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Currently, high dynamic range (HDR) videos with high resolution (HR) have become popular due to the display and the rendered technological advancements. However, making ultra-high definition (UHD) with HDR videos is expensive. The legacy low-resolution (LR) standard dynamic range (SDR) format is still largely used in practice. It is necessary to search for a solution to transform LR SDR videos into UHD HDR format. In this paper, we consider joint super resolution and learning inverse tone mapping an issue of high-frequency reconstruction and local contrast enhancement, and we propose an architecture based on a generative adversarial network to apply joint SR-ITM learning. Specifically, we include the residual ResNeXt block (RRXB) as a basic module to better capture high-frequency textures and adopt YUV interpolation to achieve local contrast enhancement. By adopting a generative adversarial network as a pivotal training mechanism, our designs show advantages in both integration and performance. Our code is now available on GitHub: SR-ITM-GAN.

This paper presents a robust two stage maximum power point tracking (MPPT) system of the photovoltaic (PV) module using an improved restricted control set model predictive control (iRCS-MPC) technique. The suggested work is improved in two aspects; a revision in conventional P&O algorithm is made by employing three distinct step sizes for different conditions, and an improvement in conventional MPC algorithm. The improved MPC algorithm is based on the single step prediction horizon that provides less computational load and swift tracking of maximum power point (MPP) by applying the control pulses directly to the converter switch. The computer aided experimental results for various environmental scenarios revealed that compared with the conventional method (conventional P&O + MPC), for the PV power and inductor current, the undershoot and overshoot is decreased to 68% and 35% respectively under stiff environmental conditions. In addition, the settling time needed to reach a stable state is significantly reduced in the proposed system. The viability of the solution suggested is verified in MATLAB/Simulink and by hardware experimentation.

Environmental concerns and depletion of traditional energy lead to the booming development of renewable distributed generation (RDG) in the past decade. However, due to intermittent nature of renewable energy sources, to what extent RDG could provide capacity to power systems becomes a critical issue to the utility company when implementing long-term system strategic (generation expansion) planning. On the other hand, in a smart-grid frame, the popularization of different varieties of demand-side resources enables the system to operate at more flexible modes ever before. The potential variability in load demand not only introduces additional dynamics and uncertainties to the system, but could also affect the reliability benefits of RDG. Therefore, in practice, to effectively estimate the reliability value of RDG in power systems, the potential interaction between generation- and demand-side must be properly captured. In this paper, a study to assess the capacity credit (CC) of RDG in a context of distributed generation system is performed with consideration of the impacts of demand response (DR). A compound reliability model for DR is presented, which considers the uncertainties involved in both instant response and follow-on load recovery processes. On this basis, an assessment framework for the CC of RDG based on sequential Monte Carlo simulation is developed by which the inter-temporal characteristics of DR resources can be fully captured. The numerical study is implemented based on the IEEE-38 bus test case. The calculation results demonstrate that the CC of RDG would depend on a variety of factors, including penetration level, responsiveness of load demand and the correlations between RDG and DR availability. Also, it is shown that accounting for the underlying effect of DR is of absolute importance, otherwise the CC of RDG might be estimated erroneously in real practices.

With the popularity of social media, including micro-blog, mining effective information in short texts has become an increasingly important issue. However, due to the sparseness, high dimensionality and large amount of data, mining this information is a very challenging task. In this paper, we propose a method to extend the Dirichlet multinomial mixture (DMM) topic model by combining the user-LDA topic model based on internal data expansion with the potential feature vector representation of words trained on a very large external corpus (we refer to it as ULW-DMM). The experimental results show that the ULW-DMM model produces a relatively large improvement in topic consistency and classification tasks for topic modeling of microblog short texts.

Hybrid precoding has a great potential to achieve near-optimal performance with few radio-frequency (RF) chains in comparison to the number of antennas. This paper proposes a novel hybrid beamforming solution for sum rate maximization in a wideband multi-user multi-input multi-output (MU-MIMO) relay system. The problem at hand is non-convex and hence mathematically intractable due to the constant modulus constraints associated with the analog beamforming design at communicating nodes shared by all sub-carriers. Furthermore, relaying in MU-MIMO systems under frequency-selective channels is also a challenging task as it requires complicated signal processing. To address this problem, source analog beamformer, relay RF combiner and phase-only processing component at each destination are obtained by deriving the common RF processing matrix which enables beamforming gain over all sub-carriers. This process involves reconstruction loss minimization for designing the required analog beamforming vectors having ability to maximize end-to-end signal-to-interference plus noise ratio (SINR). The relay analog precoder is designed by maximizing the received power at each destination through gain maximization of the equivalent baseband channel over the allocated frequency spectrum. To reduce the computational complexity of the iterative approach used to find the relay RF precoding matrix, the closed form expression is derived for achieving the desired objective. Finally, digital baseband processing components are obtained from the equivalent baseband channels by suppressing the interference among transmitted signals indirectly. Simulation results for different system configurations reveal that the proposed algorithm can achieve near-optimal performance.

The increase in the scale of distribution networks significantly reduces the efficiency of intelligent planning for distributed generation (DG). To improve the efficiency of intelligent DG planning and avoid the impact of uncertainty concerning renewable energy on it, this paper proposes a sensitivity index for the bus-embedded multi-objective estimation of distribution algorithm (MEDA) based on the semi-invariant probabilistic power flow approach to achieve an optimal solution. The sensitivity indices of the buses are comprehensively enabled to obtain a new index and determine their sensitivity sequences based on the area grey incidence decision-making method. Subsequently, according to the uncertainty of wind turbine generators and photovoltaics, a probability model is established, and the semi-invariant method is used to solve for the probabilistic power flow according to a correlation model. Finally, the sensitivity of the proposed bus-embedded MEDA to enhancing the efficiency of the solution is examined. The optimal DG allocation scheme is obtained with the goal of achieving the lowest total cost in the planning year. Finally, the feasibility and effectiveness of the proposed model and method are verified using simulations of the IEEE 33-bus, IEEE 69-bus, and IEEE 118-bus test systems.

This paper proposes combining an improved particle swarm optimization and Pareto archive algorithm to solve the multi-objective reactive power optimization problem. The idea of $\varepsilon $ -greedy strategy is adopted and designed to improve particle swarm optimization algorithm. It makes some particles have stronger global search capability, meanwhile, others have stronger local search capability during the whole iteration process. Henceforth, the strategy significantly explores the possibility of optimal solution in local space at the early stage of the iteration, in addition, it mitigates the tendency to fall into the local optimal solution at the later stage of the iteration. The Pareto optimal solution selection problem is solved by minimizing the sum of the difference between each objective function and its optimal solution. The proposed approach is tested on IEEE39-bus and IEEE118-bus system, and it is demonstrated that the proposed approach not only restores the nodes voltage to the normal range and achieves better value for each objective function, but also outperforms other algorithms including standard particle swarm optimization and non-dominated sorting genetic algorithm II(NSGA-II).

In this paper, a mixture semisupervised Bayesian principal component regression-based soft sensor modeling method for nonlinear industrial process with multiple operating modes is presented. In many chemistry processes, part of output data samples may be unavailable due to the difficulties in measurement or recording. The semisupervised method is introduced to efficiently deal with the unlabeled data set. Moreover, the Bayesian regularization method is proposed to determine the unknown dimensionality of latent variables space in each submode by introducing three different formulations of two hyperparameters to construct the Gaussian prior distributions over the loading and regression matrices. The formulation of this method is derived in expectation maximization algorithm scheme, and the formulas to update unknown parameters are derived. The effectiveness of the proposed method is verified through a numerical example, the Tennessee Eastman benchmark process, and the comparisons with the existing method.

This paper presents an application of power-line communication (PLC) as a viable solution for frequency regulation in a smart microgrid. The microgrid under consideration comprises a diesel generator with a frequency controller and another synchronous generator operating in the constant power mode. Frequency regulation is achieved in the microgrid by communicating the frequency deviation (information) from the control center to the diesel generator using PLC. The information received at the other end is used for frequency regulation. A simple yet effective M -ary amplitude-shift keying (ASK) modulation-based PLC system is proposed for information transmission. A comparative analysis of M -ary ASK and M -ary frequency-shift keying (FSK)-based PLC systems is also presented. Approximate closed-form expressions for the average symbol error rate and the average achievable rate are derived for both the PLC systems, assuming that the PLC channel coefficients and the additive impulsive noise samples are derived from a log-normal distribution and a Bernoulli-Gaussian process, respectively. The results show that M -ary ASK is a better choice for PLC than M -ary FSK. Furthermore, the delay introduced in the control loop while using the proposed PLC system is negligible, and therefore, the stability of the microgrid is also ensured.

In this paper, we proposed an integrated hybrid antenna based on spoof surface plasmon polaritons (SSPPs) which combines with omnidirectional antenna (ODA) and the frequency beam scanning antenna (FBSA). The ODA is fed by the SSPPs and the operating principle of FBSA is based on the higher-order modes of the SSPPs. The radiation mechanisms of the proposed integrated antenna are that the low-pass transmission modes of SSPPs are used to excite the ODA and the higher-order modes of SSPPs are utilized to generate the frequency beam scanning waves. That is to say, the SSPP design not only serves as the feeding structure for the ODA, but also acts as the radiating element for the FBSA. Simulated results show that the proposed integrated antenna achieves two impedance bandwidths. Within the operating bandwidth 2.32 GHz to 4.42 GHz, the proposed integrated antenna achieves omnidirectional radiation patterns, which enables the simultaneous use for wireless local area network (WLAN) and 5 G applications. Meanwhile, from 11.45 GHz to 25.5 GHz, the proposed integrated antenna acts as a wide-angle frequency beam scanning characteristic and can be used for planar integrated communication systems. Additionally, a prototype of the proposed integrated hybrid antenna is fabricated and tested. The measured results have a good agreement with the simulated ones.

To deal with the problem where each instance is associated with multiple labels, a lot of multi-label learning algorithms have been developed in recent years. Some approaches have been proposed to select label-specific features to utilize discriminate features for multi-label classification. Although label correlation has been considered in learning label-specific features, the critical correlation among instances was less taken into account. In this paper, we proposed a new approach called multi-label learning with label-specific features using correlation information (LSF-CI) to learn label-specific features for each label with the consideration of both correlation information in label space and correlation information in feature space. In the LSF-CI, the instance correlation in feature space is computed by a probabilistic neighborhood graph model, and label correlation in label space is computed by cosine similarity. For multi-label data, the LSF-CI has the capability to select Label-specific features for each label as well as classify an unseen instance into a set of relevant labels. To validate the effectiveness of LSF-CI, we conducted comprehensive experiments on eight multi-label datasets. The experimental results demonstrate that the LSF-CI is capable of selecting compact label-specific features, and achieving a competitive performance in comparison with the performances of the existing multi-label learning approaches.

In traditional wireless sensor networks (WSN), data is transmitted to base stations through multiple-hop. However, multi-hop transmission will cause problems such as energy holes, uneven energy consumption, and unreliable data transmission. Aiming at the defects of traditional networks, this paper proposes a scheme for collaborative data collection using multiple mobile nodes (MN) as sink nodes. In this scenario, an important issue is how to reasonably plan the data collection path of each mobile node under a series of constraints such as energy. This paper studies routing strategy and path planning. Firstly, a dynamic clustering algorithm is used to cluster the randomly arranged sensor nodes, and then a sensor node with higher energy is manually arranged at the virtual cluster center generated by the clustering algorithm as a cluster head node to establish a data collection cluster. Then the monitoring area is divided into several parts according to the number of MN, so that the MN traverses the cluster head nodes in the respective monitoring areas for data collection. At the same time, in order to enable the MN to complete data collection within the energy limit and improve the path balance, a path-based path equalization algorithm (PEABR) is proposed to adjust the path of the MN, further reduce and equalize the path length of the MN to satisfy the constraint conditions, and optimize the path planning scheme. Finally, simulation was carried out by using Matlab simulator, and the simulation experiments which were performed in a laboratory environment verified the feasibility and effectiveness of the algorithm.

With the increase of network services, how to avoid link congestion and make full use of limited bandwidth resources in network virtualization environment have become the key challenges. In this paper, we introduce the cognitive method into virtual network topology reconfiguration and modify the connectivity to reach the target topology by sensing the traffic. Then, we formulate an optimization problem to maximize the ratio of cumulative saved resources to the square of changed link number (CSR/SCLNR) and determine which virtual link to be added or deleted. Finally, a heuristic algorithm called cognitive virtual network topology reconfiguration method based on traffic prediction and link importance (CVNTRM-TPLI) is proposed to solve this optimization problem. In the CVNTRM-TPLI, the link importance is presented in link deletion as the topological factor to avoid the ping pang effect. Also, a hybrid traffic prediction algorithm based on optimal parameter selection is put forward, where immune optimization is introduced to select the optimal parameters and the virtual network topology can react promptly to the traffic fluctuation without adding or deleting virtual link frequently. Simulation results show that the CVNTRM-TPLI not only has the highest CSR/SCLNR, but also solves the link congestion and makes full use of the limited bandwidth resources.

In the immediate aftermath of large-scale disasters, emergency logistics services play important roles in saving lives and reducing losses. Efficient relief logistics scheduling depends on the accurate transport time information for available routes. However, this information cannot be obtained precisely until a vehicle uses the road. Considering the correlation between information acquisition and logistics operations, this paper focuses on a multiperiod online decision-making problem to simulate the information acquiring process. This problem can be referenced for emergency resource scheduling scenarios in which previous decisions impact knowledge for future logistics plans. A multi-trip cumulative capacitated vehicle routing problem with uncertain transportation time is investigated as the basic model. The tradeoff between transportation efficiency and the unknown transport time discovery rate is considered in a multiobjective evolutionary algorithm (MOEA). A memetic algorithm (MA) and a robust optimization (RO) -MA for single-period post-disaster emergency logistics are also proposed to solve the problem for comparison. In these algorithms, evolutionary operators that benefit solution fixing and variation are proposed. In the experiments, a real-world instance is employed. A simulative experimental environment is established. Dynamic information gained within the process of logistics scheduling is highlighted via multi-period online optimization. Different scenarios corresponding to estimates in emergency situations are provided to validate the performance of the algorithms. The experimental results show that the hybrid strategy, MOEA+MA, can obtain the best result in more than half of the considered cases which demonstrates the necessary balance between obtaining information and transportation efficiency.

With the explosive growth of information and communication technology and its services, some popular Websites currently generate an enormous amount of Internet traffic. A content delivery network (CDN) would then become imperative for supporting such services efficiently. In this paper, we propose joint optimizing approaches for replica server placement, content caching in selected servers, and content request load assignment among the servers, aiming to minimize the ratio of unserved content request load when the network resources and server capacity are both limited. For this, we develop a mixed integer linear programming (MILP) optimization model. To mitigate the computational complexity of the MILP model, we further decompose the optimization problem into three sub-problems, including: 1) choosing the replica server nodes optimally; 2) deciding the content items to be cached in the replica servers; and 3) allocating the content request loads from users onto different servers. For these sub-problems, we develop corresponding heuristic algorithms and show that the proposed approach is not only efficient but also performs very close to the MILP model. We also find that a number of system limitations, such as different numbers of replica servers placed, link capacity, server processing capacity, and server storage capacity, jointly affect the performance of the CDN. Saturation trends are observed on the performance, which indicate that as long as sufficient resources have already been provided, augmenting the resources further may only lead to marginal additional performance improvement.

The presence of missing data is problematic in most quantitative research studies. Water distribution systems (WDSs) are not immune to this problem. In fact, missing data is an inherent feature of a WDS. There are various techniques and methods to address missing data ranging from simply deleting the data to using complex algorithms to impute missing data. This paper reviews the different imputation options available from traditional methods (such as deletion and single imputation) to more modern and advanced methods (such as multiple imputation, model-based procedures, and machine learning techniques). The concept, application, and qualitative advantages and disadvantages of these methods are discussed. In addition, a novel approach for selecting an applicable technique is presented. The approach is a “top-down bottom-up”two-prong approach for the selection of a data analysis and missing data technique. The bottom-up approach facilitates the top-down selection of a suitable technique by analyzing the data and narrowing down the selection options. As a use case, this paper also reviews techniques that are used to impute missing data in WDSs.

This paper presents a novel single-phase to single-phase multiconverter topology that can be applied in multiple areas. The proposed multiconverter is designed with only two soft power semiconductor switches (e.g. MOSFET or IGBT), four power diodes and a center-tapped transformer which makes it more compact in size, decrease the gate driving complexity, reduce the total equipment costs and enhance the energy conversion efficiency with minimized losses. Furthermore, the utilization of the transformer in the proposed converter mitigates the multiple AC source requirement problems and provides galvanic isolation which increases the reliability of the converter. Moreover, the presented multiconverter is applicable in various areas including electric traction as a speed controller, induction heating, AC and DC variable power supplies, etc. which signify the competence of this converter in energy conversion appliances. However, a comparative analysis of the offered converter with the existing AC-AC converters is also introduced in this paper with respect to the number of components, equipment costs, gate driving complexity, and application areas. In order to evaluate the performance of the proposed multiconverter, the simulation-based results carried out in MATLAB/Simulink are presented and analyzed in this paper with proper descriptions. Finally, a scaled-down prototype is developed in the laboratory to validate the simulation results and the feasibility of the proposed multiconverter.

The use of Markov random fields (MRFs) is a common approach for performing image segmentation, where the problem is modeled using MRFs that incorporate priors on neighborhood nodes to allow for efficient Maximum a Posteriori inference. These local MRF models often result in smoothed segmentation boundaries, since they penalize the assignment of different labels to neighboring pixels and are limited in the use of long-range interactions. Although recent work on fully connected random fields and deep random fields has shown to be very promising in addressing these issues, both streams of approaches face certain limitations, which could affect inference performance and computational tractability. In this paper, we introduce the concept of deep randomly connected conditional random fields DRCRF, which fuse fully-connected random fields and deep random fields together to obtain benefits from long-range interactions while allowing for efficient inference using arbitrary potential functions. Leveraging random graph theory, the concept of stochastic cliques is incorporated into a deep CRF structure to take better advantage of long-range interactions while maintaining computational tractability. The experimental results demonstrate that the proposed DRCRF framework outperforms existing fully connected CRF frameworks and provides results comparable to the principled deep random field framework, which is among the state of the art in random field frameworks for image segmentation.

Mobile devices regularly broadcast WiFi probe requests in order to discover available proximal WiFi access points for connection. A probe request, sent automatically in the active scanning mode, consisting of the MAC address of the device expresses an advertisement of its presence. A real-time wireless sniffing system is able to sense WiFi packets and analyse wireless traffic. This provides an opportunity to obtain insights into the interaction between the humans carrying the mobile devices and the environment. Susceptibility to loss of the wireless data transmission is an important limitation on this idea, and this is complicated by the lack of a standard specification for real deployment of WiFi sniffers. In this paper, we present an experimental analysis of sniffing performance under different wireless environments using off-the-shelf products. Our objective is to identify the possible factors including channel settings and access point configurations that affect sniffing behaviours and performances, thereby enabling the design of a protocol for a WiFi sniffing system under the optimal monitoring strategy in a real deployment. Our preliminary results show that four main factors affect the sniffing performance: the number of access points and their corresponding operating channels, the signal strength of the access point and the number of devices in the vicinity. In terms of a real field deployment, we propose assignment of one sniffing device to each specific sub-region based on the local access point signal strength and coverage area and fixing the monitoring channel belongs to the local strongest access point.

Occupational health and safety hazards in the extreme work environment of underground mines remained a serious concern for both mine management and regulatory agency. Miners are often employed to perform different mining activities across the mine and are always exposed to health risks such as lung cancer. With the technology advancements, it is now possible to keep track of mine individuals and their health parameters. The current practice of health analysis is periodic in nature and is highly dependent on voluntary participation. Wearable health sensing system is an alternative solution to overcome these challenges and is able to provide insights on miners’ health conditions. Timely analysis of physiological parameters of the miners is immensely helpful to minimize the injuries and can also provide preventive measures for potential health hazards. In this paper, we propose a wireless health monitoring system, especially for underground mines. The contributions of this paper are twofold. First, it presents and discusses our proposed system architecture and solution followed by challenges of such system in the context of underground mines. Second, as a preliminary analysis, detailed discussion on the wireless link behavior for reliable data transmission and communication are presented. We performed real-world experimental measurements in an operational underground coal mine considering several deployment settings in straight, near face and curved mine galleries. The communication metrics (e.g., received signal strength and packet reception rate) are extensively evaluated.

The two-way relaying (TWR) technique has been confirmed to achieve higher spectral efficiency and average sum rate compared with the one-way relaying (OWR) technique in ultra-dense next generation networks with limited spectal resources. In this paper, an enhanced secrecy cooperative TWR scheme for cooperative non-orthogonal multiple access (NOMA) networks against untrusted relaying is highlighted. Specifically, with the application of NOMA principle, a base station (Bs) communicates with two trusted users, i.e., namely a near and a far users, where the communication with the far user takes place only via an untrusted relay (UR) employing both TWR and analog network coding (ANC) mechanisms. To minimize information leakage at the UR, the far user transmits its uplink signal simultaneously with Bs’s downlink signal to confuse the eavesdropping capability of the UR by increasing inter-user interference (IUI). To investigate the benefits of the proposed scheme, asymptotic lower bound expressions of the ergodic secrecy sum rate (ESSR) of uplink/downlink rates and their scaling law are derived to characterize the secrecy performance. The system parameters have been carefully studied to maitain the desired ESSR performance where we obtain an optimal value for the uplink power sharing coefficient with an arbitrary known value of the downlink one. Analytical and simulation results show that the proposed scheme can achieve scaling gain of $\boldsymbol {\frac {1}{8}\ln {\rho }}$ of positive ESSR over the untrusted OWR scheme with adaptive uplink/downlink jamming and a significant gain over the other conventional NOMA uplink/downlink schemes in two time slots of communication.

Many hazardous industrial incidents can occur due to the inadequate and inefficient monitoring of the offshore plants. Manual inspections of the offshore plants on a regular basis is not only time consuming but also dangerous regarding to human safety. For considering the safety measurement and alleviating the burden of the manual inspection, unmanned aerial vehicles (UAVs) can be effectively utilized to collect data from the remote industrial environment. In an industrial scenario, less delay is required for emergency packets and high throughput is needed for monitoring packets. This paper proposes a priority-aware fast MAC (PF-MAC) protocol for UAV-assisted industrial Internet-of-things (IIoT) systems, ensuring fast and robust data delivery. At first, the IoT devices under the UAV communication range transmit a reservation frame to the UAV to catch transmission opportunities using CSMA/CA. The devices utilize static traffic priority and a novel adaptive backoff mechanism during CSMA/CA. After receiving the reservation frames from the IoT devices, the UAV calculates the dynamic device priority based on their static traffic priority, communication duration, sampling frequency, and remaining energy. Then, time slot is assigned by the UAV to each device for data transmission. To ensure fairness, if a device fails in contention during the CSMA/CA period, the static traffic priority is raised in the next retransmission. There is no prior work in the literature that considers both the traffic priority and the device priority to ensure Quality of Service in IIoT and related systems. According to our performance study, the proposed PF-MAC outperforms the conventional protocols in terms of delay and throughput.

To ensure a long-time stable operation of the rolling bearing, it is important to accurately assess their working performance, especially the incipient degradation based on the massive service process data. As a new and effective tool, deep learning model is applied widely in the field of fault diagnosis but limited to rare labeled data. In this paper, a bearing performance assessment method based on signal component tracking is proposed to realize the bearing degradation detection. More general features are obtained by local convolution operation to represent the local characteristics in the spectrum or time–frequency distribution of vibration signal, which follows the forward features mapping process of the convolutional neural network (CNN). Then, a novel quantification criterion based on the comparison of those local features is used to provide the selection strategy of optimal fault components. The proposed method takes into account the abnormal information in degradation monitoring and utilizes it to achieve bearing incipient fault diagnosis. The experimental results prove that the features extracted by the proposed method possess high recognition efficiency when being used in incipient failure detection and diagnosis.

Vehicular ad hoc networks (VANETs) are a special type of wireless ad hoc network that requires highly scalable routing strategies to establishing reliable end-to-end communication. Because of the high dynamic of VANETs, the mobility of vehicle nodes increases the control traffic overhead. Accordingly, establishing reliable end-to-end communication paths depends entirely on the routing mechanism and the type of nodes mobility information. In this paper, we propose a new improvement to the mechanism of the Optimized Link State Routing Protocol(OLSR) protocol, named Cluster-based Adept Cooperative Algorithm (CACA), where each vehicle estimate a reliable low-overhead path using the cluster-based QoS algorithm. The CACA algorithm is introduced to improve the ability of the MPR scheme for maintaining long-lived routes. Moreover, the network scalability is enhanced by adaptively selecting most sustainable paths based on a signal strength beacon and the mobility degree of a node, which reduces significantly minimizes the size of control messages overhead as well the routing tables recalculation process. Simulation experiments using the network simulator are presented to demonstrate the effectiveness of our solution. The results show that the proposed algorithm can improve network performance effectively relative to other algorithms.

Device-free localization (DFL), requiring no extra devices equipped with a target, is an important field of research on the Internet of Thing (IoT). Energy efficiency issue is essential for the development of the IoT, but seldom of the existing papers are focus on it. So we investigate this issue with quantized data of only several bits under the compressive sensing (CS) framework, which can both reduce the required wireless link number and the bit number in the DFL scheme. First, through exploiting the discrete property of CS theory, we calculate the discrete measurement probability bypass computing the complex or uncalculated measurement probability density function (pdf), which can well represent the measurement distribution characteristic. Second, we design a unique quantization scheme for each wireless link according to their measurement probability and build a novel DFL model by analyzing the quantization error. Third, a new DF-QVBI algorithm is proposed to recover the target location, which can make great use of the quantization error. Finally, numerical simulations show the superiority and robustness of the proposed method.

In the past two decades, reversible data hiding (RDH), also referred to as lossless or invertible data hiding, has gradually become a very active research area in the field of data hiding. This has been verified by more and more papers on increasingly wide-spread subjects in the field of RDH research that have been published these days. In this paper, the various RDH algorithms and researches have been classified into the following six categories: 1) RDH into image spatial domain; 2) RDH into image compressed domain (e.g., JPEG); 3) RDH suitable for image semi-fragile authentication; 4) RDH with image contrast enhancement; 5) RDH into encrypted images, which is expected to have wide application in the cloud computation; and 6) RDH into video and into audio. For each of these six categories, the history of technical developments, the current state of the arts, and the possible future researches are presented and discussed. It is expected that the RDH technology and its applications in the real word will continue to move ahead.

The concept of the sharing economy has attracted wide attention due to its huge impact on transforming traditional industries. Online car-hailing, combining the sharing economy and ICT technologies, shapes a new landscape, which can greatly shorten the traveler's waiting time and reduce the empty-run rate of cars (such as Uber and DiDi). However, a few comprehensive studies have been conducted on the sustainable development of online car-hailing considering both the user experience and the operational cost. To address this issue, this paper systematically studies the influencing factors, their relations, and their impacts on online car-hailing in an empirical way. First, an index system in four key aspects, namely service, price, safety, and traveling time, is established to evaluate the user experience. Second, the Bayesian network theory is employed to model the complexity of each factor and the extent of its influence on the online car-hailing system with expert scores. The two most important influence paths affecting the passengers' choices of online car-hailing are determined. Third, we further construct an investment allocation model with the aim of minimizing the economic cost of the online car-hailing system while maintaining the system performance, considering the limiting factors, such as the complexity and cost. Finally, we perform a simulation experiment, which generates some practical suggestions for improving the online car-hailing system.

Directional modulation (DM) as a physical layer security technique has been studied from many different aspects recently. Normally all existing designs based on antenna arrays lead to varying weight coefficients for different antennas and for different signal symbols, which poses a particular challenge for feed circuits design in analogue implementation. In this paper, to reduce the implementation complexity, a constant magnitude constraint is proposed for the first time, and the resultant non-convex constraint can be modified to a convex form so that the problem can be solved conveniently by existing convex optimisation toolboxes. Design examples are provided to show the effectiveness of the proposed design.

This paper presents a method to design an integrated chassis controller with four-wheel independent steering (4WIS) under the constraint on front slip angles for electric vehicles (EVs) adopting in-wheel motor (IWM) driving system. To improve lateral stability and maneuverability of a vehicle, direct yaw moment control strategy is adopted. A control allocation method is adopted to distribute control yaw moment into tire forces, generated by 4WIS. If corrective steering angles of 4WIS are added to front steering angles generated by a driver, it can deteriorate control performance because the lateral tire force of front wheels easily saturated and it causes loss of required yaw moment needed to stabilize a vehicle. To cope with the problem, it is necessary to impose constraints on front slip angles. To compensate the loss of control yaw moment caused by the constraint on front slip angles, a constrained control allocation method is presented. Simulation on driving simulation tool, CarSim®, shows that the proposed integrated chassis controller is capable of maintaining lateral stability and maneuverability without performance deterioration under the constraint on the front slip angles.

Multiple-object tracking (MOT) has received an increasing attention due to the rapid development of autonomous driving. However, the MOT problem is still challenging mainly due to the occlusion and scale variation. Motivated by the fact that the discriminative correlation filters-based (DCFB) tracking algorithms can tackle these problems and significantly improve the accuracy of single object tracking, how to exploit the DCFB tracking algorithms for MOT is worthy studying. Moreover, the corrupted training samples due to the occlusion make DCFB tracking methods to update the appearance model of target uncorrected and result in tracking drift. In this paper, we exploit Markov decision process to integrate the DCFB tracking method into our MOT framework and address the update problem of the appearance model in DCFB tracking method. Moreover, in order to overcome the challenges of occlusion and scale variation, to prevent target drift during tracking, we use two DCFB trackers with different update frequencies and a novel update strategy to predict the location of targets. The part-based method is used to extract robust features to tackling the challenges of occlusion and scale change. In order to verify the efficiency of our algorithm, experiments are performed based on KITTI tracking benchmark. The results demonstrate that our method achieves state-of-the-art performance and outperforms the state-of-the-art algorithms in road scenarios.

Intelligent Transportation Systems (ITS) research and applications benefit from accurate short-term traffic state forecasting. To improve the forecasting accuracy, this paper proposes a deep learning based multitask learning Gated Recurrent Units (MTL-GRU) with residual mappings. To enhance the performance of the MTL-GRU, feature engineering is introduced to select the most informative features for the forecasting. Then, based on real-world datasets, numerical results show that the MTL-GRU can well estimate traffic flow and speed simultaneously, and performs better than other counterparts. Experiments also show that the deep learning based MTL-GRU model can overpower the bottleneck caused by enlarging training datasets and continue to gain benefits. The results suggest the proposed MTL-GRU model with residual mappings is promising to forecast short-term traffic state.

With the development of mobile positioning technology, a large number of Origin-Destination (OD) flow data with spatial and temporal details have been produced. These OD flow could give us a great opportunity to research geographical phenomena such as spatial interaction and mobility patterns. The OD flow clustering approach is an effective way to explore the main mobility patterns of the objects. At the same time, similarity measurement plays a key role in OD flow clustering. However, most of the previous OD flow similarity measurement methods failed to make full use of the spatial information of the flow including spatial proximity and geometric similarity. In this paper, we considered both position information and geometric properties of OD flow and propose a new method to measure the spatial similarity between OD flows. Specifically, the proposed method sets the neighbor threshold with the length of OD flows and the parameter $\alpha $ dynamically. Based on the constraint of the OD points’ location, the directions of the flows are implicitly restricted. The sole-parameter $\alpha $ has a practical value as it determines the maximum length difference and the maximum directional difference that can be tolerated between similar flows. The proposed method passed a simulation experiment with synthetic flows and a case study with 283,008 taxi trips in Beijing in one day. The results show that the proposed method can discover the dominant mobility pattern from a large number of flow data effectively. In the case study, the dominant flow clusters reveal the taxi mobility patterns of residents at different distances in Beijing.

Hyperspectral transmission imaging may provide a means for rapid screening of breast tumors, but tissue has a strong nature of scattering, thus causing a great difficulty in identifying heterogeneity. In this paper, a combination of frame accumulation and deep learning was proposed to detect heterogeneity, and we designed the simulation experiment of collecting phantom images. On the basis of frame accumulation preprocessing, the heterogeneous detection is performed on multispectral images by using faster regions with convolutional neural networks (R-CNN) features and a single shot multibox detector (SSD), two typical detection frameworks of deep learning. The results show that the mean average precision (mAP) of faster R-CNN and SSD reach 90.8% and 95.1%, respectively, when three classes (including background) are detected with the help of the dataset provided in this paper, and the mAP of two frameworks both reach 99.9% when two classes (including background) are detected. The detection efficiency of the SSD is higher than faster, SSD’s detection speed can reach 50 fps, and the detection accuracy of the images after frame accumulation preprocessing is higher than that without frame accumulation processing. In summary, we validate the possibility of employing faster R-CNN and SSD to detect heterogeneity in multispectral images based on frame accumulation that improves image grayscale resolution, and it has a certain degree of reference significance for the application of deep learning in multispectral image detection.

Negative capacitance field-effect transistor (NCFET) addresses one of the key fundamental limits in technology scaling, akin to the non-scalable Boltzmann factor, by offering a sub-threshold swing below 60 mV/decade. In this paper, we investigate how the NCFET technology can open the doors not only for the continuation of Moore's law, which is approaching its end, but also for reviving Dennard's scaling, which stopped more than a decade ago. We study NCFET for the 7-nm FinFET technology node, from physics to processors and demonstrate that prior trends in processor design with respect to voltage and frequency can be revived with the NCFET technology. Our work focuses on answering the following three questions towards drawing the impact of NCFET technology on computing efficiency: In how far NCFET technology will enable processors: 1) to operate at higher frequencies without increasing voltage; 2) to operate at higher frequencies without increasing power density, which is substantial, because maintaining on-chip power densities under tight constraints due to limited cooling capabilities is inevitable; and 3) to operate at lower voltages, while still fulfilling performance requirements, which is substantial for the emerging Internet of Things, in which available power budgets for such devices are typically very restricted.

In this paper, a new image segmentation method is proposed by combining the FCM clustering algorithm with a rough set theory. First, the attribute value table is constructed based on the segmentation results of FCM under different clustering numbers, and the image is divided into several small regions based on the indistinguishable relationship of attributes. Then, the weight values of each attribute are obtained by value reduction and used as the basis to calculate the difference between regions and then the similarity evaluation of each region is realized through the equivalence relationship defined by the difference degree. Finally, the final equivalence relation defined by similarity is used to merge regions and complete image segmentation. This method is validated in the segmentation of artificially generated images, brain CT images, and MRI images. The experimental results show that compared with the FCM method, the proposed method can reduce the error rate and achieve better segmentation results for the fuzzy boundary region. And, the experimental results also prove that the algorithm has strong anti-noise ability.

Recently, many countries have spent great efforts on wind power generation. Although there have been many methods in the field of wind power forecasting, the persistence statistics model based on historical data is still being challenged due to the randomness and uncontrollability in wind power. Hence, a more accurate and effective wind power forecasting method is still required. In this paper, a new forecasting method is proposed by combining stacked auto-encoders (SAE) and the back propagation (BP) algorithm. First, an SAE with three hidden layers is designed to extract the characteristics from the reference data sequence, and the subsequent loss function is used in the pre-training process to obtain the optimal initial connection weights of the deep network. Second, after adding one output layer to the stacked auto encoders, the BP algorithm is used to fine tune the weights of the whole network. To achieve the best network architecture, the particle swarm optimization is adopted to decide the number of neurons of the hidden layer and the learning rate of each auto encoder. Experimental results show that, for short-term wind power forecasting, the proposed method achieves more stable and effective performance than the existing BP neural network and support vector machines. The improvement in accuracy is 12% on average under different time steps.

The diagnosis of biopsy tissue with hematoxylin and eosin (H&E) stained images has been widely used by pathologists to detect the lesions and assess the malignancy. Nevertheless, the diagnostic result relies on the visual observation of pathologists which may vary from person to person under different circumstances. With the advantage of automatically and adaptively learning features at multiple levels of abstraction, Convolutional Neural Networks (CNNs) have rapidly become promising alternatives for pathological image analysis. Therefore, in this paper, we propose an effective method for tumor classification called Hard Example Guided CNN. Our contribution is twofold: firstly, to optimize image representation, we design the CNN architecture as dual-branch, used for extracting global features and local features simultaneously. Secondly, we propose a re-weight training algorithm, which improves learning accuracy and accelerates the convergence by increasing the weight of hard examples. Extensive experiments on multiple datasets demonstrate the superiority of our proposed classification method.

Biometric recognition is already a big player in how we interact with our phones and access control systems. This is a result of its comfort of use, speed, and security. For the case of border control, it eases the task of person identification and black-list checking. Although the performance rates for verification and identification have dropped in the last decades, protection against vulnerabilities is still under heavy development. This paper will focus on the detection of presentation attacks in fingerprint biometrics, i.e., attacks that are performed at the sensor level, and from a hardware perspective. Most research on presentation attacks has been carried out on software techniques due to its lower price as, in general, hardware solutions require additional subsystems. For this paper, two low-cost handheld microscopes with special lighting conditions were used to capture real and fake fingerprints, obtaining a total of 7704 images from 17 subjects. After several analyses of wavelengths and classification, it was concluded that only one of the wavelengths is already enough to obtain a very low error rate compared with other solutions: an attack presentation classification error rate of 1.78% and a bona fide presentation classification error rate (BPCER) of 1.33%, even including non-conformant fingerprints in the database. On a specific wavelength, a BPCER of 0% was achieved (having 1926 samples). Thus, the solution can be low cost and efficient. The evaluation and reporting were done following ISO/IEC 30107-3.

In this paper, the design of reduced-order and full-order interval observers for discrete-time system is investigated. First, the original system is transformed into a descriptor system with the ability to estimate the unknown noise of sensors. Next, a reduced-order interval observer design method for descriptor system is presented. The state matrix of the system is obtained by linear transformation, and a Sylvester equation is constructed to find the appropriate observer gain matrix. Then, an existing design method of full-order interval observer is introduced in this paper. Finally, a simulation example is given to demonstrate that the reduced-order interval observer has more accurate interval estimation results than the full-order interval observer.

The use of supplementary controllers for mitigating subsynchronous interaction (SSI) in series compensated DFIG-based wind farms is quite promising due to high effectiveness and low cost. Implementation of such a controller requires effective communications between individual turbines and the wind farm controller, where the control performance is very much affected by the communication delays involved. This paper delivers the first detailed analysis on the impact of communication delays on SSI damping controller performance. A novel algorithm is proposed to calculate the stability delay margin (SDM) of the closed-loop system based on Rekasius Substitution and Guardian Map Theorem with the advantage of reduced computational burden particularly for high-order systems. Based on the proposed algorithm, the impacts of wind farm operating conditions and turbine control parameters on the SDM are investigated. To strengthen the SSI damping controller performance against communication delays, a Smith predictor scheme is also developed. The effectiveness of the proposed delay analysis framework and Smith predictor scheme based delay immune controller is validated through Electromagnetic Transient (EMT) simulations on a realistic test system with multiple series capacitor compensated lines considering various operation conditions.

The scope, scale, and intensity of real, as well as potential attacks, on the Smart Grid have been increasing and thus gaining more attention. An important component of Smart Grid cybersecurity efforts addresses the availability and access to the power and related information and communications infrastructures. We overload the term, Denial-of-Service (DoS), to refer to these attacks in the Smart Grid. In this paper, we provide a holistic and methodical presentation of the DoS attack taxonomies as well as a survey of potential solution techniques to help draw a more concerted and coordinated research into this area, lack of which may have profound consequences. To the best of our knowledge, the literature does not have such a comprehensive survey study of the DoS attacks and solutions for the Smart Grid.

Fingerprint-based recognition is widely deployed in different domains. However, the traditional fingerprint recognition systems are vulnerable to presentation attack, which utilizes an artificial replica of the fingerprint to deceive the sensors. In such scenarios, Fingerprint Liveness Detection (FLD) is required to ensure the actual presence of a live fingerprint. In this paper, a fingerprint matching method fused with liveness detection is proposed. Firstly, the similarity between two fingerprint images is calculated based on Octantal Neatest-Neighborhood Structure (ONNS), where the closest minutia to the central minutia is found from each sector of octant. Secondly, the FLD score of the fingerprint image is obtained by using the modified Residual Network (Slim-ResCNN). Finally, a score-level fusion is performed on the results of fingerprint matching and FLD by generating interaction features and polynomial features as the score feature vector. To classify whether a fingerprint image is a genuine live fingerprint or a spoof attack (including impostor live and fake fingerprints), the score feature vector is processed using logistic regression (LR) classifiers. The proposed method won the first place in the Fingerprint Liveness Detection Competition 2019 with an overall accuracy of 96.88%, which indicates it can effectively protect the fingerprint recognition systems from spoof attacks.

Nowadays, Wireless Sensor Networks (WSNs) are playing a vital and sustainable role in many verticals touching different aspects of our lives including civil, public, and military applications. WSNs majorly consist of a few to several sensor nodes, that are connected to each other via wireless communication links and require real-time or delayed data transfer. In this paper, we propose an autonomous Unmanned Aerial Vehicle (UAV)-enabled data gathering mechanism for delay-tolerant WSN applications. The objective is to employ a self-trained UAV as a flying mobile unit collecting data from ground sensor nodes spatially distributed in a given geographical area during a predefined period of time. In this approach, two Reinforcement Learning (RL) approaches, specifically Deep Deterministic Gradient Decent (DDPG) and Q-learning (QL) algorithms, are jointly employed to train the UAV to understand the environment and provide effective scheduling to accomplish its data collection mission. The DDPG is used to autonomously decide the best trajectory to adopt in an obstacle-constrained environment, while the QL is developed to determine the order of nodes to visit such that the data collection time is minimized. The schedule is obtained while considering the limited battery capacity of the flying unit, its need to return the charging station, the time windows of data acquisition, and the priority of certain sensor nodes. Customized reward functions are designed for each RL model and, through numerical simulations, we investigate their training performances. We also analyze the behavior of the autonomous UAV for different selected scenarios and corroborate the ability of the proposed approach in performing effective data collection. A comparison with the deterministic optimal solution is provided to validate the performance of the learning-based approach.

Frame arrival detection is the first crucial task for a digital communication receiver. In this paper, we propose a new frame arrival detection method based on the concept of matched filtering. The proposed training sequence consists of three repeated subsequences, each including a few repeated segments and exhibiting high sparsity in frequency domain. The proposed detection method includes the following two stages. The first matched filtering stage employs the subsequence as the filter coefficients, which matches the frequency sparsity of the received subsequence and could greatly improve the output signal to noise ratio. The second stage adopts delayed autocorrelation on the filtered signal to detect the presence of training sequence. It is demonstrated that the proposed method could outperform the conventional methods in terms of both missed detection and false alarm probability. We derive the theoretical analysis for both missed detection and false alarm performance. We further extend our performance analysis and numerical evaluation to the system with carrier frequency offset (CFO). The results illustrate accuracy of the analytical results and robustness of the proposed method against practical levels of CFO.

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Edge Computing has grown into a key solution for coping with the stringent latency requirements of 5G scenarios. Nevertheless, the edge node placement problem raises critical concerns regarding deployment and operational expenditures (i.e., mainly due to the number of nodes to be deployed),current backhaul network capabilities, non-technical placement limitations, etc. In this paper, a novel framework called EdgeON is presented aiming at reducing the overall expenses when deploying and operating an Edge Computing (EC) network, taking into account the usage and characteristics of the in-place backhaul network. The framework implements several placement and optimization strategies targeting the heavily constrained network-aware Edge Node Placement Problem (ENPP). The results obtained by our solution are promising, achieving an average of30%less Edge Nodes (ENs) deployed and 25% higher average usage ratio when compared to other widely used heuristics. Furthermore, our strategy achieved as core offset of less than2%in comparison to the implemented Mixed Integer Linear Programming (MILP).

This paper presents a novel algorithm for solving a Student-Project Allocation problem, a variation of a two-sided matching algorithm, where a large number of students are allocated to numerous projects with limited available allocation places. Several new or improved algorithms are proposed, inspired by recommender systems and combined into a genetic algorithm. The main two algorithms are (i) variating the master list of student average grade ranking inspired by a fuzzy approach and (ii) extending the incomplete project preference lists by exploring the similarities of student choices, thus minimizing the number of randomly allocated students. The algorithm was implemented for allocating more than 500 students to more than 200 projects, in a course at the University of Zagreb, Faculty of Electrical Engineering and Computing. We compared our algorithm with the Deferred Acceptance (DA) Algorithm to check its validity. Using the fuzzy approach, the number of unallocated students decreased by 10%, with almost no effect to the top-ranked students. The combined usage of all proposed algorithms increased the number of successfully allocated students by more than 25%.

Polygon shape is an important factor affecting the efficiency of vector polygon overlay analysis. Aiming at the problems that polygon shape is difficult to accurately measure and different algorithms have different sensitivities to the same polygon in vector polygon overlay analysis, 27 shape variables are selected from literature to establish the shape complexity models for Vatti, Greiner-Hormann and Martinez algorithms by multiple linear regression analysis in this paper. These three algorithms are suitable for arbitrary polygon clipping and include intersection, union, difference and symmetric difference operators. The experimental results show that the calculated performances of the intersection, union, difference and symmetric difference operators belonging to the same algorithm are similar to their sensitivity to the same polygon shape. The fitting correlation coefficient R2 of the shape complexity model based on the Greiner-Hormann algorithm is the highest among the three algorithms, which can explain 99% of the performance of this algorithm.

Hajj, the annual pilgrimage to Makkah, is one of the most massive gathering events in the world. It is a mandatory religious activity, once in a lifetime for every sane well-off Muslim. Every year around 3 million Muslims across the globe visit Makkah to perform this spatiotemporally constrained set of religious activities, like circumambulation around the Kaaba and the encampment at Mina, Arafat and Muzdalifah. The key ritual at Rami-al-Jamarat (the stoning-of-the-pillars) is a place seen particularly crowded. The diversified nature of the crowd in terms of race, age, language and culture brings several administrative challenges for local organizers whose responsibility is to ensure the smooth organization of the event. The advent of digital technologies has helped researchers to explore and propose modern methods for better crowd management and devise control strategies to organize such a mass gathering. In this paper, we present a taxonomy that summarizes our survey on the utilization of technology areas for providing needful services and improving crowd management during the Hajj season. These technologies are Wireless, Computer Vision, Spatial Computing, Data Analytics, Mobile Applications, Immersive Technologies, and Crowd Modelling and Simulation. We have also included the current research work and its implementation plan for Hajj 2020. One dimension of the taxonomy is to understand different possible technologies for Crowd management. Another dimension is to understand the impact of technologies to handle such a massive crowd. The discussion section covers a comparison of technologies with multiple aspects that may help researchers to contribute more towards dealing with future mass gathering events.

High accuracy relative navigation (RELNAV) information is crucial for datalink network members in the situation of joint operations, especially the situations that some datalink network members may not be able to obtain their location with absolute navigation. In this paper, relative navigation algorithm based on JTIDS/INS/BA is proposed, in this system datalink joint units(JUs) are positioned in two-dimensional plane with multi-slots joint maximum likelihood estimation (MLE), and altitude is located by a height filter independently, which overcome the problem of poor positioning geometry of altitude. Multi-slots joint MLE convert TOA measurements in different slots to one moment with short-time inertial navigation system(INS) information, which lead to a higher localization accuracy in two-dimensional plane. We simulate a joint tactical information distribution system (JTIDS) network with multiple members, to verify the performance and analyze the influence factors of the proposed algorithm. The simulation results show that compared to EKF that process TOA measurements sequentially in each navigation slot and MLE without multi-slots joint processing, the multi-slots joint MLE relative navigation algorithm based on INS/JTIDS/BA performs better in two-dimensional plane location, and the height filter based on barometric altimeter (BA) improves the relative height positioning accuracy greatly.

This paper addresses the problem of target-directed exploration (TDE) in initially unknown and large-scale indoor environments. In such scenarios, the inference on an unknown space can improve the search performance under the assumption that the context of a particular space (i.e., the functional category of the space) is correlated with the existence of a target. The space inference is promising in that there is a strong statistical correlation between the semantic categories of indoor spaces and their adjacency because the spaces are designed to reflect universal human preferences. In this point of view, we propose a novel TDE scheme leveraging the semantic-spatial relations of an indoor floorplan dataset. Whereas existing works dealing with the data-driven space inferences consider only the one-to-one relation statistics of the spaces or utilize heuristic counting-based matching algorithms without building a trainable latent model, we propose the pattern cognitive Multivariate Bernoulli Distribution-based Graphical Space Inference Model (MBD-GSIM). MBD-GSIM efficiently captures the core contexts of the discrete semantic-spatial relations to predict an unknown space by using the latent Multivariate Bernoulli Distribution model. We also suggest utilizing the MBD-GSIM in a cost-utility based frontier exploration scheme for TDE problems. The proposed scheme is constructed in the Robot Operating System (ROS); its efficiency is investigated in the Gazebo simulation environment.