Date
All
Search in:AllTitleAbstractAuthor name
Publications
(33M+)
Patents
(1M+)
Grants
(2M+)
Pathways
(531)
Clinical trials
(347K+)
Publication
Journal: Sensors
January/21/2022
Abstract
The three-dimensional (3D) symmetry shape plays a critical role in the reconstruction and recognition of 3D objects under occlusion or partial viewpoint observation. Symmetry structure prior is particularly useful in recovering missing or unseen parts of an object. In this work, we propose Sym3DNet for single-view 3D reconstruction, which employs a three-dimensional reflection symmetry structure prior of an object. More specifically, Sym3DNet includes 2D-to-3D encoder-decoder networks followed by a symmetry fusion step and multi-level perceptual loss. The symmetry fusion step builds flipped and overlapped 3D shapes that are fed to a 3D shape encoder to calculate the multi-level perceptual loss. Perceptual loss calculated in different feature spaces counts on not only voxel-wise shape symmetry but also on the overall global symmetry shape of an object. Experimental evaluations are conducted on both large-scale synthetic 3D data (ShapeNet) and real-world 3D data (Pix3D). The proposed method outperforms state-of-the-art approaches in terms of efficiency and accuracy on both synthetic and real-world datasets. To demonstrate the generalization ability of our approach, we conduct an experiment with unseen category samples of ShapeNet, exhibiting promising reconstruction results as well.
Keywords: 3D object reconstruction; deep learning; reflection symmetry.
Related with
Publication
Journal: Sensors
January/21/2022
Abstract
In P2P networks, self-organizing anonymous peers share different resources without a central entity controlling their interactions. Peers can join and leave the network at any time, which opens the door to malicious attacks that can damage the network. Therefore, trust management systems that can ensure trustworthy interactions between peers are gaining prominence. This paper proposes AntTrust, a trust management system inspired by the ant colony. Unlike other ant-inspired algorithms, which usually adopt a problem-independent approach, AntTrust follows a problem-dependent (problem-specific) heuristic to find a trustworthy peer in a reasonable time. It locates a trustworthy file provider based on four consecutive trust factors: current trust, recommendation, feedback, and collective trust. Three rival trust management paradigms, namely, EigenTrust, Trust Network Analysis with Subjective Logic (TNA-SL), and Trust Ant Colony System (TACS), were tested to benchmark the performance of AntTrust. The experimental results demonstrate that AntTrust is capable of providing a higher and more stable success rate at a low running time regardless of the percentage of malicious peers in the network.
Keywords: peer-to-peer networks; privacy; trust management; wireless sensor networks.
Publication
Journal: Sensors
January/21/2022
Abstract
Energy efficiency is one of the critical challenges in wireless sensor networks (WSNs). WSNs collect and transmit data through sensor nodes. However, the energy carried by the sensor nodes is limited. The sensor nodes need to save energy as much as possible to prolong the network lifetime. This paper proposes a game theory-based energy-efficient clustering algorithm (GEC) for wireless sensor networks, where each sensor node is regarded as a player in the game. According to the length of idle listening time in the active state, the sensor node can adopt favorable strategies for itself, and then decide whether to sleep or not. In order to avoid the selfish behavior of sensor nodes, a penalty mechanism is introduced to force the sensor nodes to adopt cooperative strategies in future operations. The simulation results show that the use of game theory can effectively save the energy consumption of the sensor network and increase the amount of network data transmission, so as to achieve the purpose of prolonging the network lifetime.
Keywords: game theory; idle listening time; network lifetime; penalty mechanism; wireless sensor network.
Related with
Publication
Journal: Viruses
January/21/2022
Abstract
The most effective intervention for influenza prevention is vaccination. However, there are conflicting data on influenza vaccine antibody responses in obese children. Cardio-metabolic parameters such as waist circumference, cholesterol, insulin sensitivity, and blood pressure are used to subdivide individuals with overweight or obese BMI into 'healthy' (MHOO) or 'unhealthy' (MUOO) metabolic phenotypes. The ever-evolving metabolic phenotypes in children may be elucidated by using vaccine stimulation to characterize cytokine responses. We conducted a prospective cohort study evaluating influenza vaccine responses in children. Participants were identified as either normal-weight children (NWC) or overweight/obese using BMI. Children with obesity were then characterized using metabolic health metrics. These metrics consisted of changes in serum cytokine and chemokine concentrations measured via multiplex assay at baseline and repeated at one month following vaccination. Changes in NWC, MHOO and MUOO were compared using Chi-square/Fisher's exact test for antibody responses and Kruskal-Wallis test for cytokines. Differences in influenza antibody responses in normal, MHOO and MUOO children were statistically indistinguishable. IL-13 was decreased in MUOO children compared to NWC and MHOO children (p = 0.04). IL-10 approached a statistically significant decrease in MUOO compared to MHOO and NWC (p = 0.07). Influenza vaccination does not provoke different responses in NCW, MHOO, or MUOO children, suggesting that obesity, whether metabolically healthy or unhealthy, does not alter the efficacy of vaccination. IL-13 levels in MUO children were significantly different from levels in normal and MHOO children, indicating that the metabolically unhealthy phenotypes may be associated with an altered inflammatory response. A larger sample size with greater numbers of metabolically unhealthy children may lend more insight into the relationship of chronic inflammation secondary to obesity with vaccine immunity.
Keywords: influenza vaccine; metabolic health; pediatric obesity.
Publication
Journal: Sensors
January/21/2022
Abstract
The Internet of Things (IoT) connects billions of sensors to share and collect data at any time and place. The Advanced Metering Infrastructure (AMI) is one of the most important IoT applications. IoT supports AMI to collect data from smart sensors, analyse and measure abnormalities in the energy consumption pattern of sensors. However, two-way communication in distributed sensors is sensitive and tends towards security and privacy issues. Before deploying distributed sensors, data confidentiality and privacy and message authentication for sensor devices and control messages are the major security requirements. Several authentications and encryption protocols have been developed to provide confidentiality and integrity. However, many sensors in distributed systems, resource constraint smart sensors, and adaptability of IoT communication protocols in sensors necessitate designing an efficient and lightweight security authentication scheme. This paper proposes a Payload Encryption-based Optimisation Scheme for lightweight authentication (PEOS) on distributed sensors. The PEOS integrates and optimises important features of Datagram Transport Layer Security (DTLS) in Constrained Application Protocol (CoAP) architecture instead of implementing the DTLS in a separate channel. The proposed work designs a payload encryption scheme and an Optimised Advanced Encryption Standard (OP-AES). The PEOS modifies the DTLS handshaking and retransmission processes in PEOS using payload encryption and NACK messages, respectively. It also removes the duplicate features of the protocol version and sequence number without impacting the performance of CoAP. Moreover, the PEOS attempts to improve the CoAP over distributed sensors in the aspect of optimised AES operations, such as parallel execution of S-boxes in SubBytes and delayed Mixcolumns. The efficiency of PEOS authentication is evaluated on Conitki OS using the Cooja simulator for lightweight security and authentication. The proposed scheme attains better throughput while minimising the message size overhead by 9% and 23% than the existing payload-based mutual authentication PbMA and basic DTLS/CoAP scheme in random network topologies with less than 50 nodes.
Keywords: AES; CoAP; DTLS; IoT; lightweight; payload encryption; security; sensors.
Publication
Journal: Sensors
January/21/2022
Abstract
This work presents the design and analysis of newly developed reconfigurable, flexible, inexpensive, optically-controlled, and fully printable chipless Arabic alphabet-based radio frequency identification (RFID) tags. The etching of the metallic copper tag strip is performed on a flexible simple thin paper substrate (ϵr = 2.31) backed by a metallic ground plane. The analysis of investigated tags is performed in CST MWS in the frequency range of 1-12 GHz for the determination of the unique signature resonance characteristics of each tag in terms of its back-scattered horizontal and vertical mono-static radar cross section (RCS). The analysis reflects that each tag has its own unique electromagnetic signature (EMS) due to the changing current distribution of metallic resonator. This EMS of each tag could be used for the robust detection and recognition of all realized 28 Arabic alphabet tags. The study also discusses, for the first time, the effect of the change in font type and size of realized tags on their EMS. The robustness and reliability of the obtained EMS of letter tags is confirmed by comparing the RCS results for selective letter tags using FDTD and MoM numerical methods, which shows very good agreement. The proposed tags could be used for smart internet of things (IoT) and product marketing applications.
Keywords: Arabic alphabets; cheap RFID; chipless RFID; font size; font type; paper-based RFID; printable RFID; radar cross-section (RCS).
Publication
Journal: Sensors
January/21/2022
Abstract
Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive task that is prone to human error and tainted with plausible breaches of privacy. This paper presents a privacy-preserving adaptive social distance estimation and crowd monitoring solution for camera surveillance systems. We develop a novel person localization strategy through pose estimation, build a privacy-preserving adaptive smoothing and tracking model to mitigate occlusions and noisy/missing measurements, compute inter-personal distances in the real-world coordinates, detect social distance infractions, and identify overcrowded regions in a scene. Performance evaluation is carried out by testing the system's ability in person detection, localization, density estimation, anomaly recognition, and high-risk areas identification. We compare the proposed system to the latest techniques and examine the performance gain delivered by the localization and smoothing/tracking algorithms. Experimental results indicate a considerable improvement, across different metrics, when utilizing the developed system. In addition, they show its potential and functionality for applications other than social distancing.
Keywords: COVID-19; crowd monitoring; person detection and tracking; pose estimation; social distancing; video surveillance.
Publication
Journal: Sensors
January/21/2022
Abstract
For low input radio frequency (RF) power from -35 to 5 dBm, a novel quad-band RF energy harvester (RFEH) with an improved impedance matching network (IMN) is proposed to overcome the poor conversion efficiency and limited RF power range of the ambient environment. In this research, an RF spectral survey was performed in the semi-urban region of Malaysia, and using these results, a multi-frequency highly sensitive RF energy harvester was designed to harvest energy from available frequency bands within the 0.8 GHz to 2.6 GHz frequency range. Firstly, a new IMN is implemented to improve the rectifying circuit's efficiency in ambient conditions. Secondly, a self-complementary log-periodic higher bandwidth antenna is proposed. Finally, the design and manufacture of the proposed RF harvester's prototype are carried out and tested to realize its output in the desired frequency bands. For an accumulative -15 dBm input RF power that is uniformly universal across the four radio frequency bands, the harvester's calculated dc rectification efficiency is about 35 percent and reaches 52 percent at -20 dBm. Measurement in an ambient RF setting shows that the proposed harvester is able to harvest dc energy at -20 dBm up to 0.678 V.
Keywords: RF energy harvesting; RF spectral survey; ambient; broadband rectifier; log periodic antenna; quad band.
Related with
Publication
Journal: Sensors
January/21/2022
Abstract
In this paper, an encryption and trust evaluation model is proposed on the basis of a blockchain in which the identities of the Aggregator Nodes (ANs) and Sensor Nodes (SNs) are stored. The authentication of ANs and SNs is performed in public and private blockchains, respectively. However, inauthentic nodes utilize the network's resources and perform malicious activities. Moreover, the SNs have limited energy, transmission range and computational capabilities, and are attacked by malicious nodes. Afterwards, the malicious nodes transmit wrong information of the route and increase the number of retransmissions due to which the SNs' energy is rapidly consumed. The lifespan of the wireless sensor network is reduced due to the rapid energy dissipation of the SNs. Furthermore, the throughput increases and packet loss increase with the presence of malicious nodes in the network. The trust values of SNs are computed to eradicate the malicious nodes from the network. Secure routing in the network is performed considering residual energy and trust values of the SNs. Moreover, the Rivest-Shamir-Adleman (RSA), a cryptosystem that provides asymmetric keys, is used for securing data transmission. The simulation results show the effectiveness of the proposed model in terms of high packet delivery ratio.
Keywords: Rivest–Shamir–Adleman; authentication; blockchain; secure routing; smart contract; trust evaluation; wireless sensor network.
Publication
Journal: Sensors
January/21/2022
Abstract
Considering the increasing scale and severity of damage from recent cybersecurity incidents, the need for fundamental solutions to external security threats has increased. Hence, network separation technology has been designed to stop the leakage of information by separating business computing networks from the Internet. However, security accidents have been continuously occurring, owing to the degradation of data transmission latency performance between the networks, decreasing the convenience and usability of the work environment. In a conventional centralized network connection concept, a problem occurs because if either usability or security is strengthened, the other is weakened. In this study, we proposed a distributed authentication mechanism for secure network connectivity (DAM4SNC) technology in a distributed network environment that requires security and latency performance simultaneously to overcome the trade-off limitations of existing technology. By communicating with separated networks based on the authentication between distributed nodes, the inefficiency of conventional centralized network connection solutions is overcome. Moreover, the security is enhanced through periodic authentication of the distributed nodes and differentiation of the certification levels. As a result of the experiment, the relative efficiency of the proposed scheme (REP) was about 420% or more in all cases.
Keywords: decentralized authentication; distributed network; frame structure; n-factor authentication; network separation; trust level.
Publication
Journal: Sensors
January/21/2022
Abstract
While the majority of social scientists still rely on traditional research instruments (e.g., surveys, self-reports, qualitative observations), multimodal sensing is becoming an emerging methodology for capturing human behaviors. Sensing technology has the potential to complement and enrich traditional measures by providing high frequency data on people's behavior, cognition and affects. However, there is currently no easy-to-use toolkit for recording multimodal data streams. Existing methodologies rely on the use of physical sensors and custom-written code for accessing sensor data. In this paper, we present the EZ-MMLA toolkit. This toolkit was implemented as a website and provides easy access to multimodal data collection algorithms. One can collect a variety of data modalities: data on users' attention (eye-tracking), physiological states (heart rate), body posture (skeletal data), gestures (from hand motion), emotions (from facial expressions and speech) and lower-level computer vision algorithms (e.g., fiducial/color tracking). This toolkit can run from any browser and does not require dedicated hardware or programming experience. We compare this toolkit with traditional methods and describe a case study where the EZ-MMLA toolkit was used by aspiring educational researchers in a classroom context. We conclude by discussing future work and other applications of this toolkit, potential limitations and implications.
Keywords: computer vision; data mining; sensor applications and deployments.
Related with
Publication
Journal: Sensors
January/21/2022
Abstract
In this paper, focusing on the slow time-varying characteristics, a series of works have been conducted to implement an accurate quality prediction for batch processes. To deal with the time-varying characteristics along the batch direction, sliding windows can be constructed. Then, the start-up process is identified and the whole process is divided into two modes according to the steady-state identification. In the most important mode, the process data matrix, used to establish the regression model of the current batch, is expanded to involve the process data of previous batches, which is called batch augmentation. Thus, the process data of previous batches, which have an important influence on the quality of the current batch, will be identified and form a new batch augmentation matrix for modeling using the partial least squares (PLS) method. Moreover, considering the multiphase characteristic, batch augmentation analysis and modeling is conducted within each phase. Finally, the proposed method is applied to a typical batch process, the injection molding process. The quality prediction results are compared with those of the traditional quality prediction method based on PLS and the ridge regression method under the proposed batch augmentation analysis framework. The conclusion is obtained that the proposed method based on the batch augmentation analysis is superior.
Keywords: batch augmentation; batch process; partial least squares; quality prediction.
Publication
Journal: Viruses
January/21/2022
Abstract
The central nervous system (CNS) HIV reservoir is an obstacle to achieving an HIV cure. The basal ganglia harbor a higher frequency of SIV than other brain regions in the SIV-infected rhesus macaques of Chinese-origin (chRMs) even on suppressive combination antiretroviral therapy (ART). Since residual HIV/SIV reservoir is associated with inflammation, we characterized the neuroinflammation by gene expression and systemic levels of inflammatory molecules in healthy controls and SIV-infected chRMs with or without ART. CCL2, IL-6, and IFN-γ were significantly reduced in the cerebrospinal fluid (CSF) of animals receiving ART. Moreover, there was a correlation between levels of CCL2 in plasma and CSF, suggesting the potential use of plasma CCL2 as a neuroinflammation biomarker. With higher SIV frequency, the basal ganglia of untreated SIV-infected chRMs showed an upregulation of secreted phosphoprotein 1 (SPP1), which could be an indicator of ongoing neuroinflammation. While ART greatly reduced neuroinflammation in general, proinflammatory genes, such as IL-9, were still significantly upregulated. These results expand our understanding of neuroinflammation and signaling in SIV-infected chRMs on ART, an excellent model to study HIV/SIV persistence in the CNS.
Keywords: antiretroviral therapy; central nervous system; human immunodeficiency virus; immune activation; neuroinflammation; non-human primates; reservoir; rhesus macaques; simian immunodeficiency virus.
Publication
Journal: Viruses
January/21/2022
Abstract
Hepatitis A virus (HAV) is an emerging public health concern and there is an urgent need for ways to rapidly identify cases so that outbreaks can be managed effectively. Conventional testing for HAV relies on anti-HAV IgM seropositivity. However, studies estimate that 10-30% of patients may not be diagnosed by serology. Molecular assays that can directly detect viral nucleic acids have the potential to improve diagnosis, which is key to prevent the spread of infections. In this study, we developed a real-time PCR (RT-PCR) assay to detect HAV RNA for the identification of acute HAV infection. Primers were designed to target the conserved 5'-untranslated region (5'-UTR) of HAV, and the assay was optimized on both the Qiagen Rotor-Gene and the BD MAX. We successfully detected HAV from patient serum and stool samples with moderate differences in sensitivity and specificity depending on the platform used. Our results highlight the clinical utility of using a molecular assay to detect HAV from various specimen types that can be implemented in hospitals to assist with diagnostics, treatment and prevention.
Keywords: clinical diagnostics; real-time PCR; viral hepatitis.
Publication
Journal: Vaccines
January/21/2022
Abstract
Group A Streptococcus (GAS) causes a variety of diseases globally. The DNases in GAS promote GAS evasion of neutrophil killing by degrading neutrophil extracellular traps (NETs). Sda1 is a prophage-encoded DNase associated with virulent GAS strains. However, protective immunity against Sda1 has not been determined. In this study, we explored the potential of Sda1 as a vaccine candidate. Sda1 was used as a vaccine to immunize mice intranasally. The effect of anti-Sda1 IgG in neutralizing degradation of NETs was determined and the protective role of Sda1 was investigated with intranasal and systemic challenge models. Antigen-specific antibodies were induced in the sera and pharyngeal mucosal site after Sda1 immunization. The anti-Sda1 IgG efficiently prevented degradation of NETs by supernatant samples from different GAS serotypes with or without Sda1. Sda1 immunization promoted clearance of GAS from the nasopharynx independent of GAS serotypes but did not reduce lethality after systemic GAS challenge. Anti-Sda1 antibody can neutralize degradation of NETs by Sda1 and other phage-encoded DNases and decrease GAS colonization at the nasopharynx across serotypes. These results indicate that Sda1 can be a potential vaccine candidate for reduction in GAS reservoir and GAS tonsillitis-associated diseases.
Keywords: DNases; Sda1; group A streptococcus; neutrophil extracellular traps; pharyngeal colonization.
Publication
Journal: Sensors
January/21/2022
Abstract
Unpressurized aircraft circuits facilitate the initiation of electrical discharges in wiring systems, with consequent damage to related insulation materials and safety hazards, that can and have already caused severe incidents and accidents. Specific sensors and solutions must be developed to detect these types of faults at a very incipient stage, before further damage occurs. Electrical discharges in air generate the corona effect, which is characterized by emissions of bluish light, which are found in the ultraviolet (UV) and visible spectra. However, due to sunlight interference, the corona effect is very difficult to detect at the very initial stage, so the use of solar-blind sensors can be a possible solution. This work analyzes the feasibility of using inexpensive non-invasive solar-blind sensors in a range of pressures compatible with aircraft environments to detect the electrical discharges at a very incipient stage. Their behavior and sensitivity compared with other alternatives, i.e., an antenna sensor and a CMOS imaging sensor, is also assessed. Experimental results presented in this paper show that the analyzed solar-blind sensors can be applied for the on-line detection of electrical discharges in unpressurized aircraft environments at the very initial stage, thus facilitating and enabling the application of predictive maintenance strategies. They also offer the possibility to be combined with existing electrical protections to expand their capabilities and improve their sensitivity to detect very early discharges, thus allowing the timely identification of their occurrence.
Keywords: aircraft power systems; low pressure; solar-blind sensors; ultraviolet radiation.
Related with
Publication
Journal: Sensors
January/21/2022
Abstract
In this paper, we present the design, development and implementation of an integrated system for the management of COVID-19 patient, using the LoRaWAN communication infrastructure. Our system offers certain advantages when compared to other similar solutions, allowing remote symptom and health monitoring that can be applied to isolated or quarantined people, without any external interaction with the patient. The IoT wearable device can monitor parameters of health condition like pulse, blood oxygen saturation, and body temperature, as well as the current location. To test the performance of the proposed system, two persons under quarantine were monitored, for a complete 14-day standard quarantine time interval. Based on the data transmitted to the monitoring center, the medical staff decided, after several days of monitoring, when the measured values were outside of the normal parameters, to do an RT-PCR test for one of the two persons, confirming the SARS-CoV2 virus infection. We have to emphasize the high degree of scalability of the proposed solution that can oversee a large number of patients at the same time, thanks to the LoRaWAN communication protocol used. This solution can be successfully implemented by local authorities to increase monitoring capabilities, also saving lives.
Keywords: COVID-19 sensors; Internet of Things; IoT wearable device; quarantine monitoring; remote healthcare; telemedicine.
Related with
Publication
Journal: Sensors
January/21/2022
Abstract
Airborne angle-only sensors can be used to track stationary or mobile ground targets. In order to make the problem observable in 3-dimensions (3-D), the height of the target (i.e., the height of the terrain) from the sea-level is needed to be known. In most of the existing works, the terrain height is assumed to be known accurately. However, the terrain height is usually obtained from Digital Terrain Elevation Data (DTED), which has different resolution levels. Ignoring the terrain height uncertainty in a tracking algorithm will lead to a bias in the estimated states. In addition to the terrain uncertainty, another common source of uncertainty in angle-only sensors is the sensor biases. Both these uncertainties must be handled properly to obtain better tracking accuracy. In this paper, we propose algorithms to estimate the sensor biases with the target(s) of opportunity and algorithms to track targets with terrain and sensor bias uncertainties. Sensor bias uncertainties can be reduced by estimating the biases using the measurements from the target(s) of opportunity with known horizontal positions. This step can be an optional step in an angle-only tracking problem. In this work, we have proposed algorithms to pick optimal targets of opportunity to obtain better bias estimation and algorithms to estimate the biases with the selected target(s) of opportunity. Finally, we provide a filtering framework to track the targets with terrain and bias uncertainties. The Posterior Cramer-Rao Lower Bound (PCRLB), which provides the lower bound on achievable estimation error, is derived for the single target filtering with an angle-only sensor with terrain uncertainty and measurement biases. The effectiveness of the proposed algorithms is verified by Monte Carlo simulations. The simulation results show that sensor biases can be estimated accurately using the target(s) of opportunity and the tracking accuracies of the targets can be improved significantly using the proposed algorithms when the terrain and bias uncertainties are present.
Keywords: angle-only sensor; bias estimation; path planning; posterior Cramer–Rao lower bound; terrain uncertainty.
Publication
Journal: Sensors
January/21/2022
Abstract
Household appliances, climate control machines, vehicles, elevators, cash counting machines, etc., are complex machines with key contributions to the smart city. Those devices have limited memory and processing power, but they are not just actuators; they embed tens of sensors and actuators managed by several microcontrollers and microprocessors communicated by control buses. On the other hand, predictive maintenance and the capability of identifying failures to avoid greater damage of machines is becoming a topic of great relevance in Industry 4.0, and the large amount of data to be processed is a concern. This article proposes a layered methodology to enable complex machines with automatic fault detection or predictive maintenance. It presents a layered structure to perform the collection, filtering and extraction of indicators, along with their processing. The aim is to reduce the amount of data to work with, and to optimize them by generating indicators that concentrate the information provided by data. To test its applicability, a prototype of a cash counting machine has been used. With this prototype, different failure cases have been simulated by introducing defective elements. After the extraction of the indicators, using the Kullback-Liebler divergence, it has been possible to visualize the differences between the data associated with normal and failure operation. Subsequently, using a neural network, good results have been obtained, being able to correctly classify the failure in 90% of the cases. The result of this application demonstrates the proper functioning of the proposed approach in complex machines.
Keywords: artificial neural network; data reduction; fault detection; feature analysis; feature selection; indicators; industry 4.0; sensor data.
Publication
Journal: Sensors
January/21/2022
Abstract
The rapid development of intelligent networked vehicles (ICVs) has brought many positive effects. Unfortunately, connecting to the outside exposes ICVs to security threats. Using secure protocols is an important approach to protect ICVs from hacker attacks and has become a hot research area for vehicle security. However, most of the previous studies were carried out on V2X networks, while those on in-vehicle networks (IVNs) did not involve Ethernet. To this end, oriented to the new IVNs based on Ethernet, we designed an efficient secure scheme, including an authentication scheme using the Scalable Service-Oriented Middleware over IP (SOME/IP) protocol and a secure communication scheme modifying the payload field of the original SOME/IP data frame. The security analysis shows that the designed authentication scheme can provide mutual identity authentication for communicating parties and ensure the confidentiality of the issued temporary session key; the designed authentication and secure communication scheme can resist the common malicious attacks conjointly. The performance experiments based on embedded devices show that the additional overhead introduced by the secure scheme is very limited. The secure scheme proposed in this article can promote the popularization of the SOME/IP protocol in IVNs and contribute to the secure communication of IVNs.
Keywords: AEAD; Ethernet; SOME/IP; authentication; in-vehicle network; key agreement; secret; security.
Publication
Journal: Sensors
January/21/2022
Abstract
In this paper, we propose a novel time reversal-based localization method for pipeline leakage. In the proposed method, a so-called TR self-adaptive cancellation is developed to improve the leak localization resolution. First of all, the proposed approach time reverses and back-propagates the captured signals. Secondly, the time reversed signals with the various coefficients are superposed. Due to the synchronous temporal and spatial focusing characteristic of time reversal, those time reversed signals will cancel each other out. Finally, the leakage location is distinguished by observing the energy distribution of the superposed signal. In this investigation, the proposed method was employed to monitor a 58 m PVC pipeline. Three manually controllable valves were utilized to simulate the leakages. Six piezoceramic sensors equipped on the pipeline, recorded the NWP signals generated by the three valves. The experimental results show that the leak positions can accurately revealed by using the proposed approach. Furthermore, the resolution of the proposed approach can be ten times that of the conventional TR localization method.
Keywords: PZT transducer; localization; negative pressure wave; pipeline leakage; time reversal.
Publication
Journal: Viruses
January/21/2022
Abstract
Ebola virus disease (EVD) is a lethal disease caused by the highly pathogenic Ebola virus (EBOV), and its major symptoms in severe cases include vascular leakage and hemorrhage. These symptoms are caused by abnormal activation and disruption of endothelial cells (ECs) whose mediators include EBOV glycoprotein (GP) without the need for viral replication. However, the detailed molecular mechanisms underlying virus-host interactions remain largely unknown. Here, we show that EBOV-like particles (VLPs) formed by GP, VP40, and NP activate ECs in a GP-dependent manner, as demonstrated by the upregulation of intercellular adhesion molecules-1 (ICAM-1) expression. VLPs-mediated ECs activation showed a different kinetic pattern from that of TNF-α-mediated activation and was associated with apoptotic ECs disruption. In contrast to TNF-α, VLPs induced ICAM-1 overexpression at late time points. Furthermore, screening of host cytoskeletal signaling inhibitors revealed that focal adhesion kinase inhibitors were found to be potent inhibitors of ICAM-1 expression mediated by both TNF-α and VLPs. Our results suggest that EBOV GP stimulates ECs to induce endothelial activation and dysfunction with the involvement of host cytoskeletal signaling factors, which represent potential therapeutic targets for EVD.
Keywords: Ebola GP; ICAM-1; Rho signaling pathway; TNF-α; antivirals; cytoskeletal signaling; focal adhesion kinase; host factor; micropinocytosis; virus–host interactions.
Publication
Journal: Sensors
January/21/2022
Abstract
From a biological point of view, alcohol human attentional impairment occurs before reaching a Blood Alcohol Content (BAC index) of 0.08% (0.05% under the Italian legislation), thus generating a significant impact on driving safety if the drinker subject is driving a car. Car drivers must keep a safe driving dynamic, having an unaltered physiological status while processing the surrounding information coming from the driving scenario (e.g., traffic signs, other vehicles and pedestrians). Specifically, the identification and tracking of pedestrians in the driving scene is a widely investigated problem in the scientific community. The authors propose a full, deep pipeline for the identification, monitoring and tracking of the salient pedestrians, combined with an intelligent electronic alcohol sensing system to properly assess the physiological status of the driver. More in detail, the authors propose an intelligent sensing system that makes a common air quality sensor selective to alcohol. A downstream Deep 1D Temporal Residual Convolutional Neural Network architecture will be able to learn specific embedded alcohol-dynamic features in the collected sensing data coming from the GHT25S air-quality sensor of STMicroelectronics. A parallel deep attention-augmented architecture identifies and tracks the salient pedestrians in the driving scenario. A risk assessment system evaluates the sobriety of the driver in case of the presence of salient pedestrians in the driving scene. The collected preliminary results confirmed the effectiveness of the proposed approach.
Keywords: alcohol detection; artificial neural networks; driver safety.
Related with
Publication
Journal: Sensors
January/21/2022
Abstract
Green citrus detection in citrus orchards provides reliable support for production management chains, such as fruit thinning, sunburn prevention and yield estimation. In this paper, we proposed a lightweight object detection YOLOv5-CS (Citrus Sort) model to realize object detection and the accurate counting of green citrus in the natural environment. First, we employ image rotation codes to improve the generalization ability of the model. Second, in the backbone, a convolutional layer is replaced by a convolutional block attention module, and a detection layer is embedded to improve the detection accuracy of the little citrus. Third, both the loss function CIoU (Complete Intersection over Union) and cosine annealing algorithm are used to get the better training effect of the model. Finally, our model is migrated and deployed to the AI (Artificial Intelligence) edge system. Furthermore, we apply the scene segmentation method using the "virtual region" to achieve accurate counting of the green citrus, thereby forming an embedded system of green citrus counting by edge computing. The results show that the mAP@.5 of the YOLOv5-CS model for green citrus was 98.23%, and the recall is 97.66%. The inference speed of YOLOv5-CS detecting a picture on the server is 0.017 s, and the inference speed on Nvidia Jetson Xavier NX is 0.037 s. The detection and counting frame rate of the AI edge system-side counting system is 28 FPS, which meets the counting requirements of green citrus.
Keywords: AI edge system; YOLOv5-CS; green citrus; object detection; virtual region.
load more...