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With the rapid development of digital media and entertainment industry, the importance of 3D mesh animation has become increasingly prominent. However, the traditional production methods are faced with problems such as high computational complexity and unnatural effects when dealing with complex motion. To improve the production efficiency and quality of 3D mesh animation, this study innovatively integrates integer programming, nonlinear constraint optimization, and machine learning algorithms to construct a new 3D mesh animation optimization algorithm. Comparative analysis of the improved machine learning algorithm shows that the mean absolute error of the algorithm is 0.00048 and the fit degree is 98.8%, which is better than the comparison algorithm. Then, the performance of the proposed 3D mesh animation optimization algorithm is analyzed. The results show that the rendering speed and average frame rate of the proposed algorithm are 29.5 FPS and 28.7 FPS, respectively, which is superior to the comparison algorithm. The algorithm can effectively improve the efficiency and quality of animation production, and inject new vitality into the development of digital media and entertainment industry. This study not only provides new ideas and methods for optimizing 3D mesh animation, but also provides useful references for research and applications in related fields.

Authentication for the Internet of Vehicles (IoV) is essential for safe IoV connection. However, because of the peculiar structure of IoV, current authentication schemes heavily rely on Road Side Units (RSUs). An excessive number of RSUs will make it difficult to synchronize updates, which will make system authentication inefficient overall and prevent the reuse of authentication results. Based on this, this paper proposes a blockchain-based secure distributed authentication scheme for IoV. First, data processing and storage are decentralized to the edge of the network to reduce the communication delay and response time with the trusted authority (TA). Second, in the IoV authentication process, smart contract technology is used to achieve automatic triggering of the authentication process, and an optimized Practical Byzantine Fault Tolerant Consensus (PBFT) algorithm is designed to add the authentication information to the blockchain ledger so that the authentication results can be reused. Finally, this paper performs formal security verification and informal security analysis using and Real-Or-Random (ROR) oracle model and AVISPA tool. In addition, this paper performs a detailed evaluation of computation cost and communication cost and uses SUMO for simulation verification. The security analysis and performance evaluation show that the scheme meets the security requirements of the IoV while having less communication and computation cost.

In this paper, the secrecy performance in internet of things (IoTs) networks with cooperative eavesdroppers has been investigated, where simultaneous wireless information and power transfer (SWIPT) nodes perform as relay nodes. The secrecy energy efficiency (SEE) maximization problem for single relay node has been introduced to extend the life-time for IoT nodes without power supply. The optimization problem is firstly transformed into two sub-problems, and then each sub-problem is solved by bisection method. Furthermore, the model has been extended into multiple relay nodes and a novel relay selection algorithm for SEE maximization has been proposed. Finally, the advantage of the proposed algorithm has been verified from simulations.

Coded caching is widely regarded as an important tool to reduce pressure on the data transmission during the peak traffic times in heterogeneous wireless networks. In this paper, we will provide two concatenating constructions of coded caching schemes based on the schemes derived by Shangguan et al. in 2018 and Yan et al. in 2017, respectively. Then we show that our new schemes have good performance in reducing packet number. Furthermore, in some case, we present that our schemes have better performances on user number, memory ratio, packet number and transmission rate than the schemes obtained by Chittoor et al. in 2019 and Krishnan in 2018.

Consensus-based estimators have been applied in the state estimation for cooperative multi-sensor systems, and most of current studies are for the continuous-time or discrete-time case. With regards to some engineering applications, such as ballistic target tracking, it is more suitable to adopt the continuous-discrete state-space model to formulate a dynamic system, which can capture the evolution characteristics of this state process more accurately. This paper presents a novel consensus continuous-discrete Gaussian filtering (CCDGF) estimator. On the basis of strong Taylor approximation for continuous state, the estimator utilizes the fully symmetric interpolatory quadrature (FSIQ) rule to numerically resolve the first two moments of propagated Gaussian density. Then, the average consensus protocol is leveraged to iterate the local innovations of Gaussian filtering framework at each sensor. The consensus estimates with odd-degree accuracy can be obtained through sufficient exchanges of neighborhood information. Finally, it is demonstrated by simulation examples that the CCDGF estimator can achieve performance close to its centralized counterpart, and has higher tracking accuracy with the increase of quadrature degree.

This paper studies the energy efficiency (EE) maximization problem of fixed-wing Unmanned aerial vehicles (UAVs) enabled Internet of Things (IoT) system, where an UAV is employed as an aerial relay with enough cache capacity to amplify and forward (AF) received signals between narrow band (NB)-IoT device and eNodeB. This paper optimizes the unconstrained trajectory of the UAV to maximize the EE of UAV-enabled IoT system to achieve the green communication. Due to the objective function is a fraction and is non-convex, it's hard to solve the optimization problem. Therefore, this paper alternately optimizes trajectory in source subspace and destination subspace with the other fixed and comes up with an algorithm based on successive convex approximation (SCA) method and Dinkelbach method to obtain a local optimal solution. Numerical results show that the proposed UAV trajectory design method can obtain much bigger EE than the running track (RT) trajectory and circular trajectory. Besides, the proposed cache-enabled AF strategy can obtain much bigger EE than non cache-enabled AF strategy.

Stack Overflow is a popular Community-based Question Answer (CQA) website focused on software programming and has attracted more and more users in recent years. However, duplicate questions frequently appear in Stack Overflow and they are manually marked by the users with high reputation. Automatic duplicate question detection alleviates labor and effort for users with high reputation. Although existing approaches extract textual features to automatically detect duplicate questions, these approaches are limited since semantic information could be lost. To tackle this problem, we explore the use of powerful deep learning techniques, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM), to detect duplicate questions in Stack Overflow. In addition, we use Word2Vec to obtain the vector representations of words. They can fully capture semantic information at document-level and word-level respectively. Therefore, we construct three deep learning approaches WV-CNN, WV-RNN and WV-LSTM, which are based on Word2Vec, CNN, RNN and LSTM, to detect duplicate questions in Stack Overflow. Evaluation results show that WV-CNN and WV-LSTM have made significant improvements over four baseline approaches (i.e., DupPredictor, Dupe, DupPredictorRep-T, and DupeRep) and three deep learning approaches (i.e., DQ-CNN, DQ-RNN, and DQ-LSTM) in terms of recall-rate@5, recall-rate@10 and recall-rate@20. Furthermore, the experimental results indicate that our approaches WV-CNN, WV-RNN, and WV-LSTM outperform four machine learning approaches based on Support Vector Machine, Logic Regression, Random Forest and eXtreme Gradient Boosting in terms of recall-rate@5, recall-rate@10 and recall-rate@20.

Sustainability is necessary for improving the quality of the manufacturing industry, which is an important pillar of the economy. The existing studies can hardly realize a comprehensive reflection of the research status of sustainability in manufacturing. Thus, this study adopts science mapping as the main research method for the systematic and accurate mastery of the overall research status, development track, research hotspots, and trends within this field. Moreover, this work provides a systematic review of related studies. This study examines 6,591 articles (1999-2019), including in core journals on Web of Science as research samples, and deeply explores core journal, core author, core country, discipline distribution, keyword co-occurrence, and literature co-citation via CiteSpace software. According to results, authoritative journals regarding sustainability in manufacturing place particular emphasis on theory, method, or application in different degrees. The number of published articles in the U.S.A. tops the ranking, and the influence of Chinese research institutes and scholars is continuously enhanced, where the main research fields are engineering, environmental science, and so on. Sustainable innovativeness in the manufacturing industry is generally at a high level. Moreover, research hotspots have evolved from traditional to modern and undergone three phases: theory construction-industry or enterprise technology construction-the emergence of new problems and application of new technologies. This study further analyzes the four clusters with citation bursts, namely, additive manufacturing, power consumption, green supply chain, and green information system in terms of grasping the knowledge structures and probing the classical research results. Future research should emphasize 3D printing design and application in the information era, the business model integrating the manufacturing industry and service industry, optimization of production modeling, and so on. This study aims to provide a systematic, comprehensive, dynamic, and objective review of the literature on sustainability in manufacturing to deepen and perfect the research in this field.

Convolutional dictionary learning (CDL) is an unsupervised learning method to seek a translation-invariant sparse representation for signals, and has gained a lot of interest in various image processing and computer vision applications. However, 3D hyperspectral images pose unique challenges due to their high dimensionality and complex structures, making optimization of the dictionary and its application to inverse problems difficult. This paper proposes an efficient CDL algorithm that neither explicit evaluation of the Hessians nor their inversion is required in the optimization process, which leads to substantial acceleration and memory savings. Furthermore, we exploit the learned kernels as the convolutional sparse coding (CSC) image prior for the compressive chromo-tomographic (CCT) reconstruction problem, and examine the usability and performances of the proposed method for CCT reconstruction. Numerical experiments show that, for CCT, 1) the proposed CSC can provide an efficient representation for HSI by using several tens of 3D filters; 2) the learned convolutional dictionary has reliable generalization capability; and 3) the proposed CSC-based method outperforms the classical reconstruction method using an analytic sparsifying basis.

Ultra-Dense Network (UDN) is one of the key techniques for the next generation of mobile network due to providing high system throughput. However, severe interference often occurs in UDN, which greatly impact the data rates of cell-edge users. User-centric wireless access virtualization has been widely adopted in UDN to mitigate the interference of cell-edge users by sharing resources and eliminating cell boundary. However, it's only effective for moderate scale networks. Moreover, the efficiency needs further improvement. In this paper, we study effective cooperative clustering method for large scale UDN with less computations in order to improve the throughput of cell-edge users. We formulate a convex optimization problem in which the objective is to maximize the system throughput with overlapping virtual cells. We propose a clustering method to solve this optimization problem. We design a fast-convergent iterative algorithm called K-Nearest Neighbor (KNN) algorithm to perform users clustering. Simulation results show that our proposed algorithm has better throughput performance for both average and cell-edge users. Especially, the per-carrier throughput is improved, which leads to more serviceable users with limited resources.

In Smart Grid, the integration of Information and Communications Technology (ICT) systems and power systems has enabled real-time services and distributed controls, while the fusion of technologies necessitates a profound and versatile platform for the interdisciplinary research and evaluation. This article introduces a Software Defined Smart Grid testbed architecture, which integrates real-world wireless communication systems and Artificial Intelligence algorithms to provide a re-configurable framework to meet different real-time Smart Grid testbed design requirements. Through prototyping and experiments, the architecture shows great potential in addressing co-evaluation challenges of ICT systems and power systems, as well as supporting real-time Smart Grid evaluations.

Drift detection has been a difficult problem in the field of sensor fault diagnosis. In this article, a sensor drift detection method using discrete wavelet transform (DWT) and a grey model GM(1,1) is proposed. DWT is used to separate the noise part from the trend part of the sensor data. Then, the GM(1,1) model is used for time series prediction in the trend part. Finally, residuals generated by predicted and current denoised sensor data are calculated and compared with a pre-selected threshold for drift detection. The residuals may not necessarily be Gaussian distribution. Therefore, the pre-selected threshold is chosen by using the kernel density estimation (KDE) method without Gaussian assumption. The effectiveness of the proposed method has been demonstrated using a simulated temperature sensor output from a sensor model on a continuous stirred-tank reactor (CSTR), as well as measurements from a physical temperature sensor in the nuclear power control test facility (NPCTF).

Li et al. introduced coded distributed computing (CDC) scheme to reduce the communication load in general distributed computing frameworks such as MapReduce. They also proposed cascaded CDC schemes where each output function is computed multiple times, and proved that such schemes achieved the fundamental trade-off between computation load and communication load. However, these schemes require exponentially large numbers of input files and output functions when the number of computing nodes gets large. In this paper, by using the structure of placement delivery arrays (PDAs), we construct several infinite classes of cascaded CDC schemes. We also show that the numbers of output functions in all the new schemes are only a factor of the number of computing nodes, and the number of input files in our new schemes is much smaller than that of input files in CDC schemes derived by Li et al.

Dynamic wireless charging for Electric Vehicles (EVs) can promote the take-up of EVs due to its potential of extending the driving range and reducing the size and cost of batteries of EVs. However, its dynamic charging demand and rigorous operation requirements may stress the power grid and increase carbon emissions. A novel adaptive dynamic wireless charging system is proposed that enables mobile EVs to be powered by renewable wind energy by taking advantages of our proposed traffic flow-based charging demand prediction programme. The aim is to cut down the system cost and carbon emissions at the same time, whilst realising fast demand prediction and supply response as well as relieving the peak demand on the power grid. Simulation results show that the proposed system can adaptively adjust the demand side energy response according to customers' welfare analysis and charging price, thereby to determine the power supply method. Moreover, due to the prioritised use of renewable energy, EV charging system requires less electricity from the power grid and thus the overall carbon emissions are reduced by 63.7%.

Deep learning (DL) can effectively handle explosive data and solve the complicated non-convex problems. Recently, it is used to reduce the overhead of channel state information (CSI) feedback and improve the performance of the limited feedback massive MIMO systems. However, the complex wireless environment and the high-dimensional data of massive MIMO channel degrades the performance of the DL based codebook design. To address this issue, we propose a deep clustering (DC) based codebook design for massive MIMO systems. At first, deep neural network (DNN) learns the key propagation characteristics of the wireless channel according to the training model. And then, the clustering algorithm outputs the centroids of each key propagation characteristic, which can be used to reconstruct the valid space of massive MIMO channel. Finally, massive MIMO codebook is constructed by the valid space. Since the amount of the key propagation characteristics are far less than the dimension of massive MIMO channel, the proposed design can significantly simplify the parameters and the structures of DNN. Moreover, the clustering algorithm output the information with minimum sum-distance with the real channel sequences which can acquire the maximum sum-rate of massive MIMO codebook design. The robustness of the proposed codebook design is validated by carrying out extensive simulation in various scenarios and antenna array structures. Simulation results consist with theoretical analysis in terms of the achievable rate, and demonstrate that the proposed codebook design outperforms the traditional schemes.

An ambient backscatter system could utilize the exiting ambient RF signals as both energy source and information carrier to enable the communications among battery-free devices and is deemed as a promising communication primitive for the Internet of Things (IoT). In this paper, we propose a time splitting strategy (TSS) and power splitting strategy (PSS) for the ambient backscatter system, where the tag-reflection coefficient is optimized according to dynamic channel variation. We also derive the analytical expressions of the outage probability and the optimal throughput for both the TSS and the PSS, which provides insights on the impact of system parameters, e.g., channel variance, RF source power, and circuit power consumption. The simulations showed that the proposed strategies can significantly outperform the traditional counterparts that rely on fixed reflection coefficient.

Dynamic subarray achieves a compromise between the sum rate and hardware complexity of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, in which antenna elements are dynamically partitioned to radio frequency (RF) chain according to the channel state information. However, most prior works on multi-user hybrid precoding only considered the fully connected architecture or the fixed subarray architecture. In this paper, a novel multi-user hybrid precoding framework is proposed for the dynamic subarray architecture. Different from the existing schemes, the base station (BS) first selects the multi-user set based on the analog effective channel. Then, the antenna partitioning algorithm allocates each antenna element to the RF chain according to the maximum increment of the signal-to-interference-noise ratio (SINR). Finally, the hybrid precoding is optimized for the dynamic subarray architecture. By calculating the SINRs on the analog effective channels of the selected users, the antenna partitioning can greatly reduce the computation complexity and the size of the search space. Moreover, it also guarantees the user fairness because each antenna element is allocated to acquire the maximum increment of the SINR for all selected users. The extensive simulation results demonstrate that both the energy efficiency and sum rate of the proposed solution obviously outperform those of the fixed subarrays, and the proposed solution obtains higher energy efficiency with a slight loss of sum rate compared with the fully connected architecture.

In this paper, we propose a homogenized Chebyshev-Arnold map (HCAM) by homogenizing the linear coupling of Chebyshev map and Arnold map. The proposed HCAM has complex dynamical behaviors and can avoid the problems of the original Chebyshev map when used in image encryption. Based on the HCAM, we present a color image encryption algorithm that contains confusion and diffusion processes. In the confusion stage, we use the random chaotic matrix transform (RCMT) to randomize the shifting steps, which can eliminate the regular pattern of the original CMT and enhance the security level. In the diffusion stage, we use a SHA-512- and SHA-384-based fast pixel substitution scheme to perform the bit-level exclusive-or operation, which can obtain outstanding self-adaptiveness and high efficiency. The experimental results and security analysis demonstrate that the proposed algorithm has high level of security and robust to the potential attacks.

In GitHub, integrators inspect submitted code changes, make evaluation decision, and close pull requests. Some pull requests may be reopened for further modification and code review. It remains unknown why some pull requests are reopened, and how they affect software development. In this paper, we conduct a case study to understand reopened pull requests. We collect 100,622 pull requests from 7 popular projects in GitHub. We study the impacts of reopened pull requests on the code review process. We then qualitatively analyze the root reasons that lead to reopening pull requests. Finally, we examine the characteristics of pull requests caused by various reasons. Our main observations are: 1) Reopened pull requests have lower acceptance rates, more comments, and longer evaluation time than non-reopened pull requests. 2) Several key reasons that lead to reopened pull request are identified such as bugs, change minds, insufficient tests, incompatible version, tests fails, and so on. 3) Reopened pull requests in different categories have various acceptance rates, comment amounts, and evaluation time. Reopened pull requests in the category rebase and change branch has the largest number of comments and the longest evaluation time. These findings provide insights to decrease reopened pull requests, develop a variety of assistant tools, and reduce the maintenance cost of open source projects.

Smart grids are the next generation of power distribution network, using information and communications technologies to increase overall energy efficiency and service quality of the power grid. A significant challenge in smart grid development is the rapidly rising number of smart devices and how to meet the associated load on the backbone communication infrastructure. This paper designs an Internetof-Things smart grid testbed simulator to provide crucial insight into communication network optimization. Simulation for a large number of smart devices under various heterogeneous network topologies is used to analyze the maximum number of clients supportable for a given demand-response latency requirement. This latency includes all protocol overheads, retransmissions and traffic congestion, and simulator processing time is successfully eliminated from the final delay calculation via data post-processing. For a specific three-tier topology, given a round-trip latency requirement, the effect of number of smart devices per local hub and overall number of local hubs on network performance is analyzed, and crucial design insights are drawn relevant to cost-efficiency optimization of network deployment.