Road traffic signals and traffic infrastructure have a significant impact on the behaviour of highly automated or autonomous vehicles. However, the increase in automation does not always mean an advantage. Generally, highly automated vehicles strictly follow the traffic rules resulting in near-accident situations, although their goal is to avoid and reduce them. Malfunctions of the automated functions

In this paper, a new method is presented to study the impacts of telemedicine on the performance of an emergency department in Spain. Spain’s Demographics indicate that this country is experiencing population aging, resulting in overcrowding of emergency departments and significant demand on the healthcare system. However, it has been reported that most patients visiting emergency departments are not

A klystron-like relativistic backward-wave oscillator (KL-RBWO) with transverse electron beam confinement and waveguide reflection, which works well under a magnetic-field strength below that of the cyclotron resonance, is proposed. For this device operating with a low magnetic field, effective suppression of the transverse velocity of electron beams is introduced by a local electric field near the

Multi-cloud storage systems are becoming more popular due to the ever-expanding amount of consumer data. This growth is accompanied by increasing concerns regarding security, privacy, and reliability of cloud storage solutions. Ensuring data consistency in such systems is especially challenging due to their architecture and characteristics. Specifically, passive cloud storage cannot run coordination

This article develops a 3GPP inspired analytical framework for cache-enabled in-band full-duplex (IBFD) integrated access and backhaul (IAB) heterogeneous network in the FR2 (millimeter wave) band. In particular, by assuming wide-band FR2 channel model, we analyze the performance of cache-enabled IAB nodes that serve multiple users in access in IBFD mode through hybrid beamforming architectures. Since

In this article, we propose a load balancing algorithm that accelerates ray tracing effectively and simply manner. The algorithm was developed in a hybrid system consisting of a CPU and hardware dedicated to ray tracing. Tree-building is processed on the CPU, and rendering is executed by the ray tracing-dedicated hardware. Because these components operate independently of each other, the final performance

With the advent of Internet of Health (IoH) age, traditional medical or healthy services are gradually migrating to the Web or Internet and have been producing a considerable amount of medical data associated with patients, doctors, medicine, medical infrastructure and so on. Effective fusion and analyses of these IoH data are of positive significances for the scientific disaster diagnosis and medical

Recently, unsupervised deep hashing has attracted increasing attention, mainly because of its potential ability to learn binary codes without identity annotations. However, because the labels are predicted by their pretext tasks, unsupervised deep hashing becomes unstable when learning with noisy labels. To mitigate this issue, we propose a simple but effective approach to self-supervised hash learning

This paper presents the latest progress on pricing and revenue management. Consumer’s switching purchase behavior occurs frequently in perishable goods retail market, and we propose a two-period dynamic pricing model among online and offline retailers under heterogeneous purchase behavior of myopic consumers and strategic consumers. Through equilibrium solution and analysis, this study discusses the

Generative Adversarial Networks (GANs) have made great progress in synthesizing realistic images in recent years. However, they are often trained on image datasets with either too few samples or too many classes belonging to different data distributions. Consequently, GANs are prone to underfitting or overfitting, making the analysis of them difficult and constrained. Therefore, in order to conduct

The structure of blood vessels play a crucial role in diagnoses of the various vision threatening diseases including Glaucoma and Diabetic Retinopathy (DR). The correct segmentation of retinal blood vessels is a crucial step in the study of retinal fundus images. We proposed a simple unsupervised approach by using a combination of Hessian based approach and intensity transformation approach. We have

Multispectral images are increasingly used for pedestrian detection. Preliminary fusion strategies would fail to exploit informative features from cross-spectral images, or worse, may introduce additional interference. In this paper, we propose an attention based multi-layer fusion network in the triple-stream deep convolutional neural network architecture for multispectral pedestrian detection. The

Advances in Artificial Intelligence and Image Processing are changing the way people interacts with digital images and video. Widespread mobile apps like FACEAPP make use of the most advanced Generative Adversarial Networks (GAN) to produce extreme transformations on human face photos such gender swap, aging, etc. The results are utterly realistic and extremely easy to be exploited even for non-experienced

High impedance faults (HIFs) in distribution networks are hard to describe and be detected precisely because of the complexity and randomness of their features. Therefore, traditional feature analysis methods may lack sufficient reliability and generalization, which makes data-based methods a more appropriate option. However, according to previous statistical analyses, in practical scenarios, only

In natural language processing, semantic document exchange ensures unambiguity and shares the same meaning for documents sender and receiver cross different natural languages (e.g., English to Chinese), this difference makes the translation between natural languages becomes complex and inaccurate. This paper proposed a novel framework of Context-Free Machine Universal Grammar which consists of local

Although the Internet of Things (IoT) can increase efficiency and productivity through intelligent and remote management, it also increases the risk of cyber-attacks. The potential threats to IoT applications and the need to reduce risk have recently become an interesting research topic. It is crucial that effective Intrusion Detection Systems (IDSs) tailored to IoT applications be developed. Such

As the demand for more functions and capabilities in the system increases, the application of multiprocessors has brought advantages in many ways. Many systems now have multiprocessors, and safety-critical systems with real-time properties are no exception. In these systems where the satisfaction of real-time properties is directly linked to the safety of life, the predictability of the behavior is

As a combination of q-rung orthopair fuzzy sets (q-ROFSs) and hesitant fuzzy sets (HFSs), q-rung orthopair hesitant fuzzy sets (q-ROHFSs) are more effective, powerful, and meaningful in solving the complexity, ambiguity, and expert hesitancy of membership and non-membership in multi-attribute decision-making (MADM) problems. And so, based on the advantages of q-ROHFSs, we herein propose an improved

Fifth-generation (5G) telecommunication systems are expected to meet the world market demands of accessing and delivering services anywhere and anytime. The Non-Terrestrial Network (NTN) systems are able to satisfy the requests of anywhere and anytime connections by offering wide-area coverage and ensuring service availability, continuity, and scalability. In this work, we review the 3GPP NTN features

This article addresses the problem of detecting misleading information related to COVID-19. We propose a misleading-information detection model that relies on the World Health Organization, UNICEF, and the United Nations as sources of information, as well as epidemiological material collected from a range of fact-checking websites. Obtaining data from reliable sources should assure their validity.

As a critical component in green supply chain activities, green supplier selection modelled as the typical multicriteria group decision-making (MCGDM) problem, has received tremendous attention both from scholars and practitioners. This article aims to advance an integrated approach for precisely addressing this urgent problem under Pythagorean fuzzy (PF) scenario. Firstly, analytic hierarchy process

Speeding up the simulation of discrete-event wafer-fabrication models is essential for fast decision-making to handle unexpected events in smart semiconductor manufacturing because decision-parameter optimization requires repeated simulation execution based on the current manufacturing situation. In this paper, we present a runtime abstraction-level conversion approach for discrete-event fab models

In view of the shortcomings of traditional fault diagnosis methods based on time domain vibration analysis, which require complicated calculations of feature vectors, and are over-dependent on a prior diagnosis knowledge, effort for the design of the feature extraction algorithms, and have limited ability to extract the complex relationships in fault signals, in this paper we propose a convolutional

Software-Defined Network (SDN) has been developed to reduce network complexity through control and manage the whole network from a centralized location. Today, SDN is widely implemented in many data center’s network environments. Nevertheless, emerging technology itself can lead to many vulnerabilities and threats which are still challenging for manufacturers to address it. Therefore, deploying Intrusion

Landslide-induced impulse waves in a river-valley reservoir region have become a serious threat to life and property intactness. Compared with previous studies focusing on landslides in open-water regions, this article systematically carried out numerical investigations on the hydrodynamic characteristics of landslide-induced impulse waves in narrow river-valley reservoirs by applying software package

Both turbo Hadamard codes and concatenated zigzag Hadamard codes are ultimate-Shannon-limit-approaching channel codes. The former one requires the use of Bahl-Cocke-Jelinek-Raviv (BCJR) in the iterative decoding process, making the decoder structure more complex and limiting its throughput. The latter one, however, does not involve BCJR decoding. Hence its decoder structure can be much simpler and

Designing resource-efficient deep neural networks (DNNs) is a challenging task due to the enormous diversity of applications as well as their time-consuming design, training, optimization, and evaluation cycles, especially the resource-constrained embedded systems. To address these challenges, we propose a novel DNN design framework called accuracy-and-performance-aware neural architecture search (APNAS)

In order to deal with the problems of background mixing, pedestrian blur and pedestrian multi-scale in pedestrian tunnels, we proposed an improved faster region based convolution neural network (IF-RCNN) pedestrian detection method, which uses deep CNN to automatically extract features from pictures instead of traditional manual design features. In this paper, an improved region proposal network (RPN)

Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to academic study, leading to a vast number of proposed approaches. However, they have mostly been treated as separate problems, and have typically relied on non-linear first-principles models with parameters

3D Convolution Neural Networks (CNNs), an important deep learning model, has good performance in recognizing actions in videos. When recognizing actions from videos, 3D CNNs usually down-sample in temporal dimension, leading to loss of the temporal information. To obtain more temporal information from the videos, this work proposed a new model based on the Inflated 3D ConvNet (I3D), named as I3D-T

Multi-core techniques have been widely used in various hardware platforms, promoting development of concurrent programs. Unfortunately, concurrent programs are prone to concurrency errors. At present, it is still an open issue to provide high coverage detection of concurrency errors. In this paper, we present an enhanced dynamic concurrency error detection technique, called $PVcon$ , which can detect

Background : International agencies recognize the lack of knowledge to further establish standards and guidelines to protect the workers and the public from extremely low-frequency magnetic fields (ELF-MF). In that regard, postural control has been proposed as a biomarker of potential adverse effects in humans. Considering its crucial role in postural control and its specific neurophysiological characteristics

This article focuses on the production scheduling problem in the flexible job shop (FJS) environment with limited buffers. Limited manufacturing resources and buffers may lead to blockage and deadlock phenomenon. In order to establish production scheduling with minimum makespan, the timed Petri net (PN) model of a production process is established. Based on this PN model, a novel Hybrid Estimation-of-Distribution

Airborne LiDAR has been adopted as a powerful survey tool for overhead power transmission line (TL) cruising so that the operator can quickly search for, locate and eliminate the risk objects at the power line corridor scene. However, the TLs are moving objects, which positions are dynamically affected by working conditions (e.g. temperature variation, wind-induced conductor motion) while they are

Due to the increasing demand of electrical vehicles (EVs), prognostics of the battery state is of paramount importance. The nonlinearity of the signal (e.g. voltage) results in the complexity of analyzing the degradation of the battery. Aging characteristics extracted from the voltage, current, and temperature when the battery is fully charged/discharged were commonly used by previous researchers to

With the popularity of electric multi-rotor unmanned aerial vehicles (EMRUAV), the problem of low efficiency of the motor propeller system (MPS) becomes serious especially for EMRUAVs which have multiple working states. To solve this problem, an integrated optimization design method for high-efficiency MPS is proposed using an improved parallel particle swarm optimization and differential evolution

A large number of facts prove that Global Navigation Satellite System (GNSS) has certain security risks and is threatened by attacks. In particular, GNSS civilian signal receivers have some vulnerability in responding to spoofing attack and jamming attack, because the format and modulation of GNSS civilian signals are public. Based on the concerns of existing GNSS spoofing scenarios, this article reviews

Multipath transmission control protocol (MPTCP) is a promising transport layer protocol that enables a device to utilize multiple communication interfaces simultaneously, thereby achieving high throughput. A congestion control algorithm (CCA) employed in MPTCP constitutes a key part that controls the data flow through different subflows (SFs). There are two fundamental challenges associated with MPTCP

Raw statistical features can imitate the amplitude, average, energy and time, and frequency series distribution of a raw vibration signal. However, these raw statistical features are either not very sensitive to weak incipient faults or are unsuitable for more severe faults, thus affecting the fault detection and classification accuracy. To tackle this problem, this paper proposes a discriminant feature

The modulation asymmetry of a multifunction integrated optic circuit (MIOC) indicates the difference in the phase response to modulation electric fields with the same magnitude but different directions, and is partly caused by the vertical component of the modulation signal. This introduces a nonlinear phase error that is difficult to compensate in fiber optic gyroscopes (FOGs), which needs to be suppressed

The communication security of constrained objects such as radio frequency identification (RFID) tags and wireless sensor network (WSN) is very challenging because it is not always possible to use the conventional on-the-shelf solutions for them, due to their limited available power and computational capabilities. To deal with this demand, many security protocols have been developed by the researchers

Service-oriented computing (SOC) promises a world of cooperating services loosely connected, constructing agile Web applications in heterogeneous environments conveniently. Web application interface (API) as an emerging technique attracts more and more enterprises and organizations to publish their deep computing functionalities and big data on the Internet, Web API has become the backbone to promote

Since the appearance of 5G, Internet of Things (IoT) has gained an increased interest, with multiple technologies emerging and converging to cover different user needs. One of the biggest challenges today is to have global IoT coverage, ensuring seamless communication with IoT devices placed in rural and even remote areas. Satellite constellations, and in particular CubeSats orbiting in Low Earth Orbit

In order to ensure the safe and reliable operation of three-phase four-wire (3P4W) three-level grid-connected converter (TL-GCC) from the point view of neutral point voltage (NPV) self-equilibrium, a sufficient condition based on carrier-based pulse width modulation (CBPWM) strategy is presented. The analysis of NPV self-equilibrium based on the sufficient condition is presented while different forms

The coevolution of 5G technological development and the growing market toward the 5G era has generated an innovation enhancement pattern. Identifying this pattern will reveal the 5G services in demand in the market and the infrastructure required for improving the 5G era. Herein, the patterns of 5G technological innovation enhancements are identified as expected in the Indonesian market. We use a novel

Accurate and reliable wind speed forecasting is crucial for wind farm planning and grid operation security. To improve the accuracy of wind speed forecasting, a novel combined model is proposed for wind speed forecasting in this article. First, the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and permutation entropy (PE) are employed to decompose the original wind speed time

Graph neural network, as a deep learning based graph representation technology, can capture the structural information encapsulated in graphs well and generate more effective feature embedding. We have recently witnessed an emerging research interests on it. However, existing models are primarily focused on handling homogeneous graphs. When designing graph neural networks for heterogeneous graphs,

To improve the anti-interference for ELF/VLF communication based on modulated heating lower-ionosphere, chirp spread spectrum technology with strong anti-interference is combined with square wave amplitude modulation, a novel method of chirp-BOK modulated heating lower-ionosphere is proposed in this paper. On this basis, to improve the radiation efficiency of ELF/VLF, the effects of heating parameters

Anomaly detection is essential for many real-world applications, such as video surveillance, disease diagnosis, and visual inspection. With the development of neural networks, many neural networks have been used for anomaly detection by learning the distribution of normal data. However, they are vulnerable to distinguishing abnormalities when the normal and abnormal images are not significantly different

This paper proposes an optimal rate control model based on deep neural network (DNN) features to improve the coding tree unit (CTU)-level rate control in high-efficiency video coding for conversational videos. The proposed algorithm extracts high-level features from the original and previously reconstructed CTU blocks based on a predefined DNN model of the visual geometry group (VGG-16) network. Then

When a pole-to-pole short-circuit fault occurs in an LCC-MMC hybrid DC transmission system, the fault characteristics are affected by the distributed capacitance and the transition resistance that cause a dead zone in the fault location and poor accuracy in determining the fault location. In view of this, this paper proposes a fault location method for the LCC-MMC hybrid DC transmission system. By

A significant number of processing cores in any many-core systems nowadays and likely in the future have to be switched off or forced to be idle to become dark cores, in light of ever increasing power density and chip temperature. Although these dark cores cannot make direct contributions to the chip’s throughput, they can still be allocated to applications currently running in the system for the sole

In this paper, we propose a lossless snubber cell for a soft-switched bidirectional buck–boost converter (BBC). The proposed snubber cell provides all switches with soft-switching conditions in both the bidirectional power flows of the BBC. In addition, the cell also achieves a relatively low circulating current for the snubber operation over a wide load range. In particular, the reverse recovery problem

Visual sensor network (VSN) requires a multi-focus image or video frame fusion technique involving focus measure computation in the DCT-domain to generate an all-in-focus image. Such techniques are implemented on resource-constrained on-board systems requiring hardware-friendly implementations. In this article, we first show that components of the Laplacian matrix are related to the discrete cosine

In this article, we propose an effective algorithm of pipelined dividers for double-precision floating-point numbers. This reduces the latency of the previous pipelined dividers without increasing the lookup table size. The experimental results show that the proposed divider reduces the overall latency up to 16% compared to the previous divider.

The location-based social network (LBSN) contains a large amount of user check-in data informations, in order to better improve the recommendation performance and avoid the impact of user check-in data sparsity. It is proposed to mine the time-category informations in the user’s check-in data to carry out network modeling; by using the belief propagation algorithm on the time-category Markov network

Cold-start problem has been recognized as the most crucial challenge in recommender systems. Many recommendation algorithms work well when lots of preference information is available but start to degrade in cold-start settings. Inspired by the spirit of meta-learning, we identified that the appeal of cold-start problems and the superiority of meta-learning are congruous where meta-learning aims to

Patients with breast cancer are prone to serious health-related complications with higher mortality. The primary reason might be a misinterpretation of radiologists in recognizing suspicious lesions due to technical issues in imaging qualities and heterogeneous breast densities which increases the false-(positive and negative) ratio. Early intervention is significant in establishing an up-to-date prognosis

In this paper, we propose a multi-frame depth super-resolution (SR) method based on $L_{1}$ data fidelity with the total variation regularization (TV- $L_{1}$ ) model. The majority of time-of-flight (ToF) sensors exhibit limited spatial resolution compared to RGB sensors and the improvement of the depth image resolution is an inherently ill-posed problem. To overcome this under-determined problem,

This paper proposes an adaptive energy management strategy for hybrid electric vehicles by combining deep reinforcement learning (DRL) and transfer learning (TL). This work aims to address the defect of DRL in tedious training time. First, an optimization control modeling of a hybrid tracked vehicle is built, wherein the elaborate powertrain components are introduced. Then, a bi-level control framework