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The combination of pervasive edge computing and blockchain technologies opens up significant possibilities for industrial Internet of Things (IIoT) applications, but there are several critical limitations regarding efficient storage and rapid response for large-scale low-delay IIoT scenarios. To address these limitations, in this article we propose a hierarchical edge-cloud blockchain called LayerChain . Specifically, to promote scalability, we design a layered structure to hierarchically store the blockchain data in multiple distributed clouds and edge nodes. Next, we propose a node classification method to accommodate differences between the edge nodes when deploying the blockchain. Moreover, to mitigate lengthy delays during block propagation, we propose a tree-based clustering algorithm where blocks are propagated through different clusters with a compressed tree depth. Simulation results show that our LayerChain efficiently reduces the system's resource requirements and block propagation time, making it well-suited for large-scale low-delay IIoT applications.

The continuous development and wide application of CT in medical practice have raised public concern over the associated radiation dose to the patient. However, reducing the radiation dose may result in increasing the noise and artifacts, which may adversely interfere with the judgment and belief of radiologists. Therefore, we propose a low-dose CT denoising model based on multiscale parallel convolution neural network to improve the visual effect. Residual learning is utilized to reduce the difficulty of network learning, and batch normalization is adopted to solve the problem of performance degradation due to the increase in neural network layers. Specifically, we introduce the dilated convolution to expand the receptive field by inserting weights of zero in the standard convolution kernel, while not increasing the extra parameters. Furthermore, the multiscale parallel method is utilized to extract multiscale detail features from lung images. Compared to the traditional methods such as Wiener filter, NLM, and models based on CNN, e.g., SCNN, DnCNN, our extensive experimental results demonstrate that our proposed model (CT-ReCNN) can not only reduce the LDCT lung images noise level, but also retain more exact information as well.

This paper proposes an event-triggered reinforcement learning (RL) control strategy to stabilize the quadrotor unmanned aerial vehicle (UAV) with actuator saturation. As the quadrotor UAV equips with a complex dynamic is difficult to be model accurately, a model free reinforcement learning scheme is designed. Due to the practical limitation of actuators, the end of controller is constrained with a bounded function. In order to reduce the calculation consumption for the onboard computer, an event-triggered mechanism is developed, which only update the controller when the triggered condition is satisfied. The proposed controller is implemented with two neural networks which are called critic and actor. Some advanced RL technologies are utilized for speeding up the train process, e.g. off-policy training, experience replay, etc. The stability of closed-loop system is proved by the Lyapunov analysis. The simulation results including a stability task and a tracking task verify the theoretical analysis, in which we find the updating frequency of controller is decreased greatly.

This brief addresses the fixed-time event/self-triggered leader-follower consensus problems for networked multi-agent systems subject to nonlinear dynamics. First, we present an event-triggered control strategy to achieve the fixed-time consensus, and a new measurement error is designed to avoid Zeno behavior. Then, two new self-triggered control strategies are presented to avoid continuous triggering condition monitoring. Moreover, under the proposed self-triggered control strategies, a strictly positive minimal triggering interval of each follower is given to exclude Zeno behavior. Compared with the existing fixed-time event-triggered results, we propose two new self-triggered control strategies, and the nonlinear term is more general. Finally, the performances of the consensus tracking algorithms are illustrated by a simulation example.

By using the generating tree technique and the obstinate kernel method, Kim and Lin confirmed a conjecture due to Martinez and Savage which asserts that inversion sequences e = ( e 1 , e 2 , … , e n ) containing no three indices i j k such that e i ≥ e j , e j ≥ e k and e i > e k are counted by Baxter numbers. In this paper, we provide a bijective proof of this conjecture via an intermediate structure of open partition diagrams, in answer to a problem posed by Beaton-Bouvel-Guerrini-Rinaldi. Moreover, we show that two new classes of pattern-avoiding inversion sequences are also counted by Baxter numbers.

In this paper, we study the practical fixed-time event-triggered time-varying formation tracking problem of leader-follower multi-agent systems with multi-dimensional dynamics. Fixed-time event-triggered control schemes with continuous communication and intermittent communication are developed, respectively. Continuous communication and measurement are avoided, and computation cost is reduced greatly in the latter scheme. And the settling time is to be specified regardless of initial states of agents. Meanwhile, tracking errors are adjustable as desired with expected settling time. It is demonstrated that time-varying formation tracking can be achieved under the two proposed control schemes and Zeno behavior can be excluded. Finally, numerical examples are provided to illustrate the effectiveness of the proposed control strategies.

Crowdsourcing is a novel distributed problem-solving mechanism that can provide the collection and share of marine data in the Internet of Things for the smart ocean. Nevertheless, the privacy leakage issue caused by multirole information interaction in crowdsourcing brings a serious challenge to the smart ocean. In this article, we propose a crowdsourcing privacy protection scheme based on multiauthority ciphertext-policy attribute-based encryption to enhance privacy protection in the data sharing environment. In this scheme, we design an independent key component distribution approach through multiple authorities, which could effectively disperse the security responsibility from the crowdsourcing platform. Then, we present the idea of partial decryption on the platform to reduce the computing cost of mobile users and prevent the platform from snooping on users’ data. Moreover, we put forward an efficient attribute revocation mechanism and a task search function in the scheme to achieve dynamic on-demand services while ensuring the forward and backward security of tasks. Theoretical analysis proves the correctness of both decryption and keywords matching, and the security of each involved entity. The simulation results show that our proposed scheme achieves a significant improvement in reducing time consumption compared with several related schemes.

Mobile crowdsourcing is a promising strategy for trusted data collection in Internet-of-Things (IoT) applications. In this article, we propose a new fog-blockchain distributed approach for crowdsourcing reputation management to prevent user’s privacy leakage, malicious users’ participation, and reputation tampering in wireless IoT systems. To protect the user’s privacy, we design a cross-layer privacy protection model to separate the user’s identity and tasks flexibly by means of a hierarchical structure based on fog computing. Moreover, considering the multiconstraint requirement of crowdsourcing tasks, we present a multifactor reputation evaluation method to accurately identify malicious users. Furthermore, to solve the multi-identity problem of users on multiple fog nodes, we propose an adaptive fog-blockchain reputation storage method, which efficiently reduces the system resource consumption by analyzing the adaptive classification of fog nodes. Exhaustive experimental simulation results validate the security and efficiency of our proposed reputation management system.

With the rapid increase in service requirements driven by Internet of Things (IoT) networks, mobile crowdsourcing has become a compelling paradigm that can efficiently solve complex tasks in the physical world. Nevertheless, we found that most IoT tasks have constraints on deadline, location, and resource consumption, which limit the application of crowdsourcing platforms in the IoT networks. In this article, we innovatively propose a reliable fog-based temporal–spatial crowdsourcing for serving the above tasks. In this scenario, the key point is to achieve the best match of the attributes among tasks, fog nodes, and workers. As the bridge of the other two parts, fog nodes determine the orientation of tasks. Therefore, we present a temporal–spatial task allocation (TS-TA) scheme in the fog layer, aiming to make task results more reliable. In this scheme, we build a temporal–spatial attribute learning model based on the user behaviors. Then, we use the users’ interest attribute matching model to identify the candidate fog nodes that satisfy the requirements of temporal–spatial tasks. We choose the fog nodes with low spatial correlation that is benefit to defense the attack on the nodes in the intensive area. Meanwhile, we assign the redundancy nodes for intrusion response through replacing the attacked/negative node. Both theoretical and real-topology simulation results validate that the proposed scheme can get better performance in system resource consumption and system robustness compared with other benchmark schemes.

This paper investigates the synchronization problem of the Kuramoto-oscillator network with non-identical oscillators. The fixed-time event-triggered synchronization control strategies are developed for phase agreement and frequency synchronization under both continuous and intermittent communication. With the developed fixed-time controller, the synchronization can be achieved within a pre-defined time for any initial phase of each oscillator. The event-triggered mechanism avoids continuous controller update and data transmission, which significantly saves the computation and communication resources. Furthermore, theoretical analysis shows that the fixed-time convergence can be guaranteed and the Zeno behavior is avoided by the proposed methods. The numerical simulations of each situation also verify the effectiveness of the proposed synchronization control strategies.

The explosion pressure and minimum explosible concentration (MEC) properties of metal sulfide ore dust clouds are valuable for the prevention and control of metal sulfide ore dust explosions. In this study, a 20 L explosion sphere vessel was used to investigate the effect of sulfur content, particle size, and concentration on the explosion pressure and minimum explosible concentration of metal sulfide ore dust clouds. Four samples with different sulfur contents were selected (30%–40%, 20%–30%, 10%–20%, and 0%–10%). Before and after the explosion, samples were tested by X-ray diffraction. The results indicate that the metal sulfide ore dust is explosive dust with St1 grade explosion pressure. With an increase in concentration, the maximum explosion pressure increased at first and then decreased. With an increase in sulfide content, the explosion pressure of metal sulfide ore dust increased, while the minimum explosible concentration decreased. As particle size decreased, the MEC also decreased. The sulfur content, particle size, and concentration of metal sulfide ore dust were the main factors affecting the explosion hazard.

In this article, a new generation of magnet-free circulators with high performance is proposed. Circulators are crucial devices in modern communication systems due to their ability to enable full-duplexing and double the spectral efficiency directly in the physical layer of the radio-frequency (RF) front end. Traditionally, the Lorentz reciprocity is broken by applying the magnetic bias to ferrite materials; therefore, conventional circulators are bulky and expensive. In this article, this problem is addressed by replacing the magnetic bias with periodic spatiotemporal modulation. Compared to previous works, the proposed circulator is constructed using surface acoustic wave (SAW) filters instead of transmission lines (TLs), which reduces the modulation frequency by at least a factor of 20 and ensures ultra-low power consumption and high linearity. The miniaturized high quality ( ${Q}$ ) factor SAW filters also lead to a low-loss nonreciprocal band with strong isolation (IX) and broad bandwidth (BW) on a chip scale, therefore addressing such limitations in previous magnet-free demonstrations. Furthermore, compared to the conventional differential circuit configuration, a novel quad configuration is developed, which doubles the intermodulation-free BW.

In this paper, we study the fixed-time event-triggered consensus problem of the uncertain nonlinear multiagent systems. Two fixed-time event-triggered consensus controllers are proposed. In contrast to finite-time results, the convergence time of fixed-time results is independent of initial conditions. Furthermore, continuous communications can be avoided both in the update of controllers and in the triggering condition monitoring. It is proved that there is no Zeno behavior under the fixed-time event-triggered consensus control strategies. The availability of the control algorithms is verified by numerical simulations.

The energy use prediction of building systems is crucial to design a high-efficiency building and maintain low energy consumption operation, which is also important in optimizing building system control and retrofitting. This paper demonstrates a comparative study of four data-driven methods used in online building energy predictions involving large-scale data extracted from several types of buildings. The characteristics of building electricity use and data reliability were addressed through the data pre-treatment process including visualization, cleaning, parsing and filtering. Mathematical algorithms and their applications in previous studies were summarized and compared, and evaluation methods were developed. The performance and suitable application scenarios of the proposed algorithms were conducted via the comparison of monitoring data and predicted results. The study indicates that the most complex method which requires the highest computation ability, i.e., the Artificial Neural Network (ANN), does not lead to the highest accuracy, while as the fastest computation method, Gaussian Process Regression (GPR) usually has the results with the lowest accuracy. Support Vector Machine (SVM) and Multivariate Linear Regression (MLR) methods usually perform better in the case scenarios studied. All the prediction accuracies can meet the requirements of RMSE

During the last couple of years, sustainability has become topical in transnational public–private partnership (TPPP) projects primarily because of the enormous impact TPPP projects have on the environment, economic, and well‐being of people. This paper aims to develop a model that could be used to evaluate the sustainability levels of TPPP projects. The proposed model consists of four categories of social responsibility factors; these include economic, environment, social, and people‐oriented factors. Of the four categories, social factors obtained the highest coefficient (i.e., 0.255) in the TPPP sustainability index equation. The substantive model will enable practitioners to quantify the sustainability levels of TPPP projects prior to their implementations. Moreover, the outputs of this study inform private investors of the key areas to focus on to enhance sustainable development through TPPP project implementations.

A novel high step-up DC-DC converter with a three-winding-coupled-inductor and an output capacitor in series is proposed in this study. By charging for two windings of dotted terminal connection in series by input source, the proposed converter can achieve a higher gain by using smaller turns ratios when the active switch is turned on. The passive clamping circuits are introduced for not only recycling leakage energy but also alleviating voltage spike on the main switch effectively. Meanwhile, the reverse-recovery problem of the diode is alleviated by the leakage inductor. Thus, the efficiency can be improved. In addition, output capacitors are in series to provide energy so that the volume of the output capacitor is reduced. The operating principle and steady-state analyses of the proposed converter are discussed in detail. Then, the performance of the proposed converter is compared with existing converters. Finally, a prototype circuit is built with a 40-V input voltage, 380-V output voltage, and 400-W output power to verify the performance of the proposed converter.

This paper investigates the fixed-time consensus control problem of multi-agent systems (MASs) with input delay and uncertain disturbances. The fixed-time consensus is obtained via an event-triggered control, and it can be reached within a settling time for any initial state. Continuous communication can be avoided both in the update of the controller and triggering condition monitoring. Consequently, the energy consumption can be significantly reduced. It is proven that no Zeno behavior occurs under the proposed event-triggered consensus control strategies. Finally, some numerical simulations are provided to illustrate the effectiveness of the proposed controllers.

The Network Functions Virtualization (NFV) extends the functionality provided by Software-Defined Networking (SDN). It is a virtualization technology that aims to replace the functionality provided by traditional networking hardware using software solutions. Thereby, enabling cheaper and efficient network deployment and management. The use of NFV and SDN is anticipated to enhance the performance of Infrastructure-as-a-Service (IaaS) clouds. However, due to the presence of a large number of network devices in IaaS clouds offering a plethora of networked services, there is need to develop a traffic monitoring system for the efficient network. This paper proposes and validates an extensible SDN and NFV-enabled network traffic monitoring system. Using extensive experiments, we show that the proposed system can closely match the performance of traditional networks at cheaper costs and by adding more flexibility to network management tasks.

In this paper, an adaptive fuzzy neural network (FNN) control scheme is developed for a class of multiple-input and multiple-output (MIMO) nonlinear systems subject to unknown dynamics and state constraints. FNNs are used to approximate the unknown dynamics that comprises the effects of uncertain parameters and functions. Also, integral Lyapunov functions are introduced to address state constraints. A neural-network-based observer is designed to estimate the unmeasurable states. With state-feedback and output feedback tracking control, the stability of closed-loop system is guaranteed via Lyapunov’s stability theory. Two cases of simulations for MIMO systems with state constraints are conducted to verify the effectiveness of the proposed control.

The control problem for quadrotor UAVs is difficult and challenging due to the complex nonlinear dynamics and ever-changing disturbances. In this paper, a supplementary controller based on reinforcement learning (RL) is proposed to improve the control performance of quadrotor UAVs. The proposed RL method is constructed by an actor-critic structure and some improved technologies, e.g., Q-learning, temporal difference, and experience replay. With the proposed method, the speed and stability of training can be improved greatly. On one hand, the supplementary controller can work together with the traditional controller online, which can guarantee the stability of the system. On the other hand, the model uncertainties and external disturbances could be restrained through online RL training. The Lyapunov theory is used to prove the convergence of the RL controller's weights theoretically. Finally, three simulations are provided to illustrate the effectiveness of the proposed controller.

In this paper, in order to analyze the single-link flexible structure, the assumed mode method is employed to develop the dynamic model. Based on the discrete dynamic model, fuzzy neural network (NN) control is investigated to track the desired trajectory accurately and to suppress the flexible vibration maximally. To ensure the stability rigorously as the goal, the system is proved to be uniform ultimate boundedness by Lyapunov's stability method. Eventually, simulations verify that the proposed control strategy is effective, and the control performance is compared with the proportion derivative control. The experiments are implemented on the Quanser platform to further demonstrate the feasibility of the proposed fuzzy NN control.

This paper presents a novel magnetically coupled dual switches three level dc–dc converter with high-voltage boost and reduced power device voltage stress ability. The topology can achieve high-voltage boost ability by using coupled inductor and low MOSFET voltage stress by using three-level idea. However, unlike other high step-up three level converters, its gain is increased by reducing turns ratio. The name inverse is used for representing the inverse proportion principle of the proposed converter. Aiming at existing leakage inductance, introduced diode and capacitor circuit not only absorbs the leakage energy but also further boosts the gain. In addition, the proposed converter only utilises one magnetic core. Operating principles and relevant theoretical analysis of the proposed converter are described. In order to verify the proposed converter's performance, the relevant experiment is performed.

In recommender systems, modeling user-item behaviors is essential for user representation learning. Existing sequential recommenders consider the sequential correlations between historically interacted items for capturing users' historical preferences. However, since users' preferences are by nature time-evolving and diversified, solely modeling the historical preference (without being aware of the time-evolving trends of preferences) can be inferior for recommending complementary or fresh items and thus hurt the effectiveness of recommender systems. In this paper, we bridge the gap between the past preference and potential future preference by proposing the future-aware diverse trends (FAT) framework. By future-aware, for each inspected user, we construct the future sequences from other similar users, which comprise of behaviors that happen after the last behavior of the inspected user, based on a proposed neighbor behavior extractor. By diverse trends, supposing the future preferences can be diversified, we propose the diverse trends extractor and the time-aware mechanism to represent the possible trends of preferences for a given user with multiple vectors. We leverage both the representations of historical preference and possible future trends to obtain the final recommendation. The quantitative and qualitative results from relatively extensive experiments on real-world datasets demonstrate the proposed framework not only outperforms the state-of-the-art sequential recommendation methods across various metrics, but also makes complementary and fresh recommendations.

Matching items for a user from a travel item pool of large cardinality have been the most important technology for increasing the business at Fliggy, one of the most popular online travel platforms (OTPs) in China. There are three major challenges facing OTPs: sparsity, diversity, and implicitness. In this paper, we present a novel Fliggy ITinerary-aware deep matching NETwork (FitNET) to address these three challenges. FitNET is designed based on the popular deep matching network, which has been successfully employed in many industrial recommendation systems, due to its effectiveness. The concept itinerary is firstly proposed under the context of recommendation systems for OTPs, which is defined as the list of unconsumed orders of a user. All orders in a user itinerary are learned as a whole, based on which the implicit travel intention of each user can be more accurately inferred. To alleviate the sparsity problem, users' profiles are incorporated into FitNET. Meanwhile, a series of itinerary-aware attention mechanisms that capture the vital interactions between user's itinerary and other input categories are carefully designed. These mechanisms are very helpful in inferring a user's travel intention or preference, and handling the diversity in a user's need. Further, two training objectives, i.e., prediction accuracy of user's travel intention and prediction accuracy of user's click behavior, are utilized by FitNET, so that these two objectives can be optimized simultaneously. An offline experiment on Fliggy production dataset with over 0.27 million users and 1.55 million travel items, and an online A/B test both show that FitNET effectively learns users' travel intentions, preferences, and diverse needs, based on their itineraries and gains superior performance compared with state-of-the-art methods. FitNET now has been successfully deployed at Fliggy, serving major online traffic., 12 pages

With the vigorous development of big data and the 5G era, in the process of communication, the number of information that needs to be forwarded is increasing. The traditional end-to-end communication mode has long been unable to meet the communication needs of modern people. Therefore, it is particularly important to improve the success rate of information forwarding under limited network resources. One method to improve the success rate of information forwarding in opportunistic social networks is to select appropriate relay nodes so as to reduce the number of hops and save network resources. However, the existing routing algorithms only consider how to select a more suitable relay node, but do not exclude untrusted nodes before choosing a suitable relay node. To select a more suitable relay node under the premise of saving network resources, a routing algorithm based on intuitionistic fuzzy decision-making model is proposed. By analyzing the real social scene, the algorithm innovatively proposes two universal measurement indexes of node attributes and quantifies the support degree and opposition degree of node social attributes to help node forward by constructing intuitionistic fuzzy decision-making matrix. The relay nodes are determined more accurately by using the multi-attribute decision-making method. Simulation results show that, in the best case, the forwarding success rate of IFMD algorithm is 0.93, and the average end-to-end delay, network load, and energy consumption are the lowest compared with Epidemic algorithm, Spray and Wait algorithm, NSFRE algorithm, and FCNS algorithm.

A colocated multiple-input multiple-output (MIMO) radar system may be interested in sending different data stream to multiple communication sinks widely separated in different directions with a physical layer security feature, i.e., a time-division dual function radar communications (DFRC) mode. Conventional channel precoding methods do not depend on the contents of the data streams. In this paper, given communication channels, we directly optimize constant-modulus transmit waveforms for a colocated MIMO radar, such that those communication sinks can decode the data stream in their own manner. Given the constellations of communication sinks, we design codes for different transmit antennas. Different from the precoding method, we transmit the optimized codes instead of precoded codes into space if a code combination needs to be transmitted into space. Meanwhile, the code optimization method enables to distribute different powers into space and can avoid eavesdroppers in other spatial directions from decoding the information transmitted. Numerical results with phase shift keying (PSK) constellations indicate that this method can form several intended code constellations in different spatial directions and the accuracy is higher if more transmit antennas are available. Meanwhile, the eavesdroppers in other space directions have a messed code constellations and it is hard to decode useful information from received signal.

Blockchain has unique advantages in data privacy protection and data integrity. We can solve many security problems, such as the single point of failure and data sharing in the current centralized system through blockchain approach. Existing studies have demonstrated that the application of blockchain in the medical system could improve the patient's medical experience. However, we found that the blockchain-based medical systems didn't consider the problems of long insurance claim cycle and complicated procedures. There are also very few healthcare systems that provide targeted sharing protocols for medical data and personal health data. In this paper, we propose a multi-role healthcare data sharing system framework based on blockchain. In this system, we reduce the storage cost through the collaborative storage of blockchain and IPFS. Then we design a smart contract on insurance to help patients achieve automatic insurance claim. In addition, we design two different sharing protocols to realize the fine management of personal data. The system analysis shows that our proposed blockchain-based multi-role healthcare data sharing system can effectively address the actual needs of users and has perfect performance in data storage, privacy protection, insurance claims and personal data management.

This paper studies the time-varying formation problem of general multi-agent systems with undirected topology via observer-based event-triggered control schemes. The distributed event-triggered control scheme and self-triggered control scheme are developed respectively. Each agent updates its next event time using current information without continuous communication in self-triggered control strategy. Formation error and state estimation error are considered simultaneously to get more accurate formation. Meanwhile, it is only when a request is received that each agent broadcasts its information. It is demonstrated that the time-varying formation can be achieved asymptotically under the two proposed control schemes and Zeno behavior can be excluded. Finally, numerical examples are provided to illustrate the effectiveness of the proposed observer-based event-triggered control strategies.

This paper presents the research results of detecting gastric polyps with deep learning object detection method in gastroscopic images. Gastric polyps have various sizes. The difficulty of polyp detection is that small polyps are difficult to detect from the background. We propose a feature extraction and fusion module and combine it with the YOLOv3 network to form our network. This method performs better than other methods in the detection of small polyps because it can fuse the semantic information of high-level feature maps with low-level feature maps to help small polyps detection. In this work, we use a dataset of gastric polyps created by ourselves, containing 1433 training images and 508 validation images. We train and validate our network on our dataset. In comparison with other methods of polyps detection, our method has a significant improvement in precision, recall rate, F1, and F2 score. The precision, recall rate, F1 score, and F2 score of our method can achieve 91.6%, 86.2%, 88.8%, and 87.2%.

Carbon emissions cost is a potential effective measure to restrict hydrocarbon pollution in the international shipping trade. The minimization of the total cost is pursued by ship operators, whereas voyage cost is increasingly involving the replacement of clean fuel and changing the cost of the shipping route. A flexible optimization method focusing on maximizing the total profit is developed in terms of sailing speed optimization and single port skips integrate carbon emission influence. An actual ocean shipping route from Shanghai to Rotterdam is applied to validate the effectiveness of the proposed models. The results have shown that the shipping route profit is volatile along with the sailing speed and the number of port calls. However, the profit will be maximized when applying the single port skip and will slow down the sailing speed at the same time. The demand of planned skip port can be supported by a short line container. A system composed of ocean container liner and short line container can improve the profit by 4.05% and reduce the carbon emission by 19.70%. Furthermore, sensitive results show that the profit is less affected by the changing of the carbon emission cost. A small size container has enough ability to solve the short transportation demand in adjacent ports and convert extraberthing cost into profit. These findings can provide reliable support for the shipping route decision process considering future carbon emission costs.

Analyzing network vulnerability, especially connectivity vulnerability, is vital for network security planning. Traditionally, network vulnerability analysis methods separate the studies of global connectivity vulnerability and critical area vulnerability, and thus ignore joint failure of network connectivity and critical-area integrity that may cause grave damage to a network. To this end, this paper proposes a prioritizing critical area approach for connectivity analysis to identify the corresponding vulnerable elements. Specifically, we consider the worst-case scenario of a network and aim at finding the minimum disruption-cost set of elements whose removal not only severely damages network connectivity but also disrupts the critical-area integrity. Since the above optimization problem is NP-hard, a heuristic algorithm based on spectral partitioning is developed to solve it. Simulation results validate the effectiveness of our proposed scheme in accurately identifying the vulnerable elements in critical areas to prevent significant loss in the overall network connectivity and performance.

In this paper, we consider a multiuser multiple-input multiple-output (MIMO) downlink communication system with simultaneous wireless information and power transfer (SWIPT). In particular, we focus on a realistic and efficient multi-receiver multi-eavesdropper MIMO SWIPT system, in which the channel state information (CSI) of each legitimate receiver and energy receiver (i.e., potential eavesdropper) is partially known to the transmitter. Based on this, we propose a robust artificial noise (AN)-aided secure transmission scheme for the system, where the channel uncertainties are modeled by the worst-case model. In the proposed scheme, we aim to maximize the worst-case achievable secrecy rate under the transmit power constraint and the energy harvesting (EH) constraint, by jointly optimizing the transmit precoding matrix and the AN covariance matrix. We utilize the S-Procedure and Taylor series approximation to transform the non-convex problem. Then, we apply the interior point method to tackle the transformed convex problem, obtaining the approximate optimal matrices and the corresponding maximum worst-case secrecy rate. Simulation results show that our proposed scheme achieves significant performance improvements in terms of convergence and the worst-case achievable secrecy rate.

We present a real-time Truncated Signed Distance Field (TSDF)-based three-dimensional (3D) semantic reconstruction for LiDAR point cloud, which achieves incremental surface reconstruction and highly accurate semantic segmentation. The high-precise 3D semantic reconstruction in real time on LiDAR data is important but challenging. Lighting Detection and Ranging (LiDAR) data with high accuracy is massive for 3D reconstruction. We so propose a line-of-sight algorithm to update implicit surface incrementally. Meanwhile, in order to use more semantic information effectively, an online attention-based spatial and temporal feature fusion method is proposed, which is well integrated into the reconstruction system. We implement parallel computation in the reconstruction and semantic fusion process, which achieves real-time performance. We demonstrate our approach on the CARLA dataset, Apollo dataset, and our dataset. When compared with the state-of-art mapping methods, our method has a great advantage in terms of both quality and speed, which meets the needs of robotic mapping and navigation.

GPS/INS combination is a commonly used integrated navigation mode. Inertial navigation system (INS) is a typical navigation mode, which can complete the acquisition of navigation parameters, but it will cause error accumulation problem when it is used alone, so using GPS to provide INS with a useful correction information. This article considers that the GPS antenna and the INS are not in the same position in the actual UAV integrated navigation system. Therefore, there is a lever arm that makes the observation equation nonlinear, In order to solve the problems, an improved filter is adopted. Finally, the data are used for comparative experiments, the stability of the algorithm is verified.

In this paper, we propose a UAV dynamic path planning algorithm to solve the path planning problem of a single UAV in a dynamic environment. The contributions of this paper mainly include the following two folds: (1) Using a combination of global and local path planning to improve planning efficiency. (2) The improved Q-Learning algorithm and artificial potential field method are combined to solve the problem that effective path planning cannot be performed between two path nodes. Finally, simulation results with Matlab proves the effectiveness of the algorithm.

In this paper, we study the sampled-data- based event-triggered practical formation tracking problem of multi-agent systems with integrator dynamics. Two control scheme using sampled information are developed respectively. Because of the sampling scheme, Zeno behavior is excluded in both methods. The amount of communication, sampling and controller updates is reduced greatly in self-triggered strategy. It is demonstrated that the formation tracking can be achieved asymptotically in finite-time under the two proposed control schemes. Numerical examples are provided to illustrate the effectiveness of the proposed control strategies.

This paper investigates the synchronization problem for a class of Kuramoto-oscillator network model, which has nonidentical oscillators coupled by a time-varying topology. The distributed fixed-time event-triggered control strategies are adopted to achieve phase agreement. To deal with the time-varying problem of the connection topology of oscillators, we propose a multilayered control structure, which makes it possible to design the upper bound of the settling time independent of the initial phase diameter. Moreover, the fixed-time global stability of the proposed method is guaranteed by the Lyapunov analysis. And we also prove that there is no Zeno behavior in the settling progress. Finally, numerical simulations validate the effectiveness and correctness of the proposed approach.

This article addresses the team-triggered fixed-time consensus problems for a class of double-integrator agents subject to uncertain disturbance. Compared with the finite-time results, the convergence time of the fixed-time results is independent of the initial conditions. Furthermore, a novel team-triggered control (TTC) strategy is presented. This control strategy incorporates the event-triggered control (ETC) and self-triggered control (STC). The ETC and STC are proposed to achieve the fixed-time consensus of second-order multiagent systems (MASs), and no Zeno behavior occurs. The TTC scheme, derived by combining the ETC scheme and the STC scheme, is able to relax the requirement of continuous communication and thus lowering the energy consumption of communication while ensuring the performance of the system. The effectiveness of the proposed algorithms is validated by numerical simulations.

IMS (IP Multimedia Subsystem) is an essential framework for providing 4G/5G multimedia services. It has been deployed worldwide to support two call services: VoLTE (Voice over LTE) and VoWi-Fi (Voice over Wi-Fi). VoWi-Fi enables telephony calls over the Wi-Fi network to complement VoLTE. In this work, we uncover that the VoWi-Fi signaling session can be hijacked to maliciously manipulate the IMS call operation. An adversary can easily make ghost calls to launch a stealthy call DoS (Denial of Service) attack against specific cellular users. Only phone numbers, but not any malware or network information, are required from the victims. This sophisticated attack harnesses a design defect of the IMS call state machine, but not simply flooding or a crash trigger. To stealthily detect attackable phones at run time, we exploit a vulnerability of the 4G network infrastructure, call information leakage, which we explore using machine learning. We validate these vulnerabilities in operational 4G networks of 4 top-tier carriers across Asia and North America countries with 7 phone brands. Our result shows that the call DoS attack can prevent the victims from receiving incoming calls up to 99.0% time without user awareness. We finally propose and evaluate recommended solutions.

With the flourishing of big data and the 5G era, the amount of data to be transmitted in the communication process is increasing, and end-to-end communication in traditional social networks has been unable to meet the current communication needs. Therefore, in order to improve the success rate of data forwarding, social networks propose that the sender of the message should reasonably choose the next hop node. However, existing routing and forwarding algorithms do not take into account nodes that are live in different scenarios, and the applicable next hop node metrics are also different. These algorithms only consider the forwarding preferences of the nodes during working hours and do not consider the forwarding preferences of the nodes during non-working hours. We propose a routing algorithm based on fuzzy decision theory, which aims at a more accurate decision on selecting the next hop. A routing and forwarding algorithm based on fuzzy decision is proposed in this paper. This algorithm symmetrical divides scenes in opportunistic social networks into working time and non-working time according to real human activity. In addition, metrics are designed symmetrically for these two scenarios. Simulation results demonstrate that, in the best case, the proposed scheme presents an average delivery ratio of 0.95 and reduces the average end-to-end delay and average overhead compared with the epidemic routing algorithm, the EIMSTalgorithm, the ICMT algorithm, and the FCNSalgorithm.

Background Cervical cancer is the second most common female cancer globally, and it is vital to detect cervical cancer with low cost at an early stage using automated screening methods of high accuracy, especially in areas with insufficient medical resources. Automatic detection of cervical intraepithelial neoplasia (CIN) can effectively prevent cervical cancer. Objectives Due to the deficiency of standard and accessible colposcopy image datasets, we present a dataset containing 4753 colposcopy images acquired from 679 patients in three states (acetic acid reaction, green filter, and iodine test) for detection of cervical intraepithelial neoplasia. Based on this dataset, a new computer-aided method for cervical cancer screening was proposed. Methods We employed a wide range of methods to comprehensively evaluate our proposed dataset. Hand-crafted feature extraction methods and deep learning methods were used for the performance verification of the multistate colposcopy image (MSCI) dataset. Importantly, we propose a gated recurrent convolutional neural network (C-GCNN) for colposcopy image analysis that considers time series and combined multistate cervical images for CIN grading. Results The experimental results showed that the proposed C-GCNN model achieves the best classification performance in CIN grading compared with hand-crafted feature extraction methods and classic deep learning methods. The results showed an accuracy of 96.87 %, a sensitivity of 95.68 %, and a specificity of 98.72 %. Conclusion A multistate colposcopy image dataset (MSCI) is proposed. A CIN grading model (C-GCNN) based on the MSCI dataset is established, which provides a potential method for automated cervical cancer screening.

We present a novel approach for person images synthesis in this paper, that can generate person images in arbitrary poses, shapes and views. Unlike existing methods just using keypoints’ locations in heatmaps format, we propose to render SMPL model to UV maps, which can provide human structural information about poses and shapes. Thus, by varying the parameters of poses, shapes and camera in SMPL model, we can generate different person images with various attributions in a simple way, while in most cases we can only obtain new shapes of people by computer graphics methods. We train an end to end generative adversarial network with unlabeled data. As our SMPL parameters come from a pretrained model, we call our overall network semi- supervised. Our network keeps a global appearance during the fine-tuning stage of the target person, thus we can get a complete appearance of the target person, rather than the inaccurate appearance caused by inferencing without enough information. Experiments on Human3.6M Dataset and a self-collected dataset demonstrate the excellent effectiveness of our approach on person images synthesis for different applications.

With the explosion of online streaming, artificial intelligence and mobile computing, higher wireless data-rates are required, which in 5G NR are achieved by adopting massive MIMO and Carrier Aggregation (CA) with a wide channel bandwidth (BW) up to 200MHz. Together with the requirement for LTE+NR Dual Connectivity (EN-DC), the transceiver (XCVR) and RF front-end complexity increases dramatically requiring new techniques to maintain a small and cost-effective RF implementation. In this work, a 12nm CMOS 2/3/4/5G multi-RAT (Radio Access Technology) XCVR is presented, capable of supporting up to 6 inter-band CA DL with flexible 4×4 MIMO port selection and 2 inter-band CA UL with coherent 2×2 MIMO. In NR 200MHz 4×4 MIMO 256-QAM mode a throughput up to 5Gb/s can be achieved.

We present for the first time a real-time reconfigurable MEMS filter microsystem that is able to exhibit bandpass (BP) or bandstop (BS) responses in the Radio Frequency (RF) spectrum. The unit cell of the reconfigurable filter consists of a Double-Pole, Double-Throw (DPDT) switch periodically modulating two Thin-Film Bulk-Acoustic Resonators (FBARs) on the same resonance frequency. Unlike traditonal FBAR ladder filters which involve higher fabrication complexity for multi-frequency FBARs on the same chip, we propose a novel system to acheive a sharp BS filteing response by periodically commutating multiple identical FBARs. Thanks to the excellent spectral selectivity of those resonators, sharp notches can be obtained in the same frequency range by reconfiguring the commutation frequency. Experimental results demonstrate a 30 dB rejection in BS mode and a 2.5 dB Insertion Loss (IL) in BP mode, within a bandwidth set by the modulation frequency (up to 3.6% of the RF signal frequency). In BP mode, the filter exhibits a more than 20 dB out-of-band rejection for a single stage.

Time-varying output formation control problems for high-order time-invariant swarm systems are studied with nonlinear uncertainties and directed network topology in this paper. A robust controller which consists of a nominal controller and a robust compensator is applied to achieve formation control. The nominal controller based on the output feedback is designed to achieve desired time-varying formation properties for the nominal system. And the robust compensator based on the robust signal compensator technology is constructed to restrain nonlinear uncertainties. The time-varying formation problem is transformed into the stability problem. And the formation errors can be arbitrarily small with expected convergence rate. Numerical examples are provided to illustrate the effectiveness of the proposed strategy.

In this paper, a micro-acoustic RF circulator with novel “quad” configuration is reported. Four micro-acoustic filters are periodically modulated by RF switches to break the reciprocity. The use of high-Q micro-acoustic filters significantly reduces the modulation frequency (14 MHz) compared to previous demonstrations based on transmission lines (e.g., 333 MHz in [1]). The low modulation frequency translates to an ultra-low power consumption (202 uW, 3600 times smaller than [1]) and one of the highest linearity ( $\mathrm{P}1\mathrm{dB}=31\ \mathrm{dBm}$ ) reported for magnet-free circulators. Furthermore, compared to the more conventional differential configuration, this quad configuration shows advantages in terms of intermodulation products (IMPs) suppression for all the in-band IMPs, guaranteeing a pseudo-linear-time-invariant (pseudo-LTI) operation. Furthermore, the isolation of the circulator is further improved by an additional cancellation circuit, achieving more than 50 dB of isolation in a 10MHz bandwidth (BW). This high level of isolation enables the use of this magnet-free circulator in full-duplex radio, which requires large level of self-interference suppression between the transmitter and the receiver nodes.

Purpose This paper aims to develop sliding mode control (SMC) methods for second-order multi-agent systems (MAS) in the presence of mismatched uncertainties. Design/methodology/approach Based on the disturbance observer (DOB), discontinuous and continuous sliding mode protocols are designed to achieve finite-time consensus in spite of the disturbances. Findings Compared with integral SMC, numerical simulation results show that the proposed control methods exhibit better performance with respect to reduction of chattering. Originality/value The main contributions are the following: MAS described with mismatched uncertainties are considered; both discontinuous and continuous sliding mode controllers are considered; with the proposed sliding mode controller, the desired sliding surface can be reached in finite time and the DOB is introduced in the controller to alleviate the chattering phenomenon.

By considering the Simultaneous Wireless Information and Power Transfer (SWIPT) schemes, this paper focuses on secure transmission model design in multiple-input-single-output (MISO) channels. In these channels, the channel state information is assumed to be perfect. Our objective is to maximize the worst-case secrecy rate with respect to both potential eavesdroppers and obvious eavesdroppers under the constraints of energy-harvesting and total transmission power. We present an optimization model to indirectly obtain maximum security rate in a single receiver system. Due to the high computational complexity of the solution process caused by the formulated non-convex optimization problem, we propose a novel indirect method to handle this issue. Then, a Semi-Definite Programming (SDP) relaxation method is used to approach the optimal solution. Moreover, we reveal the conditions for ensuring that the above semi-definite relaxation is compact. Simulation results demonstrate that the gained performance in our system is much better than those of the existing competing schemes.

This paper addresses the robust compensator design problem for the attitude tracking of an indoor micro quadrotor subject to unknown stochastic input delays and input constraints. The attit...

In order to improve the performance of thermal energy storage (TES) systems, a multiple phase change material (multi-PCM) based TES unit for use in conventional air-conditioning systems was studied. Three PCMs (PCM-1, PCM-2, and PCM-3) with phase change temperatures of 5.3 °C, 6.5 °C and 10 °C, respectively, were used. Water was used as the heat transfer fluid (HTF). A three-dimensional model (3D) was developed in ANSYS FLUENT to investigate the charging process of multi-PCM TES unit. In order to validate the model, an experimental system was set up. The effect of volume ratio of multi-PCM, HTF inlet temperature and flow rate on the charging process of TES unit was investigated. The simulation results indicate that TES unit using multi-PCM with volume ratio 1:2:3 intensify the charging process in comparison with using single-PCM. The total charging capacity of multi-PCM TES unit with 1:2:3 proportions was 3637.2 kJ and increased by approximately 32.22% as compared to the single-PCM. For the HTF flow rate of 0.3 kg/s, decreasing the inlet temperature of HTF sped up the charging capacity, and obviously shortened the complete charging time of TES unit. However, the HTF inlet temperature did not appreciably change the total charging capacity.