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A cooperative intelligent transport system (C-ITS) enables information sharing among ITS subsystems, such as vehicle and roadside infrastructure, with vehicle-to-everything (V2X) communications. Novel C-ITS applications aim to reduce traffic congestion and improve road safety. For example, a roadside unit (RSU) can sense the traffic status of an intersection and share the information to nearby vehicles to prevent potential collision, i.e., the intersection movement assist (IMA) scenario. Typically, C-ITS applications are developed in a simulated world to mitigate the high costs and safety hazards associated with the real-world counterpart. Unfortunately, existing simulation tools focus primarily on modeling vehicle-to-vehicle (V2V) communications, rather than the vehicle-to-infrastructure (V2I) communications involved in the above example. This poses a great challenge for developing V2I-based applications, especially for the application-level performance assessment on such systems. This work proposes a software framework that enables the simulation of full software stacks for the ITS subsystems, i.e., vehicle and RSU. This full-stack simulation capability enables hardware-in-the-loop simulations, evaluating the application-level performance and relative cost in the early stage of system development. This can shorten the time to market for C-ITS applications. We believe that this framework paves the way toward the development of novel V2I applications.

Road information plays an increasingly important role in applications, such as map updating, urban planning, and intelligent supervision. However, roads in remote sensing images may be shaded by trees and buildings or interfered with by farmland. These intrinsic image features can cause road extraction results to suffer from breakage and misidentification problems. To address these problems, this article improves on D-LinkNet and proposes a dual codec structure network, namely RUW-Net. Specifically, we use ReSidual U-blocks instead of ordinary residual blocks to extract more global contextual information during the encoding stage. Moreover, we propose a decoder-encoder combination (DEC) module to build a dual codec structure. The DEC module links the decoder of the first U-block and the encoder of the following U-block to narrow the semantic gap in the encoding and decoding process. The RUW-Net model can extract more multiscale contextual features and effectively use them to enhance the semantic information of road entities. Therefore, the RUW-Net model can obtain more accurate extraction results. We conducted a series of experiments on public datasets, such as DeepGlobe, including comparative, robustness, and ablation experiments. The results show that the proposed model alleviates the road extraction breakage and misidentification problems. Compared with other representative methods, the RUW-Net performs better in terms of completeness and accuracy of road extraction results; overall, its extraction results are also the best. The RUW-Net model provides a new idea for road extraction from remote sensing images.

Fabric-based pneumatic actuators (FPAs) are extensively employed in the design of lightweight and compliant soft wearable assistive gloves. However, conventional FPAs typically exhibit limited output force, thereby restricting the applications of such gloves. This paper presents the development of a novel honeycomb pneumatic actuator (HPA) constructed using flexible thermoplastic polyurethane (TPU) coating through hot pressing or ultrasonic welding techniques. Compared to the previously utilized double-layer fabric-based pneumatic actuators (DLFPAs), the HPAs yields a remarkable 862% increase in end output force. It can produce a tip force of 13.57 N at a pressure of 150 kPa. The integration of HPAs onto a soft pneumatic glove enables the facilitation of various activities of daily living. A series of trials involving nine patients were conducted to assess the effectiveness of the soft glove. The experimental results indicate that when assisted by the glove, the patients’ finger metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints achieved angles of 87.67 ± 19.27° and 64.2 ± 30.66°, respectively. Additionally, the average fingertip force reached 10.16 ± 4.24 N, the average grip force reached 26.04 ± 15.08 N, and the completion rate of daily functions for the patients increased from 39% to 76%. These outcomes demonstrate that the soft glove effectively aids in finger movements and significantly enhances the patients’ daily functioning.

Deploying pigeon age detection model on edge equipment can solve the problem of video transmission delay and reduce the pressure of network transmission. Based on the deployment of edge devices, we made some improvements to You Only Look Once version 5 (YOLOv5) to form a new, lightweight, high-performance detector named LN-STEP-YOLO. In order to reduce the size of the model, we halved the number of channels in the YOLOv5s model. However, the decrease in the number of channels brings some problems, such as low global information acquisition, retention of image redundancy information, attention deficit, etc. To solve these problems, we did the following work. First, we proposed a new convolution structure with the effect of a large convolution kernel, StepConv. Second, a $2\times 2$ convolution with step size 2 was used at the input to split each image into individual patches. Third, External Attention (EA) was introduced in the bottleneck structure. Fourth, a modified extremely separated convolutional block (XsepConv) was used for downsampling. Finally, we replaced the batch normalization (BN) of all non-downsampled layers with layer normalization (LN). The results showed that the improved algorithm outperformed generic lightweight networks such as Mixnet, Mobilenetv3, and Ghostnet to distinguish small-sized, overlapping pigeons, achieving 92.8% mean average precision (mAP) at about 17% of YOLOv5s parameters, 0.1% lower than that achieved by use of YOLOv5s. In addition, the improved method had 3.7G floating point operations (FLOPs) and 1.25G parameters, which allowed the detection of the growth stages of pigeons in real environments and provided a reference to guide placement of feeders in automated pigeon farming.

This paper presents a switch-mode rectifier (SMR) fed synchronous reluctance motor (SynRM) drive. Good driving performance over wide operating range is achieved via: (i) boosted and well-regulated DC-link voltage established by the SMR; (ii) real-time commutation angle tuning by the adaptive commutation scheme (ACS) using on-line fitted motor key parameters, and equivalently by the model-free intuitive commutation tuning (ICT) scheme; (iii) robust current control considering slotting harmonic effects; and (iv) quantitative and robust speed controls. First, the SynRM drive with fixed DC-link voltage is established. The real-time commutation tuning scheme with motor total loss minimization is proposed. Then the dynamic current and speed control schemes are designed. The feedback controllers are augmented with simple robust control schemes to have satisfactory responses. Under well-regulated DC-link voltage, good line drawn power quality is obtained simultaneously. Moreover, the regenerative braking power can be recovered back to the utility grid. Finally, the whole SMR-fed SynRM drive is evaluated. The motor driving characteristics under higher speeds and heavier loads are enhanced.

Many patients with genetic syndromes have special facial features, which boast significant potential value for clinical diagnosis. Deep learning and computer vision technology can be employed to diagnose genetic diseases by analyzing facial features of patients. As a matter of fact, the application of deep learning technology in the area of genetic diseases is confined owing to the difficulties of patient data acquisition. This study develops BioFace, a deep learning framework that can recognize multiple genetic diseases facial attributes based on limited datasets. BioFace is a deep neural network architecture designed premised on Resnet. To increase the weight of effective features and weaken the weight of invalid or unobvious features during extraction of facial features, we add Squeeze-and-Excitation (SE) blocks in the network. In combination with this network architecture, we designed a cross-loss training method based on transfer learning. This method can transfer the ability learned from the task of face identification to the task of recognition of genetic diseases facial attribute, and improve the inter-class distance of different genetic diseases and the intra-class distance of similar genetic diseases simultaneously. These render it possible for deep learning to be applied to recognition of multiple genetic diseases facial attribute with very small amount of data. In this research, we tested 10 syndromes with our framework and the Top-1 accuracy was 93.5%, which is the state-of-the-art in multiple genetic syndromes recognition research. In practical clinical applications, our framework and methods can be extended to the disease identification of more small datasets, potentially offering valuable assistance for the auxiliary clinical application of genetic diagnosis and other related genetic research.

By caching multiple copies of the sensed data and suppressing repeated content deliveries from the sensor nodes, the in-network caching exhibits strong potentials to reduce energy consumption and avoid service loss in the Internet of Things (IoT). In this paper, we review the fundamentals of the in-network caching in IoT, especially the deployment principles of the cache-enabled brokers. Since in-network caching is not the optimal choice in some cases, we propose the dynamic in-network caching scheme to decide whether the sensed data should be cached in brokers or not, where the location and delay requirement of the clients, traffic load of the brokers as well as the energy level of the sensor nodes are encapsulated in the proposed scheme. Next, we give a practical case study to demonstrate the effectiveness of the proposed in-network caching scheme in the IoT scenario. It shows that the proposed caching scheme is superior to the traditional sensor-to-client scheme in terms of energy saving. Finally, we conclude this paper, and summarize the future research directions to achieve the in-network caching for implementing the green IoT.

A wireless power transfer (WPT) system with bidirectional power flow control for charging batteries and drawing electric power from batteries is presented in this paper for e-bike sharing applications. The proposed WPT consists of a bidirectional DC-DC converter and a sandwiched coil set, which includes primary winding with ferrite pad in charging stations and a movable secondary coil without ferrite pad, which is inserted into the primary coils to receive or transmit energy, on the e-bike. Hence, the proposed coil set could provide high coupling factor, better tolerance of misalignment, and a compact size that enables easy installation in the current mechanism of bike-sharing systems. The Maxwell 3D is adopted to design coil sets, which are based on the known resistance of Litz wire, and the effect of the ferrite pad and misalignment are analyzed to achieve good performance. To select a suitable converter, the characteristics of predesigned coils are simulated using Maxwell 3D, MATLAB and PSIM. Then, a full-bridge CLLC converter with the same resonant frequency for charging and discharging modes is chosen to provide minimal loss by using the synchronous rectifier on the 48-V side and voltage gain without the loading effect. Finally, a digital signal processor-based digitally controlled CLLC converter is constructed to verify the performance of the proposed WPT, which can provide 200 to 48 V/500 W charging and discharging functions with constant current/constant voltage modes and fast-response current control. In addition, the maximum efficiency is 96% at 52 V and 75% rated load in charging mode.

In this paper, a multi-state circularly polarized antenna with gain enhancement is proposed based on the polarization conversion metasurface(PCM). The antenna is composed of a simple monopole antenna and a modified PCM coating which is made up of a series of arrow-shaped elements distributed partly on the back of the substrate. The dimension of the antenna is 42 × 43 mm. And Genetic Algorithm (GA) is adopted to obtain the best performance of the PCM. The coating is used as a reflector, compared with the radiating wave, the amplitude of the reflected wave is equal and the phase is 90° difference. Therefore, circularly polarized antenna is obtained with a low profile. By rotating the orientation of the element, the antenna changes from LHCP to RHCP which means the antenna can work in multi-state. In addition, the gain of the antenna is improved. Both of the simulated and measured results have verified the design. The simulated and measured results show that the operating band of the proposed CP radiation is 10.2 to 10.9 GHz. And the gain is improved for 1 dB in average with the peak being 2 dB at 10.9 GHz.

A novel and simple broadband antenna array loaded with high impedance surface is proposed in this letter. The unit cell is composed of a main radiating patch and a pair of EBG arrays placed symmetrically at the two radiating edges of the main radiating patch. The main radiating patch is responsible for the original TM10 mode while the electromagnetic bandgap (EBG) arrays is for another resonant mode characterized as quasi-TM20 mode. Thus, wideband performance is obtained due to the combination of the two generated resonant modes. And on this basis, by adding symmetrical mushroom structures at the other two edges of the main radiating patch, the impedance bandwidth and sidelobe suppression are improved. The current distributions on the all metal patches are almost uniform, therefore high gains can be obtained during the whole operating bandwidth. The antenna array is formed by 4 × 4 unit cells and the prototype is fabricated to validate the design. The measured results show that the impedance bandwidth is broadened from 11.1 to 15.6 GHz with the peak-gain reaching as high as 18.8dBi. Besides, the proposed antenna is easy fabrication with low profile which provides great convenience in the actual beam and point-to-point communication applications.

With the exponential growth of information and communication technology, the traditional power system is gradually evolving into a cyber physical energy system (CPES) with frequently interactions between physical and cyber components. CPES indeed revolutionize the power grid efficiency and operational performance, but it also gives rise to new security challenges, causing catastrophic consequences in power system. Therefore, this article proposes a new false data injection attack (FDIA) detection mechanism to automatically recognize the intrusions and thereby improve the cybersecurity of CPES. In this mechanism, a two-stage FDIA model is developed to generate training dataset for the machine learning-based detector, and an extreme gradient boosting (XGBoost) classifier is meticulously designed by combining with active learning and Bayesian optimization techniques to improve training efficiency and model performance respectively. The proposed FDIA defense strategy can properly extract the nonstationary and nonlinear attacking features and thus locate the biased states with a high accuracy. The obtained numerical results on the standard IEEE 14-, 57- and 118-bus systems highlight the destructive effects of the developed two-stage FDIA and comprehensively demonstrate the effectiveness of the proposed FDIA detection mechanism.

Nowadays, Over-The-Top (OTT) voice call service has become more popular than other Voice over Internet Protocol services provided by mobile operators due to cheaper costs. For devices with more than one network interfaces, the OTT software may need to handover between heterogeneous networks during a call. Unnecessary handover may lead to signal overload, especially in a Wi-Fi intensive area where users may handover frequently between Wi-Fi and cellular networks. For example, if the period between when a user handovers from Wi-Fi to Long Term Evolution (LTE) and when the user handovers from LTE back to Wi-Fi is too short to transmit voice data, the handover procedures between Wi-Fi and LTE can be considered unnecessary and power consuming. To tackle this issue, this paper proposes delay handover mechanism (DHM) to avoid the unnecessary handover procedures by postponing the timing to handover from Wi-Fi to LTE. We then propose an analytical model as well as a simulation model to investigate how many handovers can be reduced. The analytical model is validated against the simulation model. With extensive simulation experiments, the performance evaluation of DHM offers a guideline to configure parameters based on the trade-off between reduced handover ratio and packet loss rate. As a whole, DHM is an application-based mechanism for avoiding unnecessary handovers as well as cutting down LTE usages.

This paper proposes a two-stage model for the estimation of origin-destination (OD) demands by the time of day over the year with the use of offline traffic data from the real-time travel information system. In the first stage, a travel time recursive function is proposed to use the offline travel speed data for the investigation of the spatial and temporal relationships between time-dependent OD demands and traffic counts. As such, it is not required to carry out the time-consuming dynamic traffic assignment (DTA) process which is frequently used in the conventional time-dependent OD estimation models. Using the results in the first stage together with the available traffic count data, a least-squares method is adopted to formulate the time-dependent OD demand estimation problem as a quadratic programming model in the second stage. A solution algorithm is adapted for solving the proposed model. Then, the proposed method is easy for implementation in practice. Particularly, when the traffic accident occurs in the network, the estimated time-dependent OD demands can be helpful for understanding the complex travel behavior (e.g., departure time choice) under uncertainty condition. The numerical examples are presented to illustrate the applications of the proposed model.

The analysis of severity causality for traffic crash is essential for enhancing the crash rescue responding speed, thereby reducing the casualties and property losses caused by roadway crashes. This study constructs a severity causation network to explore the relationship between risk factors and crash severity by combining information entropy and Bayesian network. The impacts of different risk factors on the severity indexes are quantitatively estimated and compared by utilizing entropy weight method, and the key factors for severity prediction are determined by considering both the weight and the accessibility. Then, the severity indexes, i.e., the number of injuries, fatalities, and the amount of property damage, are predicted with the selected key factors based on Bayesian parameter learning. The verification results confirm that compared with severity prediction utilizing all the risk factors, the prediction utilizing selected key factors do not lead to obviously precision loss. Moreover, it significantly enhances the feasibility of crash severity prediction. Due to the appropriate abbreviation of risk factors, the prediction efficiency and practical operability in crash rescue responding is improved. The findings can be utilized in analyzing the severity causation of traffic crashes, which is serviceable for managers to find effective measures to improve traffic safety as well as reduce the casualties and property losses caused by traffic crashes. By providing the severity causation network, this study facilitates the prediction of severity level, which can provide valuable reference information for a crash response.

In this paper, a novel compact wideband and high-gain antenna based on metasurface is proposed. The antenna is an octagon with a side length of 32.3 mm and a height of 5.6 mm. The metasurface consists of two stacking layers, and both layers contain a 4×4 copper patch array. The antenna is excited by an aperture coupled structure made of an anomalous microstrip line and a narrow slot etched in the ground plane. Genetic algorithm (GA) is adopted to optimize all the parameters and obtain the valid performance of the metasurface antenna. Moreover, the antenna is modified by introducing nonuniform patch. Modification leads to varying degrees gain enhancement, and the peak is approximately 1.5 dB at 6.1GHz. The impedance bandwidth of the proposed antenna is 54.9%, ranging from 4.48-7.87 GHz. A prototype of the antenna is fabricated, and the measured results verify the design.

In this study, an integrated operation platform is proposed to study colloidal microsphere and microdroplet resonators on a chip. Via varying dielectrophoretic force induced on a specially designed concentric ring electrode, spherical optical resonators are able to be precisely controlled on their position relative to an integrated waveguide with resolution down to tens of nanometer, which is crucial to determine the coupling condition of the resonator. A solid-state polystyrene microsphere and an immersion oil droplet are demonstrated with ability to be freely operated in undercoupling, critical coupling, and overcoupling regimes. Through this platform, a metrology technique is reported to precisely measure the radius of a microdroplet by examining the resonant modes. This platform could be a useful tool to study optical resonance of spherical objects dispersed in water.

We propose and experimentally demonstrate an indoor positioning system which combines identity positioning and the radio frequency carrier allocation technique in order to reduce the signal interference from nearby light-emitting diodes (LEDs) and improve the accuracy of positioning. In addition, the solar cell is used as an optical receiver for the visible light positioning system. Due to the benefits of solar cells such as low-cost, high light sensitivity, and ease in integration with wearable devices, the proposed system could be an energy-efficient and environmentally friendly choice for indoor positioning in the future.

In the above paper [3], two references [1], [2]weremissing. The first sentence of Section III should read “This section proposes an analytical model for DHM, which is based on the mathematical analysis in [1] and [2]. Compared to [1] and [2], DHM has different trafc model and different execution condition (DHM only applies in silent period).”