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Mixed Micro/Macro Cache for Device-to-Device Caching Systems in Multi-Operator Environments
Assessment of PM2.5 Concentration at University Transit Bus Stops Using Low-Cost Aerosol Monitors by Student Commuters
Remotely Sensed Maize Yield Estimation Model Dependency Analysis
Daily Living Activity Recognition with Frequency-Shift WiFi Backscatter Tags

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), International Society for the Measurement of Physical Behaviour (ISMPB) and Chinese Society of Micro-Nano Technology (CSMNT) 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 (Chemistry, Analytical) / CiteScore - Q1 (Instrumentation)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.8 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the first half of 2024).
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, JCP and Targets.

Impact Factor: 3.4 (2023); 5-Year Impact Factor: 3.7 (2023)

Imprint Information Journal Flyer Open Access ISSN: 1424-8220

Latest Articles

20 pages, 1670 KiB

Open AccessArticle

An Approach of Query Audience’s Attention in Virtual Speech

by Hongbo Kang, Rui Yang, Ruoyang Song, Chunjie Yang and Wenqing Wang

Sensors 2024, 24(16), 5363; https://doi.org/10.3390/s24165363 (registering DOI) - 20 Aug 2024

Virtual speeches are a very popular way for remote multi-user communication, but it has the disadvantage of the lack of eye contact. This paper proposes the evaluation of an online audience attention based on gaze tracking. Our research only uses webcams to capture [...] Read more.

Virtual speeches are a very popular way for remote multi-user communication, but it has the disadvantage of the lack of eye contact. This paper proposes the evaluation of an online audience attention based on gaze tracking. Our research only uses webcams to capture the audience’s head posture, gaze time, and other features, providing a low-cost method for attention monitoring with reference values across multiple domains. Meantime, we also propose a set of indexes which can be used to evaluate the audience’s degree of attention, making up for the fact that the speaker cannot gauge the audience’s concentration through eye contact during online speeches. We selected 96 students for a 20 min group simulation session and used Spearman’s correlation coefficient to analyze the correlation between our evaluation indicators and concentration. The result showed that each evaluation index has a significant correlation with the degree of attention (p = 0.01), and all the students in the focused group met the thresholds set by each of our evaluation indicators, while the students in the non-focused group failed to reach the standard. During the simulation, eye movement data and EEG signals were measured synchronously for the second group of students. The EEG results of the students were consistent with the systematic evaluation. The performance of the measured EEG signals confirmed the accuracy of the systematic evaluation. Full article

(This article belongs to the Section Biomedical Sensors)

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24 pages, 917 KiB

Open AccessArticle

A Novel Two-Step Channel Estimation Method for RIS-Assisted mmWave Systems

by Jiarun Yu

Sensors 2024, 24(16), 5362; https://doi.org/10.3390/s24165362 (registering DOI) - 19 Aug 2024

In this work, we resolve the cascaded channel estimation problem and the reflected channel estimation problem for the reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) systems. The novel two-step method contains modified multiple population genetic algorithm (MMPGA), least squares (LS), residual network (ResNet), and [...] Read more.

In this work, we resolve the cascaded channel estimation problem and the reflected channel estimation problem for the reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) systems. The novel two-step method contains modified multiple population genetic algorithm (MMPGA), least squares (LS), residual network (ResNet), and multi-task regression model. In the first step, the proposed MMPGA-LS optimizes the crossover strategy and mutation strategy. Besides, the ResNet achieves cascaded channel estimation by learning the relationship between the cascaded channel obtained by the MMPGA-LS and the channel of the user (UE)-RIS-base station (BS). Then, the proposed multi-task-ResNet (MTRnet) is introduced for the reflected channel estimation. Relying on the output of ResNet, the MTRnet with multiple output layers estimates the coefficients of reflected channels and reconstructs the channel of UE-RIS and RIS-BS. Remarkably, the proposed MTRnet is capable of using a lower optimization model to estimate multiple reflected channels compared with the classical neural network with the single output layer. A series of experimental results validate the superiority of the proposed method in terms of a lower norm mean square error (NMSE). Besides, the proposed method also obtains a low NMSE in the RIS with the formulation of the uniform planar array. Full article

(This article belongs to the Topic Advanced Array Signal Processing for B5G/6G: Models, Algorithms, and Applications)

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21 pages, 1699 KiB

Open AccessArticle

Investigating the Impact of Lunar Rover Structure and Lunar Surface Characteristics on Antenna Performance

by Rida Gadhafi, Elham Serria, Sara AlMaeeni, Husameldin Mukhtar, Raed Abd-Alhameed and Wathiq Mansoor

Sensors 2024, 24(16), 5361; https://doi.org/10.3390/s24165361 (registering DOI) - 19 Aug 2024

This article explores the influence of lunar regolith and rover structure, such as mast design and material composition, on antenna parameters. It focuses on the distinctive difficulties of communication in the lunar environment, which need specialized antenna solutions. This study specifically examines the [...] Read more.

This article explores the influence of lunar regolith and rover structure, such as mast design and material composition, on antenna parameters. It focuses on the distinctive difficulties of communication in the lunar environment, which need specialized antenna solutions. This study specifically examines the performance of antennas on the lunar Rashid rover within the Atlas crater, a landing site on the moon, considering two antenna types: a sleeve dipole antenna and an all-metal patch antenna. Thermal analyses reveal temperatures in the Atlas crater can exceed 80°C during lunar mid-day. The findings highlight the effect of different materials used as thermal coatings for Rashid rover antennas, as well as the influence of rover materials on antenna performance. Furthermore, this study extends to analyze the conductivity and depth of lunar regolith within the Atlas crater. Given the critical role of antennas in wireless communication, understanding how lunar regolith properties affect antenna performance is essential. This research contributes to the creation of a strong communication system for the Rashid rover and future lunar missions by considering the features of the lunar regolith in addition to the rover’s size and material attributes. Full article

(This article belongs to the Section Communications)

16 pages, 10669 KiB

Open AccessArticle

Hybridization of Acoustic and Visual Features of Polish Sibilants Produced by Children for Computer Speech Diagnosis

by Agata Sage, Zuzanna Miodońska, Michał Kręcichwost and Paweł Badura

Sensors 2024, 24(16), 5360; https://doi.org/10.3390/s24165360 (registering DOI) - 19 Aug 2024

Speech disorders are significant barriers to the balanced development of a child. Many children in Poland are affected by lisps (sigmatism)—the incorrect articulation of sibilants. Since speech therapy diagnostics is complex and multifaceted, developing computer-assisted methods is crucial. This paper presents the results [...] Read more.

Speech disorders are significant barriers to the balanced development of a child. Many children in Poland are affected by lisps (sigmatism)—the incorrect articulation of sibilants. Since speech therapy diagnostics is complex and multifaceted, developing computer-assisted methods is crucial. This paper presents the results of assessing the usefulness of hybrid feature vectors extracted based on multimodal (video and audio) data for the place of articulation assessment in sibilants /s/ and /ʂ/. We used acoustic features and, new in this field, visual parameters describing selected articulators’ texture and shape. Analysis using statistical tests indicated the differences between various sibilant realizations in the context of the articulation pattern assessment using hybrid feature vectors. In sound /s/, 35 variables differentiated dental and interdental pronunciation, and 24 were visual (textural and shape). For sibilant /ʂ/, we found 49 statistically significant variables whose distributions differed between speaker groups (alveolar, dental, and postalveolar articulation), and the dominant feature type was noise-band acoustic. Our study suggests hybridizing the acoustic description with video processing provides richer diagnostic information. Full article

(This article belongs to the Section Sensing and Imaging)

16 pages, 1158 KiB

Open AccessArticle

Accelerated Fatigue Test for Electric Vehicle Reducer Based on the SVR–FDS Method

by Yudong Wu, Zhanhao Cui, Wang Yan, Haibo Huang and Weiping Ding

Sensors 2024, 24(16), 5359; https://doi.org/10.3390/s24165359 (registering DOI) - 19 Aug 2024

The reducer serves as a pivotal component within the power transmission system of electric vehicles. On one hand, it bears the torque load within the power transmission system. On the other hand, it also endures the vibration load transmitted from other vehicle components. [...] Read more.

The reducer serves as a pivotal component within the power transmission system of electric vehicles. On one hand, it bears the torque load within the power transmission system. On the other hand, it also endures the vibration load transmitted from other vehicle components. Over extended periods, these dynamic loads can cause fatigue damage to the reducer. Therefore, the reliability and durability of the reducer during use are very important for electric vehicles. In order to save time and economic costs, the durability of the reducer is often evaluated through accelerated fatigue testing. However, traditional approaches to accelerated fatigue tests typically only consider the time-domain characteristics of the load, which limits precision and reliability. In this study, an accelerated fatigue test method for electric vehicle reducers based on the SVR–FDS method is proposed to enhance the testing process and ensure the reliability of the results. By utilizing the support vector regression (SVR) model in conjunction with the fatigue damage spectrum (FDS) approach, this method offers a more accurate and efficient way to evaluate the durability of reducers. It has been proved that this method significantly reduces the testing period while maintaining the necessary level of test reliability. The accelerated fatigue test based on the SVR–FDS method represents a valuable approach for assessing the durability of electric vehicle reducers and offering insights into their long-term performance. Full article

(This article belongs to the Special Issue Fault Diagnosis and Prognosis in Rotating Machines)

12 pages, 7350 KiB

Open AccessArticle

User-Centered Evaluation of the Wearable Walker Lower Limb Exoskeleton; Preliminary Assessment Based on the Experience Protocol

by Cristian Camardella, Vittorio Lippi, Francesco Porcini, Giulia Bassani, Lucia Lencioni, Christoph Mauer, Christian Haverkamp, Carlo Alberto Avizzano, Antonio Frisoli and Alessandro Filippeschi

Sensors 2024, 24(16), 5358; https://doi.org/10.3390/s24165358 - 19 Aug 2024

Using lower limb exoskeletons provides potential advantages in terms of productivity and safety associated with reduced stress. However, complex issues in human–robot interactions are still open, such as the physiological effects of exoskeletons and the impact on the user’s subjective experience. In this [...] Read more.

Using lower limb exoskeletons provides potential advantages in terms of productivity and safety associated with reduced stress. However, complex issues in human–robot interactions are still open, such as the physiological effects of exoskeletons and the impact on the user’s subjective experience. In this work, an innovative exoskeleton, the Wearable Walker, is assessed using the EXPERIENCE benchmarking protocol from the EUROBENCH project. The Wearable Walker is a lower-limb exoskeleton that enhances human abilities, such as carrying loads. The device uses a unique control approach called Blend Control that provides smooth assistance torques. It operates two models simultaneously, one in the case in which the left foot is grounded and another for the grounded right foot. These models generate assistive torques combined to provide continuous and smooth overall assistance, preventing any abrupt changes in torque due to model switching. The EXPERIENCE protocol consists of walking on flat ground while gathering physiological signals, such as heart rate, its variability, respiration rate, and galvanic skin response, and completing a questionnaire. The test was performed with five healthy subjects. The scope of the present study is twofold: to evaluate the specific exoskeleton and its current control system to gain insight into possible improvements and to present a case study for a formal and replicable benchmarking of wearable robots. Full article

(This article belongs to the Collection Sensors for Gait, Human Movement Analysis, and Health Monitoring)

18 pages, 9349 KiB

Open AccessArticle

Object Detection and Information Perception by Fusing YOLO-SCG and Point Cloud Clustering

by Chunyang Liu, Zhixin Zhao, Yifei Zhou, Lin Ma, Xin Sui, Yan Huang, Xiaokang Yang and Xiqiang Ma

Sensors 2024, 24(16), 5357; https://doi.org/10.3390/s24165357 - 19 Aug 2024

Robots need to sense information about the external environment before moving, which helps them to recognize and understand their surroundings so that they can plan safe and effective paths and avoid obstacles. Conventional algorithms using a single sensor cannot obtain enough information and [...] Read more.

Robots need to sense information about the external environment before moving, which helps them to recognize and understand their surroundings so that they can plan safe and effective paths and avoid obstacles. Conventional algorithms using a single sensor cannot obtain enough information and lack real-time capabilities. To solve these problems, we propose an information perception algorithm with vision as the core and the fusion of LiDAR. Regarding vision, we propose the YOLO-SCG model, which is able to detect objects faster and more accurately. When processing point clouds, we integrate the detection results of vision for local clustering, improving both the processing speed of the point cloud and the detection effectiveness. Experiments verify that our proposed YOLO-SCG algorithm improves accuracy by 4.06% and detection speed by 7.81% compared to YOLOv9, and our algorithm excels in distinguishing different objects in the clustering of point clouds. Full article

(This article belongs to the Section Radar Sensors)

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22 pages, 5499 KiB

Open AccessReview

A Review on Soft Error Correcting Techniques of Aerospace-Grade Static RAM-Based Field-Programmable Gate Arrays

by Weihang Wang, Xuewu Li, Lei Chen, Huabo Sun and Fan Zhang

Sensors 2024, 24(16), 5356; https://doi.org/10.3390/s24165356 - 19 Aug 2024

Aerospace-grade SRAM-based field-programmable gate arrays (FPGAs) used in space applications are highly susceptible to single event effects, leading to soft errors in FPGAs. Additionally, as FPGAs scale up, the difficulty of correcting soft errors also increases. This paper proposes that performing soft error [...] Read more.

Aerospace-grade SRAM-based field-programmable gate arrays (FPGAs) used in space applications are highly susceptible to single event effects, leading to soft errors in FPGAs. Additionally, as FPGAs scale up, the difficulty of correcting soft errors also increases. This paper proposes that performing soft error sensitivity analysis on FPGAs can help target the more sensitive areas for detection and correction, thereby improving the efficiency of soft error repair. Firstly, in accordance with the dual-layer architecture of SRAM-based FPGAs, methods for the soft error sensitivity analysis of FPGA application layer resources and configuration bitstreams are reviewed. Subsequently, based on the analysis results, it also covers corresponding application layer memory scrubbing and configuration scrubbing techniques. A prospective look at emerging soft error mitigation technologies is discussed at the end of this review, supporting the development of highly reliable aerospace-grade SRAM-based FPGAs. Full article

(This article belongs to the Section Physical Sensors)

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19 pages, 2956 KiB

Open AccessArticle

Eddy Current Sensor Probe Design for Subsurface Defect Detection in Additive Manufacturing

by Heba E. Farag, Mir Behrad Khamesee and Ehsan Toyserkani

Sensors 2024, 24(16), 5355; https://doi.org/10.3390/s24165355 - 19 Aug 2024

Pore and crack formation in parts produced by additive manufacturing (AM) processes, such as laser powder bed fusion, is one of the issues associated with AM technology. Surface and subsurface cracks and pores are induced during the printing process, undermining the printed part [...] Read more.

Pore and crack formation in parts produced by additive manufacturing (AM) processes, such as laser powder bed fusion, is one of the issues associated with AM technology. Surface and subsurface cracks and pores are induced during the printing process, undermining the printed part durability. In-situ detection of defects will enable the real-time or intermittent control of the process, resulting in higher product quality. In this paper, a new eddy current-based probe design is proposed to detect these defects in parts with various defects that mimic pores and cracks in additively manufactured parts. Electromagnetic finite element analyses were carried out to optimize the probe geometry, followed by fabricating a prototype. Artificial defects were seeded in stainless steel plates to assess the feasibility of detecting various flaws with different widths and lengths. The smallest defect detected had a 0.17 mm radius for blind holes and a 0.43 mm notch with a 5 mm length. All the defects were 0.5 mm from the surface, and the probe was placed on the back surface of the defects. The surface roughness of the tested samples was less than 2 µm. The results show promise for detecting defects, indicating a potential application in AM. Full article

(This article belongs to the Special Issue Editorial Board Members' Collection Series: Electronic Sensors, Devices and Systems)

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31 pages, 13979 KiB

Open AccessReview

Advances in Portable and Wearable Acoustic Sensing Devices for Human Health Monitoring

by Fanhao Kong, Yang Zou, Zhou Li and Yulin Deng

Sensors 2024, 24(16), 5354; https://doi.org/10.3390/s24165354 (registering DOI) - 19 Aug 2024

The practice of auscultation, interpreting body sounds to assess organ health, has greatly benefited from technological advancements in sensing and electronics. The advent of portable and wearable acoustic sensing devices marks a significant milestone in telemedicine, home health, and clinical diagnostics. This review [...] Read more.

The practice of auscultation, interpreting body sounds to assess organ health, has greatly benefited from technological advancements in sensing and electronics. The advent of portable and wearable acoustic sensing devices marks a significant milestone in telemedicine, home health, and clinical diagnostics. This review summarises the contemporary advancements in acoustic sensing devices, categorized based on varied sensing principles, including capacitive, piezoelectric, and triboelectric mechanisms. Some representative acoustic sensing devices are introduced from the perspective of portability and wearability. Additionally, the characteristics of sound signals from different human organs and practical applications of acoustic sensing devices are exemplified. Challenges and prospective trends in portable and wearable acoustic sensors are also discussed, providing insights into future research directions. Full article

(This article belongs to the Special Issue Advanced Acoustic Sensing Technology)

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20 pages, 1026 KiB

Open AccessArticle

Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios

by Shakil Ahmed, Md Akbar Hossain, Peter Han Joo Chong and Sayan Kumar Ray

Sensors 2024, 24(16), 5353; https://doi.org/10.3390/s24165353 - 19 Aug 2024

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce [...] Read more.

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node’s energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node’s residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10–20% compared to the benchmark algorithms. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities and Urban Planning)

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21 pages, 12734 KiB

Open AccessArticle

Improved Unsupervised Stitching Algorithm for Multiple Environments SuperUDIS

by Haoze Wu, Chun Bao, Qun Hao, Jie Cao and Li Zhang

Sensors 2024, 24(16), 5352; https://doi.org/10.3390/s24165352 - 19 Aug 2024

Large field-of-view images are increasingly used in various environments today, and image stitching technology can make up for the limited field of view caused by hardware design. However, previous methods are constrained in various environments. In this paper, we propose a method that [...] Read more.

Large field-of-view images are increasingly used in various environments today, and image stitching technology can make up for the limited field of view caused by hardware design. However, previous methods are constrained in various environments. In this paper, we propose a method that combines the powerful feature extraction capabilities of the Superpoint algorithm and the exact feature matching capabilities of the Lightglue algorithm with the image fusion algorithm of Unsupervised Deep Image Stitching (UDIS). Our proposed method effectively improves the situation where the linear structure is distorted and the resolution is low in the stitching results of the UDIS algorithm. On this basis, we make up for the shortcomings of the UDIS fusion algorithm. For stitching fractures of UDIS in some complex situations, we optimize the loss function of UDIS. We use a second-order differential Laplacian operator to replace the difference in the horizontal and vertical directions to emphasize the continuity of the structural edges during training. Combined with the above improvements, the Super Unsupervised Deep Image Stitching (SuperUDIS) algorithm is finally formed. SuperUDIS has better performance in both qualitative and quantitative evaluations compared to the UDIS algorithm, with the PSNR index increasing by 0.5 on average and the SSIM index increasing by 0.02 on average. Moreover, the proposed method is more robust in complex environments with large color differences or multi-linear structures. Full article

(This article belongs to the Collection Advances in Deep-Learning-Based Sensing, Imaging, and Video Processing)

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15 pages, 15745 KiB

Open AccessArticle

Bengali-Sign: A Machine Learning-Based Bengali Sign Language Interpretation for Deaf and Non-Verbal People

by Md. Johir Raihan, Mainul Islam Labib, Abdullah Al Jaid Jim, Jun Jiat Tiang, Uzzal Biswas and Abdullah-Al Nahid

Sensors 2024, 24(16), 5351; https://doi.org/10.3390/s24165351 - 19 Aug 2024

Sign language is undoubtedly a common way of communication among deaf and non-verbal people. But it is not common among hearing people to use sign language to express feelings or share information in everyday life. Therefore, a significant communication gap exists between deaf [...] Read more.

Sign language is undoubtedly a common way of communication among deaf and non-verbal people. But it is not common among hearing people to use sign language to express feelings or share information in everyday life. Therefore, a significant communication gap exists between deaf and hearing individuals, despite both groups experiencing similar emotions and sentiments. In this paper, we developed a convolutional neural network–squeeze excitation network to predict the sign language signs and developed a smartphone application to provide access to the ML model to use it. The SE block provides attention to the channel of the image, thus improving the performance of the model. On the other hand, the smartphone application brings the ML model close to people so that everyone can benefit from it. In addition, we used the Shapley additive explanation to interpret the black box nature of the ML model and understand the models working from within. Using our ML model, we achieved an accuracy of 99.86% on the KU-BdSL dataset. The SHAP analysis shows that the model primarily relies on hand-related visual cues to predict sign language signs, aligning with human communication patterns. Full article

(This article belongs to the Section Intelligent Sensors)

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19 pages, 7808 KiB

Open AccessArticle

ANN-Based Bridge Support Fixity Quantification Using Thermal Response Data from Real-Time Wireless Sensing

by Prakash Bhandari, Shinae Jang, Ramesh B. Malla and Song Han

Sensors 2024, 24(16), 5350; https://doi.org/10.3390/s24165350 - 19 Aug 2024

Bridges are critical infrastructures that support our economic activities and daily lives. Aging bridges have been a major issue for decades, prompting researchers to improve resilience and performance through structural health monitoring. While most research focuses on superstructure damage, the majority of bridge [...] Read more.

Bridges are critical infrastructures that support our economic activities and daily lives. Aging bridges have been a major issue for decades, prompting researchers to improve resilience and performance through structural health monitoring. While most research focuses on superstructure damage, the majority of bridge failures are associated with support or joint damages, indicating the importance of bridge support. Indeed, bridge support affects the performance of both the substructure and superstructure by maintaining the load path and allowing certain movements to mitigate thermal and other stresses. The support deterioration leads to a change in fixity in the superstructure, compromising the bridge’s integrity and safety. Hence, a reliable method to determine support fixity level is essential to detecting bearing health and enhancing the accuracy of the bridge health monitoring system. However, such research is lacking because of its complexity. In this study, we developed a support fixity quantification method based on thermal responses using an Artificial Neural Network (ANN) model. A finite element (FE) model of a representative highway bridge is used to derive thermal displacement data under different bearing stiffnesses, superstructure damage, and thermal loading. The thermal displacement behavior of the bridge under different support fixity conditions is presented, and the model is trained on the simulated response. The performance of the developed FE model and ANN was validated with field monitoring data collected from two in-service bridges in Connecticut using a real-time Wireless Sensor Network (WSN). Finally, the support stiffnesses of both bridges were predicted using the ANN model for validation. Full article

(This article belongs to the Special Issue Sensing Technologies and Deep Learning Methods for Structural Health Monitoring Systems)

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13 pages, 4585 KiB

Open AccessArticle

Low-Cost and Paper-Based Micro-Electromechanical Systems Sensor for the Vibration Monitoring of Shield Cutters

by Yazhou Zhang, Xinggang Li, Jiangfan Fu, Linpeng Liu, Changchao Zhang and Ji’an Duan

Sensors 2024, 24(16), 5349; https://doi.org/10.3390/s24165349 - 19 Aug 2024

Vibration sensors are widely used in many fields like industry, agriculture, military, medicine, environment, etc. However, due to the speedy upgrading, most sensors composed of rigid or even toxic materials cause pollution to the environment and give rise to an increased amount of [...] Read more.

Vibration sensors are widely used in many fields like industry, agriculture, military, medicine, environment, etc. However, due to the speedy upgrading, most sensors composed of rigid or even toxic materials cause pollution to the environment and give rise to an increased amount of electronic waste. To meet the requirement of green electronics, biodegradable materials are advocated to be used to develop vibration sensors. Herein, a vibration sensor is reported based on a strategy of pencil-drawing graphite on paper. Specifically, a repeated pencil-drawing process is carried out on paper with a zigzag-shaped framework and parallel microgrooves, to form a graphite coating, thus serving as a functional conductive layer for electromechanical signal conversion. To enhance the sensor’s sensitivity to vibration, a mass is loaded in the center of the paper, so that higher oscillation amplitude could happen under vibrational excitation. In so doing, the paper-based sensor can respond to vibrations with a wide frequency range from 5 Hz to 1 kHz, and vibrations with a maximum acceleration of 10 g. The results demonstrate that the sensor can not only be utilized for monitoring vibrations generated by the knuckle-knocking of plastic plates or objects falling down but also can be used to detect vibration in areas such as the shield cut head to assess the working conditions of machinery. The paper-based MEMS vibration sensor exhibits merits like easy fabrication, low cost, and being environmentally friendly, which indicates its great application potential in vibration monitoring fields. Full article

(This article belongs to the Special Issue Advanced Laser Processing Technology and Its Functional Devices)

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15 pages, 20642 KiB

Open AccessArticle

Deep Edge-Based Fault Detection for Solar Panels

by Haoyu Ling, Manlu Liu and Yi Fang

Sensors 2024, 24(16), 5348; https://doi.org/10.3390/s24165348 - 19 Aug 2024

Solar panels may suffer from faults, which could yield high temperature and significantly degrade their power generation. To detect faults of solar panels in large photovoltaic plants, drones with infrared cameras have been implemented. Drones may capture a huge number of infrared images. [...] Read more.

Solar panels may suffer from faults, which could yield high temperature and significantly degrade their power generation. To detect faults of solar panels in large photovoltaic plants, drones with infrared cameras have been implemented. Drones may capture a huge number of infrared images. It is not realistic to manually analyze such a huge number of infrared images. To solve this problem, we develop a Deep Edge-Based Fault Detection (DEBFD) method, which applies convolutional neural networks (CNNs) for edge detection and object detection according to the captured infrared images. Particularly, a machine learning-based contour filter is designed to eliminate incorrect background contours. Then faults of solar panels are detected. Based on these fault detection results, solar panels can be classified into two classes, i.e., normal and faulty ones (i.e., macro ones). We collected 2060 images in multiple scenes and achieved a high macro F1 score. Our method achieved a frame rate of 28 fps over infrared images of solar panels on an NVIDIA GeForce RTX 2080 Ti GPU. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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13 pages, 504 KiB

Open AccessArticle

Category-Level Object Pose Estimation with Statistic Attention

by Changhong Jiang, Xiaoqiao Mu, Bingbing Zhang, Chao Liang and Mujun Xie

Sensors 2024, 24(16), 5347; https://doi.org/10.3390/s24165347 - 19 Aug 2024

Six-dimensional object pose estimation is a fundamental problem in the field of computer vision. Recently, category-level object pose estimation methods based on 3D-GC have made significant breakthroughs due to advancements in 3D-GC. However, current methods often fail to capture long-range dependencies, which are [...] Read more.

Six-dimensional object pose estimation is a fundamental problem in the field of computer vision. Recently, category-level object pose estimation methods based on 3D-GC have made significant breakthroughs due to advancements in 3D-GC. However, current methods often fail to capture long-range dependencies, which are crucial for modeling complex and occluded object shapes. Additionally, discerning detailed differences between different objects is essential. Some existing methods utilize self-attention mechanisms or Transformer encoder–decoder structures to address the lack of long-range dependencies, but they only focus on first-order information of features, failing to explore more complex information and neglecting detailed differences between objects. In this paper, we propose SAPENet, which follows the 3D-GC architecture but replaces the 3D-GC in the encoder part with HS-layer to extract features and incorporates statistical attention to compute higher-order statistical information. Additionally, three sub-modules are designed for pose regression, point cloud reconstruction, and bounding box voting. The pose regression module also integrates statistical attention to leverage higher-order statistical information for modeling geometric relationships and aiding regression. Experiments demonstrate that our method achieves outstanding performance, attaining an mAP of 49.5 on the 5°2 cm metric, which is 3.4 higher than the baseline model. Our method achieves state-of-the-art (SOTA) performance on the REAL275 dataset. Full article

(This article belongs to the Section Navigation and Positioning)

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19 pages, 5307 KiB

Open AccessReview

Analyzing Diabetes Detection and Classification: A Bibliometric Review (2000–2023)

by Jannatul Ferdaus, Esmay Azam Rochy, Uzzal Biswas, Jun Jiat Tiang and Abdullah-Al Nahid

Sensors 2024, 24(16), 5346; https://doi.org/10.3390/s24165346 - 19 Aug 2024

Bibliometric analysis is a rigorous method to analyze significant quantities of bibliometric data to assess their impact on a particular field. This study used bibliometric analysis to investigate the academic research on diabetes detection and classification from 2000 to 2023. The PRISMA 2020 [...] Read more.

Bibliometric analysis is a rigorous method to analyze significant quantities of bibliometric data to assess their impact on a particular field. This study used bibliometric analysis to investigate the academic research on diabetes detection and classification from 2000 to 2023. The PRISMA 2020 framework was followed to identify, filter, and select relevant papers. This study used the Web of Science database to determine relevant publications concerning diabetes detection and classification using the keywords “diabetes detection”, “diabetes classification”, and “diabetes detection and classification”. A total of 863 publications were selected for analysis. The research applied two bibliometric techniques: performance analysis and science mapping. Various bibliometric parameters, including publication analysis, trend analysis, citation analysis, and networking analysis, were used to assess the performance of these articles. The analysis findings showed that India, China, and the United States are the top three countries with the highest number of publications and citations on diabetes detection and classification. The most frequently used keywords are machine learning, diabetic retinopathy, and deep learning. Additionally, the study identified “classification”, “diagnosis”, and “validation” as the prevailing topics for diabetes identification. This research contributes valuable insights into the academic landscape of diabetes detection and classification. Full article

(This article belongs to the Section Intelligent Sensors)

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15 pages, 41773 KiB

Open AccessArticle

Long-Range Imaging of Alpha Emitters Using Radioluminescence in Open Environments: Daytime and Night-Time Applications

by Lingteng Kong, Thomas Bligh Scott, John Charles Clifford Day and David Andrew Megson-Smith

Sensors 2024, 24(16), 5345; https://doi.org/10.3390/s24165345 - 18 Aug 2024

Alpha emitters like plutonium pose severe health risks when ingested, damaging DNA and potentially causing cancer. Traditional detection methods require proximity within millimeters of the contamination source, presenting safety risks and operational inefficiencies. Long-range detection through alpha radioluminescence (RL) offers a promising alternative. [...] Read more.

Alpha emitters like plutonium pose severe health risks when ingested, damaging DNA and potentially causing cancer. Traditional detection methods require proximity within millimeters of the contamination source, presenting safety risks and operational inefficiencies. Long-range detection through alpha radioluminescence (RL) offers a promising alternative. However, most of the previous experiments have been carried out under controlled conditions that preclude the overwhelming effect of ambient light. This study demonstrates the successful detection of a 3 MBq alpha emitter in an open environment using a compact alpha camera. This camera incorporates a deep-cooled CCD and a low f-number lens system designed to minimize the blue shift effects of filters. Night-time imaging was achieved with a dual-filter system using a sandwich filter assembly centered at 337 nm and 343 nm for capturing alpha RL and subtracting background light, respectively. At night, the alpha source was detected from 1 m away within one minute, and the lowest detection limit can be calculated as 75 kBq. The system was also evaluated under simulated urban lighting conditions. For daytime imaging, a stack of tilted 276 nm short pass filters minimized sunlight interference, enabling the detection of the alpha source at 70 cm within 10 min under indirect sunlight. This research highlights the viability of long-range optical detection of alpha emitters for environmental monitoring in real-world settings. Full article

(This article belongs to the Special Issue Novel Sensing Technologies for Environmental Monitoring and Detection)

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17 pages, 5247 KiB

Open AccessArticle

Intra-Pulse Modulation Recognition of Radar Signals Based on Efficient Cross-Scale Aware Network

by Jingyue Liang, Zhongtao Luo and Renlong Liao

Sensors 2024, 24(16), 5344; https://doi.org/10.3390/s24165344 - 18 Aug 2024

Radar signal intra-pulse modulation recognition can be addressed with convolutional neural networks (CNNs) and time–frequency images (TFIs). However, current CNNs have high computational complexity and do not perform well in low-signal-to-noise ratio (SNR) scenarios. In this paper, we propose a lightweight CNN known [...] Read more.

Radar signal intra-pulse modulation recognition can be addressed with convolutional neural networks (CNNs) and time–frequency images (TFIs). However, current CNNs have high computational complexity and do not perform well in low-signal-to-noise ratio (SNR) scenarios. In this paper, we propose a lightweight CNN known as the cross-scale aware network (CSANet) to recognize intra-pulse modulation based on three types of TFIs. The cross-scale aware (CSA) module, designed as a residual and parallel architecture, comprises a depthwise dilated convolution group (DDConv Group), a cross-channel interaction (CCI) mechanism, and spatial information focus (SIF). DDConv Group produces multiple-scale features with a dynamic receptive field, CCI fuses the features and mitigates noise in multiple channels, and SIF is aware of the cross-scale details of TFI structures. Furthermore, we develop a novel time–frequency fusion (TFF) feature based on three types of TFIs by employing image preprocessing techniques, i.e., adaptive binarization, morphological processing, and feature fusion. Experiments demonstrate that CSANet achieves higher accuracy with our TFF compared to other TFIs. Meanwhile, CSANet outperforms cutting-edge networks across twelve radar signal datasets, providing an efficient solution for high-precision recognition in low-SNR scenarios. Full article

(This article belongs to the Special Issue Radar Signal Detection, Recognition and Identification)

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