static and dynamic feature relationships (FSDFR-GNNSR). The model uses a graph embedding algorithm to extract static features of users and movies, and takes the static features as input to gated recurrent unit (GRU), so that the model can take static features into consideration while modeling user dynamic behavior. Finally, we use graph attention networks to represent the dynamic influence of friends, simplify the update strategy of second-order neighbor nodes. We apply graph pooling operations to improve the generalization ability of the algorithm. Empirical analyses on real datasets show that the proposed approach achieves superior performance to existing approaches. [ABSTRACT FROM AUTHOR]

A matrix with entries 0 , 1 is graphical if it is symmetric and all its diagonal entries are zero. Let G 1 , G 2 and G 3 be graphs defined on the same set of vertices. The graph G 3 is said to be the matrix product of graphs G 1 and G 2 , if A (G 1) A (G 2) = A (G 3) , where A (G i) is the adjacency matrix of the graph G i , 1 ≤ i ≤ 3. In such a case, we say that G 1 and G 2 are companions of each other. The main purpose of this paper is to design an algorithm to check whether a given graph G has a companion. We derive conditions on n and m so that the generalized wheel graph, denoted by W m , n , has a companion and also show that the m th power of the path graph P n has no companion. Finally, we indicate a possible application of the algorithm in a problem of coloring of edges of the complete graph K n . [ABSTRACT FROM AUTHOR]

The emergence of computation intensive automotive applications poses significant challenges on computation capacity of automotive electronic systems, thus vehicular edge computing (VEC) is introduced as a new computing paradigm into the internet of vehicle (IoV) to improve its data processing capability. However, as the computation capacity is limited in VEC servers, efficient task offloading algorithms need to be proposed. This paper first proposes a multi-task offloading model and gives the related task response time analysis method, and then, both a mixed-integer linear programming (MILP)-based algorithm and a simulated annealing-based heuristic algorithm are proposed to minimize the task response time. By comparing with a baseline algorithm, the MILP-based offloading algorithm can reduce the average task response time by 91.45%, and the heuristic offloading algorithm can reduce the average task response time by 70%. [ABSTRACT FROM AUTHOR]

To solve the problem of low accuracy in the traditional FunkSVD recommendation algorithm, an improved FunkSVD algorithm (RM-FS) is proposed. RM-FS is an improvement of the traditional FunkSVD algorithm, using RMSProp, a deep learning optimization algorithm. The RM-FS algorithm can not only solve the problem of reduced accuracy of the traditional FunkSVD algorithm because of iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, achieving the effect of improving the accuracy of the traditional algorithm. The experimental results show that the RM-FS algorithm proposed in this paper effectively improves the accuracy of the recommendation algorithm, which is better than the traditional FunkSVD recommendation algorithm and other improved FunkSVD algorithms. [ABSTRACT FROM AUTHOR]

In many engineered and natural networked systems, there has been great interest in leader selection and/or edge assignment during the optimal design of controllable networks. In this paper, we present our pioneering work in leader–follower network design via memetic algorithms, which focuses on minimizing the number of leaders or the amount of control energy while ensuring network controllability. We consider three problems in this paper: (1) selecting the minimum number of leaders in a pre-defined network with guaranteed network controllability; (2) selecting the leaders in a pre-defined network with the minimum control energy; and (3) assigning edges (interactions) between nodes to form a controllable leader–follower network with the minimum control energy. The proposed framework can be applied in designing signed, unsigned, directed, or undirected networks. It should be noted that this work is the first to apply memetic algorithms in the design of controllable networks. We chose memetic algorithms because they have been shown to be more efficient and more effective than the standard genetic algorithms in solving some optimization problems. Our simulation results provide an additional demonstration of their efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, a steganography algorithm has been proposed which is based on quantization table modification and image scrambling in the DCT domain. First, the algorithm homogenizes the energy by scrambling the cover image to improve the number of DCT coefficients suitable for information embedding. Second, the embedding capacity of the DCT block is determined by the value of the quantization table in the algorithm, different quantization tables get different embedding effects. In addition, this paper proposes an optimized modified quantization table. Extensive experiments show that the proposed algorithm achieves a great potential for confidential data and indiscernible image quality. [ABSTRACT FROM AUTHOR]

Vocal fold, a significant body structure, is accountable for phonation, which regulates air motion within and out of the lungs. The disorders in the vocal fold influence the quality of life. Thus, diagnosis of vocal fold disorders has a significant need, and CT of the neck is employed for an effective imaging scheme. Accordingly, this paper proposes an advanced multi-resolution algorithm (MRA) that optimally identifies and classifies pathologies. The vocal regions are acquired using the genetic k-means algorithm. The pathology features are generated using the local directional pattern (LDP) fed to pathology classification using moth search-rider optimization-based deep convolutional neural networks (MRA-based DCNN). The hybrid optimization (MRA), integrates the standard rider optimization algorithm (ROA) and moth search algorithm (MS) that trains deep learning classifier (DCNN). The analysis using the real databases regarding the performance metrics divulge that the proposed pathology detection module obtained the accuracy, specificity, and sensitivity of 97.020%, 91.698%, and 96.624%. [ABSTRACT FROM AUTHOR]

Application-Specific ICs (ASIC) are manufactured in bulk for a long time. In this paper, an approach to High-Speed Application-Independent IC (HS-AIIC) design is discussed. A resolution-selective (RS) and resolution-adaptive (RA) 8-bit Flash ADC are designed for use in various high-speed applications. With the choice of resolution, one can work with the trade-off between speed, power consumption, and resolution for a particular application. The proposed resolution selection algorithm can be implemented for any set of resolutions for a flash ADC design. Further, an adaptive block is added to make the ADC design adaptive in nature so that we do not have to select a particular resolution manually. The proposed design entrusts on saving manufacturing cost and increases the functionality of ADC on a single chip. Proposed resolution adaptive 8-bit flash ADC design dissipates 512 mW of power with an ENOB of 7.56 bits and SNDR of 46.27 dB for 1 GHz sampling clock pulse. [ABSTRACT FROM AUTHOR]

Applications relying on the Ethernet IEEE 802.3ba and IEEE802.3b standards require secure data encryption and authentication at extremely high speeds (at least 100 Gbit/s). The Galois/Counter Mode (GCM) is currently considered the de facto standard for hardware high-speed authenticated encryption, although other algorithms have been proposed in the literature such as the Offset Codebook Mode (OCB). The challenge in terms of providing security for high-speed applications is to achieve implementations that explore the parallelism of these algorithms; however, this translates into area cost. In this work, we propose an alternative to GCM and OCB. We show that a combination of the Offset Two-Round authenticated-encryption scheme with the AES block cipher (known as AES-OTR) is exceptionally well suited for exploiting fine-grained parallelism, and can therefore be used to achieve ultra-high-speed data encryption rates. The experiments reported in this paper show that our pipeline-parallel implementation of AES-OTR outperforms the GCM and OCB schemes in terms of throughput per area while using almost half of the logic resources. Our implementation used a Stratix 4 FPGA device as well as several devices from the Virtex family. Implementations of AES-OTR on Stratix 4 used 11kALMs and achieved a throughput of 143.65 Gbit/s. On the Virtex Ultrascale, our design used 31,859 LUTs with a throughput of 204.92 Gbit/s. [ABSTRACT FROM AUTHOR]

Saddle fixed points are the centerpieces of complicated dynamics in a system. The one-dimensional stable and unstable manifolds of these saddle-points are crucial to understanding the dynamics of such systems. While the problem of sketching the unstable manifold is simple, plotting the stable manifold is not as easy. Several algorithms exist to compute the stable manifold of saddle-points, but they have their limitations, especially when the system is not invertible. In this paper, we present a new algorithm to compute the stable manifold of two-dimensional systems which can also be used for noninvertible systems. After outlining the logic of the algorithm, we demonstrate the output of the algorithm on several examples. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN) allude to gathering of spatially fragmented and committed sensors for observing and documenting various physical and climatic variables like temperature, moistness and, so on. WSN is quickly growing its work in different fields like clinical, enterprises, climate following and so on. However, the sensor nodes have restricted battery life and substitution or re-energizing of these batteries in the sensor nodes is exceptionally troublesome for the most parts. Energy effectiveness is the significant worry in the remote sensor networks as it is significant for keeping up its activity. In this paper, clustering algorithms based on sensor module energy states to strengthen the network longevity of wireless sensor networks is proposed (i.e. modified MPCT algorithm) in which cluster head determination depends on the every cluster power centroid as well as power of the sensor nodes. Correspondence between cluster leader and sink module employ a parameter distance edge for lessening energy utilization. The outcome got shows a normal increment of 60% in network lifetime compared to Low energy adaptive protocol, Energy efficient midpoint initialization algorithm (EECPK-means), Park K-means algorithm and Mobility path selection protocol. [ABSTRACT FROM AUTHOR]

Data mining has become a relatively modern platform for information retrieval. The efficient data mining techniques can increase the reliability and accuracy of internal auditing for the various community even while lowering audit risk. Existing audit data mining approaches lack significant identification of hidden connections and interactions in bid data platforms. Hence, this study extends the literature survey on the signification of audit data mining in multiple applications. This survey identifies the scope of improved association algorithms in audit data mining, a rule-based machine learning approach to determine the exciting relationship among variables in large audit datasets. Therefore, a Conceptual Framework of Improved Association Algorithm (CFiAA) and its application in audit data mining is proposed. This study examines the strengths and weaknesses of the proposed CFiAA in audit mining. The proposed model has been trained using an audit data set and validates with various audit datasets. Finally, this paper presents the comparative analysis of the proposal to show its highest performance related to existing models. Thus, CFiAA scores the performance ratio of 94.5%, accuracy ratio of 92.4%, an efficiency ratio of 92.5%, F1 measure of 91.8%, error rate 32.5%, prediction ratio of 93.7%, and the precision ratio of 92.5% compared to existing models. [ABSTRACT FROM AUTHOR]

In this paper, we focus on discussing diagonal solutions and general solutions of second-order matrix polynomial equation of high degree in complex field. By characterizing some algebraic properties of the mentioned two types of the solutions, we present sufficient conditions that a general second-order matrix polynomial equation has diagonal solutions or general solutions. Analytic expressions of the solutions, as well as the corresponding algorithms for finding the solutions are provided. An example is given so as to verify the theoretical results we have derived. [ABSTRACT FROM AUTHOR]

In many public spaces (e.g. colleges and shopping malls), people are frequently distributed discretely, and thus, single-source evacuation, which means there's only one point of origin, is not always a feasible solution. Hence, this paper discusses a multi-source evacuation model and algorithm, which are intended to evacuate all the people that are trapped within the minimum possible time. This study presents a fast flow algorithm to prioritize the most time-consuming source point under the constraint of route and exit capacity to reduce the evacuation time. This fast flow algorithm overcomes the deficiencies in the existing global optimization fast flow algorithm and capacity constrained route planner (CCRP) algorithm. For the fast flow algorithm, the first step is to determine the optimal solution to single-source evacuation and use the evacuation time of the most time-consuming source and exit gate set as the initial solution. The second step is to determine a multi-source evacuation solution by updating the lower limit of the current evacuation time and the exit gate set continually. The final step is to verify the effectiveness and feasibility of the algorithm through comparison. [ABSTRACT FROM AUTHOR]

The key expansion algorithm is an important part of a general iterated block cipher, because its security determines the security level of the encryption scheme. Thus, generating secure round keys with statistical independence and sensitivity is desirable. An irreversible parallel key expansion algorithm is proposed based on a chaotic map in this paper. First, an enhanced nondegenerate 2D exponential chaotic map (2D-ECM) with ergodicity is constructed, with analysis results demonstrating that it has better dynamical characteristics. Then, an irreversible key expansion algorithm is designed based on the 2D-ECM, which has high sensitivity to initial key with independence between round keys. Experimental results and performance analysis demonstrate that the algorithm is feasible and can resist the side channel power attack. [ABSTRACT FROM AUTHOR]

There are various applications of clustering in the fields of machine learning, data mining, data compression along with pattern recognition. The existent techniques like the Llyods algorithm (sometimes called k-means) were affected by the issue of the algorithm which converges to a local optimum along with no approximation guarantee. For overcoming these shortcomings, an efficient k-means clustering approach is offered by this paper for stream data mining. Coreset is a popular and fundamental concept for k-means clustering in stream data. In each step, reduction determines a coreset of inputs, and represents the error, where P represents number of input points according to nested property of coreset. Hence, a bit reduction in error of final coreset gets n times more accurate. Therefore, this motivated the author to propose a new coreset-reduction algorithm. The proposed algorithm executed on the Covertype dataset, Spambase dataset, Census 1990 dataset, Bigcross dataset, and Tower dataset. Our algorithm outperforms with competitive algorithms like Streamkm++, BICO (BIRCH meets Coresets for k-means clustering), and BIRCH (Balance Iterative Reducing and Clustering using Hierarchies. [ABSTRACT FROM AUTHOR]

To improve the security level of image encryption based on chaos theory, a novel bit-level transform method which could enhance the dynamical performance of chaotic maps is proposed and a new image encryption algorithm based on a random irregular blocking method is also designed in this paper. Firstly, in the bit-level transform method, three chaotic maps are combined to generate a novel chaotic sequence. From a series of performance analyses results, the generated sequence not only successfully passes NIST tests, but also performs better than the original sequence, with a larger chaotic parametrial range, high complexity, etc. Then, an image encryption algorithm based on the newly generated chaotic sequence is proposed, in which a random irregular blocking method is adopted. Random irregular blocking could solve the problem of insecurity in the fixed blocking method. With a color Lena image as an encryption example, we analyze the security of the encryption algorithm by conducting a sufficiently large number of experiments using the encrypted image. For the encrypted Lena image, the average values of information entropy, NPCR, and UACI are 7.9931, 0.9961, and 0.3352, respectively. The experimental results and security analyses results provide evidence that the proposed encryption algorithm has good performance and can resist various typical attacks. [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]

Traditional interpolation algorithms often blur the edges of the target image due to low-pass filtering effects, making it difficult to obtain satisfactory visual effects. Especially when the reduction ratio becomes small, the phenomenon of jagged edges and partial information loss will occur. In order to obtain better image scaling quality, an adaptive image scaling algorithm based on continuous fraction interpolation and multi-resolution hierarchical processing is proposed. In order to overcome the noise problem of the original image, this paper first performs wavelet decomposition on the original image to obtain multiple images with different resolutions. Secondly, in order to eliminate the influence of local area variance on the overall image, weighted average is performed on images of different resolutions. Then, in order to overcome the blurring effect of the weighted average image, by calculating the variance of the three groups of pixels around the target pixel, selecting a group of pixels with the smallest variance and using the Salzer continuous fraction interpolation equation to obtain the gray value of the target pixel. Finally, the multiple corrected images are stitched together into a scaled image. The algorithm in this paper achieves a high-order smooth transition between pixels in the same feature area, and can also adaptively modify the pixels of the image. The experimental results show that the edge of the target image obtained by the algorithm in this paper is clear, and the algorithm complexity is low, which is convenient for hardware implementation and can realize real-time image scaling. [ABSTRACT FROM AUTHOR]

In mobile cloud computing, the computing resources of mobile devices can be integrated to execute complicated applications, in order to tackle the problem of insufficient resources of mobile devices. Such applications are, in general, characterized as workflows. Scheduling workflow tasks on a mobile cloud system consisting of heterogeneous mobile devices is a NP-hard problem. In this paper, intelligent algorithms, e.g., particle swarm optimization (PSO) and simulated annealing (SA), are widely used to solve this problem. However, both PSO and SA suffer from the limitation of easily being trapped into local optima. Since these methods rely on their evolutionary mechanisms to explore new solutions in solution space, the search procedure converges once getting stuck in local optima. To address this limitation, in this paper, we propose two effective metaheuristic algorithms that incorporate the iterated local search (ILS) strategy into PSO and SA algorithms, respectively. In case that the intelligent algorithm converges to a local optimum, the proposed algorithms use a perturbation operator to explore new solutions and use the newly explored solutions to start a new round of evolution in the solution space. This procedure is iterated until no better solutions can be explored. Experimental results show that by incorporating the ILS strategy, our proposed algorithms outperform PSO and SA in reducing workflow makespans. In addition, the perturbation operator is beneficial for improving the effectiveness of scheduling algorithms in exploring high-quality scheduling solutions. [ABSTRACT FROM AUTHOR]

Based on the dispersion entropy model, combined with multiscale analysis method and fractional order information entropy theory, this paper proposes new models — the generalized fractional order multiscale dispersion entropy (GMDE) and the generalized fractional order refined composite multiscale dispersion entropy (GRCMDE). The new models take the amplitude value information of the sequence itself into consideration, which can make better use of some key information in the sequence and have a higher stability and accuracy. In addition, extending the algorithm to generalized fractional order can make the model better capture the small evolution of the signal data, which is more advantageous for studying the dynamic characteristics of complex systems. This paper verifies the effectiveness of the new models by combining theoretical analysis with empirical research, and further studies the complexity of the financial system and the nature of its multiple time scales. The results show that the proposed GMDE, GRCMDE can better detect the intrinsic nature of financial time series and can distinguish the financial market complexity of different countries. [ABSTRACT FROM AUTHOR]

Based on the standard Marching Cubes (MC) algorithm, this paper proposes an improved MC algorithm. First, the original 15 topological configurations in the MC algorithm are increased to 24, which effectively avoid the generation of voids phenomenon. To further improve the speed of three-dimensional (3D) reconstruction, in this paper, the midpoint selection method is used instead of the linear interpolation method, and the 24 configurations are divided into three types. Each class corresponds to a thread. The multi-thread parallel processing is used to improve the calculation speed. The critical region is used to realize multi-thread synchronization, and then we designed a protocol mapping table according to the idea of the message mapping table. The function pointer is triggered by macro. Processing function is called by function pointer and completes the encapsulation of the protocol mapping table, which maintains the opening and closing principle of the class and ensures the scalability of the class. Through the improved MC algorithm accuracy verification and reconstruction speed verification, it is concluded that the improved MC algorithm can make up for the voids problem. By comparing the calculation time under the two platforms of Windows and Linux, the reconstruction speed of the improved MC algorithm is approximately 30% faster than the standard MC algorithm and 40% faster than the Masala algorithm. Finally, the algorithm is applied to the medical image 3D reconstruction system, and the accuracy and applicability of the algorithm are demonstrated by two sets of examples. [ABSTRACT FROM AUTHOR]

Complex network reconfiguration has always been an important task in complex network research. Simple and effective complex network reconstruction methods can promote the understanding of the operation of complex systems in the real world. There are many complex systems, such as stock systems, social systems and thermal power systems. These systems generally produce correlated time series of data. Discovering the relationships among these multivariate time series is the focus of this research. This paper proposes a Spearman coefficient reconstruction network (SCRN) method based on the Spearman correlation coefficient. In the SCRN method, we select entities in the real world as the nodes of the network and determine connection weights of the network edges by calculating the Spearman correlation coefficients among nodes. In this paper, we selected a stock system and boiler equipment in a thermal power generation system to construct two complex network models. For the stock network model, we used the classic Girvan–Newman (GN) algorithm for community discovery to determine whether the proposed network topology is reasonable. For the boiler network model, we built a predictive model based on an support vector regression (SVR) model in machine learning, and we verified the rationality of the boiler model by predicting the amount of boiler steam. [ABSTRACT FROM AUTHOR]

Threshold segmentation has been widely used in recent years due to its simplicity and efficiency. The method of segmenting images by the two-dimensional maximum entropy is a species of the useful technique of threshold segmentation. However, the efficiency and stability of this technique are still not ideal and the traditional search algorithm cannot meet the needs of engineering problems. To mitigate the above problem, swarm intelligent optimization algorithms have been employed in this field for searching the optimal threshold vector. An effective technique of lightning attachment procedure optimization (LAPO) algorithm based on a two-dimensional maximum entropy criterion is offered in this paper, and besides, a chaotic strategy is embedded into LAPO to develop a new algorithm named CLAPO. In order to confirm the benefits of the method proposed in this paper, the other seven kinds of competitive algorithms, such as Ant–lion Optimizer (ALO) and Grasshopper Optimization Algorithm (GOA), are compared. Experiments are conducted on four different kinds of images and the simulation results are presented in several indexes (such as computational time, maximum fitness, average fitness, variance of fitness and other indexes) at different threshold levels for each test image. By scrutinizing the results of the experiment, the superiority of the introduced method is demonstrated, which can meet the needs of image segmentation excellently. [ABSTRACT FROM AUTHOR]

In this paper, the Levy flight-based chicken swarm optimization (LFCSO) is proposed to follow the highest power of grid-joined photovoltaic (PV) framework. To analyze the grid-associated PV framework, the characteristics of current, power, voltage, and irradiance are determined. Because of the low yield voltage of the source PV, a big advance up converter with big productivity is required when the source PV is associated with the matrix power. A tale great advance up converter dependent on the exchanged capacitor and inductor is illustrated in this paper. The LFCSO algorithm with the adaptive neuro-fuzzy inference system is used to generate the control pulses of the transformer-coupled inductor DC–DC converter-less switched capacitor. While using the switched capacitor-coupled inductor, the voltage addition is expanded in the DC–DC converter and the power of PV is maximized. Here, the normal CSO algorithm is updated with the help of Levy flight functions to generate optimal results. To get the accurate optimal results, the output of the proposed LFCSO algorithm is given as the input of the ANFIS technique. After that, the optimal results are generated and they provide the pulses for the system. The working guideline is analyzed and the voltage addition is derived with the utilization of the proposed technique. From that point forward, it predicts the exact maximum power of the converter according to its inputs. Under the variety of solar irradiance and partial shading conditions (PSCs), the PV system is tested and its characteristics are analyzed in different time instants. The proposed LFCSO with ANFIS method is actualized in Simulink/MATLABstage, and the tracking executing is examined with a traditional method such as genetic algorithm (GA), perturb and observe (P&O) technique–neuro-fuzzy controller (NFC) and fuzzy logic controller (FLC) technique. [ABSTRACT FROM AUTHOR]

In some circumstances, the performance of machine learning (ML) tasks are based on the quality of signal (data) that is processed in these tasks. Therefore, the pre-processing techniques, such as reconstruction and denoising methods, are important techniques in ML tasks. In reconstructed (estimated) method, the fused lasso algorithm with non-convex penalty function is an efficient method when the signal corrupted by additive white Gaussian noise (AWGN) is considered. Therefore, this paper proposes new shrinkage functions for non-convex penalty functions, modified arctangent and exponential models, in fused lasso formulation. A lot of works present the shrinkage function for arctangent penalty function. Unfortunately, there is no closed-form solution. The numerical solution is required for shrinkage function of this penalty function. However, the analytical solution is derived in this paper. Moreover, the shrinkage function of modified exponential penalty function is proposed. This shrinkage function obtains from simple iterative method, fixed-point algorithm. We demonstrate the proposed methods through simulations with standard one-dimensional signals contaminated by AWGN. The proposed techniques are compared with traditional estimation methods, such as total variation (TV) and wavelet denoising methods. In experimental results, our proposed methods outperform several exiting methods both visual quality and in terms of root mean square error (RMSE). In fact, the proposed methods can better preserve the feature of noise-free signal than the compared methods. The denoised signals produced by the proposed methods are less smooth than the denoised signals produced by the compared methods. [ABSTRACT FROM AUTHOR]

Firstly, this paper designs the process of personalized recommendation method based on knowledge graph, and constructs user interest model. Second, the traditional personalized recommendation algorithms are studied and their advantages and disadvantages are analyzed. Finally, this paper focuses on the combination of knowledge graph and collaborative filtering recommendation algorithm. They are effective to solve the problem where K value is difficult to be determined in the clustering process of traditional collaborative filtering recommendation algorithm as well as data sparsity and cold start, utilizing the ample semantic relation in knowledge graph. If we use RDF data, which is distributed by the E and P (Exploration and Development) database based on the petroleum E and P, to verify the validity of the algorithm, the result shows that collaborative filtering algorithm based on knowledge graph can build the users' potential intentions by knowledge graph. It is enlightening to query the information of users. In this way, it expands the mind of users to accomplish the goal of recommendation. In this paper, a collaborative filtering algorithm based on domain knowledge atlas is proposed. By using knowledge graph to effectively classify and describe domain knowledge, the problems are solved including clustering and the cold start in traditional collaborative filtering recommendation algorithm. The better recommendation effect has been achieved. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc NETworks (VANETs) are typically termed as a wireless ad-hoc network that contains extreme node mobility and also the network carries a great significance in various traffic-oriented commercial applications and safety services. Due to its high mobility, routing in VANET has been a challenging work and also proving a higher rate of packet delivery ratio with reduced packet loss has been more important to be considered in route formations. With that note, this paper contributes to developing a clustering model called Middle-Order Vehicle-based Clustering (MOVC) model for managing the frequent topological change and high vehicle mobility, and efficiently handling the typical road traffic scenario. Moreover, the algorithm is intended to maintain the cluster to be constant for managing the vehicles in effective ways and also to provide uninterrupted communication between the vehicles. An algorithm for Effective Cluster Head Election (ECHE) is also derived in this paper for proficiently handling the frequency variation on the highways. Further, the model is simulated and evaluated on the basis of various metrics of VANET routing, specifically packet loss, packet delivery ratio, network lifetime and throughput. The results show that the proposed mechanism outperforms the results of existing models. [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 this paper, in order to figure out the variations before an earthquake and extract abnormal signals related to it, the geomagnetic three component Z, H, F minute values of 15 geomagnetic stations within 600 km of the epicentral distance before the MS 6.6 Minxian–Zhangxian earthquake in Gansu were analyzed. The following are the results. (1) After the fractal analysis was used directly, only three geomagnetic stations in 15 geomagnetic stations showed synchronous anomalous signals; (2) After the method of this paper was used, 9 of the 15 geomagnetic stations (including the three stations in the first point) extracted two synchronous anomalous signals, and six of the nine geomagnetic stations presented additional synchronous anomalous signals. (3) Of the three abnormal signals extracted, one had a medium-term effect and two had short-term effects. (4) The anomalous duration of the Z component of nine geomagnetic stations was longer than that of H and F. And as the epicentral distance increased, duration decreased. While the proposed method could not clearly indicate the exact relationship between the anomalous signals and the earthquake, it was proved that the signals extracted are effective and well-correlative to the earthquake. [ABSTRACT FROM AUTHOR]

Many biological and biomedical research areas such as drug design require analyzing the Gene Regulatory Networks (GRNs) to provide clear insight and understanding of the cellular processes in live cells. Under normality assumption for the genes, GRNs can be constructed by assessing the nonzero elements of the inverse covariance matrix. Nevertheless, such techniques are unable to deal with non-normality, multi-modality and heavy tailedness that are commonly seen in current massive genetic data. To relax this limitative constraint, one can apply copula function which is a multivariate cumulative distribution function with uniform marginal distribution. However, since the dependency structures of different pairs of genes in a multivariate problem are very different, the regular multivariate copula will not allow for the construction of an appropriate model. The solution to this problem is using Pair-Copula Constructions (PCCs) which are decompositions of a multivariate density into a cascade of bivariate copula, and therefore, assign different bivariate copula function for each local term. In fact, in this paper, we have constructed inverse covariance matrix based on the use of PCCs when the normality assumption can be moderately or severely violated for capturing a wide range of distributional features and complex dependency structure. To learn the non-Gaussian model for the considered GRN with non-Gaussian genomic data, we apply modified version of copula-based PC algorithm in which normality assumption of marginal densities is dropped. This paper also considers the Dynamic Time Warping (DTW) algorithm to determine the existence of a time delay relation between two genes. Breast cancer is one of the most common diseases in the world where GRN analysis of its subtypes is considerably important; Since by revealing the differences in the GRNs of these subtypes, new therapies and drugs can be found. The findings of our research are used to construct GRNs with high performance, for various subtypes of breast cancer rather than simply using previous models. [ABSTRACT FROM AUTHOR]

As an important part of metal processing, welding is widely used in industrial manufacturing activities, and its application scenarios are very extensive. Due to technical limitations, the welding process always unavoidably leaves weld defects. Weld defects are extremely hazardous, and the work used must be guaranteed to be defect-free, regardless of the field. However, manual weld inspection has subjective factors such as inefficiency and easy missed detection, and although some automatic weld inspection methods have appeared, these traditional methods still do not meet actual demand in terms of detection time and detection accuracy. Therefore, there is a need for a higher quality weld image automatic detection method to replace the manual method and the traditional automatic detection method. In view of the above, this paper proposes a weld seam image recognition algorithm based on deep learning. The Adam adaptive moment estimation algorithm is chosen as the backpropagation optimization algorithm to accelerate the training of convolutional neural networks and design an independent adaptive learning rate. Through the simulation of the collected 4500 tube images, the adaptive threshold-based method is used for weld seam extraction. The algorithm proposed in this paper is compared with the weld seam recognition method based on image texture feature value distribution (ITFVD) and the SUSAN-based weld defect target detection method. The results show that the proposed method can identify weld defects in a short time on different sizes of weld images, and can further detect the type of weld defects. In addition, the method in this paper is better than the other two methods in the false detection rate, recall rate and overall recognition accuracy, which shows that the experimental results have achieved the expected results. [ABSTRACT FROM AUTHOR]

With the increasing demand for security in many fastest growing applications, biometric recognition is the most prominent authentication system. User authentication through speech and face recognition is the important biometric technique to enhance the security. This paper proposes a speech and facial feature-based multi-modal biometric recognition technique to improve the authentication of any system. Mel Frequency Cepstral Coefficients (MFCC) is extracted from audio as speech features. In visual recognition, this paper proposes cascade hybrid facial (visual) feature extraction method based on static, dynamic and key-point salient features of the face and it proves that the proposed feature extraction method is more efficient than the existing method. In this proposed method, Viola–Jones algorithm is used to detect static and dynamic features of eye, nose, lip, Scale Invariant Feature Transform (SIFT) algorithm is used to detect some stable key-point features of face. In this paper, a research on the audio-visual integration method using AND logic is also made. Furthermore, all the experiments are carried out using Artificial Neural Network (ANN) and Support Vector Machine (SVM). An accuracy of 94.90% is achieved using proposed feature extraction method. The main objective of this work is to improve the authenticity of any application using multi-modal biometric features. Adding facial features to the speech recognition improve system security because biometric features are unique and combining evidence from two modalities increases the authenticity as well as integrity of the system. [ABSTRACT FROM AUTHOR]

Software Product Lines (SPLs) are one of the ways to develop software products by increasing productivity and reducing cost and time in the software development process. In SPLs, each product has many features and it is necessary to consider the optimal and custom features of the products. In fact, selecting key features in SPLs is a challenging process. Feature selection in SPLs is an optimization problem and an NP-Hard problem. One way to select a feature is to use meta-heuristic algorithms modeled on nature, i.e., Bat Algorithm. By modeling bat behavior in prey hunting, a suitable meta-innovative algorithm is considered. This algorithm has important advantages that make it more accurate than conventional methods such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. In this paper, to select software product features, idol binary algorithm and artificial neural network are used to identify important features of software products that reduce production costs and time. The experiments in MATLAB software and datasets related to software production lines show that the rate of reduction of target performance error or feature selection cost in software production lines in the proposed method has decreased by 64.17% with increasing population. [ABSTRACT FROM AUTHOR]

Local search algorithms are widely applied in solving large-scale distributed constraint optimization problem (DCOP). Distributed stochastic algorithm (DSA) is a typical local search algorithm to solve DCOP. However, DSA has some drawbacks including easily falling into local optima and the unfairness of assignment choice. This paper presents a novel local search algorithm named VLSs to solve the issues. In VLSs, sampling according to the probability corresponding to assignment is introduced to enable each agent to choose other promising values. Besides, each agent alternately performs a greedy choice among multiple parallel solutions to reduce the chance of falling into local optima and a variance adjustment mechanism to guide the search into a relatively good initial solution in a periodic manner. We give the proof of variance adjustment mechanism rationality and theoretical explanation of impact of greed among multiple parallel solutions. The experimental results show the superiority of VLSs over state-of-the-art DCOP algorithms. [ABSTRACT FROM AUTHOR]

Siamese network-based object tracking algorithms have recently gained popularity due to continuous improvement in tracking accuracy and robustness. However, these trackers are often limited by inefficient initialization and unstable online update. The model drift accumulates as more frames are processed. To address the above issues, this paper introduced a method that combines both deep learning and conventional regression algorithms (SVR, Support Vector Regression). Specifically, we propose a feature projection algorithm, which can effectively reduce the dimension of the features extracted from deep neural networks and improve the distinguishability of them at the same time. To make the features more robust for SVR training, we further propose two feature aggregation methods at both the channel level and the spatial level. We update the SVR model online instead of the deep neural network to make the tracking process more robust. Extensive experiments on four challenging benchmarks indicate that our proposed tracker is superior to baseline methods both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Balanced hypercube is a variant of the hypercube structure; it offers desirable properties such as connectivity, diameter, and fault-tolerance. Cycles and tori are popular interconnection topologies and have been widely used in distributed-memory parallel computers. Scholars have developed diverse and numerous parallel algorithms of cycles and tori. This paper proposes an efficient algorithm, called reflected edge label sequence, to generate cycle structures in balanced hypercubes. With this algorithm, we can embed cycles of length 2 2 k + 1 , 1 ≤ k < n , into an n -dimensional balanced hypercube. The corresponding algorithm for embedding 2-dimensional 2 2 k × 2 2 k tori can be constructed on the basis of this approach, and the time complexity is linear with respect to the size of a balanced hypercube. [ABSTRACT FROM AUTHOR]

The ring laser gyro signal contains complex noise components, affecting the system's measurement accuracy. It is an engineering problem worthy of study to find an effective method to reduce the noise in the sampling signal and improve the system's accuracy. In order to reduce various noises of the ring laser gyroscope and improve its measurement accuracy, a noise reduction method combining complete ensemble empirical mode decomposition with adaptive noise and the Savitzky–Golay algorithm is proposed. First, the measured samples are mode decomposed, and the concept of weighted-permutation entropy is introduced to distinguish the noisy modes. Then, the Savitzky–Golay algorithm is used to process the noisy modes, and finally, the signal is reconstructed. Simulated test signal and actual gyro signal are used to test, and compared with the EMD noise reduction method, each indicator is improved. The paper proposes a new noise reduction method for the laser gyro signal, and introduces weighted-permutation entropy to analyze the dividing point. The test data show the effectiveness of the method. [ABSTRACT FROM AUTHOR]

This paper considers the minimax design of two-dimensional (2D) finite impulse response (FIR) half-band filters. First, the design problem is formulated in a matrix form, where the half-band constraints are expressed as a pair of matrix equations. By matrix transformations, the constrained minimax problem is transformed into an unconstrained one. Then, we propose an efficient iterative reweighted least squares (IRLS) algorithm to solve this problem. The weighted least squares (WLS) subproblems arising from the IRLS algorithm are solved using a generalized conjugate gradient (GCG) algorithm. Moreover, the GCG algorithm is guaranteed to converge in a finite number of iterations. In the proposed algorithm, the design coefficients of filters are solved in their matrix form, leading to a great saving in computations and memory space. Design examples and comparisons with existing methods are provided to demonstrate the effectiveness and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a real-time low-cost geophone-based Elephant Footstep Vibration Detection and Identification (EFVDI) system is proposed. The system design started with a real-time low-cost generalized Footstep Vibration Recording and Analyzing (FVRA) system. A series of field experiments to record elephant footstep vibration (target) signals and other possible interfering ground vibration (noise) sources are conducted using the FVRA system. System's actual field performance was evaluated in terms of maximum detection range, signal amplitude, detection ratio, signal frequency, signal time span, etc. Variations of system's performance with several input parameters are also investigated. The recorded signals from target as well as noise sources are analyzed to extract different Signal Parameters (SPs). All SPs are saved in a Ground Vibration Signal Pattern Library (GVSPL) which is then used to frame accurate indigenous Elephant Identification Algorithm (EIA). The EIA is embedded in FVRA system to reshape it as specific Elephant Footstep Vibration Detection and Identification (EFVDI) system. The EFVDI system has successfully segregated elephant footsteps from other noise vibrations with high accuracy under simulated field experiment. The results from the proposed system will provide important data to the ongoing research of developing the much needed highly accurate Elephant Early Warning System (EEWS) in future. [ABSTRACT FROM AUTHOR]

Emergent wireless communication standards, which are employed in different transmission environments, support various modulation schemes. High-order constellations are targeted to achieve high bandwidth efficiency. However, the complexity of the symbol-by-symbol Maximum A Posteriori (MAP) algorithm increases dramatically for these high-order modulation schemes. In order to reduce the hardware complexity, the suboptimal Max-Log-MAP, which is the direct transformation of the MAP algorithm into logarithmic domain, is alternatively implemented. In the literature, a great deal of research effort has been invested into Max-Log-MAP demapping. Several simplifications are presented to meet with specific constellations. In addition, the hardware implementations dedicated for Max-Log-MAP demapping vary greatly in terms of design choices, supported flexibility and performance criteria, making them a challenge to compare. This paper explores the published Max-Log-MAP algorithm simplifications and existing hardware demapper designs and presents an extensive review of the current literature. In-depth comparisons are drawn amongst the designs and different key performance characteristics are described, namely, achieved throughput, hardware resource requirements and flexibility. This survey should facilitate fair comparisons of future designs, as well as opportunities for improving the design of Max-Log-MAP demappers. [ABSTRACT FROM AUTHOR]

In this paper, a new algorithm for solving the Navier–Stokes equations (NSE) on a quantum device is presented. For the fluid flow equations, the stream function-vorticity formulation is adopted, while the lattice Boltzmann method (LBM) is utilized for solving the corresponding system of equations numerically for one time step. Following the nature of the lattice Boltzmann method, the proposed quantum algorithm consists of five major sections: initialization, collision, propagation, boundary condition implementation and calculation of macroscopic quantities. The collision and boundary condition step is quantumly implemented by applying the standard-form encoding approach, while the quantum walk procedure is applied for the propagation step. The algorithm is implemented by using IBM's quantum computing software development framework Qiskit, while for the verification purposes, two-dimensional (2D) cavity flow is simulated and compared with classical code. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to identify local damages in frame structures based on approximate Metropolis–Hastings (AMH) algorithm and statistical moment. By analyzing the sensitivity of different statistical moment-based damage indices, the fusion index of fourth-order displacement moment and eighth-order acceleration moment is selected. Then the local damages in frame structures are primarily evaluated by AMH algorithm, where Gibbs sampling is adopted. Finally, the uncertainty of identified local damages is analyzed by using probability density evolution method (PDEM). Numerical simulations have been conducted to compare the proposed method with other similar damage detection methods, showing that the proposed method is more time-saving due to the involvement of Gibbs sampling and more accurate in assessing the damage severity. Experimental study of a 12-story benchmark frame testing has also been carried out, further validating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In recent years, image encryption schemes based on optical methods have been extensively studied. However, possible optical encryption methods in combination with fractional-order laser hyperchaotic systems have not been reported. Therefore, an optical image encryption scheme based on fractional Fourier transform and five-dimensional host-induced nonlinearity fractional-order laser hyperchaotic system is studied in this paper. Firstly, the dynamical characteristics of the proposed fractional-order laser hyperchaotic system are analyzed, and the DSP platform is used to realize the system. Then, a novel image encryption scheme is proposed combining BP neural network, GF(17) domain diffusion and hyperchaotic, random point scrambling algorithm. Finally, the performance of the encryption scheme is analyzed in detail. This work provides an experimental basis and theoretical guidance for image secure communication combining fractional-order laser hyperchaotic systems and optical methods and offers a new research perspective for optical image encryption. [ABSTRACT FROM AUTHOR]

Pose estimation is the basis and key of human motion recognition. In the two-dimensional human pose estimation based on image, in order to reduce the adverse effects of mutual occlusion among multiple people and improve the accuracy of motion recognition, a structurally symmetrical two-dimensional multi-person pose estimation model combined with face detection is proposed in this paper. First, transfer learning is used to initialize each sub-branch network model. Then, MTCNN is used for face detection to predict the number of people in the image. According to the number of people, the image is input into the improved two-branch OpenPose network. What is more, the double judgment algorithm is proposed to correct the false detection of MTCNN. The experimental results show that compared with TensorPose, which is the latest improved method based on OpenPose, the Average Precision (AP) (Intersection over Union (IoU) = 0. 5) on the validation set is 8.8 higher. Furthermore, compared with OpenPose, the mean AP (IoU = 0. 5 : 0. 9 5) is 1.7 higher on the validation set and is 1.3 higher on the Test-dev test set. [ABSTRACT FROM AUTHOR]

With the rapid development of online e-commerce, traditional collaborative filtering algorithms have the disadvantages of data set reduction and sparse matrix filling cannot meet the requirements of users. This paper takes handicrafts as an example to propose the design and application of handicraft recommendation system based on an improved hybrid algorithm. Based on the theory of e-commerce system, through the traditional collaborative filtering algorithm of users, the personalized e-commerce system of hybrid algorithm is designed and analyzed. The personalized e-commerce system based on hybrid algorithm is further proposed. The component model of the business recommendation system and the specific steps of the improved hybrid algorithm based on user information are given. Finally, an experimental analysis of the improved hybrid algorithm is carried out. The results show that the algorithm can effectively improve the effectiveness and exemption of recommending handicrafts. What's more, it can reduce the user item ratings of candidate set and improve accuracy of the forecast recommendation. [ABSTRACT FROM AUTHOR]

ClusterLogs is a framework for the automatic categorization of computing jobs and resources by error messages in distributed computing systems. Initially, it was developed for high-energy physics experiments, but it can be applied in other areas. The first prototype of the framework was limited to sequential execution and did not allow the processing of a large amount of data in an acceptable time. In the next prototype, the system was significantly improved by the parallelization of several data preprocessing stages. In this paper, we focus on the parallelization of the DBSCAN algorithm, the main method used for clustering of the numeric vectors representing the error messages. [ABSTRACT FROM AUTHOR]