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
A Comprehensive Architecture for Federated Learning-Based Smart Advertising
Sensors 2024, 24(12), 3765; https://doi.org/10.3390/s24123765 (registering DOI) - 9 Jun 2024
Abstract
This paper introduces a cutting-edge data architecture designed for a smart advertising context, prioritizing efficient data flow and performance, robust security, while guaranteeing data privacy and integrity. At the core of this study lies the application of federated learning (FL) as the primary
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This paper introduces a cutting-edge data architecture designed for a smart advertising context, prioritizing efficient data flow and performance, robust security, while guaranteeing data privacy and integrity. At the core of this study lies the application of federated learning (FL) as the primary methodology, which emphasizes the authenticity and privacy of data while promptly discarding irrelevant or fraudulent information. Our innovative data model employs a semi-random role assignment strategy based on a variety of criteria to efficiently collect and amalgamate data. The architecture is composed of model nodes, data nodes, and validator nodes, where the role of each node is determined by factors such as computational capability, interconnection quality, and historical performance records. A key feature of our proposed system is the selective engagement of a subset of nodes for modeling and validation, optimizing resource use and minimizing data loss. The AROUND social network platform serves as a real-world case study, illustrating the efficacy of our data architecture in a practical setting. Both simulated and real implementations of our architecture showcase its potential to dramatically curtail network traffic and average CPU usage, while preserving the accuracy of the FL model. Remarkably, the system is capable of achieving over a 50% reduction in both network traffic and average CPU usage even when the user count escalates by twenty-fold. The click rate, user engagement, and other parameters have also been evaluated, proving that the proposed architecture’s advantages do not affect the smart advertising accuracy. These findings highlight the proposed architecture’s capacity to scale efficiently and maintain high performance in smart advertising environments, making it a valuable contribution to the evolving landscape of digital marketing and FL.
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(This article belongs to the Section Sensor Networks)
Open AccessArticle
Advancing Thrombosis Research: A Novel Device for Measuring Clot Permeability
by
Elia Landi, Marco Mugnaini, Tunahan Vatansever, Ada Fort, Valerio Vignoli, Elvira Giurranna, Flavia Rita Argento, Eleonora Fini, Giacomo Emmi, Claudia Fiorillo and Matteo Becatti
Sensors 2024, 24(12), 3764; https://doi.org/10.3390/s24123764 (registering DOI) - 9 Jun 2024
Abstract
Thromboembolism, a global leading cause of mortality, needs accurate risk assessment for effective prophylaxis and treatment. Current stratification methods fall short in predicting thrombotic events, emphasizing the need for a deeper understanding of clot properties. Fibrin clot permeability, a crucial parameter in hypercoagulable
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Thromboembolism, a global leading cause of mortality, needs accurate risk assessment for effective prophylaxis and treatment. Current stratification methods fall short in predicting thrombotic events, emphasizing the need for a deeper understanding of clot properties. Fibrin clot permeability, a crucial parameter in hypercoagulable states, impacts clot structure and resistance to lysis. Current clot permeability measurement limitations propel the need for standardized methods. Prior findings underscore the importance of clot permeability in various thrombotic conditions but call for improvements and more precise, repeatable, and standardized methods. Addressing these challenges, our study presents an upgraded, portable, and cost-effective system for measuring blood clot permeability, which utilizes a pressure-based approach that adheres to Darcy’s law. By enhancing precision and sensitivity in discerning clot characteristics, this innovation provides a valuable tool for assessing thrombotic risk and associated pathological conditions. In this paper, the authors present a device that is able to automatically perform the permeability measurements on plasma or fibrinogen in vitro-induced clots on specific holders (filters). The proposed device has been tailored to distinguish clot permeability, with high precision and sensitivity, between healthy subjects and high cardiovascular-risk patients. The precise measure of clot permeability represents an excellent indicator of thrombotic risk, thus allowing the clinician, also on the basis of other anamnestic and laboratory data, to attribute a risk score to the subject. The proposed instrument was characterized by performing permeability measurements in plasma and purified fibrinogen clots derived from 17 Behcet patients and 15 sex- and age-matched controls. As expected, our results clearly indicate a significant difference in plasma clot permeability in Behcet patients with respect to controls (0.0533 ± 0.0199 d vs. 0.0976 ± 0.0160 d, p < 0.001). This difference was confirmed in the patient’s vs. control fibrin clots (0.0487 ± 0.0170 d vs. 0.1167 ± 0.0487 d, p < 0.001). In conclusion, our study demonstrates the feasibility, efficacy, portability, and cost-effectiveness of a novel device for measuring clot permeability, allowing healthcare providers to better stratify thrombotic risk and tailor interventions, thereby improving patient outcomes and reducing healthcare costs, which could significantly improve the management of thromboembolic diseases.
Full article
(This article belongs to the Special Issue Metrology for Industry 4.0 & IoT 2023)
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Open AccessArticle
The Improved Biometric Identification of Keystroke Dynamics Based on Deep Learning Approaches
by
Łukasz Wyciślik, Przemysław Wylężek and Alina Momot
Sensors 2024, 24(12), 3763; https://doi.org/10.3390/s24123763 (registering DOI) - 9 Jun 2024
Abstract
In an era marked by escalating concerns about digital security, biometric identification methods have gained paramount importance. Despite the increasing adoption of biometric techniques, keystroke dynamics analysis remains a less explored yet promising avenue. This study highlights the untapped potential of keystroke dynamics,
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In an era marked by escalating concerns about digital security, biometric identification methods have gained paramount importance. Despite the increasing adoption of biometric techniques, keystroke dynamics analysis remains a less explored yet promising avenue. This study highlights the untapped potential of keystroke dynamics, emphasizing its non-intrusive nature and distinctiveness. While keystroke dynamics analysis has not achieved widespread usage, ongoing research indicates its viability as a reliable biometric identifier. This research builds upon the existing foundation by proposing an innovative deep-learning methodology for keystroke dynamics-based identification. Leveraging open research datasets, our approach surpasses previously reported results, showcasing the effectiveness of deep learning in extracting intricate patterns from typing behaviors. This article contributes to the advancement of biometric identification, shedding light on the untapped potential of keystroke dynamics and demonstrating the efficacy of deep learning in enhancing the precision and reliability of identification systems.
Full article
(This article belongs to the Special Issue AI Technology for Cybersecurity and IoT Applications)
Open AccessArticle
Adaptive Low-Rank Tensor Estimation Model Based Multichannel Weak Fault Detection for Bearings
by
Huiming Jiang, Yue Wu, Jing Yuan, Qian Zhao and Jin Chen
Sensors 2024, 24(12), 3762; https://doi.org/10.3390/s24123762 (registering DOI) - 9 Jun 2024
Abstract
Abstract: Multichannel signals contain an abundance of fault characteristic information on equipment and show greater potential for weak fault characteristics extraction and early fault detection. However, how to effectively utilize the advantages of multichannel signals with their information richness while eliminating interference components
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Abstract: Multichannel signals contain an abundance of fault characteristic information on equipment and show greater potential for weak fault characteristics extraction and early fault detection. However, how to effectively utilize the advantages of multichannel signals with their information richness while eliminating interference components caused by strong background noise and information redundancy to achieve accurate extraction of fault characteristics is still challenging for mechanical fault diagnosis based on multichannel signals. To address this issue, an effective weak fault detection framework for multichannel signals is proposed in this paper. Firstly, the advantages of a tensor on characterizing fault information were displayed, and the low-rank property of multichannel fault signals in a tensor domain is revealed through tensor singular value decomposition. Secondly, to tackle weak fault characteristics extraction from multichannel signals under strong background noise, an adaptive threshold function is introduced, and an adaptive low-rank tensor estimation model is constructed. Thirdly, to further improve the accurate estimation of weak fault characteristics from multichannel signals, a new sparsity metric-oriented parameter optimization strategy is provided for the adaptive low-rank tensor estimation model. Finally, an effective multichannel weak fault detection framework is formed for rolling bearings. Multichannel data from the repeatable simulation, the publicly available XJTU-SY whole lifetime datasets and an accelerated fatigue test of rolling bearings are used to validate the effectiveness and practicality of the proposed method. Excellent results are obtained in multichannel weak fault detection with strong background noise, especially for early fault detection.
Full article
(This article belongs to the Special Issue Sensors in Civil Structural Health Monitoring)
Open AccessArticle
Digital Twin Smart City: Integrating IFC and CityGML with Semantic Graph for Advanced 3D City Model Visualization
by
Phuoc-Dat Lam, Bon-Hyon Gu, Hoang-Khanh Lam, Soo-Yol Ok and Suk-Hwan Lee
Sensors 2024, 24(12), 3761; https://doi.org/10.3390/s24123761 (registering DOI) - 9 Jun 2024
Abstract
The growing interest in building data management, especially the building information model (BIM), has significantly influenced urban management, materials supply chain analysis, documentation, and storage. However, the integration of BIM into 3D GIS tools is becoming more common, showing progress beyond the traditional
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The growing interest in building data management, especially the building information model (BIM), has significantly influenced urban management, materials supply chain analysis, documentation, and storage. However, the integration of BIM into 3D GIS tools is becoming more common, showing progress beyond the traditional problem. To address this, this study proposes data transformation methods involving mapping between three domains: industry foundation classes (IFC), city geometry markup language (CityGML), and web ontology framework (OWL)/resource description framework (RDF). Initially, IFC data are converted to CityGML format using the feature manipulation engine (FME) at CityGML standard’s levels of detail 4 (LOD4) to enhance BIM data interoperability. Subsequently, CityGML is converted to the OWL/RDF diagram format to validate the proposed BIM conversion process. To ensure integration between BIM and GIS, geometric data and information are visualized through Cesium Ion web services and Unreal Engine. Additionally, an RDF graph is applied to analyze the association between the semantic mapping of the CityGML standard, with Neo4j (a graph database management system) utilized for visualization. The study’s results demonstrate that the proposed data transformation methods significantly improve the interoperability and visualization of 3D city models, facilitating better urban management and planning.
Full article
(This article belongs to the Section Intelligent Sensors)
Open AccessReview
Static and Dynamic Hand Gestures: A Review of Techniques of Virtual Reality Manipulation
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Oswaldo Mendoza Herbert, David Pérez-Granados, Mauricio Alberto Ortega Ruiz, Rodrigo Cadena Martínez, Carlos Alberto González Gutiérrez and Marco Antonio Zamora Antuñano
Sensors 2024, 24(12), 3760; https://doi.org/10.3390/s24123760 (registering DOI) - 9 Jun 2024
Abstract
This review explores the historical and current significance of gestures as a universal form of communication with a focus on hand gestures in virtual reality applications. It highlights the evolution of gesture detection systems from the 1990s, which used computer algorithms to find
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This review explores the historical and current significance of gestures as a universal form of communication with a focus on hand gestures in virtual reality applications. It highlights the evolution of gesture detection systems from the 1990s, which used computer algorithms to find patterns in static images, to the present day where advances in sensor technology, artificial intelligence, and computing power have enabled real-time gesture recognition. The paper emphasizes the role of hand gestures in virtual reality (VR), a field that creates immersive digital experiences through the Ma blending of 3D modeling, sound effects, and sensing technology. This review presents state-of-the-art hardware and software techniques used in hand gesture detection, primarily for VR applications. It discusses the challenges in hand gesture detection, classifies gestures as static and dynamic, and grades their detection difficulty. This paper also reviews the haptic devices used in VR and their advantages and challenges. It provides an overview of the process used in hand gesture acquisition, from inputs and pre-processing to pose detection, for both static and dynamic gestures.
Full article
(This article belongs to the Special Issue Robotics and Haptics: Haptic Feedback for Medical Robots)
Open AccessArticle
An Iterative 3D Correction plus 2D Inversion Procedure to Remove 3D Effects from 2D ERT Data along Embankments
by
Azadeh Hojat
Sensors 2024, 24(12), 3759; https://doi.org/10.3390/s24123759 (registering DOI) - 9 Jun 2024
Abstract
This paper addresses the problem of removing 3D effects as one of the most challenging problems related to 2D electrical resistivity tomography (ERT) monitoring of embankment structures. When processing 2D ERT monitoring data measured along linear profiles, it is fundamental to estimate and
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This paper addresses the problem of removing 3D effects as one of the most challenging problems related to 2D electrical resistivity tomography (ERT) monitoring of embankment structures. When processing 2D ERT monitoring data measured along linear profiles, it is fundamental to estimate and correct the distortions introduced by the non-uniform 3D geometry of the embankment. Here, I adopt an iterative 3D correction plus 2D inversion procedure to correct the 3D effects and I test the validity of the proposed algorithm using both synthetic and real data. The modelled embankment is inspired by a critical section of the Parma River levee in Colorno (PR), Italy, where a permanent ERT monitoring system has been in operation since November 2018. For each model of the embankment, reference synthetic data were produced in Res2dmod and Res3dmod for the corresponding 2D and 3D models. Using the reference synthetic data, reference 3D effects were calculated to be compared with 3D effects estimated by the proposed algorithm at each iteration. The results of the synthetic tests showed that even in the absence of a priori information, the proposed algorithm for correcting 3D effects converges rapidly to ideal corrections. Having validated the proposed algorithm through synthetic tests, the method was applied to the ERT monitoring data in the study site to remove 3D effects. Two real datasets from the study site, taken after dry and rainy periods, are discussed here. The results showed that 3D effects cause about ±50% changes in the inverted resistivity images for both periods. This is a critical artifact considering that the final objective of ERT monitoring data for such studies is to produce water content maps to be integrated in alarm systems for hydrogeological risk mitigation. The proposed algorithm to remove 3D effects is thus a rapid and validated solution to satisfy near-real-time data processing and to produce reliable results.
Full article
(This article belongs to the Special Issue Novel Sensing Technologies for Environmental Monitoring and Detection)
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Open AccessArticle
A Multi-Scale Natural Scene Text Detection Method Based on Attention Feature Extraction and Cascade Feature Fusion
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Nianfeng Li, Zhenyan Wang, Yongyuan Huang, Jia Tian, Xinyuan Li and Zhiguo Xiao
Sensors 2024, 24(12), 3758; https://doi.org/10.3390/s24123758 (registering DOI) - 9 Jun 2024
Abstract
Scene text detection is an important research field in computer vision, playing a crucial role in various application scenarios. However, existing scene text detection methods often fail to achieve satisfactory results when faced with text instances of different sizes, shapes, and complex backgrounds.
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Scene text detection is an important research field in computer vision, playing a crucial role in various application scenarios. However, existing scene text detection methods often fail to achieve satisfactory results when faced with text instances of different sizes, shapes, and complex backgrounds. To address the challenge of detecting diverse texts in natural scenes, this paper proposes a multi-scale natural scene text detection method based on attention feature extraction and cascaded feature fusion. This method combines global and local attention through an improved attention feature fusion module (DSAF) to capture text features of different scales, enhancing the network’s perception of text regions and improving its feature extraction capabilities. Simultaneously, an improved cascaded feature fusion module (PFFM) is used to fully integrate the extracted feature maps, expanding the receptive field of features and enriching the expressive ability of the feature maps. Finally, to address the cascaded feature maps, a lightweight subspace attention module (SAM) is introduced to partition the concatenated feature maps into several sub-space feature maps, facilitating spatial information interaction among features of different scales. In this paper, comparative experiments are conducted on the ICDAR2015, Total-Text, and MSRA-TD500 datasets, and comparisons are made with some existing scene text detection methods. The results show that the proposed method achieves good performance in terms of accuracy, recall, and F-score, thus verifying its effectiveness and practicality.
Full article
(This article belongs to the Special Issue Computer Vision and Virtual Reality: Technologies and Applications)
Open AccessCommunication
Evaluation of a Ground Subsidence Zone in an Urban Area Using Geophysical Methods
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Lara De Giorgi, Dora Francesca Barbolla, Chiara Torre, Stefano Settembrini and Giovanni Leucci
Sensors 2024, 24(12), 3757; https://doi.org/10.3390/s24123757 (registering DOI) - 9 Jun 2024
Abstract
An important geological risk to which many towns in Puglia are exposed is sinking cavities in urban areas. For urban centers, studying, mapping, providing geological and speleological descriptions, classifying, and cataloging the forms and types of cavities is essential because cavities are linked
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An important geological risk to which many towns in Puglia are exposed is sinking cavities in urban areas. For urban centers, studying, mapping, providing geological and speleological descriptions, classifying, and cataloging the forms and types of cavities is essential because cavities are linked to past local anthropic and natural processes at different sites. These circumstances could lead to the enhancement of existing underground cavities in urban areas through conservation and continuous monitoring. Unfortunately, in many cases, these underground cavities have been used as landfills and subsequently abandoned. In late March 2007, one of these cavities collapsed inside Gallipoli’s inhabited center, causing damage to the structures but fortunately not human lives. In the area surrounding the collapsed cavity, a series of geophysical investigations were undertaken using ground penetrating radar in an attempt to delimit the area of collapse and develop possible interventions for restoration. In the same area, these measures were repeated 16 years later in December 2022 due to another collapse. The comparison between data acquired in these two periods shows that there were no strong changes apart from an increased presence of subsoil moisture in 2022.
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(This article belongs to the Section Remote Sensors)
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Open AccessArticle
Artificial Vision System on Digital Devices for Real-Time Head Tilt Control
by
Miguel Ángel Tomé de la Torre, Antonio Álvarez Fernández-Balbuena, Ricardo Bernárdez-Vilaboa and Daniel Vázquez Molini
Sensors 2024, 24(12), 3756; https://doi.org/10.3390/s24123756 (registering DOI) - 9 Jun 2024
Abstract
It is common to see cases in which, when performing tasks in close vision in front of a digital screen, the posture or position of the head is not adequate, especially in young people; it is essential to have a correct posture of
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It is common to see cases in which, when performing tasks in close vision in front of a digital screen, the posture or position of the head is not adequate, especially in young people; it is essential to have a correct posture of the head to avoid visual, muscular, or joint problems. Most of the current systems to control head inclination require an external part attached to the subject’s head. The aim of this study is the validation of a procedure that, through a detection algorithm and eye tracking, can control the correct position of the head in real time when subjects are in front of a digital device. The system only needs a digital device with a CCD receiver and downloadable software through which we can detect the inclination of the head, indicating if a bad posture is adopted due to a visual problem or simply inadequate visual–postural habits, alerting us to the postural anomaly to correct it.The system was evaluated in subjects with disparate interpupillary distances, at different working distances in front of the digital device, and at each distance, different tilt angles were evaluated. The system evaluated favorably in different lighting environments, correctly detecting the subjects’ pupils. The results showed that for most of the variables, particularly good absolute and relative reliability values were found when measuring head tilt with lower accuracy than most of the existing systems. The evaluated results have been positive, making it a considerably inexpensive and easily affordable system for all users. It is the first application capable of measuring the head tilt of the subject at their working or reading distance in real time by tracking their eyes.
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(This article belongs to the Special Issue Sensors for Human Posture and Movement)
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Motor Imagery EEG Signal Classification Using Distinctive Feature Fusion with Adaptive Structural LASSO
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Weihai Huang, Xinyue Liu, Weize Yang, Yihua Li, Qiyan Sun and Xiangzeng Kong
Sensors 2024, 24(12), 3755; https://doi.org/10.3390/s24123755 (registering DOI) - 9 Jun 2024
Abstract
A motor imagery brain–computer interface connects the human brain and computers via electroencephalography (EEG). However, individual differences in the frequency ranges of brain activity during motor imagery tasks pose a challenge, limiting the manual feature extraction for motor imagery classification. To extract features
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A motor imagery brain–computer interface connects the human brain and computers via electroencephalography (EEG). However, individual differences in the frequency ranges of brain activity during motor imagery tasks pose a challenge, limiting the manual feature extraction for motor imagery classification. To extract features that match specific subjects, we proposed a novel motor imagery classification model using distinctive feature fusion with adaptive structural LASSO. Specifically, we extracted spatial domain features from overlapping and multi-scale sub-bands of EEG signals and mined discriminative features by fusing the task relevance of features with spatial information into the adaptive LASSO-based feature selection. We evaluated the proposed model on public motor imagery EEG datasets, demonstrating that the model has excellent performance. Meanwhile, ablation studies and feature selection visualization of the proposed model further verified the great potential of EEG analysis.
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(This article belongs to the Collection Machine Learning and Signal Processing in Sensing and Sensor Applications)
Open AccessArticle
Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence
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Hafeez Ur Rehman Siddiqui, Ambreen Akmal, Muhammad Iqbal, Adil Ali Saleem, Muhammad Amjad Raza, Kainat Zafar, Aqsa Zaib, Sandra Dudley, Jon Arambarri, Ángel Kuc Castilla and Furqan Rustam
Sensors 2024, 24(12), 3754; https://doi.org/10.3390/s24123754 (registering DOI) - 9 Jun 2024
Abstract
Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to
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Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%.
Full article
(This article belongs to the Section Vehicular Sensing)
Open AccessArticle
3D Camera and Single-Point Laser Sensor Integration for Apple Localization in Spindle-Type Orchard Systems
by
R. M. Rasika D. Abeyrathna, Victor Massaki Nakaguchi, Zifu Liu, Rizky Mulya Sampurno and Tofael Ahamed
Sensors 2024, 24(12), 3753; https://doi.org/10.3390/s24123753 (registering DOI) - 9 Jun 2024
Abstract
Accurate localization of apples is the key factor that determines a successful harvesting cycle in the automation of apple harvesting for unmanned operations. In this regard, accurate depth sensing or positional information of apples is required for harvesting apples based on robotic systems,
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Accurate localization of apples is the key factor that determines a successful harvesting cycle in the automation of apple harvesting for unmanned operations. In this regard, accurate depth sensing or positional information of apples is required for harvesting apples based on robotic systems, which is challenging in outdoor environments because of uneven light variations when using 3D cameras for the localization of apples. Therefore, this research attempted to overcome the effect of light variations for the 3D cameras during outdoor apple harvesting operations. Thus, integrated single-point laser sensors for the localization of apples using a state-of-the-art model, the EfficientDet object detection algorithm with an [email protected] of 0.775 were used in this study. In the experiments, a RealSense D455f RGB-D camera was integrated with a single-point laser ranging sensor utilized to obtain precise apple localization coordinates for implementation in a harvesting robot. The single-point laser range sensor was attached to two servo motors capable of moving the center position of the detected apples based on the detection ID generated by the DeepSORT (online real-time tracking) algorithm. The experiments were conducted under indoor and outdoor conditions in a spindle-type apple orchard artificial architecture by mounting the combined sensor system behind a four-wheel tractor. The localization coordinates were compared between the RGB-D camera depth values and the combined sensor system under different light conditions. The results show that the root-mean-square error (RMSE) values of the RGB-D camera depth and integrated sensor mechanism varied from 3.91 to 8.36 cm and from 1.62 to 2.13 cm under 476~600 lx to 1023~1100 × 100 lx light conditions, respectively. The integrated sensor system can be used for an apple harvesting robotic manipulator with a positional accuracy of ±2 cm, except for some apples that were occluded due to leaves and branches. Further research will be carried out using changes in the position of the integrated system for recognition of the affected apples for harvesting operations.
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(This article belongs to the Special Issue Innovative Imaging Sensors Combined with Artificial Intelligence Approaches to Support Precision Agriculture)
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Open AccessArticle
Design of Fluxgate Current Sensor Based on Magnetization Residence Times and Neural Networks
by
Jingjie Li, Wei Ren, Yanshou Luo, Xutong Zhang, Xinpeng Liu and Xue Zhang
Sensors 2024, 24(12), 3752; https://doi.org/10.3390/s24123752 (registering DOI) - 9 Jun 2024
Abstract
This study introduces a novel fluxgate current sensor with a compact, ring-shaped configuration that exhibits improved performance through the integration of magnetization residence times and neural networks. The sensor distinguishes itself with a unique magnetization profile, denoted as M waves, which emerge from
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This study introduces a novel fluxgate current sensor with a compact, ring-shaped configuration that exhibits improved performance through the integration of magnetization residence times and neural networks. The sensor distinguishes itself with a unique magnetization profile, denoted as M waves, which emerge from the interaction between the target signal and ambient magnetic interference, effectively enhancing interference suppression. These M waves highlight the non-linear coupling between the magnetic field and magnetization residence times. Detection of these residence times is accomplished using full-wave rectification circuits and a Schmitt trigger, with a digital output provided by timing sequence detection. A dual-layer feedforward neural network deciphers the target signal, exploiting this non-linear relationship. The sensor achieves a linearity error of within a measurement range of 15 A. When juxtaposed with conventional sensors utilizing the residence-time difference strategy, our sensor reduces linearity error by more than 40-fold and extends the effective measurement range by . Furthermore, it demonstrates a significant decrease in ambient magnetic interference.
Full article
(This article belongs to the Special Issue Dalian University of Technology Celebrating 75th Anniversary)
Open AccessArticle
Nitrophenyl Thiourea-Modified Polyethylenimine Colorimetric Sensor for Sulfate, Fluorine, and Acetate
by
Kediye Kuerbanjiang, Kuerbanjiang Rouzi and Si-Yu Zhang
Sensors 2024, 24(12), 3751; https://doi.org/10.3390/s24123751 (registering DOI) - 9 Jun 2024
Abstract
A thiourea-based colorimetric sensor incorporating polyethyleneimine (PEI) and chromophoric nitrophenyl groups was synthesized and utilized for detecting various anions. Structural characterization of the sensor was accomplished using FTIR and 1H-NMR spectroscopy. The sensor’s interactions and colorimetric recognition capabilities with different anions, including CI
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A thiourea-based colorimetric sensor incorporating polyethyleneimine (PEI) and chromophoric nitrophenyl groups was synthesized and utilized for detecting various anions. Structural characterization of the sensor was accomplished using FTIR and 1H-NMR spectroscopy. The sensor’s interactions and colorimetric recognition capabilities with different anions, including CI−, Br−, I−, F−, NO3−, PF6−, AcO−, H2PO4−, PO43−, and SO42−, were investigated via visual observation and UV/vis spectroscopy. Upon adding SO42−, F−, and AcO− anions, the sensor exhibited distinct color changes from colorless to yellow and yellowish, while other anions did not induce significant color alterations. UV/vis spectroscopic titration experiments conducted in a DMSO/H2O solution (9:1 volume ratio) demonstrated the sensor’s selectivity toward SO42−, F−, and AcO−. The data revealed that the formation of the main compounds and anion complexes was mediated by hydrogen bonding, leading to signal changes in the nitrophenyl thiourea-modified PEI spectrum.
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(This article belongs to the Collection Optical Chemical Sensors: Design and Applications)
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Open AccessArticle
Efficient Guided Wave Modelling for Tomographic Corrosion Mapping via One-Way Wavefield Extrapolation
by
Emiel Hassefras, Arno Volker and Martin Verweij
Sensors 2024, 24(12), 3750; https://doi.org/10.3390/s24123750 (registering DOI) - 9 Jun 2024
Abstract
Mapping corrosion depths along pipeline sections using guided-wave-based tomographic methods is a challenging task. Accurate defect sizing depends heavily on the precision of the forward model in guided wave tomography. This model is fitted to measured data using inversion techniques. This study evaluates
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Mapping corrosion depths along pipeline sections using guided-wave-based tomographic methods is a challenging task. Accurate defect sizing depends heavily on the precision of the forward model in guided wave tomography. This model is fitted to measured data using inversion techniques. This study evaluates the effectiveness of a recursive extrapolation scheme for tomography applications and full waveform inversion. It employs a table-driven approach, with precomputed extrapolation operators stored across a spectrum of wavenumbers. This enables fast modelling for extensive pipe sections, approaching the speed of ray tracing while accurately handling complex velocity models within the full frequency band. This ensures an accurate representation of diffraction phenomena. The study examines the assumptions underlying the extrapolation approach, namely, the negligible reflection and conversion of modes at defects. In our tomography approach, we intend to use multiple wave modes— , , and —and helical paths. The acoustic extrapolation method is validated through numerical studies for different wave modes, solving the 3D elastodynamic wave equation. Comparison with an experimentally measured single-mode wavefield from an aluminium plate with an artificial defect reveals good agreement.
Full article
(This article belongs to the Special Issue Guided Waves for Structural Health Monitoring (GW4SHM))
Open AccessArticle
ESC-NAS: Environment Sound Classification Using Hardware-Aware Neural Architecture Search for the Edge
by
Dakshina Ranmal, Piumini Ranasinghe, Thivindu Paranayapa, Dulani Meedeniya and Charith Perera
Sensors 2024, 24(12), 3749; https://doi.org/10.3390/s24123749 (registering DOI) - 9 Jun 2024
Abstract
The combination of deep-learning and IoT plays a significant role in modern smart solutions, providing the capability of handling task-specific real-time offline operations with improved accuracy and minimised resource consumption. This study provides a novel hardware-aware neural architecture search approach called ESC-NAS, to
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The combination of deep-learning and IoT plays a significant role in modern smart solutions, providing the capability of handling task-specific real-time offline operations with improved accuracy and minimised resource consumption. This study provides a novel hardware-aware neural architecture search approach called ESC-NAS, to design and develop deep convolutional neural network architectures specifically tailored for handling raw audio inputs in environmental sound classification applications under limited computational resources. The ESC-NAS process consists of a novel cell-based neural architecture search space built with 2D convolution, batch normalization, and max pooling layers, and capable of extracting features from raw audio. A black-box Bayesian optimization search strategy explores the search space and the resulting model architectures are evaluated through hardware simulation. The models obtained from the ESC-NAS process achieved the optimal trade-off between model performance and resource consumption compared to the existing literature. The ESC-NAS models achieved accuracies of 85.78%, 81.25%, 96.25%, and 81.0% for the FSC22, UrbanSound8K, ESC-10, and ESC-50 datasets, respectively, with optimal model sizes and parameter counts for edge deployment.
Full article
(This article belongs to the Section Internet of Things)
Open AccessArticle
Identification of Respiratory Pauses during Swallowing by Unconstrained Measuring Using Millimeter Wave Radar
by
Toma Kadono and Hiroshi Noguchi
Sensors 2024, 24(12), 3748; https://doi.org/10.3390/s24123748 (registering DOI) - 9 Jun 2024
Abstract
Breathing temporarily pauses during swallowing, and the occurrence of inspiration before and after these pauses may increase the likelihood of aspiration, a serious health problem in older adults. Therefore, the automatic detection of these pauses without constraints is important. We propose methods for
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Breathing temporarily pauses during swallowing, and the occurrence of inspiration before and after these pauses may increase the likelihood of aspiration, a serious health problem in older adults. Therefore, the automatic detection of these pauses without constraints is important. We propose methods for measuring respiratory movements during swallowing using millimeter wave radar to detect these pauses. The experiment involved 20 healthy adult participants. The results showed a correlation of 0.71 with the measurement data obtained from a band-type sensor used as a reference, demonstrating the potential to measure chest movements associated with respiration using a non-contact method. Additionally, temporary respiratory pauses caused by swallowing were confirmed by the measured data. Furthermore, using machine learning, the presence of respiring alone was detected with an accuracy of 88.5%, which is higher than that reported in previous studies. Respiring and temporary respiratory pauses caused by swallowing were also detected, with a macro-averaged F1 score of 66.4%. Although there is room for improvement in temporary pause detection, this study demonstrates the potential for measuring respiratory movements during swallowing using millimeter wave radar and a machine learning method.
Full article
(This article belongs to the Special Issue Biomedical Sensors for Diagnosis and Rehabilitation-2nd Edition)
Open AccessArticle
Lifetime Prediction of Permanent Magnet Synchronous Motor in Selective Compliance Assembly Robot Arm Considering Insulation Thermal Aging
by
Mingxu Chen, Bingye Zhang, Haibo Li, Xiang Gao, Jiajin Wang and Jian Zhang
Sensors 2024, 24(12), 3747; https://doi.org/10.3390/s24123747 (registering DOI) - 9 Jun 2024
Abstract
The direct-drive selective compliance assembly robot arm (SCARA) is widely used in high-end manufacturing fields, as it omits the mechanical transmission structures and has the advantages of high positioning accuracy and fast movement speed. However, due to the intensifying dynamic coupling problem of
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The direct-drive selective compliance assembly robot arm (SCARA) is widely used in high-end manufacturing fields, as it omits the mechanical transmission structures and has the advantages of high positioning accuracy and fast movement speed. However, due to the intensifying dynamic coupling problem of structures in the direct-drive SCARA, the permanent magnet synchronous motors (PMSMs) located at the joints will take on nonstationary loads, which causes excessive internal temperature and reduces the lifetime of PMSMs. To address these issues, the lifetime prediction of PMSMs is studied. The kinematic and dynamic models of the SCARA are established to calculate the torque curve required by the PMSM in specific typical motion tasks. Additionally, considering thermal stress as the main factor affecting lifetime, accelerated degradation tests are conducted on insulation material. Then, the reliability function of the PMSM is formulated based on the accelerated degradation model. Based on the parameters and working conditions of the PMSM, the temperature field distribution is obtained through simulation. The maximum temperature is used as the reference temperature to conduct reliability evaluation and lifetime prediction of the PMSM. The research results show that for a typical point-to-point task, the PMSM can run for 102,623 h while achieving the reliability requirement of 0.99.
Full article
(This article belongs to the Section Sensors and Robotics)
Open AccessArticle
Efficient Haze Removal from a Single Image Using a DCP-Based Lightweight U-Net Neural Network Model
by
Yunho Han, Jiyoung Kim, Jinyoung Lee, Jae-Ho Nah, Yo-Sung Ho and Woo-Chan Park
Sensors 2024, 24(12), 3746; https://doi.org/10.3390/s24123746 (registering DOI) - 9 Jun 2024
Abstract
In this paper, we propose a lightweight U-net architecture neural network model based on Dark Channel Prior (DCP) for efficient haze (fog) removal with a single input. The existing DCP requires high computational complexity in its operation. These computations are challenging to accelerate,
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In this paper, we propose a lightweight U-net architecture neural network model based on Dark Channel Prior (DCP) for efficient haze (fog) removal with a single input. The existing DCP requires high computational complexity in its operation. These computations are challenging to accelerate, and the problem is exacerbated when dealing with high-resolution images (videos), making it very difficult to apply to general-purpose applications. Our proposed model addresses this issue by employing a two-stage neural network structure, replacing the computationally complex operations of the conventional DCP with easily accelerated convolution operations to achieve high-quality fog removal. Furthermore, our proposed model is designed with an intuitive structure using a relatively small number of parameters (2M), utilizing resources efficiently. These features demonstrate the effectiveness and efficiency of the proposed model for fog removal. The experimental results show that the proposed neural network model achieves an average Peak Signal-to-Noise Ratio (PSNR) of 26.65 dB and a Structural Similarity Index Measure (SSIM) of 0.88, indicating an improvement in the average PSNR of 11.5 dB and in SSIM of 0.22 compared to the conventional DCP. This shows that the proposed neural network achieves comparable results to CNN-based neural networks that have achieved SOTA-class performance, despite its intuitive structure with a relatively small number of parameters.
Full article
(This article belongs to the Special Issue Disruptive Technologies and Wireless Sensor Network Communication Algorithms (2nd Edition))
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