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
Strain and Temperature Sensing Based on Different Temperature Coefficients fs-FBG Arrays for Intelligent Buoyancy Materials
Sensors 2024, 24(9), 2824; https://doi.org/10.3390/s24092824 (registering DOI) - 29 Apr 2024
Abstract
The temperature and strain fields monitoring during the preparation process of buoyancy materials, as well as the health status after molding, are important for mastering the mechanical properties of buoyancy materials and ensuring the safety of operators and equipment. This paper proposes a
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The temperature and strain fields monitoring during the preparation process of buoyancy materials, as well as the health status after molding, are important for mastering the mechanical properties of buoyancy materials and ensuring the safety of operators and equipment. This paper proposes a short and high-density femtosecond fiber Bragg grating (fs-FBG) array based on different temperature coefficients fibers. By optimizing the parameters of femtosecond laser point-by-point writing technology, high-performance fs-FBG arrays with millimeter level gating length and millimeter level spatial resolution were prepared on two types of fibers. These were successfully embedded in buoyancy materials to achieve in-situ online monitoring of the curing process and after molding. The experimental results show that the fs-FBG array sensor has good anti-chirp performance and achieves online monitoring of millimeter-level spatial resolution. Intelligent buoyancy materials can provide real-time feedback on the health status of equipment in harsh underwater environments. The system can achieve temperature monitoring with an accuracy of 0.56 °C and deformation monitoring with sub-millimeter accuracy; the error is in the order of micrometers, which is of great significance in the field of deep-sea exploration.
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(This article belongs to the Special Issue Fiber Grating Sensors and Applications)
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Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images
by
Steven Fussner, Aidan Boyne, Albert Han, Lauren A. Nakhleh and Zulfi Haneef
Sensors 2024, 24(9), 2823; https://doi.org/10.3390/s24092823 (registering DOI) - 29 Apr 2024
Abstract
The treatment of epilepsy, the second most common chronic neurological disorder, is often complicated by the failure of patients to respond to medication. Treatment failure with anti-seizure medications is often due to the presence of non-epileptic seizures. Distinguishing non-epileptic from epileptic seizures requires
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The treatment of epilepsy, the second most common chronic neurological disorder, is often complicated by the failure of patients to respond to medication. Treatment failure with anti-seizure medications is often due to the presence of non-epileptic seizures. Distinguishing non-epileptic from epileptic seizures requires an expensive and time-consuming analysis of electroencephalograms (EEGs) recorded in an epilepsy monitoring unit. Machine learning algorithms have been used to detect seizures from EEG, typically using EEG waveform analysis. We employed an alternative approach, using a convolutional neural network (CNN) with transfer learning using MobileNetV2 to emulate the real-world visual analysis of EEG images by epileptologists. A total of 5359 EEG waveform plot images from 107 adult subjects across two epilepsy monitoring units in separate medical facilities were divided into epileptic and non-epileptic groups for training and cross-validation of the CNN. The model achieved an accuracy of 86.9% (Area Under the Curve, AUC 0.92) at the site where training data were extracted and an accuracy of 87.3% (AUC 0.94) at the other site whose data were only used for validation. This investigation demonstrates the high accuracy achievable with CNN analysis of EEG plot images and the robustness of this approach across EEG visualization software, laying the groundwork for further subclassification of seizures using similar approaches in a clinical setting.
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(This article belongs to the Special Issue Artificial Neural Networks-Based Sensing and Biomedical Signal Processing Technology)
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Open AccessArticle
Comparison of Perioperative, Functional, and Oncologic Outcomes of Open vs. Robot-Assisted Off-Clamp Partial Nephrectomy: A Propensity Scored Match Analysis
by
Riccardo Mastroianni, Giuseppe Chiacchio, Leonard Perpepaj, Gabriele Tuderti, Aldo Brassetti, Umberto Anceschi, Mariaconsiglia Ferriero, Leonardo Misuraca, Simone D’Annunzio, Alfredo Maria Maria Bove, Salvatore Guaglianone, Rocco Simone Simone Flammia, Flavia Proietti, Marco Pula, Giulio Milanese, Costantino Leonardo, Andrea Benedetto Benedetto Galosi and Giuseppe Simone
Sensors 2024, 24(9), 2822; https://doi.org/10.3390/s24092822 (registering DOI) - 28 Apr 2024
Abstract
Off-clamp partial nephrectomy represents one of the latest developments in nephron-sparing surgery, with the goal of preserving renal function and reducing ischemia time. The aim of this study was to evaluate and compare the functional, oncologic, and perioperative outcomes between off-clamp robot-assisted partial
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Off-clamp partial nephrectomy represents one of the latest developments in nephron-sparing surgery, with the goal of preserving renal function and reducing ischemia time. The aim of this study was to evaluate and compare the functional, oncologic, and perioperative outcomes between off-clamp robot-assisted partial nephrectomy (off-C RAPN) and off-clamp open partial nephrectomy (off-C OPN) through a propensity score-matched (PSM) analysis. A 1:1 PSM analysis was used to balance variables potentially affecting postoperative outcomes. To report surgical quality, 1 year trifecta was used. Univariable Cox regression analysis was performed to identify predictors of trifecta achievement. The Kaplan–Meier method was used to compare cancer-specific survival (CSS), overall survival (OS), disease-free survival (DFS), and metastasis-free survival (MFS) probabilities between groups. Overall, 542 patients were included. After PSM analysis, two homogeneous cohorts of 147 patients were obtained. The off-C RAPN cohort experienced shorter length of stay (LoS) (3.4 days vs. 5.4 days; p < 0.001), increased likelihoods of achieving 1 year trifecta (89.8% vs. 80.3%; p = 0.03), lower postoperative Clavien–Dindo ≤ 2 complications (1.3% vs. 18.3%, p < 0.001), and lower postoperative transfusion rates (3.4% vs. 12.2%, p = 0.008). At univariable analysis, the surgical approach (off-C RAPN vs. off-C OPN, OR 2.22, 95% CI 1.09–4.46, p = 0.02) was the only predictor of 1 year trifecta achievement. At Kaplan–Meier analysis, no differences were observed between the two groups in terms of OS (log-rank p = 0.451), CSS (log-rank p = 0.476), DFS (log-rank p = 0.678), and MFS (log-rank p = 0.226). Comparing RAPN and OPN in a purely off-clamp scenario, the minimally invasive approach proved to be a feasible and safe surgical approach, with a significantly lower LoS and minor rate of postoperative complications and transfusions as a result of improved surgical quality expressed by higher 1 year trifecta achievement.
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(This article belongs to the Section Sensors and Robotics)
Open AccessArticle
Low-Cost Recognition of Plastic Waste Using Deep Learning and a Multi-Spectral Near-Infrared Sensor
by
Uriel Martinez-Hernandez, Gregory West and Tareq Assaf
Sensors 2024, 24(9), 2821; https://doi.org/10.3390/s24092821 (registering DOI) - 28 Apr 2024
Abstract
This work presents an approach for the recognition of plastics using a low-cost spectroscopy sensor module together with a set of machine learning methods. The sensor is a multi-spectral module capable of measuring 18 wavelengths from the visible to the near-infrared. Data processing
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This work presents an approach for the recognition of plastics using a low-cost spectroscopy sensor module together with a set of machine learning methods. The sensor is a multi-spectral module capable of measuring 18 wavelengths from the visible to the near-infrared. Data processing and analysis are performed using a set of ten machine learning methods (Random Forest, Support Vector Machines, Multi-Layer Perceptron, Convolutional Neural Networks, Decision Trees, Logistic Regression, Naive Bayes, k-Nearest Neighbour, AdaBoost, Linear Discriminant Analysis). An experimental setup is designed for systematic data collection from six plastic types including PET, HDPE, PVC, LDPE, PP and PS household waste. The set of computational methods is implemented in a generalised pipeline for the validation of the proposed approach for the recognition of plastics. The results show that Convolutional Neural Networks and Multi-Layer Perceptron can recognise plastics with a mean accuracy of 72.50% and 70.25%, respectively, with the largest accuracy of 83.5% for PS plastic and the smallest accuracy of 66% for PET plastic. The results demonstrate that this low-cost near-infrared sensor with machine learning methods can recognise plastics effectively, making it an affordable and portable approach that contributes to the development of sustainable systems with potential for applications in other fields such as agriculture, e-waste recycling, healthcare and manufacturing.
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(This article belongs to the Special Issue Advanced Optical Sensors Based on Machine Learning)
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Open AccessArticle
Rectified Latent Variable Model-Based EMG Factorization of Inhibitory Muscle Synergy Components Related to Aging, Expertise and Force–Tempo Variations
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Subing Huang, Xiaoyu Guo, Jodie J. Xie, Kelvin Y. S. Lau, Richard Liu, Arthur D. P. Mak, Vincent C. K. Cheung and Rosa H. M. Chan
Sensors 2024, 24(9), 2820; https://doi.org/10.3390/s24092820 (registering DOI) - 28 Apr 2024
Abstract
Muscle synergy has been widely acknowledged as a possible strategy of neuromotor control, but current research has ignored the potential inhibitory components in muscle synergies. Our study aims to identify and characterize the inhibitory components within motor modules derived from electromyography (EMG), investigate
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Muscle synergy has been widely acknowledged as a possible strategy of neuromotor control, but current research has ignored the potential inhibitory components in muscle synergies. Our study aims to identify and characterize the inhibitory components within motor modules derived from electromyography (EMG), investigate the impact of aging and motor expertise on these components, and better understand the nervous system’s adaptions to varying task demands. We utilized a rectified latent variable model (RLVM) to factorize motor modules with inhibitory components from EMG signals recorded from ten expert pianists when they played scales and pieces at different tempo–force combinations. We found that older participants showed a higher proportion of inhibitory components compared with the younger group. Senior experts had a higher proportion of inhibitory components on the left hand, and most inhibitory components became less negative with increased tempo or decreased force. Our results demonstrated that the inhibitory components in muscle synergies could be shaped by aging and expertise, and also took part in motor control for adapting to different conditions in complex tasks.
Full article
(This article belongs to the Special Issue Advances in Biomedical Sensing, Instrumentation and Systems: 2nd Edition)
Open AccessArticle
Flexible Force Sensor Based on a PVA/AgNWs Nanocomposite and Cellulose Acetate
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Dulce Natalia Castillo-López, Luz del Carmen Gómez-Pavón, Alfredo Gutíerrez-Nava, Placido Zaca-Morán, Cesar Augusto Arriaga-Arriaga, Jesús Manuel Muñoz-Pacheco and Arnulfo Luis-Ramos
Sensors 2024, 24(9), 2819; https://doi.org/10.3390/s24092819 (registering DOI) - 28 Apr 2024
Abstract
Nanocomposites are materials of special interest for the development of flexible electronic, optical, and mechanical devices in applications such as transparent conductive electrodes and flexible electronic sensors. These materials take advantage of the electrical, chemical, and mechanical properties of a polymeric matrix, especially
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Nanocomposites are materials of special interest for the development of flexible electronic, optical, and mechanical devices in applications such as transparent conductive electrodes and flexible electronic sensors. These materials take advantage of the electrical, chemical, and mechanical properties of a polymeric matrix, especially in force sensors, as well as the properties of a conductive filler such as silver nanowires (AgNWs). In this work, the fabrication of a force sensor using AgNWs synthesized via the polyol chemical technique is presented. The nanowires were deposited via drop-casting in polyvinyl alcohol (PVA) to form the active (electrode) and resistive (nanocomposite) sensor films, with both films separated by a cellulose acetate substrate. The dimensions of the resulting sensor are 35 mm × 40 mm × 0.1 mm. The sensor shows an applied force ranging from 0 to 3.92 N, with a sensitivity of 0.039 N. The sensor stand-off resistance, exceeding 50 MΩ, indicates a good ability to detect changes in applied force without an external force. Additionally, studies revealed a response time of 10 ms, stabilization of 9 s, and a degree of hysteresis of 1.9%. The voltage response of the sensor under flexion at an angle of 85° was measured, demonstrating its functionality over a prolonged period. The fabricated sensor can be used in applications that require measuring pressure on irregular surfaces or systems with limited space, such as for estimating movement in robot joints.
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(This article belongs to the Special Issue Editorial Board Members' Collection Series: Nanomaterials-Based Electronics Devices and Sensors)
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Open AccessArticle
The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis
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Chengyuan Zha, Lei Li, Fangting Zhu and Yanzhe Zhao
Sensors 2024, 24(9), 2818; https://doi.org/10.3390/s24092818 (registering DOI) - 28 Apr 2024
Abstract
The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection, which detects the patient’s exhaled VOCs, combined with sensor arrays of convolutional neural network (CNN) algorithms as a new lung cancer detection
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The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection, which detects the patient’s exhaled VOCs, combined with sensor arrays of convolutional neural network (CNN) algorithms as a new lung cancer detection is attracting more researchers’ attention. However, the low accuracy, high-complexity computation and large number of parameters make the CNN algorithms difficult to transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients. Compared to currently popular lightweight CNN models, such as EfficientNet, LTNet has fewer parameters (32 K) and its training weight size is only 0.155 MB. LTNet achieved an overall classification accuracy of 99.06% and 99.14% in the own mixed gas dataset and the University of California (UCI) dataset, which are both higher than the scores of the six existing models, and it also offers the shortest training (844.38 s and 584.67 s) and inference times (23 s and 14 s) in the same validation sets. Compared to the existing CNN models, LTNet is more suitable for resource-limited POCT devices.
Full article
(This article belongs to the Section Sensor Networks)
Open AccessArticle
Optimizing Human–Robot Teaming Performance through Q-Learning-Based Task Load Adjustment and Physiological Data Analysis
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Soroush Korivand, Gustavo Galvani, Arash Ajoudani, Jiaqi Gong and Nader Jalili
Sensors 2024, 24(9), 2817; https://doi.org/10.3390/s24092817 (registering DOI) - 28 Apr 2024
Abstract
The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human–robot teaming (HRT) performance,
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The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human–robot teaming (HRT) performance, it is crucial to predict performance without negatively affecting task execution. Therefore, to predict performance indirectly, significant factors affecting human performance, such as engagement and task load (i.e., amount of cognitive, physical, and/or sensory resources required to perform a particular task), must be considered. Hence, we propose a framework to predict and maximize the HRT performance. For the prediction of task performance during the development phase, our methodology employs features extracted from physiological data as inputs. The labels for these predictions—categorized as accurate performance or inaccurate performance due to high/low task load—are meticulously crafted using a combination of the NASA TLX questionnaire, records of human performance in quality control tasks, and the application of Q-Learning to derive task-specific weights for the task load indices. This structured approach enables the deployment of our model to exclusively rely on physiological data for predicting performance, thereby achieving an accuracy rate of 95.45% in forecasting HRT performance. To maintain optimized HRT performance, this study further introduces a method of dynamically adjusting the robot’s speed in the case of low performance. This strategic adjustment is designed to effectively balance the task load, thereby enhancing the efficiency of human–robot collaboration.
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(This article belongs to the Special Issue Sensors in 2024)
Open AccessArticle
Vision-Based On-Site Construction Waste Localization Using Unmanned Aerial Vehicle
by
Zeli Wang, Xincong Yang, Xianghan Zheng and Heng Li
Sensors 2024, 24(9), 2816; https://doi.org/10.3390/s24092816 (registering DOI) - 28 Apr 2024
Abstract
In the context of construction and demolition waste exacerbating environmental pollution, the lack of recycling technology has hindered the green development of the industry. Previous studies have explored robot-based automated recycling methods, but their efficiency is limited by movement speed and detection range,
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In the context of construction and demolition waste exacerbating environmental pollution, the lack of recycling technology has hindered the green development of the industry. Previous studies have explored robot-based automated recycling methods, but their efficiency is limited by movement speed and detection range, so there is an urgent need to integrate drones into the recycling field to improve construction waste management efficiency. Preliminary investigations have shown that previous construction waste recognition techniques are ineffective when applied to UAVs and also lack a method to accurately convert waste locations in images to actual coordinates. Therefore, this study proposes a new method for autonomously labeling the location of construction waste using UAVs. Using images captured by UAVs, we compiled an image dataset and proposed a high-precision, long-range construction waste recognition algorithm. In addition, we proposed a method to convert the pixel positions of targets to actual positions. Finally, the study verified the effectiveness of the proposed method through experiments. Experimental results demonstrated that the approach proposed in this study enhanced the discernibility of computer vision algorithms towards small targets and high-frequency details within images. In a construction waste localization task using drones, involving high-resolution image recognition, the accuracy and recall were significantly improved by about 2% at speeds of up to 28 fps. The results of this study can guarantee the efficient application of drones to construction sites.
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(This article belongs to the Section Internet of Things)
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Electro-Mechanical Characterization and Modeling of a Broadband Piezoelectric Microgenerator Based on Lithium Niobate
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Namanu Panayanthatta, Giacomo Clementi, Merieme Ouhabaz, Samuel Margueron, Ausrine Bartasyte, Mickael Lallart, Skandar Basrour, Roberto La Rosa, Edwige Bano and Laurent Montes
Sensors 2024, 24(9), 2815; https://doi.org/10.3390/s24092815 (registering DOI) - 28 Apr 2024
Abstract
Vibration energy harvesting based on piezoelectric transducers is an attractive choice to replace single-use batteries in powering Wireless Sensor Nodes (WSNs). As of today, their widespread application is hindered due to low operational bandwidth and the conventional use of lead-based materials. The Restriction
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Vibration energy harvesting based on piezoelectric transducers is an attractive choice to replace single-use batteries in powering Wireless Sensor Nodes (WSNs). As of today, their widespread application is hindered due to low operational bandwidth and the conventional use of lead-based materials. The Restriction of Hazardous Substances legislation (RoHS) implemented in the European Union restricts the use of lead-based piezoelectric materials in future electronic devices. This paper investigates lithium niobate ( ) as a lead-free material for a high-performance broadband Piezoelectric Energy Harvester (PEH). A single-clamped, cantilever beam-based piezoelectric microgenerator with a mechanical footprint of 1 cm2, working at a low resonant frequency of 200 Hz, with a high piezoelectric coupling coefficient and broad bandwidth, was designed and microfabricated, and its performance was evaluated. The PEH device, with an acceleration of 1 g delivers a maximum output RMS power of nearly 35 μW/cm2 and a peak voltage of 6 V for an optimal load resistance at resonance. Thanks to a high squared piezoelectric electro-mechanical coupling coefficient ( ), the device offers a broadband operating frequency range above 10% of the central frequency. The Mason electro-mechanical equivalent circuit was derived, and a SPICE model of the device was compared with experimental results. Finally, the output voltage of the harvester was rectified to provide a DC output stored on a capacitor, and it was regulated and used to power an IoT node at an acceleration of as low as 0.5 g.
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(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Driving toward Connectivity: Vehicular Visible Light Communications Receiver with Adaptive Field of View for Enhanced Noise Resilience and Mobility
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Alin-Mihai Căilean, Sebastian-Andrei Avătămăniței and Cătălin Beguni
Sensors 2024, 24(9), 2814; https://doi.org/10.3390/s24092814 (registering DOI) - 28 Apr 2024
Abstract
Wireless communication represents the basis for the next generation of vehicle safety systems, whereas visible light communication (VLC) is one of the most suitable technologies for this purpose. In this context, this work introduces a novel VLC receiver architecture that integrates a field-of-view
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Wireless communication represents the basis for the next generation of vehicle safety systems, whereas visible light communication (VLC) is one of the most suitable technologies for this purpose. In this context, this work introduces a novel VLC receiver architecture that integrates a field-of-view (FoV) adaptation mechanism in accordance with the optical noise generated by the sun. In order to demonstrate the benefits of this concept, a VLC prototype was experimentally tested in an infrastructure-to-vehicle (I2V) VLC configuration, which uses an LED traffic light as the transmitter. At the receiver side, an automatic FoV adaptation mechanism was designed based on a mechanical iris placed in front of a photodetector. Adjustments were made based on the values recorded by a multi-angle light sensor, built with an array of IR photodiodes covering an elevation from 0° to 30° and an azimuth from −30° to 30°. Depending on the incidence of solar light, the mechanical iris can adjust the FoV from ±1° to ±22°, taking into account both the light irradiance and the sun’s position relative to the VLC receiver. For experimental testing, two identical VLC receivers were used: one with an automatic FoV adjustment, and the other with a ±22° fixed FoV. The test results performed at a distance of 50 m, in the presence of solar irradiance reaching up to 67,000 µW/cm2, showed that the receiver with a fixed FoV saturated and lost the communication link most of the time, whereas the receiver with an adjustable FoV maintained an active link throughout the entire period, with a bit error rate (BER) of less than 10−7.
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(This article belongs to the Special Issue Advances in Intelligent Transportation Systems Based on Sensor Fusion)
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Open AccessArticle
RadarTCN: Lightweight Online Classification Network for Automotive Radar Targets Based on TCN
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Yuan Li, Mengmeng Zhang, Hongyuan Jing and Zhi Liu
Sensors 2024, 24(9), 2813; https://doi.org/10.3390/s24092813 (registering DOI) - 28 Apr 2024
Abstract
Automotive radar is one of the key sensors for intelligent driving. Radar image sequences contain abundant spatial and temporal information, enabling target classification. For existing radar spatiotemporal classifiers, multi-view radar images are usually employed to enhance the information of the target and 3D
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Automotive radar is one of the key sensors for intelligent driving. Radar image sequences contain abundant spatial and temporal information, enabling target classification. For existing radar spatiotemporal classifiers, multi-view radar images are usually employed to enhance the information of the target and 3D convolution is employed for spatiotemporal feature extraction. These models consume significant hardware resources and are not applicable to real-time applications. In this paper, RadarTCN, a novel lightweight network, is proposed that achieves high-accuracy online target classification using single-view radar image sequences only. In RadarTCN, 2D convolution and 3D-TCN are employed to extract spatiotemporal features sequentially. To reduce data dimensionality and computational complexity, a multi-layer max pooling down-sampling method is designed in a 2D convolution module. Meanwhile, the 3D-TCN module is improved through residual pruning and causal convolution is introduced for leveraging the performance of online target classification. The experimental results demonstrate that RadarTCN can achieve high-precision online target recognition for both range-angle and range-Doppler map sequences. Compared to the reference models on the CARRADA dataset, RadarTCN exhibits better classification performance, with fewer parameters and lower computational complexity.
Full article
(This article belongs to the Section Radar Sensors)
Open AccessArticle
Minimizing Task Age upon Decision for Low-Latency MEC Networks Task Offloading with Action-Masked Deep Reinforcement Learning
by
Zhouxi Jiang, Jianfeng Yang and Xun Gao
Sensors 2024, 24(9), 2812; https://doi.org/10.3390/s24092812 (registering DOI) - 28 Apr 2024
Abstract
In this paper, we consider a low-latency Mobile Edge Computing (MEC) network where multiple User Equipment (UE) wirelessly reports to a decision-making edge server. At the same time, the transmissions are operated with Finite Blocklength (FBL) codes to achieve low-latency transmission. We introduce
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In this paper, we consider a low-latency Mobile Edge Computing (MEC) network where multiple User Equipment (UE) wirelessly reports to a decision-making edge server. At the same time, the transmissions are operated with Finite Blocklength (FBL) codes to achieve low-latency transmission. We introduce the task of Age upon Decision (AuD) aimed at the timeliness of tasks used for decision-making, which highlights the timeliness of the information at decision-making moments. For the case in which dynamic task generation and random fading channels are considered, we provide a task AuD minimization design by jointly selecting UE and allocating blocklength. In particular, to solve the task AuD minimization problem, we transform the optimization problem to a Markov Decision Process problem and propose an Error Probability-Controlled Action-Masked Proximal Policy Optimization (EMPPO) algorithm. Via simulation, we show that the proposed design achieves a lower AuD than baseline methods across various network conditions, especially in scenarios with significant channel Signal-to-Noise Ratio (SNR) differences and low average SNR, which shows the robustness of EMPPO and its potential for real-time applications.
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(This article belongs to the Special Issue Edge Computing in IoT Networks Based on Artificial Intelligence)
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Open AccessArticle
Classification of Sleep Quality and Aging as a Function of Brain Complexity: A Multiband Non-Linear EEG Analysis
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Lucía Penalba-Sánchez, Gabriel Silva, Mark Crook-Rumsey, Alexander Sumich, Pedro Miguel Rodrigues, Patrícia Oliveira-Silva and Ignacio Cifre
Sensors 2024, 24(9), 2811; https://doi.org/10.3390/s24092811 (registering DOI) - 28 Apr 2024
Abstract
Understanding and classifying brain states as a function of sleep quality and age has important implications for developing lifestyle-based interventions involving sleep hygiene. Current studies use an algorithm that captures non-linear features of brain complexity to differentiate awake electroencephalography (EEG) states, as a
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Understanding and classifying brain states as a function of sleep quality and age has important implications for developing lifestyle-based interventions involving sleep hygiene. Current studies use an algorithm that captures non-linear features of brain complexity to differentiate awake electroencephalography (EEG) states, as a function of age and sleep quality. Fifty-eight participants were assessed using the Pittsburgh Sleep Quality Inventory (PSQI) and awake resting state EEG. Groups were formed based on age and sleep quality (younger adults n = 24, mean age = 24.7 years, SD = 3.43, good sleepers n = 11; older adults n = 34, mean age = 72.87; SD = 4.18, good sleepers n = 9). Ten non-linear features were extracted from multiband EEG analysis to feed several classifiers followed by a leave-one-out cross-validation. Brain state complexity accurately predicted (i) age in good sleepers, with 75% mean accuracy (across all channels) for lower frequencies (alpha, theta, and delta) and 95% accuracy at specific channels (temporal, parietal); and (ii) sleep quality in older groups with moderate accuracy (70 and 72%) across sub-bands with some regions showing greater differences. It also differentiated younger good sleepers from older poor sleepers with 85% mean accuracy across all sub-bands, and 92% at specific channels. Lower accuracy levels (<50%) were achieved in predicting sleep quality in younger adults. The algorithm discriminated older vs. younger groups excellently and could be used to explore intragroup differences in older adults to predict sleep intervention efficiency depending on their brain complexity.
Full article
(This article belongs to the Special Issue Recent Advances in the Acquisition and Processing of Biomedical Signals and Images)
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Open AccessArticle
3D Shape Measurement of Aeroengine Blade Based on Fringe Projection Profilometer Improved by Multi-Layer Concentric Ring Calibration
by
Ze Chen, Yuhang Ju, Chuanzhi Sun, Yinchu Wang, Yongmeng Liu and Jiubin Tan
Sensors 2024, 24(9), 2810; https://doi.org/10.3390/s24092810 (registering DOI) - 28 Apr 2024
Abstract
The precision requirements for aeroengine blade machining are exceedingly stringent. This study aims to improve the accuracy of existing aeroengine blade measurement methods while achieving comprehensive measurement. Therefore, this study proposes a new concentric ring calibration method and designs a multi-layer concentric ring
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The precision requirements for aeroengine blade machining are exceedingly stringent. This study aims to improve the accuracy of existing aeroengine blade measurement methods while achieving comprehensive measurement. Therefore, this study proposes a new concentric ring calibration method and designs a multi-layer concentric ring calibration plate. The effectiveness of this calibration method was verified through actual testing of standard ball gauges. Compared with the checkerboard-grid calibration method, the average deviation of the multilayer concentric ring calibration method for measuring the center distance of the standard sphere is 0.02352, which improves the measurement accuracy by 3–4 times. On the basis of multi-layer concentric ring calibration, this study builds a fringe projection profiler based on the three-frequency twelve-step phase shift method. Compared with the CMM, the average deviation of the blade chord length measured by this solution is 0.064, which meets the measurement index requirements of aeroengine fan blades.
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(This article belongs to the Collection 3D/4D Optical Imaging Sensors for Surface Measurement, Processing and Applications)
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Open AccessArticle
Use of Unmanned Surface Vehicles (USVs) in Water Chemistry Studies
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Georgios Katsouras, Elias Dimitriou, Sotirios Karavoltsos, Stylianos Samios, Aikaterini Sakellari, Angeliki Mentzafou, Nikolaos Tsalas and Michael Scoullos
Sensors 2024, 24(9), 2809; https://doi.org/10.3390/s24092809 (registering DOI) - 28 Apr 2024
Abstract
Unmanned surface vehicles (USVs) equipped with integrated sensors are a tool valuable to several monitoring strategies, offering enhanced temporal and spatial coverage over specific timeframes, allowing for targeted examination of sites or events of interest. The elaboration of environmental monitoring programs has relied
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Unmanned surface vehicles (USVs) equipped with integrated sensors are a tool valuable to several monitoring strategies, offering enhanced temporal and spatial coverage over specific timeframes, allowing for targeted examination of sites or events of interest. The elaboration of environmental monitoring programs has relied so far on periodic spot sampling at specific locations, followed by laboratory analysis, aiming at the evaluation of water quality at a catchment scale. For this purpose, automatic telemetric stations for specific parameters have been installed by the Institute of Marine Biological Resources and Inland Waters of Hellenic Centre for Marine Research (IMBRIW-HCMR) within several Greek rivers and lakes, providing continuous and temporal monitoring possibilities. In the present work, USVs were deployed by the Athens Water and Sewerage Company (EYDAP) as a cost-effective tool for the environmental monitoring of surface water bodies of interest, with emphasis on the spatial fluctuations of chlorophyll α, electrical conductivity, dissolved oxygen and pH, observed in Koumoundourou Lake and the rivers Acheloos, Asopos and Kifissos. The effectiveness of an innovative heavy metal (HM) system installed in the USV for the in situ measurements of copper and lead was also evaluated herewith. The results obtained demonstrate the advantages of USVs, setting the base for their application in real-time monitoring of chemical parameters including metals. Simultaneously, the requirements for accuracy and sensitivity improvement of HM sensors were noted, in order to permit full exploitation of USVs’ capacities.
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(This article belongs to the Section Remote Sensors)
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Open AccessArticle
High-Performance Four-Channel Tactile Sensor for Measuring the Magnitude and Orientation of Forces
by
Mingyao Zhang, Yong Shi, Haitao Ge, Guopeng Sun, Zihan Lian and Yifei Lu
Sensors 2024, 24(9), 2808; https://doi.org/10.3390/s24092808 (registering DOI) - 28 Apr 2024
Abstract
Flexible sensors have gained popularity in recent years. This study proposes a novel structure of a resistive four-channel tactile sensor capable of distinguishing the magnitude and direction of normal forces acting on its sensing surface. The sensor uses EcoflexTM00-30 as the
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Flexible sensors have gained popularity in recent years. This study proposes a novel structure of a resistive four-channel tactile sensor capable of distinguishing the magnitude and direction of normal forces acting on its sensing surface. The sensor uses EcoflexTM00-30 as the substrate and EGaIn alloy as the conductive filler, featuring four mutually perpendicular and curved channels to enhance the sensor’s dynamic responsiveness. Experiments and simulations show that the sensor has a large dynamic range (31.25–100 mΩ), high precision (deviation of repeated pressing below 0.1%), linearity (R2 above 0.97), fast response/recovery time (0.2 s/0.15 s), and robust stability (with fluctuations below 0.9%). This work uses an underactuated robotic hand equipped with a four-channel tactile sensor to grasp various objects. The sensor data collected effectively predicts the shapes of the objects grasped. Furthermore, the four-channel tactile sensor proposed in this work may be employed in smart wearables, medical diagnostics, and other industries.
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(This article belongs to the Section Sensors Development)
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Open AccessArticle
A Novel Architecture for an Intrusion Detection System Utilizing Cross-Check Filters for In-Vehicle Networks
by
Hyungchul Im, Donghyeon Lee and Seongsoo Lee
Sensors 2024, 24(9), 2807; https://doi.org/10.3390/s24092807 (registering DOI) - 28 Apr 2024
Abstract
The Controller Area Network (CAN), widely used for vehicular communication, is vulnerable to multiple types of cyber-threats. Attackers can inject malicious messages into the CAN bus through various channels, including wireless methods, entertainment systems, and on-board diagnostic ports. Therefore, it is crucial to
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The Controller Area Network (CAN), widely used for vehicular communication, is vulnerable to multiple types of cyber-threats. Attackers can inject malicious messages into the CAN bus through various channels, including wireless methods, entertainment systems, and on-board diagnostic ports. Therefore, it is crucial to develop a reliable intrusion detection system (IDS) capable of effectively distinguishing between legitimate and malicious CAN messages. In this paper, we propose a novel IDS architecture aimed at enhancing the cybersecurity of CAN bus systems in vehicles. Various machine learning (ML) models have been widely used to address similar problems; however, although existing ML-based IDS are computationally efficient, they suffer from suboptimal detection performance. To mitigate this shortcoming, our architecture incorporates specially designed rule-based filters that cross-check outputs from the traditional ML-based IDS. These filters scrutinize message ID and payload data to precisely capture the unique characteristics of three distinct types of cyberattacks: DoS attacks, spoofing attacks, and fuzzy attacks. Experimental evidence demonstrates that the proposed architecture leads to a significant improvement in detection performance across all utilized ML models. Specifically, all ML-based IDS achieved an accuracy exceeding 99% for every type of attack. This achievement highlights the robustness and effectiveness of our proposed solution in detecting potential threats.
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(This article belongs to the Section Vehicular Sensing)
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Online Detection of Hydrogen Fluoride under Corona Discharge in Gas-Insulated Switchgear Based on Photoacoustic Spectroscopy
by
Liujie Wan, Xiaohe Zhao and Kang Li
Sensors 2024, 24(9), 2806; https://doi.org/10.3390/s24092806 (registering DOI) - 27 Apr 2024
Abstract
Internal discharge and overheating faults in sulfur hexafluoride (SF6) gas-insulated electrical equipment will generate a series of characteristic gas products. Hydrogen fluoride (HF) is one of the main decomposition gases under discharge failure. Because of its extremely corrosive nature, it can
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Internal discharge and overheating faults in sulfur hexafluoride (SF6) gas-insulated electrical equipment will generate a series of characteristic gas products. Hydrogen fluoride (HF) is one of the main decomposition gases under discharge failure. Because of its extremely corrosive nature, it can react with other materials in gas-insulated switchgear (GIS), resulting in a short existence time, so it needs to be detected online. Resonant gas photoacoustic spectroscopy has the advantage of high sensitivity, fast response, and no sample gas consumption, and can be used for the online detection of flowing gas. In this paper, a simulated GIS corona discharge experimental platform was built, and the HF generated in the discharge was detected online by gas photoacoustic spectroscopy. The absorption peak of HF molecule near 1312.59 nm was selected as the absorption spectral line, and a resonant photoacoustic cell was designed. To improve the detection sensitivity of HF, wavelength modulation and second-harmonic detection technology were used. The online monitoring of HF in the simulated GIS corona discharge fault was successfully realized. The experimental results show that the sensitivity of the designed photoacoustic spectroscopy detection system for HF is 0.445 μV/(μL/L), and the limit of detection (LOD) is 0.611 μL/L.
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(This article belongs to the Special Issue Photoacoustic Sensors and Devices for Gas Detection)
Open AccessArticle
Combining the Benefits of Biotin–Streptavidin Aptamer Immobilization with the Versatility of Ni-NTA Regeneration Strategies for SPR
by
Eliza K. Hanson and Rebecca J. Whelan
Sensors 2024, 24(9), 2805; https://doi.org/10.3390/s24092805 (registering DOI) - 27 Apr 2024
Abstract
The high affinity of the biotin–streptavidin interaction has made this non-covalent coupling an indispensable strategy for the immobilization and enrichment of biomolecular affinity reagents. However, the irreversible nature of the biotin–streptavidin bond renders surfaces functionalized using this strategy permanently modified and not amenable
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The high affinity of the biotin–streptavidin interaction has made this non-covalent coupling an indispensable strategy for the immobilization and enrichment of biomolecular affinity reagents. However, the irreversible nature of the biotin–streptavidin bond renders surfaces functionalized using this strategy permanently modified and not amenable to regeneration strategies that could increase assay reusability and throughput. To increase the utility of biotinylated targets, we here introduce a method for reversibly immobilizing biotinylated thrombin-binding aptamers onto a Ni-nitrilotriacetic acid (Ni-NTA) sensor chip using 6xHis-tagged streptavidin as a regenerable capture ligand. This approach enabled the reproducible immobilization of aptamers and measurements of aptamer–protein interaction in a surface plasmon resonance assay. The immobilized aptamer surface was stable during five experiments over two days, despite the reversible attachment of 6xHis-streptavidin to the Ni-NTA surface. In addition, we demonstrate the reproducibility of this immobilization method and the affinity assays performed using it. Finally, we verify the specificity of the biotin tag–streptavidin interaction and assess the efficiency of a straightforward method to regenerate and reuse the surface. The method described here will allow researchers to leverage the versatility and stability of the biotin–streptavidin interaction while increasing throughput and improving assay efficiency.
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(This article belongs to the Special Issue Surface Plasmon Resonance-Based Biosensor)
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