Journal Description
Sensors
Sensors
is an international, peer-reviewed, open access journal on the science and technology of sensors. Sensors is published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE), Japan Society of Photogrammetry and Remote Sensing (JSPRS), Spanish Society of Biomedical Engineering (SEIB) and International Society for the Measurement of Physical Behaviour (ISMPB) are affiliated with Sensors and their members receive a discount on the article processing charges.
- Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, MEDLINE, PMC, Ei Compendex, Inspec, Astrophysics Data System, and other databases.
- Journal Rank: JCR - Q2 (Instruments & Instrumentation) / CiteScore - Q1 (Instrumentation)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about Sensors.
- Companion journals for Sensors include: Chips, Automation, JCP and Targets.
Impact Factor:
3.9 (2022);
5-Year Impact Factor:
4.1 (2022)
Latest Articles
Online Detection of Hydrogen Fluoride under Corona Discharge in Gas-Insulated Switchgear Based on Photoacoustic Spectroscopy
Sensors 2024, 24(9), 2806; https://doi.org/10.3390/s24092806 (registering DOI) - 27 Apr 2024
Abstract
Internal discharge and overheating faults in sulfur hexafluoride (SF6) gas-insulated electrical equipment will generate a series of characteristic gas products. Hydrogen fluoride (HF) is one of the main decomposition gases under discharge failure. Because of its extremely corrosive nature, it can
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Internal discharge and overheating faults in sulfur hexafluoride (SF6) gas-insulated electrical equipment will generate a series of characteristic gas products. Hydrogen fluoride (HF) is one of the main decomposition gases under discharge failure. Because of its extremely corrosive nature, it can react with other materials in gas-insulated switchgear (GIS), resulting in a short existence time, so it needs to be detected online. Resonant gas photoacoustic spectroscopy has the advantage of high sensitivity, fast response, and no sample gas consumption, and can be used for the online detection of flowing gas. In this paper, a simulated GIS corona discharge experimental platform was built, and the HF generated in the discharge was detected online by gas photoacoustic spectroscopy. The absorption peak of HF molecule near 1312.59 nm was selected as the absorption spectral line, and a resonant photoacoustic cell was designed. To improve the detection sensitivity of HF, wavelength modulation and second-harmonic detection technology were used. The online monitoring of HF in the simulated GIS corona discharge fault was successfully realized. The experimental results show that the sensitivity of the designed photoacoustic spectroscopy detection system for HF is 0.445 μV/(μL/L), and the limit of detection (LOD) is 0.611 μL/L.
Full article
(This article belongs to the Special Issue Photoacoustic Sensors and Devices for Gas Detection)
Open AccessArticle
Combining the Benefits of Biotin–Streptavidin Aptamer Immobilization with the Versatility of Ni-NTA Regeneration Strategies for SPR
by
Eliza K. Hanson and Rebecca J. Whelan
Sensors 2024, 24(9), 2805; https://doi.org/10.3390/s24092805 (registering DOI) - 27 Apr 2024
Abstract
The high affinity of the biotin–streptavidin interaction has made this non-covalent coupling an indispensable strategy for the immobilization and enrichment of biomolecular affinity reagents. However, the irreversible nature of the biotin–streptavidin bond renders surfaces functionalized using this strategy permanently modified and not amenable
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The high affinity of the biotin–streptavidin interaction has made this non-covalent coupling an indispensable strategy for the immobilization and enrichment of biomolecular affinity reagents. However, the irreversible nature of the biotin–streptavidin bond renders surfaces functionalized using this strategy permanently modified and not amenable to regeneration strategies that could increase assay reusability and throughput. To increase the utility of biotinylated targets, we here introduce a method for reversibly immobilizing biotinylated thrombin-binding aptamers onto a Ni-nitrilotriacetic acid (Ni-NTA) sensor chip using 6xHis-tagged streptavidin as a regenerable capture ligand. This approach enabled the reproducible immobilization of aptamers and measurements of aptamer–protein interaction in a surface plasmon resonance assay. The immobilized aptamer surface was stable during five experiments over two days, despite the reversible attachment of 6xHis-streptavidin to the Ni-NTA surface. In addition, we demonstrate the reproducibility of this immobilization method and the affinity assays performed using it. Finally, we verify the specificity of the biotin tag–streptavidin interaction and assess the efficiency of a straightforward method to regenerate and reuse the surface. The method described here will allow researchers to leverage the versatility and stability of the biotin–streptavidin interaction while increasing throughput and improving assay efficiency.
Full article
(This article belongs to the Special Issue Surface Plasmon Resonance-Based Biosensor)
Open AccessArticle
Developing a Novel Ontology for Cybersecurity in Internet of Medical Things-Enabled Remote Patient Monitoring
by
Kulsoom S. Bughio, David M. Cook and Syed Afaq A. Shah
Sensors 2024, 24(9), 2804; https://doi.org/10.3390/s24092804 (registering DOI) - 27 Apr 2024
Abstract
IoT has seen remarkable growth, particularly in healthcare, leading to the rise of IoMT. IoMT integrates medical devices for real-time data analysis and transmission but faces challenges in data security and interoperability. This research identifies a significant gap in the existing literature regarding
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IoT has seen remarkable growth, particularly in healthcare, leading to the rise of IoMT. IoMT integrates medical devices for real-time data analysis and transmission but faces challenges in data security and interoperability. This research identifies a significant gap in the existing literature regarding a comprehensive ontology for vulnerabilities in medical IoT devices. This paper proposes a fundamental domain ontology named MIoT (Medical Internet of Things) ontology, focusing on cybersecurity in IoMT (Internet of Medical Things), particularly in remote patient monitoring settings. This research will refer to similar-looking acronyms, IoMT and MIoT ontology. It is important to distinguish between the two. IoMT is a collection of various medical devices and their applications within the research domain. On the other hand, MIoT ontology refers to the proposed ontology that defines various concepts, roles, and individuals. MIoT ontology utilizes the knowledge engineering methodology outlined in Ontology Development 101, along with the structured life cycle, and establishes semantic interoperability among medical devices to secure IoMT assets from vulnerabilities and cyberattacks. By defining key concepts and relationships, it becomes easier to understand and analyze the complex network of information within the IoMT. The MIoT ontology captures essential key terms and security-related entities for future extensions. A conceptual model is derived from the MIoT ontology and validated through a case study. Furthermore, this paper outlines a roadmap for future research, highlighting potential impacts on security automation in healthcare applications.
Full article
(This article belongs to the Section Internet of Things)
Open AccessArticle
In-Laboratory Polysomnography Worsens Obstructive Sleep Apnea by Changing Body Position Compared to Home Testing
by
Raquel Chartuni Pereira Teixeira and Michel Burihan Cahali
Sensors 2024, 24(9), 2803; https://doi.org/10.3390/s24092803 (registering DOI) - 27 Apr 2024
Abstract
(1) Background: Home sleep apnea testing, known as polysomnography type 3 (PSG3), underestimates respiratory events in comparison with in-laboratory polysomnography type 1 (PSG1). Without head electrodes for scoring sleep and arousal, in a home environment, patients feel unfettered and move their bodies more
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(1) Background: Home sleep apnea testing, known as polysomnography type 3 (PSG3), underestimates respiratory events in comparison with in-laboratory polysomnography type 1 (PSG1). Without head electrodes for scoring sleep and arousal, in a home environment, patients feel unfettered and move their bodies more naturally. Adopting a natural position may decrease obstructive sleep apnea (OSA) severity in PSG3, independently of missing hypopneas associated with arousals. (2) Methods: Patients with suspected OSA performed PSG1 and PSG3 in a randomized sequence. We performed an additional analysis, called reduced polysomnography, in which we blindly reassessed all PSG1 tests to remove electroencephalographic electrodes, electrooculogram, and surface electromyography data to estimate the impact of not scoring sleep and arousal-based hypopneas on the test results. A difference of 15 or more in the apnea–hypopnea index (AHI) between tests was deemed clinically relevant. We compared the group of patients with and without clinically relevant differences between lab and home tests (3) Results: As expected, by not scoring sleep, there was a decrease in OSA severity in the lab test, similar to the home test results. The group of patients with clinically relevant differences between lab and home tests presented more severe OSA in the lab compared to the other group (mean AHI, 42.5 vs. 20.2 events/h, p = 0.002), and this difference disappeared in the home test. There was no difference between groups in the shift of OSA severity by abolishing sleep scoring in the lab. However, by comparing lab and home tests, there were greater variations in supine AHI and time spent in the supine position in the group with a clinically relevant difference, either with or without scoring sleep, showing an impact of the site of the test on body position during sleep. These variations presented as a marked increase or decrease in supine outcomes according to the site of the test, with no particular trend. (4) Conclusions: In-lab polysomnography may artificially increase OSA severity in a subset of patients by inducing marked changes in body position compared to home tests. The location of the sleep test seems to interfere with the evaluation of patients with more severe OSA.
Full article
(This article belongs to the Special Issue Sensors for Breathing Monitoring)
Open AccessArticle
Advanced Detection of Failed LEDs in a Short Circuit for Automotive Lighting Applications
by
Jose R. Martínez-Pérez, Miguel A. Carvajal, Juan J. Santaella, Nuria López-Ruiz, Pablo Escobedo and Antonio Martínez-Olmos
Sensors 2024, 24(9), 2802; https://doi.org/10.3390/s24092802 (registering DOI) - 27 Apr 2024
Abstract
This paper addresses the issue of LED short-circuit fault detection in signaling and lighting systems in the automotive industry. The conventional diagnostic method commonly implemented in newer vehicles relies on measuring the voltage drop across different LED branches and comparing it with threshold
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This paper addresses the issue of LED short-circuit fault detection in signaling and lighting systems in the automotive industry. The conventional diagnostic method commonly implemented in newer vehicles relies on measuring the voltage drop across different LED branches and comparing it with threshold values indicating faults caused by open circuits or LED short circuits. With this algorithm, detecting cases of a few LEDs short-circuited within a branch, particularly a single malfunctioning LED, is particularly challenging. In this work, two easily implementable algorithms are proposed to address this issue within the vehicle’s control unit. One is based on a mathematical prediction model, while the other utilizes a neural network. The results obtained offer a 100% LED short-circuit fault detection rate in the majority of analyzed cases, representing a significant improvement over the conventional method, even in scenarios involving a single malfunctioning LED within a branch. Additionally, the neural network-based model can accurately predict the number of failed LEDs.
Full article
(This article belongs to the Section Industrial Sensors)
Open AccessArticle
Low-Complexity 2D-DOD and 2D-DOA Estimation in Bistatic MIMO Radar Systems: A Reduced-Dimension MUSIC Algorithm Approach
by
Mushtaq Ahmad, Xiaofei Zhang, Xin Lai, Farman Ali and Xinlei Shi
Sensors 2024, 24(9), 2801; https://doi.org/10.3390/s24092801 (registering DOI) - 27 Apr 2024
Abstract
This paper presents a new technique for estimating the two-dimensional direction of departure (2D-DOD) and direction of arrival (2D-DOA) in bistatic uniform planar array Multiple-Input Multiple-Output (MIMO) radar systems. The method is based on the reduced-dimension (RD) MUSIC algorithm, aiming to achieve improved
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This paper presents a new technique for estimating the two-dimensional direction of departure (2D-DOD) and direction of arrival (2D-DOA) in bistatic uniform planar array Multiple-Input Multiple-Output (MIMO) radar systems. The method is based on the reduced-dimension (RD) MUSIC algorithm, aiming to achieve improved precision and computational efficiency. Primarily, this pioneering approach efficiently transforms the four-dimensional (4D) estimation problem into two-dimensional (2D) searches, thus reducing the computational complexity typically associated with conventional MUSIC algorithms. Then, exploits the spatial diversity of array response vectors to construct a 4D spatial spectrum function, which is crucial in resolving the complex angular parameters of multiple simultaneous targets. Finally, the objective is to simplify the spatial spectrum to a 2D search within a 4D measurement space to achieve an optimal balance between efficiency and accuracy. Simulation results validate the effectiveness of our proposed algorithm compared to several existing approaches, demonstrating its robustness in accurately estimating 2D-DOD and 2D-DOA across various scenarios. The proposed technique shows significant computational savings and high-resolution estimations and maintains high precision, setting a new benchmark for future explorations in the field.
Full article
(This article belongs to the Topic Advanced Array Signal Processing for B5G/6G: Models, Algorithms, and Applications)
Open AccessArticle
Experimental Testing on Tuned Liquid Dampers for Implementation in Industrial Chimneys
by
Giancarlo Marulli and Carlos Moutinho
Sensors 2024, 24(9), 2800; https://doi.org/10.3390/s24092800 (registering DOI) - 27 Apr 2024
Abstract
A TLD is a passive damping device that works by dissipating energy through the sloshing of the liquid and the effect of wave breaking, thereby controlling the vibrations of the structure. One of the applications where TLDs are of great interest is in
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A TLD is a passive damping device that works by dissipating energy through the sloshing of the liquid and the effect of wave breaking, thereby controlling the vibrations of the structure. One of the applications where TLDs are of great interest is in the case of industrial chimneys since these structures often have a very low natural frequency, which can be easily achieved in a control device of this type. The main objective of this study is to evaluate the behaviour of an annular TLD composed of multiple cells through laboratory tests and investigate if it is adequate to design it as an agglomeration of smaller rectangular TLDs. The influence of the amplitude of displacement on the behaviour of the annular TLD will also be analysed. The tests were performed on a shaking table and recurring with pendulums of the same length but of different masses. Three reservoirs were studied as TLDs: a rectangular one, a cell of an annular TLD and a quarter-ring of an annular TLD. This study concluded that the analytical methods developed in previous studies were, in general, adequate for the design of a rectangular TLD and that it was reasonable to design the annular TLD studied as a combination of rectangular ones, as its cells were a close match to a rectangle of similar dimensions. It was also concluded that a compartmentalised annular TLD is an adequate solution for the vibration control of structures with high displacements.
Full article
(This article belongs to the Special Issue Novel Sensors for Structural Health Monitoring)
Open AccessArticle
An End-to-End Artificial Intelligence of Things (AIoT) Solution for Protecting Pipeline Easements against External Interference—An Australian Use-Case
by
Umair Iqbal, Johan Barthelemy and Guillaume Michal
Sensors 2024, 24(9), 2799; https://doi.org/10.3390/s24092799 (registering DOI) - 27 Apr 2024
Abstract
High-pressure pipelines are critical for transporting hazardous materials over long distances, but they face threats from third-party interference activities. Preventive measures are implemented, but interference accidents can still occur, making the need for high-quality detection strategies vital. This paper proposes an end-to-end Artificial
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High-pressure pipelines are critical for transporting hazardous materials over long distances, but they face threats from third-party interference activities. Preventive measures are implemented, but interference accidents can still occur, making the need for high-quality detection strategies vital. This paper proposes an end-to-end Artificial Intelligence of Things (AIoT) solution to detect potential interference threats in real time. The solution involves developing a smart visual sensor capable of processing images using state-of-the-art computer vision algorithms and transmitting alerts to pipeline operators in real time. The system’s core is based on the object-detection model (e.g., You Only Look Once version 4 (YOLOv4) and DETR with Improved deNoising anchOr boxes (DINO)), trained on a custom Pipeline Visual Threat Assessment (Pipe-VisTA) dataset. Among the trained models, DINO was able to achieve the best Mean Average Precision (mAP) of 71.2% for the unseen test dataset. However, for the deployment on a limited computational-ability edge computer (i.e., the NVIDIA Jetson Nano), the simpler and TensorRT-optimized YOLOv4 model was used, which achieved a mAP of 61.8% for the test dataset. The developed AIoT device captures the image using a camera, processes on the edge using the trained YOLOv4 model to detect the potential threat, transmits the threat alert to a Fleet Portal via LoRaWAN, and hosts the alert on a dashboard via a satellite network. The device has been fully tested in the field to ensure its functionality prior to deployment for the SEA Gas use-case. The AIoT smart solution has been deployed across the 10km stretch of the SEA Gas pipeline across the Murray Bridge section. In total, 48 AIoT devices and three Fleet Portals are installed to ensure the line-of-sight communication between the devices and portals.
Full article
(This article belongs to the Section Sensing and Imaging)
Open AccessArticle
Particle-Filter-Based Fault Diagnosis for the Startup Process of an Open-Cycle Liquid-Propellant Rocket Engine
by
Jihyoung Cha, Sangho Ko and Soon-Young Park
Sensors 2024, 24(9), 2798; https://doi.org/10.3390/s24092798 (registering DOI) - 27 Apr 2024
Abstract
This study introduces a fault diagnosis algorithm based on particle filtering for open-cycle liquid-propellant rocket engines (LPREs). The algorithm serves as a model-based method for the startup process, accounting for more than 30% of engine failures. Similar to the previous fault detection and
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This study introduces a fault diagnosis algorithm based on particle filtering for open-cycle liquid-propellant rocket engines (LPREs). The algorithm serves as a model-based method for the startup process, accounting for more than 30% of engine failures. Similar to the previous fault detection and diagnosis (FDD) algorithm for the startup process, the algorithm in this study is composed of a nonlinear filter to generate residuals, a residual analysis, and a multiple-model (MM) approach to detect and diagnose faults from the residuals. In contrast to the previous study, this study makes use of the modified cumulative sum (CUSUM) algorithm, widely used in change-detection monitoring, and a particle filter (PF), which is theoretically the most accurate nonlinear filter. The algorithm is confirmed numerically using the CUSUM and MM methods. Subsequently, the FDD algorithm is compared with an algorithm from a previous study using a Monte Carlo simulation. Through a comparative analysis of algorithmic performance, this study demonstrates that the current PF-based FDD algorithm outperforms the algorithm based on other nonlinear filters.
Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
Open AccessArticle
Uniqueness of Iris Pattern Based on the Auto-Regressive Model
by
Natalia A. Schmid, Matthew C. Valenti, Katelyn M. Hampel, Jinyu Zuo, Priyanka Das, Stephanie Schuckers and Joseph Skufca
Sensors 2024, 24(9), 2797; https://doi.org/10.3390/s24092797 (registering DOI) - 27 Apr 2024
Abstract
In this paper, we evaluate the uniqueness of a hypothetical iris recognition system that relies upon a nonlinear mapping of iris data into a space of Gaussian codewords with independent components. Given the new data representation, we develop and apply a sphere packing
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In this paper, we evaluate the uniqueness of a hypothetical iris recognition system that relies upon a nonlinear mapping of iris data into a space of Gaussian codewords with independent components. Given the new data representation, we develop and apply a sphere packing bound for Gaussian codewords and a bound similar to Daugman’s to characterize the maximum iris population as a function of the relative entropy between Gaussian codewords of distinct iris classes. As a potential theoretical approach leading toward the realization of the hypothetical mapping, we work with the auto-regressive model fitted into iris data, after some data manipulation and preprocessing. The distance between a pair of codewords is measured in terms of the relative entropy (log-likelihood ratio statistic is an alternative) between distributions of codewords, which is also interpreted as a measure of iris quality. The new approach to iris uniqueness is illustrated using two toy examples involving two small datasets of iris images. For both datasets, the maximum sustainable population is presented as a function of image quality expressed in terms of relative entropy. Although the auto-regressive model may not be the best model for iris data, it lays the theoretical framework for the development of a high-performance iris recognition system utilizing a nonlinear mapping from the space of iris data to the space of Gaussian codewords with independent components.
Full article
(This article belongs to the Section Biosensors)
Open AccessArticle
An Audio-Based SLAM for Indoor Environments: A Robotic Mixed Reality Presentation
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Elfituri S. F. Lahemer and Ahmad Rad
Sensors 2024, 24(9), 2796; https://doi.org/10.3390/s24092796 (registering DOI) - 27 Apr 2024
Abstract
In this paper, we present a novel approach referred to as the audio-based virtual landmark-based HoloSLAM. This innovative method leverages a single sound source and microphone arrays to estimate the voice-printed speaker’s direction. The system allows an autonomous robot equipped with a single
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In this paper, we present a novel approach referred to as the audio-based virtual landmark-based HoloSLAM. This innovative method leverages a single sound source and microphone arrays to estimate the voice-printed speaker’s direction. The system allows an autonomous robot equipped with a single microphone array to navigate within indoor environments, interact with specific sound sources, and simultaneously determine its own location while mapping the environment. The proposed method does not require multiple audio sources in the environment nor sensor fusion to extract pertinent information and make accurate sound source estimations. Furthermore, the approach incorporates Robotic Mixed Reality using Microsoft HoloLens to superimpose landmarks, effectively mitigating the audio landmark-related issues of conventional audio-based landmark SLAM, particularly in situations where audio landmarks cannot be discerned, are limited in number, or are completely missing. The paper also evaluates an active speaker detection method, demonstrating its ability to achieve high accuracy in scenarios where audio data are the sole input. Real-time experiments validate the effectiveness of this method, emphasizing its precision and comprehensive mapping capabilities. The results of these experiments showcase the accuracy and efficiency of the proposed system, surpassing the constraints associated with traditional audio-based SLAM techniques, ultimately leading to a more detailed and precise mapping of the robot’s surroundings.
Full article
(This article belongs to the Section Navigation and Positioning)
Open AccessArticle
Real-Time Multi-Person Video Synthesis with Controllable Prior-Guided Matting
by
Aoran Chen, Hai Huang, Yueyan Zhu and Junsheng Xue
Sensors 2024, 24(9), 2795; https://doi.org/10.3390/s24092795 (registering DOI) - 27 Apr 2024
Abstract
In order to enhance the matting performance in multi-person dynamic scenarios, we introduce a robust, real-time, high-resolution, and controllable human video matting method that achieves state of the art on all metrics. Unlike most existing methods that perform video matting frame by frame
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In order to enhance the matting performance in multi-person dynamic scenarios, we introduce a robust, real-time, high-resolution, and controllable human video matting method that achieves state of the art on all metrics. Unlike most existing methods that perform video matting frame by frame as independent images, we design a unified architecture using a controllable generation model to solve the problem of the lack of overall semantic information in multi-person video. Our method, called ControlMatting, uses an independent recurrent architecture to exploit temporal information in videos and achieves significant improvements in temporal coherence and detailed matting quality. ControlMatting adopts a mixed training strategy comprised of matting and a semantic segmentation dataset, which effectively improves the semantic understanding ability of the model. Furthermore, we propose a novel deep learning-based image filter algorithm that enforces our detailed augmentation ability on both matting and segmentation objectives. Our experiments have proved that prior information about the human body from the image itself can effectively combat the defect masking problem caused by complex dynamic scenarios with multiple people.
Full article
(This article belongs to the Special Issue Computer Vision and Virtual Reality: Technologies and Applications)
Open AccessArticle
Bridging Requirements, Planning, and Evaluation: A Review of Social Robot Navigation
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Jarosław Karwowski, Wojciech Szynkiewicz and Ewa Niewiadomska-Szynkiewicz
Sensors 2024, 24(9), 2794; https://doi.org/10.3390/s24092794 (registering DOI) - 27 Apr 2024
Abstract
Navigation lies at the core of social robotics, enabling robots to navigate and interact seamlessly in human environments. The primary focus of human-aware robot navigation is minimizing discomfort among surrounding humans. Our review explores user studies, examining factors that cause human discomfort, to
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Navigation lies at the core of social robotics, enabling robots to navigate and interact seamlessly in human environments. The primary focus of human-aware robot navigation is minimizing discomfort among surrounding humans. Our review explores user studies, examining factors that cause human discomfort, to perform the grounding of social robot navigation requirements and to form a taxonomy of elementary necessities that should be implemented by comprehensive algorithms. This survey also discusses human-aware navigation from an algorithmic perspective, reviewing the perception and motion planning methods integral to social navigation. Additionally, the review investigates different types of studies and tools facilitating the evaluation of social robot navigation approaches, namely datasets, simulators, and benchmarks. Our survey also identifies the main challenges of human-aware navigation, highlighting the essential future work perspectives. This work stands out from other review papers, as it not only investigates the variety of methods for implementing human awareness in robot control systems but also classifies the approaches according to the grounded requirements regarded in their objectives.
Full article
(This article belongs to the Collection Advances in Human-Robot Interaction: Sensing, Cognition and Control)
Open AccessReview
Enhancing Healthcare through Sensor-Enabled Digital Twins in Smart Environments: A Comprehensive Analysis
by
Sasan Adibi, Abbas Rajabifard, Davood Shojaei and Nilmini Wickramasinghe
Sensors 2024, 24(9), 2793; https://doi.org/10.3390/s24092793 (registering DOI) - 27 Apr 2024
Abstract
This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and
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This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML, and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being.
Full article
(This article belongs to the Special Issue Sensor-Enabled Digital Twins for Healthcare Applications: Unlocking Their Potential)
Open AccessArticle
Multibody Model with Foot-Deformation Approach for Estimating Ground Reaction Forces and Moments and Joint Torques during Level Walking through Optical Motion Capture without Optimization Techniques
by
Naoto Haraguchi and Kazunori Hase
Sensors 2024, 24(9), 2792; https://doi.org/10.3390/s24092792 (registering DOI) - 27 Apr 2024
Abstract
The biomechanical-model-based approach with a contact model offers advantages in estimating ground reaction forces (GRFs) and ground reaction moments (GRMs), as it does not rely on the need for training data and gait assumptions. However, this approach faces the challenge of long computational
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The biomechanical-model-based approach with a contact model offers advantages in estimating ground reaction forces (GRFs) and ground reaction moments (GRMs), as it does not rely on the need for training data and gait assumptions. However, this approach faces the challenge of long computational times due to the inclusion of optimization processes. To address this challenge, the present study developed a new optical motion capture (OMC)-based method to estimate GRFs, GRMs, and joint torques without prolonged computational times. The proposed approach performs the estimation process by distributing external forces, as determined by a multibody model, between the left and right feet based on foot deformations, thereby predicting the GRFs and GRMs without relying on optimization techniques. In this study, prediction accuracies during level walking were confirmed by comparing a general analysis using a force plate with the estimation results. The comparison revealed excellent or strong correlations between the prediction and the measurements for all GRFs, GRMs, and lower-limb-joint torques. The proposed method, which provides practical estimation with low computational cost, facilitates efficient biomechanical analysis and rapid feedback of analysis results, contributing to its increased applicability in clinical settings.
Full article
(This article belongs to the Special Issue Applications and Development of Intelligent Sensors for Sports, Health, and Medicine)
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Open AccessArticle
A Real-Time License Plate Detection and Recognition Model in Unconstrained Scenarios
by
Lingbing Tao, Shunhe Hong, Yongxing Lin, Yanbing Chen, Pingan He and Zhixin Tie
Sensors 2024, 24(9), 2791; https://doi.org/10.3390/s24092791 (registering DOI) - 27 Apr 2024
Abstract
Accurate and fast recognition of vehicle license plates from natural scene images is a crucial and challenging task. Existing methods can recognize license plates in simple scenarios, but their performance degrades significantly in complex environments. A novel license plate detection and recognition model
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Accurate and fast recognition of vehicle license plates from natural scene images is a crucial and challenging task. Existing methods can recognize license plates in simple scenarios, but their performance degrades significantly in complex environments. A novel license plate detection and recognition model YOLOv5-PDLPR is proposed, which employs YOLOv5 target detection algorithm in the license plate detection part and uses the PDLPR algorithm proposed in this paper in the license plate recognition part. The PDLPR algorithm is mainly designed as follows: (1) A Multi-Head Attention mechanism is used to accurately recognize individual characters. (2) A global feature extractor network is designed to improve the completeness of the network for feature extraction. (3) The latest parallel decoder architecture is adopted to improve the inference efficiency. The experimental results show that the proposed algorithm has better accuracy and speed than the comparison algorithms, can achieve real-time recognition, and has high efficiency and robustness in complex scenes.
Full article
(This article belongs to the Section Sensing and Imaging)
Open AccessArticle
Cognitive Control Architecture for the Practical Realization of UAV Collision Avoidance
by
Qirui Zhang, Ruixuan Wei and Songlin Huang
Sensors 2024, 24(9), 2790; https://doi.org/10.3390/s24092790 (registering DOI) - 27 Apr 2024
Abstract
A highly intelligent system often draws lessons from the unique abilities of humans. Current humanlike models, however, mainly focus on biological behavior, and the brain functions of humans are often overlooked. By drawing inspiration from brain science, this article shows how aspects of
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A highly intelligent system often draws lessons from the unique abilities of humans. Current humanlike models, however, mainly focus on biological behavior, and the brain functions of humans are often overlooked. By drawing inspiration from brain science, this article shows how aspects of brain processing such as sensing, preprocessing, cognition, obstacle learning, behavior, strategy learning, pre-action, and action can be melded together in a coherent manner with cognitive control architecture. This work is based on the notion that the anti-collision response is activated in sequence, which starts from obstacle sensing to action . In the process of collision avoidance, cognition and learning modules continuously control the UAV’s repertoire. Furthermore, simulated and experimental results show that the proposed architecture is effective and feasible.
Full article
(This article belongs to the Topic Target Tracking, Guidance, and Navigation for Autonomous Systems)
Open AccessArticle
Experimental Evaluation of an SDR-Based UAV Localization System
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Cristian Codău, Rareș-Călin Buta, Andra Păstrăv, Paul Dolea, Tudor Palade and Emanuel Puschita
Sensors 2024, 24(9), 2789; https://doi.org/10.3390/s24092789 (registering DOI) - 27 Apr 2024
Abstract
UAV communications have seen a rapid rise in the last few years. The drone class of UAV has particularly become more widespread around the world, and illicit behavior using drones has become a problem. Therefore, localization, tracking, and even taking control of drones
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UAV communications have seen a rapid rise in the last few years. The drone class of UAV has particularly become more widespread around the world, and illicit behavior using drones has become a problem. Therefore, localization, tracking, and even taking control of drones have also gained interest. Knowing the frequency of a target signal, its position can be determined (as the angle of arrival with respect to a fixed receiver point) using radio frequency-based localization techniques. One such technique is represented by the subspace-based algorithms that offer highly accurate results. This paper presents the implementation of the MUSIC algorithm on an SDR-based system using a uniform circular antenna array and its experimental evaluation in relevant outdoor environments for drone localization. The results show the capability of the system to indicate the AoA of the target signal. The results are compared with the actual direction computed from the log files of the drone application and validated with a professional direction-finding solution (i.e., Narda SignalShark equipped with the automatic direction-finding antenna).
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(This article belongs to the Section Communications)
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Open AccessArticle
Improved Finite Element Model Updating of a Highway Viaduct Using Acceleration and Strain Data
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Doron Hekič, Diogo Ribeiro, Andrej Anžlin, Aleš Žnidarič and Peter Češarek
Sensors 2024, 24(9), 2788; https://doi.org/10.3390/s24092788 (registering DOI) - 27 Apr 2024
Abstract
Most finite element model updating (FEMU) studies on bridges are acceleration-based due to their lower cost and ease of use compared to strain- or displacement-based methods, which entail costly experiments and traffic disruptions. This leads to a scarcity of comprehensive studies incorporating strain
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Most finite element model updating (FEMU) studies on bridges are acceleration-based due to their lower cost and ease of use compared to strain- or displacement-based methods, which entail costly experiments and traffic disruptions. This leads to a scarcity of comprehensive studies incorporating strain measurements. This study employed the strain- and acceleration-based FEMU analyses performed on a more than 50-year-old multi-span concrete highway viaduct. Mid-span strains under heavy vehicles were considered for the strain-based FEMU, and frequencies and mode shapes for the acceleration-based FEMU. The analyses were performed separately for up to three variables, representing Young’s modulus adjustment factors for different groups of structural elements. FEMU studies considered residual minimisation and the error-domain model falsification (EDMF) methodology. The residual minimisation utilised four different single-objective optimisations focusing on strains, frequencies, and mode shapes. Strain- and frequency-based FEMU analyses resulted in an approximately 20% increase in the overall superstructure’s design stiffness. This study shows the benefits of the intuitive EDMF over residual minimisation for FEMU, where information gained from the strain data, in addition to the acceleration data, manifests more sensible updated variables. EDMF finally resulted in a 25–50% overestimated design stiffness of internal main girders.
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(This article belongs to the Special Issue Structural Health Monitoring: Advanced Sensing, Diagnostics and Prognostics)
Open AccessArticle
Facile Synthesis of Fe-Doped, Algae Residue-Derived Carbon Aerogels for Electrochemical Dopamine Biosensors
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Hao Wu, Qin Wen, Xin Luan, Weiwei Yang, Lei Guo and Gang Wei
Sensors 2024, 24(9), 2787; https://doi.org/10.3390/s24092787 (registering DOI) - 27 Apr 2024
Abstract
An abnormal level of dopamine (DA), a kind of neurotransmitter, correlates with a series of diseases, including Parkinson’s disease, Willis-Ekbom disease, attention deficit hyperactivity disorder, and schizophrenia. Hence, it is imperative to achieve a precise, rapid detection method in clinical medicine. In this
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An abnormal level of dopamine (DA), a kind of neurotransmitter, correlates with a series of diseases, including Parkinson’s disease, Willis-Ekbom disease, attention deficit hyperactivity disorder, and schizophrenia. Hence, it is imperative to achieve a precise, rapid detection method in clinical medicine. In this study, we synthesized nanocomposite carbon aerogels (CAs) doped with iron and iron carbide, based on algae residue-derived biomass materials, using Fe(NO3)3 as the iron source. The modified glassy carbon electrode (GCE) for DA detection, denoted as CAs-Fe/GCE, was prepared through surface modification with this composite material. X-ray photoelectron spectroscopy and X-ray diffraction characterization confirmed the successful doping of iron into the as-prepared CAs. Additionally, the electrochemical behavior of DA on the modified electrode surface was investigated and the results demonstrate that the addition of the CAs-Fe promoted the electron transfer rate, thereby enhancing their sensing performance. The fabricated electrochemical DA biosensor exhibits an accurate detection of DA in the concentration within the range of 0.01~200 µM, with a detection limit of 0.0033 µM. Furthermore, the proposed biosensor is validated in real samples, showing its high applicability for the detection of DA in beverages.
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(This article belongs to the Special Issue Recent Advances in Nanomaterial-Based Electrochemical Sensors)
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