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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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11 pages, 8257 KiB  
Article
Fibre Optic Method for Detecting Oil Fluorescence in Marine Sediments
by Emilia Baszanowska, Zbigniew Otremba and Maria Kubacka
Sensors 2025, 25(1), 173; https://doi.org/10.3390/s25010173 - 31 Dec 2024
Viewed by 790
Abstract
The aim of this study is to verify the possibility of detecting oil in the bottom sediment using a fibre optic system. The presence of oil is assessed on excitation–emission spectra obtained from spectral fluorescence signals of the sediment sample. A factory spectrofluorometer [...] Read more.
The aim of this study is to verify the possibility of detecting oil in the bottom sediment using a fibre optic system. The presence of oil is assessed on excitation–emission spectra obtained from spectral fluorescence signals of the sediment sample. A factory spectrofluorometer coupled with an experimental fibre optic measurement system was used. During the determination of spectra, the fibre optic system is set at a 45° angle to the sediment surface and placed above its surface. The light exciting the fluorescence and the light emitted by the sediment are transmitted in a combined bundle of fibre optic threads. The analysis of excitation–emission spectra of sediments contaminated with oil shows variability of the shapes of fluorescence spectra depending on the type and degree of oil contamination, which indicates the feasibility of the sensor design for detecting oil in the sediment in situ. Full article
(This article belongs to the Special Issue Sensor Technologies for Ocean Environments: Impact Assessment, Monitoring and Protection)
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16 pages, 8862 KiB  
Article
Development of Automatic Method for Glucose Detection Based on Platinum Octaethylporphyrin Sol–Gel Film with Long-Term Stability
by Yujie Niu, Yongda Wang, Lu Li, Xiyu Zhang and Ting Liu
Sensors 2025, 25(1), 186; https://doi.org/10.3390/s25010186 - 31 Dec 2024
Cited by 1 | Viewed by 1094
Abstract
In this study, an approach has been proposed in response to the urgent need for a sensitive and stable method for glucose detection at low concentrations. Platinum octaethylporphyrin (PtOEP) was chosen as the probe and embedded into the matrix material to yield a [...] Read more.
In this study, an approach has been proposed in response to the urgent need for a sensitive and stable method for glucose detection at low concentrations. Platinum octaethylporphyrin (PtOEP) was chosen as the probe and embedded into the matrix material to yield a glucose-sensing film, i.e., Pt/TE-MTS, through a sol–gel process. The optical parameter (OP) was defined as the ratio of phosphorescence in the absence and presence of glucose, and the relationship between OP and glucose concentration (GC) was established in a theoretical way based on the Stern–Volmer equation and further obtained by photoluminescence measurement. OP exhibited a linear relationship with GC in a range of 0–720 μM. The time required by the photoluminescence of the film to reach equilibrium was measured to ensure the completion of the reaction, and it was found that the equilibrium time decreased as the GC increased. The photobleaching behavior and stabilization of the film were monitored, and the result showed that the film exhibited excellent resistance to photobleaching and was quite stable in an aqueous solution. Additionally, a LabVIEW-based GC-detection system was developed to achieve the practical application of the sensing film. In summary, the Pt/TE-MTS film exhibited high sensitivity in detecting the GC with excellent reproducibility, which is of high value in applications. Full article
(This article belongs to the Section Nanosensors)
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14 pages, 3937 KiB  
Article
Concentration vs. Optical Density of ESKAPEE Bacteria: A Method to Determine the Optimum Measurement Wavelength
by Bruno Wacogne, Marine Belinger Podevin, Naïs Vaccari, Claudia Koubevi, Céline Codjiová, Emilie Gutierrez, Lucie Davoine, Marjorie Robert-Nicoud, Alain Rouleau and Annie Frelet-Barrand
Sensors 2024, 24(24), 8160; https://doi.org/10.3390/s24248160 - 21 Dec 2024
Cited by 3 | Viewed by 3651
Abstract
Optical density measurement has been used for decades to determine the microorganism concentration and more rarely for mammalian cells. Although this measurement can be carried out at any wavelength, studies report a limited number of measurement wavelengths, mainly around 600 nm, and no [...] Read more.
Optical density measurement has been used for decades to determine the microorganism concentration and more rarely for mammalian cells. Although this measurement can be carried out at any wavelength, studies report a limited number of measurement wavelengths, mainly around 600 nm, and no consensus seems to be emerging to propose an objective method for determining the optimum measurement wavelength for each microorganism. In this article, we propose a method for analyzing the absorbance spectra of ESKAPEE bacteria and determining the optimum measurement wavelength for each of them. The method is based on the analysis of the signal-to-noise ratio of the relationships between concentrations and optical densities when the measurement wavelength varies over the entire spectral range of the absorbance spectra measured for each bacterium. These optimum wavelengths range from 612 nm for Enterococcus faecium to 705 nm for Acinetobacter baumannii. The method can be directly applied to any bacteria, any culture method, and also to any biochemical substance with an absorbance spectrum without any particular feature such as an identified maximum. Full article
(This article belongs to the Special Issue Spectroscopy for Biochemical Imaging and Sensing)
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27 pages, 20405 KiB  
Article
Implementation and Experimental Application of Industrial IoT Architecture Using Automation and IoT Hardware/Software
by David Calderón, Francisco Javier Folgado, Isaías González and Antonio José Calderón
Sensors 2024, 24(24), 8074; https://doi.org/10.3390/s24248074 - 18 Dec 2024
Cited by 13 | Viewed by 2572
Abstract
The paradigms of Industry 4.0 and Industrial Internet of Things (IIoT) require functional architectures to deploy and organize hardware and software taking advantage of modern digital technologies in industrial systems. In this sense, a lot of the literature proposes and describes this type [...] Read more.
The paradigms of Industry 4.0 and Industrial Internet of Things (IIoT) require functional architectures to deploy and organize hardware and software taking advantage of modern digital technologies in industrial systems. In this sense, a lot of the literature proposes and describes this type of architecture with a conceptual angle, without providing experimental validation or with scarce details about the involved equipment under real operation. Aiming at overcoming these limitations, this paper presents the experimental application of an IIoT architecture divided into four functional layers, namely, Sensing, Network, Middleware and Application layers. Automation and IoT hardware and software are used to implement and apply the architecture. Special attention is put on the software Grafana, chosen in the top layer to deploy graphical user interfaces that are remotely accessible via web. A pilot microgrid integrating photovoltaic energy and hydrogen served as scenario to test and prove the suitability of the architecture in four application cases. Full article
(This article belongs to the Special Issue Industry 4.0: Sensor-Based Broadband Advances towards the Industrial Internet of Things Ecosystem)
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21 pages, 4888 KiB  
Article
Digital Twin-Assisted Lightpath Provisioning and Nonlinear Mitigation in C+L+S Multiband Optical Networks
by Sadegh Ghasrizadeh, Prasunika Khare, Nelson Costa, Marc Ruiz, Antonio Napoli, Joao Pedro and Luis Velasco
Sensors 2024, 24(24), 8054; https://doi.org/10.3390/s24248054 - 17 Dec 2024
Cited by 1 | Viewed by 1080
Abstract
Multiband (MB) optical transmission targets increasing the capacity of operators’ optical transport networks. However, nonlinear impairments (NLI) affect each optical channel in the C+L+S bands differently, and, therefore, the routing and spectrum assignment (RSA) problem needs to be complemented with fast and accurate [...] Read more.
Multiband (MB) optical transmission targets increasing the capacity of operators’ optical transport networks. However, nonlinear impairments (NLI) affect each optical channel in the C+L+S bands differently, and, therefore, the routing and spectrum assignment (RSA) problem needs to be complemented with fast and accurate tools to consider the quality of transmission (QoT) within the provisioning process. This paper proposes a digital twin-assisted approach for lightpath provisioning to provide a complete solution for the RSA problem that ensures the required QoT in MB optical networks. The OCATA time domain digital twin is proposed, not only to estimate the QoT of a selected path but also to support the QoT-based channel assignment process. OCATA is based on a Deep Neural Network (DNN) to model the propagation of the optical signal. However, because of the different impacts of nonlinear noise on each channel and the large number of channels that need to be considered in C+L+S MB scenarios, OCATA needs to be adapted to make it scalable, while keeping its high accuracy and fast QoT estimation characteristics. In consequence, a complete methodology is proposed in this work that limits the number of channels being modeled to just a few. Moreover, OCATA-MB helps to mitigate NLI noise by programming the receiver at the provisioning time and thus with very little complexity compared to its equivalent implemented during the operation. NLI noise mitigation can be applied in the case when a lightpath cannot be provisioned because none of the available channels can provide the required QoT, making it an advantageous tool for reducing connection blocking. Exhaustive simulation results demonstrate the remarkable accuracy of OCATA-MB in estimating the QoT for any channel. Interestingly, by utilizing the proposed OCATA-MB-assisted lightpath provisioning approach, a reduction of the blocking ratio exceeding 50% when compared to traditional approaches is shown when NLI noise mitigation is not applied. If NLI mitigation is implemented, an additional over 50% blocking reduction is achieved. Full article
(This article belongs to the Special Issue Sensing Technologies and Optical Communication)
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110 pages, 4916 KiB  
Review
Revolutionizing Free-Space Optics: A Survey of Enabling Technologies, Challenges, Trends, and Prospects of Beyond 5G Free-Space Optical (FSO) Communication Systems
by Isiaka A. Alimi and Paulo P. Monteiro
Sensors 2024, 24(24), 8036; https://doi.org/10.3390/s24248036 - 16 Dec 2024
Cited by 5 | Viewed by 7944
Abstract
As the demand for high-speed, low-latency communication continues to grow, free-space optical (FSO) communication has gained prominence as a promising solution for supporting the next generation of wireless networks, especially in the context of the 5G and beyond era. It offers high-speed, low-latency [...] Read more.
As the demand for high-speed, low-latency communication continues to grow, free-space optical (FSO) communication has gained prominence as a promising solution for supporting the next generation of wireless networks, especially in the context of the 5G and beyond era. It offers high-speed, low-latency data transmission over long distances without the need for a physical infrastructure. However, the deployment of FSO systems faces significant challenges, such as atmospheric turbulence, weather-induced signal degradation, and alignment issues, all of which can impair performance. This paper offers a comprehensive survey of the enabling technologies, challenges, trends, and future prospects for FSO communication in next-generation networks, while also providing insights into the current mitigation strategies. The survey explores the critical enabling technologies such as adaptive optics, modulation schemes, and error correction codes that are revolutionizing FSO communication and addressing the unique challenges of FSO links. Also, the integration of FSO with radio frequency, millimeter-wave, and Terahertz technologies is explored, emphasizing hybrid solutions that enhance reliability and coverage. Additionally, the paper highlights emerging trends, such as the integration of FSO with artificial intelligence-driven optimization techniques and the growing role of machine learning in enhancing FSO system performance for dynamic environments. By analyzing the current trends and identifying key challenges, this paper emphasizes the prospects of FSO communication in the evolving landscape of 5G and future networks. In this regard, it assesses the potential of FSO to meet the demands for high-speed, low-latency communication and offers insights into its scalability, reliability, and deployment strategies for 5G and beyond. The paper concludes by identifying the open challenges and future research directions critical to realizing the full potential of FSO in next-generation communication systems. Full article
(This article belongs to the Special Issue Advanced Optical Technologies for Communications, Perception, and Chips)
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18 pages, 2211 KiB  
Article
Accuracy Evaluation of 3D Pose Reconstruction Algorithms Through Stereo Camera Information Fusion for Physical Exercises with MediaPipe Pose
by Sebastian Dill, Arjang Ahmadi, Martin Grimmer, Dennis Haufe, Maurice Rohr, Yanhua Zhao, Maziar Sharbafi and Christoph Hoog Antink
Sensors 2024, 24(23), 7772; https://doi.org/10.3390/s24237772 - 4 Dec 2024
Cited by 2 | Viewed by 4018
Abstract
In recent years, significant research has been conducted on video-based human pose estimation (HPE). While monocular two-dimensional (2D) HPE has been shown to achieve high performance, monocular three-dimensional (3D) HPE poses a more challenging problem. However, since human motion happens in a 3D [...] Read more.
In recent years, significant research has been conducted on video-based human pose estimation (HPE). While monocular two-dimensional (2D) HPE has been shown to achieve high performance, monocular three-dimensional (3D) HPE poses a more challenging problem. However, since human motion happens in a 3D space, 3D HPE offers a more accurate representation of the human, granting increased usability for complex tasks like analysis of physical exercise. We propose a method based on MediaPipe Pose, 2D HPE on stereo cameras and a fusion algorithm without prior stereo calibration to reconstruct 3D poses, combining the advantages of high accuracy in 2D HPE with the increased usability of 3D coordinates. We evaluate this method on a self-recorded database focused on physical exercise to research what accuracy can be achieved and whether this accuracy is sufficient to recognize errors in exercise performance. We find that our method achieves significantly improved performance compared to monocular 3D HPE (median RMSE of 30.1 compared to 56.3, p-value below 10−6) and can show that the performance is sufficient for error recognition. Full article
(This article belongs to the Special Issue Deep Learning Applications for Pose Estimation and Human Action Recognition)
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14 pages, 5456 KiB  
Article
A Hybrid Photoplethysmography (PPG) Sensor System Design for Heart Rate Monitoring
by Farjana Akter Jhuma, Kentaro Harada, Muhamad Affiq Bin Misran, Hin-Wai Mo, Hiroshi Fujimoto and Reiji Hattori
Sensors 2024, 24(23), 7634; https://doi.org/10.3390/s24237634 - 29 Nov 2024
Cited by 3 | Viewed by 4362
Abstract
A photoplethysmography (PPG) sensor is a cost-effective and efficacious way of measuring health conditions such as heart rate, oxygen saturation, and respiration rate. In this work, we present a hybrid PPG sensor system working in a reflective mode with an optoelectronic module, i.e., [...] Read more.
A photoplethysmography (PPG) sensor is a cost-effective and efficacious way of measuring health conditions such as heart rate, oxygen saturation, and respiration rate. In this work, we present a hybrid PPG sensor system working in a reflective mode with an optoelectronic module, i.e., the combination of an inorganic light-emitting diode (LED) and a circular-shaped organic photodetector (OPD) surrounding the LED for efficient light harvest followed by the proper driving circuit for accurate PPG signal acquisition. The performance of the hybrid sensor system was confirmed by the heart rate detection process from the PPG using fast Fourier transform analysis. The PPG signal obtained with a 50% LED duty cycle and 250 Hz sampling rate resulted in accurate heart rate monitoring with an acceptable range of error. The effects of the LED duty cycle and the LED luminous intensity were found to be crucial to the heart rate accuracy and to the power consumption, i.e., indispensable factors for the hybrid sensor. Full article
(This article belongs to the Section Biosensors)
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15 pages, 4402 KiB  
Article
Segmentation of Low-Grade Brain Tumors Using Mutual Attention Multimodal MRI
by Hiroyuki Seshimo and Essam A. Rashed
Sensors 2024, 24(23), 7576; https://doi.org/10.3390/s24237576 - 27 Nov 2024
Cited by 3 | Viewed by 2051
Abstract
Early detection and precise characterization of brain tumors play a crucial role in improving patient outcomes and extending survival rates. Among neuroimaging modalities, magnetic resonance imaging (MRI) is the gold standard for brain tumor diagnostics due to its ability to produce high-contrast images [...] Read more.
Early detection and precise characterization of brain tumors play a crucial role in improving patient outcomes and extending survival rates. Among neuroimaging modalities, magnetic resonance imaging (MRI) is the gold standard for brain tumor diagnostics due to its ability to produce high-contrast images across a variety of sequences, each highlighting distinct tissue characteristics. This study focuses on enabling multimodal MRI sequences to advance the automatic segmentation of low-grade astrocytomas, a challenging task due to their diffuse and irregular growth patterns. A novel mutual-attention deep learning framework is proposed, which integrates complementary information from multiple MRI sequences, including T2-weighted and fluid-attenuated inversion recovery (FLAIR) sequences, to enhance the segmentation accuracy. Unlike conventional segmentation models, which treat each modality independently or simply concatenate them, our model introduces mutual attention mechanisms. This allows the network to dynamically focus on salient features across modalities by jointly learning interdependencies between imaging sequences, leading to more precise boundary delineations even in regions with subtle tumor signals. The proposed method is validated using the UCSF-PDGM dataset, which consists of 35 astrocytoma cases, presenting a realistic and clinically challenging dataset. The results demonstrate that T2w/FLAIR modalities contribute most significantly to the segmentation performance. The mutual-attention model achieves an average Dice coefficient of 0.87. This study provides an innovative pathway toward improving segmentation of low-grade tumors by enabling context-aware fusion across imaging sequences. Furthermore, the study showcases the clinical relevance of integrating AI with multimodal MRI, potentially improving non-invasive tumor characterization and guiding future research in radiological diagnostics. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning in Image/Video Processing and Sensing)
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37 pages, 2256 KiB  
Review
Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review
by Bryan Nsoh, Abia Katimbo, Hongzhi Guo, Derek M. Heeren, Hope Njuki Nakabuye, Xin Qiao, Yufeng Ge, Daran R. Rudnick, Joshua Wanyama, Erion Bwambale and Shafik Kiraga
Sensors 2024, 24(23), 7480; https://doi.org/10.3390/s24237480 - 23 Nov 2024
Cited by 8 | Viewed by 6978
Abstract
This systematic review critically evaluates the current state and future potential of real-time, end-to-end smart, and automated irrigation management systems, focusing on integrating the Internet of Things (IoTs) and machine learning technologies for enhanced agricultural water use efficiency and crop productivity. In this [...] Read more.
This systematic review critically evaluates the current state and future potential of real-time, end-to-end smart, and automated irrigation management systems, focusing on integrating the Internet of Things (IoTs) and machine learning technologies for enhanced agricultural water use efficiency and crop productivity. In this review, the automation of each component is examined in the irrigation management pipeline from data collection to application while analyzing its effectiveness, efficiency, and integration with various precision agriculture technologies. It also investigates the role of the interoperability, standardization, and cybersecurity of IoT-based automated solutions for irrigation applications. Furthermore, in this review, the existing gaps are identified and solutions are proposed for seamless integration across multiple sensor suites for automated systems, aiming to achieve fully autonomous and scalable irrigation management. The findings highlight the transformative potential of automated irrigation systems to address global food challenges by optimizing water use and maximizing crop yields. Full article
(This article belongs to the Special Issue Sensor and AI Technologies in Intelligent Agriculture: 2nd Edition)
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18 pages, 4163 KiB  
Article
Privacy-Preserving Synthetic Data Generation Method for IoT-Sensor Network IDS Using CTGAN
by Saleh Alabdulwahab, Young-Tak Kim and Yunsik Son
Sensors 2024, 24(22), 7389; https://doi.org/10.3390/s24227389 - 20 Nov 2024
Cited by 3 | Viewed by 2549
Abstract
The increased usage of IoT networks brings about new privacy risks, especially when intrusion detection systems (IDSs) rely on large datasets for machine learning (ML) tasks and depend on third parties for storing and training the ML-based IDS. This study proposes a privacy-preserving [...] Read more.
The increased usage of IoT networks brings about new privacy risks, especially when intrusion detection systems (IDSs) rely on large datasets for machine learning (ML) tasks and depend on third parties for storing and training the ML-based IDS. This study proposes a privacy-preserving synthetic data generation method using a conditional tabular generative adversarial network (CTGAN) aimed at maintaining the utility of IoT sensor network data for IDS while safeguarding privacy. We integrate differential privacy (DP) with CTGAN by employing controlled noise injection to mitigate privacy risks. The technique involves dynamic distribution adjustment and quantile matching to balance the utility–privacy tradeoff. The results indicate a significant improvement in data utility compared to the standard DP method, achieving a KS test score of 0.80 while minimizing privacy risks such as singling out, linkability, and inference attacks. This approach ensures that synthetic datasets can support intrusion detection without exposing sensitive information. Full article
(This article belongs to the Special Issue Recent Trends and Advances in Sensors Cybersecurity)
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15 pages, 874 KiB  
Article
Deep Reinforcement Learning-Driven Jamming-Enhanced Secure Unmanned Aerial Vehicle Communications
by Zhifang Xing, Yunhui Qin, Changhao Du, Wenzhang Wang and Zhongshan Zhang
Sensors 2024, 24(22), 7328; https://doi.org/10.3390/s24227328 - 16 Nov 2024
Cited by 1 | Viewed by 1084
Abstract
Despite its flexibility, unmanned aerial vehicle (UAV) communications are susceptible to eavesdropping due to the open nature of wireless channels and the broadcasting nature of wireless signals. This paper studies secure UAV communications and proposes a method to optimize the minimum secrecy rate [...] Read more.
Despite its flexibility, unmanned aerial vehicle (UAV) communications are susceptible to eavesdropping due to the open nature of wireless channels and the broadcasting nature of wireless signals. This paper studies secure UAV communications and proposes a method to optimize the minimum secrecy rate of the system by using interference technology to enhance it. To this end, the system not only deploys multiple UAV base stations (BSs) to provide services to legitimate users but also assigns dedicated UAV jammers to send interference signals to active or potential eavesdroppers to disrupt their eavesdropping effectiveness. Based on this configuration, we formulate the optimization process of parameters such as the user association variables, UAV trajectory, and output power as a sequential decision-making problem and use the single-agent soft actor-critic (SAC) algorithm and twin delayed deep deterministic policy gradient (TD3) algorithm to achieve joint optimization of the core parameters. In addition, for specific scenarios, we also use the multi-agent soft actor-critic (MASAC) algorithm to solve the joint optimization problem mentioned above. The numerical results show that the normalized average secrecy rate of the MASAC algorithm increased by more than 6.6% and 14.2% compared with that of the SAC and TD3 algorithms, respectively. Full article
(This article belongs to the Special Issue Novel Signal Processing Techniques for Wireless Communications)
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20 pages, 3171 KiB  
Article
Multimodal Trajectory Prediction for Diverse Vehicle Types in Autonomous Driving with Heterogeneous Data and Physical Constraints
by Maoning Ge, Kento Ohtani, Ming Ding, Yingjie Niu, Yuxiao Zhang and Kazuya Takeda
Sensors 2024, 24(22), 7323; https://doi.org/10.3390/s24227323 - 16 Nov 2024
Cited by 1 | Viewed by 2577
Abstract
The accurate prediction of vehicle behavior is crucial for autonomous driving systems, impacting their safety and efficiency in complex urban environments. To address the challenge of multi-agent trajectory prediction, we propose a novel model integrating multiple input modalities, including historical trajectories, map data, [...] Read more.
The accurate prediction of vehicle behavior is crucial for autonomous driving systems, impacting their safety and efficiency in complex urban environments. To address the challenge of multi-agent trajectory prediction, we propose a novel model integrating multiple input modalities, including historical trajectories, map data, vehicle features, and interaction information. Our approach employs a Conditional Variational Autoencoder (CVAE) framework with a decoder that predicts control actions using the Gaussian Mixture Model (GMM) and then converts these actions into dynamically feasible trajectories through a bicycle model. Evaluated on the nuScenes dataset, the model achieves great performance across key metrics, including minADE5 of 1.26 and minFDE5 of 2.85, demonstrating robust performance across various vehicle types and prediction horizons. These results indicate that integrating multiple data sources, physical models, and probabilistic methods significantly improves trajectory prediction accuracy and reliability for autonomous driving. Our approach generates diverse yet realistic predictions, capturing the multimodal nature of future outcomes while adhering to Physical Constraints and vehicle dynamics. Full article
(This article belongs to the Special Issue AI-Driving for Autonomous Vehicles)
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16 pages, 5554 KiB  
Article
Unmanned Aerial Vehicle Photogrammetry for Monitoring the Geometric Changes of Reclaimed Landfills
by Grzegorz Pasternak, Klaudia Pasternak, Eugeniusz Koda and Paweł Ogrodnik
Sensors 2024, 24(22), 7247; https://doi.org/10.3390/s24227247 - 13 Nov 2024
Cited by 1 | Viewed by 1327
Abstract
Monitoring reclaimed landfills is essential for ensuring their stability and monitoring the regularity of facility settlement. Insufficient recognition of the magnitude and directions of these changes can lead to serious damage to the body of the landfill (landslides, sinkholes) and, consequently, threaten the [...] Read more.
Monitoring reclaimed landfills is essential for ensuring their stability and monitoring the regularity of facility settlement. Insufficient recognition of the magnitude and directions of these changes can lead to serious damage to the body of the landfill (landslides, sinkholes) and, consequently, threaten the environment and the life and health of people near landfills. This study focuses on using UAV photogrammetry to monitor geometric changes in reclaimed landfills. This approach highlights the advantages of UAVs in expanding the monitoring and providing precise information critical for decision-making in the reclamation process. This study presents the result of annual photogrammetry measurements at the Słabomierz–Krzyżówka reclaimed landfill, located in the central part of Poland. The Multiscale Model to Model Cloud Comparison (M3C2) algorithm was used to determine deformation at the landfill. The results were simultaneously compared with the landfill’s reference (angular–linear) measurements. The mean vertical displacement error determined by the photogrammetric method was ±2.3 cm. The results showed that, with an appropriate measurement methodology, it is possible to decide on changes in geometry reliably. The collected 3D data also gives the possibility to improve the decision-making process related to repairing damage or determining the reclamation direction of the landfill, as well as preparing further development plans. Full article
(This article belongs to the Special Issue AI-Based Object Detection and Tracking in UAVs: Challenges and Research Directions)
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37 pages, 3817 KiB  
Review
A Comprehensive Review of Biomarker Sensors for a Breathalyzer Platform
by Pardis Sadeghi, Rania Alshawabkeh, Amie Rui and Nian Xiang Sun
Sensors 2024, 24(22), 7263; https://doi.org/10.3390/s24227263 - 13 Nov 2024
Cited by 2 | Viewed by 2715
Abstract
Detecting volatile organic compounds (VOCs) is increasingly recognized as a pivotal tool in non-invasive disease diagnostics. VOCs are metabolic byproducts, mostly found in human breath, urine, feces, and sweat, whose profiles may shift significantly due to pathological conditions. This paper presents a thorough [...] Read more.
Detecting volatile organic compounds (VOCs) is increasingly recognized as a pivotal tool in non-invasive disease diagnostics. VOCs are metabolic byproducts, mostly found in human breath, urine, feces, and sweat, whose profiles may shift significantly due to pathological conditions. This paper presents a thorough review of the latest advancements in sensor technologies for VOC detection, with a focus on their healthcare applications. It begins by introducing VOC detection principles, followed by a review of the rapidly evolving technologies in this area. Special emphasis is given to functionalized molecularly imprinted polymer-based biochemical sensors for detecting breath biomarkers, owing to their exceptional selectivity. The discussion examines SWaP-C considerations alongside the respective advantages and disadvantages of VOC sensing technologies. The paper also tackles the principal challenges facing the field and concludes by outlining the current status and proposing directions for future research. Full article
(This article belongs to the Section Biosensors)
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14 pages, 2896 KiB  
Article
Sensor Arrays for Electrochemical Detection of PCR-Amplified Genes Extracted from Cells Suspended in Environmental Waters
by Hiroshi Aoki, Mai Kawaguchi, Yukiko Kumakura, Hiroki Kamo, Kazuki Miura, Yuki Hiruta, Siro Simizu and Daniel Citterio
Sensors 2024, 24(22), 7182; https://doi.org/10.3390/s24227182 - 8 Nov 2024
Viewed by 1364
Abstract
Ecological surveys of living things based on DNAs from environmental samples are attractive. However, despite simplicity of water sampling from the target environment, it is still necessary to transport the samples to the laboratory for DNA analysis based on skillful next-generation sequencers. To [...] Read more.
Ecological surveys of living things based on DNAs from environmental samples are attractive. However, despite simplicity of water sampling from the target environment, it is still necessary to transport the samples to the laboratory for DNA analysis based on skillful next-generation sequencers. To perform DNA-oriented surveys based on a simple protocol without any special training, we demonstrated, in this study, the detection of genes from cell-containing environmental waters using gene sensor arrays that require no DNA labeling and no external indicators. Cell-suspended PBS or river water were used as models of environmental waters containing living things, and DNA samples were prepared by PCR amplification. Ferrocene-terminated probes were synthesized and immobilized on an electrode array to develop a sensor array. The sensor array showed a large response to a target DNA complementary to the probe and no response to a mismatched DNA, indicating sequence-specific detection. For DNA samples prepared from the cells in PBS, they showed good responses similar to those for the target DNA. They also significantly detected DNA samples from the cells in river water at a general environmental concentration (38 cells mL−1) with 28-fold larger responses than those for 0 cells mL−1. Full article
(This article belongs to the Special Issue Electrochemical Sensor Applications for Environment Monitoring)
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11 pages, 2010 KiB  
Article
Validation of a 3D Markerless Motion Capture Tool Using Multiple Pose and Depth Estimations for Quantitative Gait Analysis
by Mathis D’Haene, Frédéric Chorin, Serge S. Colson, Olivier Guérin, Raphaël Zory and Elodie Piche
Sensors 2024, 24(22), 7105; https://doi.org/10.3390/s24227105 - 5 Nov 2024
Cited by 3 | Viewed by 2483
Abstract
Gait analysis is essential for evaluating walking patterns and identifying functional limitations. Traditional marker-based motion capture tools are costly, time-consuming, and require skilled operators. This study evaluated a 3D Marker-less Motion Capture (3D MMC) system using pose and depth estimations with the gold-standard [...] Read more.
Gait analysis is essential for evaluating walking patterns and identifying functional limitations. Traditional marker-based motion capture tools are costly, time-consuming, and require skilled operators. This study evaluated a 3D Marker-less Motion Capture (3D MMC) system using pose and depth estimations with the gold-standard Motion Capture (MOCAP) system for measuring hip and knee joint angles during gait at three speeds (0.7, 1.0, 1.3 m/s). Fifteen healthy participants performed gait tasks which were captured by both systems. The 3D MMC system demonstrated good accuracy (LCC > 0.96) and excellent inter-session reliability (RMSE < 3°). However, moderate-to-high accuracy with constant biases was observed during specific gait events, due to differences in sample rates and kinematic methods. Limitations include the use of only healthy participants and limited key points in the pose estimation model. The 3D MMC system shows potential as a reliable tool for gait analysis, offering enhanced usability for clinical and research applications. Full article
(This article belongs to the Special Issue Biomedical Sensors for Diagnosis and Rehabilitation—2nd Edition)
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19 pages, 10067 KiB  
Article
A Novel Panorama Depth Estimation Framework for Autonomous Driving Scenarios Based on a Vision Transformer
by Yuqi Zhang, Liang Chu, Zixu Wang, He Tong, Jincheng Hu and Jihao Li
Sensors 2024, 24(21), 7013; https://doi.org/10.3390/s24217013 - 31 Oct 2024
Cited by 1 | Viewed by 1788
Abstract
An accurate panorama depth estimation result is crucial to risk perception in autonomous driving practice. In this paper, an innovative framework is presented to address the challenges of imperfect observation and projection fusion in panorama depth estimation, enabling the accurate capture of distances [...] Read more.
An accurate panorama depth estimation result is crucial to risk perception in autonomous driving practice. In this paper, an innovative framework is presented to address the challenges of imperfect observation and projection fusion in panorama depth estimation, enabling the accurate capture of distances from surrounding images in driving scenarios. First, the Patch Filling method is proposed to alleviate the imperfect observation of panoramic depth in autonomous driving scenarios, which constructs a panoramic depth map based on the sparse distance data provided by the 3D point cloud. Then, in order to tackle the distortion challenge faced by outdoor panoramic images, a method for image context learning, ViT-Fuse, is proposed and specifically designed for equirectangular panoramic views. The experimental results show that the proposed ViT-Fuse reduces the estimation error by 9.15% on average in driving scenarios compared with the basic method and exhibits more robust and smoother results on the edge details of the depth estimation maps. Full article
(This article belongs to the Special Issue Large AI Models for Positioning and Perception in Autonomous Driving)
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17 pages, 978 KiB  
Review
TRPV4—A Multifunctional Cellular Sensor Protein with Therapeutic Potential
by Sanna Koskimäki and Sari Tojkander
Sensors 2024, 24(21), 6923; https://doi.org/10.3390/s24216923 - 29 Oct 2024
Cited by 2 | Viewed by 2657
Abstract
Transient receptor potential vanilloid (TRPV) channel proteins belong to the superfamily of TRP proteins that form cationic channels in the animal cell membranes. These proteins have various subtype-specific functions, serving, for example, as sensors for pain, pressure, pH, and mechanical extracellular stimuli. The [...] Read more.
Transient receptor potential vanilloid (TRPV) channel proteins belong to the superfamily of TRP proteins that form cationic channels in the animal cell membranes. These proteins have various subtype-specific functions, serving, for example, as sensors for pain, pressure, pH, and mechanical extracellular stimuli. The sensing of extracellular cues by TRPV4 triggers Ca2+-influx through the channel, subsequently coordinating numerous intracellular signaling cascades in a spatio-temporal manner. As TRPV channels play such a wide role in various cellular and physiological functions, loss or impaired TRPV protein activity naturally contributes to many pathophysiological processes. This review concentrates on the known functions of TRPV4 sensor proteins and their potential as a therapeutic target. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2024)
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32 pages, 15095 KiB  
Article
Multi-Sensor Soil Probe and Machine Learning Modeling for Predicting Soil Properties
by Sabine Grunwald, Mohammad Omar Faruk Murad, Stephen Farrington, Woody Wallace and Daniel Rooney
Sensors 2024, 24(21), 6855; https://doi.org/10.3390/s24216855 - 25 Oct 2024
Cited by 4 | Viewed by 6397
Abstract
We present a data-driven, in situ proximal multi-sensor digital soil mapping approach to develop digital twins for multiple agricultural fields. A novel Digital Soil CoreTM (DSC) Probe was engineered that contains seven sensors, each of a distinct modality, including sleeve friction, tip [...] Read more.
We present a data-driven, in situ proximal multi-sensor digital soil mapping approach to develop digital twins for multiple agricultural fields. A novel Digital Soil CoreTM (DSC) Probe was engineered that contains seven sensors, each of a distinct modality, including sleeve friction, tip force, dielectric permittivity, electrical resistivity, soil imagery, acoustics, and visible and near-infrared spectroscopy. The DSC System integrates the DSC Probe, DSC software (v2023.10), and deployment equipment components to sense soil characteristics at a high vertical spatial resolution (mm scale) along in situ soil profiles up to a depth of 120 cm in about 60 s. The DSC Probe in situ proximal data are harmonized into a data cube providing vertical high-density knowledge associated with physical–chemical–biological soil conditions. In contrast, conventional ex situ soil samples derived from soil cores, soil pits, or surface samples analyzed using laboratory and other methods are bound by a substantially coarser spatial resolution and multiple compounding errors. Our objective was to investigate the effects of the mismatched scale between high-resolution in situ proximal sensor data and coarser-resolution ex situ soil laboratory measurements to develop soil prediction models. Our study was conducted in central California soil in almond orchards. We collected DSC sensor data and spatially co-located soil cores that were sliced into narrow layers for laboratory-based soil measurements. Partial Least Squares Regression (PLSR) cross-validation was used to compare the results of testing four data integration methods. Method A reduced the high-resolution sensor data to discrete values paired with layer-based soil laboratory measurements. Method B used stochastic distributions of sensor data paired with layer-based soil laboratory measurements. Method C allocated the same soil analytical data to each one of the high-resolution multi-sensor data within a soil layer. Method D linked the high-density multi-sensor soil data directly to crop responses (crop performance and behavior metrics), bypassing costly laboratory soil analysis. Overall, the soil models derived from Method C outperformed Methods A and B. Soil predictions derived using Method D were the most cost-effective for directly assessing soil–crop relationships, making this method well suited for industrial-scale precision agriculture applications. Full article
(This article belongs to the Section Smart Agriculture)
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22 pages, 1654 KiB  
Article
A New Scene Sensing Model Based on Multi-Source Data from Smartphones
by Zhenke Ding, Zhongliang Deng, Enwen Hu, Bingxun Liu, Zhichao Zhang and Mingyang Ma
Sensors 2024, 24(20), 6669; https://doi.org/10.3390/s24206669 - 16 Oct 2024
Viewed by 1194
Abstract
Smartphones with integrated sensors play an important role in people’s lives, and in advanced multi-sensor fusion navigation systems, the use of individual sensor information is crucial. Because of the different environments, the weights of the sensors will be different, which will also affect [...] Read more.
Smartphones with integrated sensors play an important role in people’s lives, and in advanced multi-sensor fusion navigation systems, the use of individual sensor information is crucial. Because of the different environments, the weights of the sensors will be different, which will also affect the method and results of multi-source fusion positioning. Based on the multi-source data from smartphone sensors, this study explores five types of information—Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), cellular networks, optical sensors, and Wi-Fi sensors—characterizing the temporal, spatial, and mathematical statistical features of the data, and it constructs a multi-scale, multi-window, and context-connected scene sensing model to accurately detect the environmental scene in indoor, semi-indoor, outdoor, and semi-outdoor spaces, thus providing a good basis for multi-sensor positioning in a multi-sensor navigation system. Detecting environmental scenes provides an environmental positioning basis for multi-sensor fusion localization. This model is divided into four main parts: multi-sensor-based data mining, a multi-scale convolutional neural network (CNN), a bidirectional long short-term memory (BiLSTM) network combined with contextual information, and a meta-heuristic optimization algorithm. Full article
(This article belongs to the Special Issue Smart Sensor Systems for Positioning and Navigation)
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17 pages, 8226 KiB  
Article
Design of a Capacitive Tactile Sensor Array System for Human–Computer Interaction
by Fei Fei, Zhenkun Jia, Changcheng Wu, Xiong Lu and Zhi Li
Sensors 2024, 24(20), 6629; https://doi.org/10.3390/s24206629 - 14 Oct 2024
Cited by 2 | Viewed by 1422
Abstract
This paper introduces a novel capacitive sensor array designed for tactile perception applications. Utilizing an all-in-one inkjet deposition printing process, the sensor array exhibited exceptional flexibility and accuracy. With a resolution of up to 32.7 dpi, the sensor array was capable of capturing [...] Read more.
This paper introduces a novel capacitive sensor array designed for tactile perception applications. Utilizing an all-in-one inkjet deposition printing process, the sensor array exhibited exceptional flexibility and accuracy. With a resolution of up to 32.7 dpi, the sensor array was capable of capturing the fine details of touch inputs, making it suitable for applications requiring high spatial resolution. The design incorporates two multiplexers to achieve a scanning rate of 100 Hz, ensuring the rapid and responsive data acquisition that is essential for real-time feedback in interactive applications, such as gesture recognition and haptic interfaces. To evaluate the performance of the capacitive sensor array, an experiment that involved handwritten number recognition was conducted. The results demonstrated that the sensor accurately captured fingertip inputs with a high precision. When combined with an Auxiliary Classifier Generative Adversarial Network (ACGAN) algorithm, the sensor system achieved a recognition accuracy of 98% for various handwritten numbers from “0” to “9”. These results show the potential of the capacitive sensor array for advanced human–computer interaction applications. Full article
(This article belongs to the Section Sensors Development)
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21 pages, 2895 KiB  
Article
Traffic Flow Prediction in 5G-Enabled Intelligent Transportation Systems Using Parameter Optimization and Adaptive Model Selection
by Hanh Hong-Phuc Vo, Thuan Minh Nguyen, Khoi Anh Bui and Myungsik Yoo
Sensors 2024, 24(20), 6529; https://doi.org/10.3390/s24206529 - 10 Oct 2024
Cited by 2 | Viewed by 1934
Abstract
This study proposes a novel hybrid method, FVMD-WOA-GA, for enhancing traffic flow prediction in 5G-enabled intelligent transportation systems. The method integrates fast variational mode decomposition (FVMD) with optimization techniques, namely, the whale optimization algorithm (WOA) and genetic algorithm (GA), to improve the accuracy [...] Read more.
This study proposes a novel hybrid method, FVMD-WOA-GA, for enhancing traffic flow prediction in 5G-enabled intelligent transportation systems. The method integrates fast variational mode decomposition (FVMD) with optimization techniques, namely, the whale optimization algorithm (WOA) and genetic algorithm (GA), to improve the accuracy of overall traffic flow based on models tailored for each decomposed sub-sequence. The selected predictive models—long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU)—were considered to capture diverse temporal dependencies in traffic data. This research explored a multi-stage approach, where the decomposition, optimization, and selection of models are performed systematically to improve prediction performance. Experimental validation on two real-world traffic datasets further underscores the method’s efficacy, achieving root mean squared errors (RMSEs) of 152.43 and 7.91 on the respective datasets, which marks improvements of 3.44% and 12.87% compared to the existing methods. These results highlight the ability of the FVMD-WOA-GA approach to improve prediction accuracy significantly, reduce inference time, enhance system adaptability, and contribute to more efficient traffic management. Full article
(This article belongs to the Section Sensor Networks)
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18 pages, 5232 KiB  
Article
Vehicle and Pedestrian Traffic Signal Performance Measures Using LiDAR-Derived Trajectory Data
by Enrique D. Saldivar-Carranza, Jairaj Desai, Andrew Thompson, Mark Taylor, James Sturdevant and Darcy M. Bullock
Sensors 2024, 24(19), 6410; https://doi.org/10.3390/s24196410 - 3 Oct 2024
Viewed by 1802
Abstract
Light Detection and Ranging (LiDAR) sensors at signalized intersections can accurately track the movement of virtually all objects passing through at high sampling rates. This study presents methodologies to estimate vehicle and pedestrian traffic signal performance measures using LiDAR trajectory data. Over 15,000,000 [...] Read more.
Light Detection and Ranging (LiDAR) sensors at signalized intersections can accurately track the movement of virtually all objects passing through at high sampling rates. This study presents methodologies to estimate vehicle and pedestrian traffic signal performance measures using LiDAR trajectory data. Over 15,000,000 vehicle and 170,000 pedestrian waypoints detected during a 24 h period at an intersection in Utah are analyzed to describe the proposed techniques. Sampled trajectories are linear referenced to generate Purdue Probe Diagrams (PPDs). Vehicle-based PPDs are used to estimate movement level turning counts, 85th percentile queue lengths (85QL), arrivals on green (AOG), highway capacity manual (HCM) level of service (LOS), split failures (SF), and downstream blockage (DSB) by time of day (TOD). Pedestrian-based PPDs are used to estimate wait times and the proportion of people that traverse multiple crosswalks. Although vehicle signal performance can be estimated from several days of aggregated connected vehicle (CV) data, LiDAR data provides the ability to measure performance in real time. Furthermore, LiDAR can measure pedestrian speeds. At the studied location, the 15th percentile pedestrian walking speed was estimated to be 3.9 ft/s. The ability to directly measure these pedestrian speeds allows agencies to consider alternative crossing times than those suggested by the Manual on Uniform Traffic Control Devices (MUTCD). Full article
(This article belongs to the Section Radar Sensors)
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26 pages, 542 KiB  
Review
WiFi-Based Human Identification with Machine Learning: A Comprehensive Survey
by Manal Mosharaf, Jae B. Kwak and Wooyeol Choi
Sensors 2024, 24(19), 6413; https://doi.org/10.3390/s24196413 - 3 Oct 2024
Cited by 2 | Viewed by 3980
Abstract
In the modern world of human–computer interaction, notable advancements in human identification have been achieved across fields like healthcare, academia, security, etc. Despite these advancements, challenges remain, particularly in scenarios with poor lighting, occlusion, or non-line-of-sight. To overcome these limitations, the utilization of [...] Read more.
In the modern world of human–computer interaction, notable advancements in human identification have been achieved across fields like healthcare, academia, security, etc. Despite these advancements, challenges remain, particularly in scenarios with poor lighting, occlusion, or non-line-of-sight. To overcome these limitations, the utilization of radio frequency (RF) wireless signals, particularly wireless fidelity (WiFi), has been considered an innovative solution in recent research studies. By analyzing WiFi signal fluctuations caused by human presence, researchers have developed machine learning (ML) models that significantly improve identification accuracy. This paper conducts a comprehensive survey of recent advances and practical implementations of WiFi-based human identification. Furthermore, it covers the ML models used for human identification, system overviews, and detailed WiFi-based human identification methods. It also includes system evaluation, discussion, and future trends related to human identification. Finally, we conclude by examining the limitations of the research and discussing how researchers can shift their attention toward shaping the future trajectory of human identification through wireless signals. Full article
(This article belongs to the Special Issue Wireless Sensors and Wireless Sensor Networks for Engineering Applications)
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12 pages, 3136 KiB  
Article
Enhancing Time-of-Flight Diffraction (TOFD) Inspection through an Innovative Curved-Sole Probe Design
by Irati Sanchez Duo, Jose Luis Lanzagorta, Iratxe Aizpurua Maestre and Lander Galdos
Sensors 2024, 24(19), 6360; https://doi.org/10.3390/s24196360 - 30 Sep 2024
Viewed by 1796
Abstract
Time-of-Flight Diffraction (TOFD) is a method of ultrasonic testing (UT) that is widely established as a non-destructive technique (NDT) mainly used for the inspection of welds. In contrast to other established UT techniques, TOFD is capable of identifying discontinuities regardless of their orientation. [...] Read more.
Time-of-Flight Diffraction (TOFD) is a method of ultrasonic testing (UT) that is widely established as a non-destructive technique (NDT) mainly used for the inspection of welds. In contrast to other established UT techniques, TOFD is capable of identifying discontinuities regardless of their orientation. This paper proposes a redesign of the typical TOFD transducers, featuring an innovative curved sole aimed at enhancing their defect detection capabilities. This design is particularly beneficial for thick-walled samples, as it allows for deeper inspections without compromising the resolution near the surface area. During this research, an evaluation consisting in simulations of the ultrasonic beam distribution and experimental tests on a component with artificially manufactured defects at varying depths has been performed to validate the new design. The results demonstrate a 30 to 50% higher beam distribution area as well as an improvement in the signal-to-noise ratio (SNR) resulting in a 24% enhancement in the capability of defect detection compared to the traditional approach. Full article
(This article belongs to the Section Industrial Sensors)
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18 pages, 7421 KiB  
Article
Enhanced Visual SLAM for Collision-Free Driving with Lightweight Autonomous Cars
by Zhihao Lin, Zhen Tian, Qi Zhang, Hanyang Zhuang and Jianglin Lan
Sensors 2024, 24(19), 6258; https://doi.org/10.3390/s24196258 - 27 Sep 2024
Cited by 5 | Viewed by 2076
Abstract
The paper presents a vision-based obstacle avoidance strategy for lightweight self-driving cars that can be run on a CPU-only device using a single RGB-D camera. The method consists of two steps: visual perception and path planning. The visual perception part uses ORBSLAM3 enhanced [...] Read more.
The paper presents a vision-based obstacle avoidance strategy for lightweight self-driving cars that can be run on a CPU-only device using a single RGB-D camera. The method consists of two steps: visual perception and path planning. The visual perception part uses ORBSLAM3 enhanced with optical flow to estimate the car’s poses and extract rich texture information from the scene. In the path planning phase, the proposed method employs a method combining a control Lyapunov function and control barrier function in the form of a quadratic program (CLF-CBF-QP) together with an obstacle shape reconstruction process (SRP) to plan safe and stable trajectories. To validate the performance and robustness of the proposed method, simulation experiments were conducted with a car in various complex indoor environments using the Gazebo simulation environment. The proposed method can effectively avoid obstacles in the scenes. The proposed algorithm outperforms benchmark algorithms in achieving more stable and shorter trajectories across multiple simulated scenes. Full article
(This article belongs to the Special Issue Intelligent Control Systems for Autonomous Vehicles)
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65 pages, 19918 KiB  
Review
Radiation Detectors and Sensors in Medical Imaging
by Christos Michail, Panagiotis Liaparinos, Nektarios Kalyvas, Ioannis Kandarakis, George Fountos and Ioannis Valais
Sensors 2024, 24(19), 6251; https://doi.org/10.3390/s24196251 - 26 Sep 2024
Cited by 5 | Viewed by 6262
Abstract
Medical imaging instrumentation design and construction is based on radiation sources and radiation detectors/sensors. This review focuses on the detectors and sensors of medical imaging systems. These systems are subdivided into various categories depending on their structure, the type of radiation they capture, [...] Read more.
Medical imaging instrumentation design and construction is based on radiation sources and radiation detectors/sensors. This review focuses on the detectors and sensors of medical imaging systems. These systems are subdivided into various categories depending on their structure, the type of radiation they capture, how the radiation is measured, how the images are formed, and the medical goals they serve. Related to medical goals, detectors fall into two major areas: (i) anatomical imaging, which mainly concerns the techniques of diagnostic radiology, and (ii) functional-molecular imaging, which mainly concerns nuclear medicine. An important parameter in the evaluation of the detectors is the combination of the quality of the diagnostic result they offer and the burden of the patient with radiation dose. The latter has to be minimized; thus, the input signal (radiation photon flux) must be kept at low levels. For this reason, the detective quantum efficiency (DQE), expressing signal-to-noise ratio transfer through an imaging system, is of primary importance. In diagnostic radiology, image quality is better than in nuclear medicine; however, in most cases, the dose is higher. On the other hand, nuclear medicine focuses on the detection of functional findings and not on the accurate spatial determination of anatomical data. Detectors are integrated into projection or tomographic imaging systems and are based on the use of scintillators with optical sensors, photoconductors, or semiconductors. Analysis and modeling of such systems can be performed employing theoretical models developed in the framework of cascaded linear systems analysis (LCSA), as well as within the signal detection theory (SDT) and information theory. Full article
(This article belongs to the Special Issue Multiple Sensor Signal and Image Processing for Clinical Application)
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17 pages, 6457 KiB  
Article
A Cumulant-Based Method for Acquiring GNSS Signals
by He-Sheng Wang, Hou-Yu Wang and Dah-Jing Jwo
Sensors 2024, 24(19), 6234; https://doi.org/10.3390/s24196234 - 26 Sep 2024
Cited by 3 | Viewed by 1182
Abstract
Global Navigation Satellite Systems (GNSS) provide positioning, velocity, and time services for civilian applications. A critical step in the positioning process is the acquisition of visible satellites in the sky. Modern GNSS systems, such as Galileo—developed and maintained by the European Union—utilize a [...] Read more.
Global Navigation Satellite Systems (GNSS) provide positioning, velocity, and time services for civilian applications. A critical step in the positioning process is the acquisition of visible satellites in the sky. Modern GNSS systems, such as Galileo—developed and maintained by the European Union—utilize a new modulation technique known as Binary Offset Carrier (BOC). However, BOC signals introduce multiple side-peaks in their autocorrelation function, which can lead to significant errors during the acquisition process. In this paper, we propose a novel acquisition method based on higher-order cumulants that effectively eliminates these side-peaks. This method is capable of simultaneously acquiring both conventional ranging signals, such as GPS C/A code, and BOC-modulated signals. The effectiveness of the proposed method is demonstrated through the acquisition of simulated signals, with a comparison to traditional methods. Additionally, we apply the proposed method to real satellite signals to further validate its performance. Our results show that the proposed method successfully suppresses side-peaks, improves acquisition accuracy in weak signal environments, and demonstrates potential for indoor GNSS applications. The study concludes that while the method may increase computational load, its performance in challenging conditions makes it a promising approach for future GNSS receiver designs. Full article
(This article belongs to the Special Issue Advances in GNSS Signal Processing and Navigation)
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10 pages, 945 KiB  
Article
The Validity of Apple Watch Series 9 and Ultra 2 for Serial Measurements of Heart Rate Variability and Resting Heart Rate
by Ben O’Grady, Rory Lambe, Maximus Baldwin, Tara Acheson and Cailbhe Doherty
Sensors 2024, 24(19), 6220; https://doi.org/10.3390/s24196220 - 26 Sep 2024
Cited by 7 | Viewed by 11761
Abstract
The widespread use of wearable devices has enabled continuous monitoring of biometric data, including heart rate variability (HRV) and resting heart rate (RHR). However, the validity of these measurements, particularly from consumer devices like Apple Watch, remains underexplored. This study aimed to validate [...] Read more.
The widespread use of wearable devices has enabled continuous monitoring of biometric data, including heart rate variability (HRV) and resting heart rate (RHR). However, the validity of these measurements, particularly from consumer devices like Apple Watch, remains underexplored. This study aimed to validate HRV measurements obtained from Apple Watch Series 9 and Ultra 2 against the Polar H10 chest strap paired with the Kubios HRV software, which together served as the reference standard. A prospective cohort of 39 healthy adults provided 316 HRV measurements over a 14-day period. Generalized Estimating Equations were used to assess the difference in HRV between devices, accounting for repeated measures. Apple Watch tended to underestimate HRV by an average of 8.31 ms compared to the Polar H10 (p = 0.025), with a mean absolute percentage error (MAPE) of 28.88% and a mean absolute error (MAE) of 20.46 ms. The study found no significant impact of RHR discrepancies on HRV differences (p = 0.156), with RHR showing a mean difference of −0.08 bpm, an MAPE of 5.91%, and an MAE of 3.73 bpm. Equivalence testing indicated that the HRV measurements from Apple Watch did not fall within the pre-specified equivalence margin of ±10 ms. Despite accurate RHR measurements, these findings underscore the need for improved HRV algorithms in consumer wearables and caution in interpreting HRV data for clinical or performance monitoring. Full article
(This article belongs to the Collection Sensors for Gait, Human Movement Analysis, and Health Monitoring)
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26 pages, 9754 KiB  
Review
Gas Sensing Properties of Indium–Oxide–Based Field–Effect Transistor: A Review
by Chengyao Liang, Zhongyu Cao, Jiongyue Hao, Shili Zhao, Yuanting Yu, Yingchun Dong, Hangyu Liu, Chun Huang, Chao Gao, Yong Zhou and Yong He
Sensors 2024, 24(18), 6150; https://doi.org/10.3390/s24186150 - 23 Sep 2024
Cited by 1 | Viewed by 2834
Abstract
Excellent stability, low cost, high response, and sensitivity of indium oxide (In2O3), a metal oxide semiconductor, have been verified in the field of gas sensing. Conventional In2O3 gas sensors employ simple and easy–to–manufacture resistive components as [...] Read more.
Excellent stability, low cost, high response, and sensitivity of indium oxide (In2O3), a metal oxide semiconductor, have been verified in the field of gas sensing. Conventional In2O3 gas sensors employ simple and easy–to–manufacture resistive components as transducers. However, the swift advancement of the Internet of Things has raised higher requirements for gas sensors based on metal oxides, primarily including lowering operating temperatures, improving selectivity, and realizing integrability. In response to these three main concerns, field–effect transistor (FET) gas sensors have garnered growing interest over the past decade. When compared with other metal oxide semiconductors, In2O3 exhibits greater carrier concentration and mobility. The property is advantageous for manufacturing FETs with exceptional electrical performance, provided that the off–state current is controlled at a sufficiently low level. This review presents the significant progress made in In2O3 FET gas sensors during the last ten years, covering typical device designs, gas sensing performance indicators, optimization techniques, and strategies for the future development based on In2O3 FET gas sensors. Full article
(This article belongs to the Special Issue Inorganic Nanostructure-Based Sensors: Design and Applications)
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19 pages, 575 KiB  
Article
Jointly Optimization of Delay and Energy Consumption for Multi-Device FDMA in WPT-MEC System
by Danxia Qiao, Lu Sun, Dianju Li, Huajie Xiong, Rina Liang, Zhenyuan Han and Liangtian Wan
Sensors 2024, 24(18), 6123; https://doi.org/10.3390/s24186123 - 22 Sep 2024
Cited by 2 | Viewed by 1800
Abstract
With the rapid development of mobile edge computing (MEC) and wireless power transfer (WPT) technologies, the MEC-WPT system makes it possible to provide high-quality data processing services for end users. However, in a real-world WPT-MEC system, the channel gain decreases with the transmission [...] Read more.
With the rapid development of mobile edge computing (MEC) and wireless power transfer (WPT) technologies, the MEC-WPT system makes it possible to provide high-quality data processing services for end users. However, in a real-world WPT-MEC system, the channel gain decreases with the transmission distance, leading to “double near and far effect” in the joint transmission of wireless energy and data, which affects the quality of the data processing service for end users. Consequently, it is essential to design a reasonable system model to overcome the “double near and far effect” and make reasonable scheduling of multi-dimensional resources such as energy, communication and computing to guarantee high-quality data processing services. First, this paper designs a relay collaboration WPT-MEC resource scheduling model to improve wireless energy utilization efficiency. The optimization goal is to minimize the normalization of the total communication delay and total energy consumption while meeting multiple resource constraints. Second, this paper imports a BK-means algorithm to complete the end terminals cluster to guarantee effective energy reception and adapts the whale optimization algorithm with adaptive mechanism (AWOA) for mobile vehicle path-planning to reduce energy waste. Third, this paper proposes an immune differential enhanced deep deterministic policy gradient (IDDPG) algorithm to realize efficient resource scheduling of multiple resources and minimize the optimization goal. Finally, simulation experiments are carried out on different data, and the simulation results prove the validity of the designed scheduling model and proposed IDDPG. Full article
(This article belongs to the Special Issue Integrated Sensing, Communication, and Computing Networks for IoT Applications)
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26 pages, 3911 KiB  
Review
Emerging Paradigms in Fetal Heart Rate Monitoring: Evaluating the Efficacy and Application of Innovative Textile-Based Wearables
by Md Raju Ahmed, Samantha Newby, Prasad Potluri, Wajira Mirihanage and Anura Fernando
Sensors 2024, 24(18), 6066; https://doi.org/10.3390/s24186066 - 19 Sep 2024
Cited by 3 | Viewed by 6075
Abstract
This comprehensive review offers a thorough examination of fetal heart rate (fHR) monitoring methods, which are an essential component of prenatal care for assessing fetal health and identifying possible problems early on. It examines the clinical uses, accuracy, and limitations of both modern [...] Read more.
This comprehensive review offers a thorough examination of fetal heart rate (fHR) monitoring methods, which are an essential component of prenatal care for assessing fetal health and identifying possible problems early on. It examines the clinical uses, accuracy, and limitations of both modern and traditional monitoring techniques, such as electrocardiography (ECG), ballistocardiography (BCG), phonocardiography (PCG), and cardiotocography (CTG), in a variety of obstetric scenarios. A particular focus is on the most recent developments in textile-based wearables for fHR monitoring. These innovative devices mark a substantial advancement in the field and are noteworthy for their continuous data collection capability and ergonomic design. The review delves into the obstacles that arise when incorporating these wearables into clinical practice. These challenges include problems with signal quality, user compliance, and data interpretation. Additionally, it looks at how these technologies could improve fetal health surveillance by providing expectant mothers with more individualized and non-intrusive options, which could change the prenatal monitoring landscape. Full article
(This article belongs to the Section Wearables)
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25 pages, 12251 KiB  
Article
Laser Scanner-Based Hyperboloid Cooling Tower Geometry Inspection: Thickness and Deformation Mapping
by Maria Makuch, Pelagia Gawronek and Bartosz Mitka
Sensors 2024, 24(18), 6045; https://doi.org/10.3390/s24186045 - 18 Sep 2024
Cited by 2 | Viewed by 1430
Abstract
Hyperboloid cooling towers are counted among the largest cast-in-place industrial structures. They are an essential element of cooling systems used in many power plants in service today. Their main structural component, a reinforced-concrete shell in the form of a one-sheet hyperboloid with bidirectional [...] Read more.
Hyperboloid cooling towers are counted among the largest cast-in-place industrial structures. They are an essential element of cooling systems used in many power plants in service today. Their main structural component, a reinforced-concrete shell in the form of a one-sheet hyperboloid with bidirectional curvature continuity, makes them stand out against other towers and poses very high construction and service requirements. The safe service and adequate durability of the hyperboloid structure are guaranteed by the proper geometric parameters of the reinforced-concrete shell and monitoring of their condition over time. This article presents an original concept for employing terrestrial laser scanning to conduct an end-to-end assessment of the geometric condition of a hyperboloid cooling tower as required by industry standards. The novelty of the proposed solution lies in the use of measurements of the interior of the structure to determine the actual thickness of the hyperboloid shell, which is generally disregarded in geometric measurements of such objects. The proposal involves several strategies and procedures for a reliable verification of the structure’s verticality, the detection of signs of ovalisation of the shell, the estimation of the parameters of the structure’s theoretical model, and the analysis of the distribution of the thickness and geometric imperfections of the reinforced-concrete shell. The idea behind the method for determining the actual thickness of the shell (including its variation due to repairs and reinforcement operations), which is generally disregarded when measuring the geometry of such structures, is to estimate the distance between point clouds of the internal and external surfaces of the structure using the M3C2 algorithm principle. As a particularly dangerous geometric anomaly of hyperboloid cooling towers, shell ovalisation is detected with an innovative analysis of the bimodality of the frequency distribution of radial deviations in horizontal cross-sections. The concept of a complete assessment of the geometry of a hyperboloid cooling tower was devised and validated using three measurement series of a structure that has been continuously in service for fifty years. The results are consistent with data found in design and service documents. We identified a permanent tilt of the structure’s axis to the northeast and geometric imperfections of the hyperboloid shell from −0.125 m to +0.136 m. The results also demonstrated no advancing deformation of the hyperboloid shell over a two-year research period, which is vital for its further use. Full article
(This article belongs to the Section Industrial Sensors)
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12 pages, 8700 KiB  
Article
A Sensor Probe with Active and Passive Humidity Management for In Situ Soil CO2 Monitoring
by Jacob F. Anderson, David P. Huber and Owen A. Walsh
Sensors 2024, 24(18), 6034; https://doi.org/10.3390/s24186034 - 18 Sep 2024
Viewed by 2130
Abstract
Soil CO2 concentration and flux measurements are important in diverse fields, including geoscience, climate science, soil ecology, and agriculture. However, practitioners in these fields face difficulties with existing soil CO2 gas probes, which have had problems with high costs and frequent [...] Read more.
Soil CO2 concentration and flux measurements are important in diverse fields, including geoscience, climate science, soil ecology, and agriculture. However, practitioners in these fields face difficulties with existing soil CO2 gas probes, which have had problems with high costs and frequent failures when deployed. Confronted with a recent research project’s need for long-term in-soil CO2 monitoring at a large number of sites in harsh environmental conditions, we developed our own CO2 logging system to reduce expense and avoid the expected failures of commercial instruments. Our newly developed soil probes overcome the central challenge of soil gas probes—surviving continuous exposure to soil moisture while remaining open to soil gases—via three approaches: a 3D printed housing (economical for small-scale production) following design principles that correct the usual water permeability flaw of 3D printed materials; passive moisture protection via a hydrophobic, CO2-permeable PTFE membrane; and active moisture protection via a low-power micro-dehumidifier. Our CO2 instrumentation performed well and yielded a high-quality dataset that includes signals related to a prescribed fire as well as seasonal and diel cycles. We expect our technology to support underground CO2 monitoring in fields where it is already practiced and stimulate its expansion into diverse new fields. Full article
(This article belongs to the Section Environmental Sensing)
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16 pages, 4816 KiB  
Article
Aerogel-Lined Capillaries as Liquid-Core Waveguides for Raman Signal Gain of Aqueous Samples: Advanced Manufacturing and Performance Characterization
by Felix Spiske, Lara Sophie Jakob, Maximilian Lippold, Parvaneh Rahimi, Yvonne Joseph and Andreas Siegfried Braeuer
Sensors 2024, 24(18), 5979; https://doi.org/10.3390/s24185979 - 14 Sep 2024
Viewed by 1956
Abstract
An advanced process for the manufacturing of aerogel-lined capillaries is presented; these are applicable as liquid-core waveguides for gaining the Raman signal of aqueous samples. With respect to the spin-coating process we have used so far for the manufacturing of aerogel-lined capillaries, the [...] Read more.
An advanced process for the manufacturing of aerogel-lined capillaries is presented; these are applicable as liquid-core waveguides for gaining the Raman signal of aqueous samples. With respect to the spin-coating process we have used so far for the manufacturing of aerogel-lined capillaries, the here-presented manufacturing process is advanced as it enables (i) the lining of longer capillaries, (ii) the adjustment of the lining-thickness via the lining velocity, and (iii) the reproducible generation of crack-free linings. The key parameters of the advanced process and their effect on the fabrication of aerogel-lined capillaries with optimal Raman signal gain are reported and related to the thickness and topography of the aerogel linings by the support of scanning electron microscopy. Full article
(This article belongs to the Section Sensors Development)
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14 pages, 4333 KiB  
Article
Eddy Current-Based Delamination Imaging in CFRP Using Erosion and Thresholding Approaches
by Dario J. Pasadas, Mohsen Barzegar, Artur L. Ribeiro and Helena G. Ramos
Sensors 2024, 24(18), 5932; https://doi.org/10.3390/s24185932 - 13 Sep 2024
Cited by 1 | Viewed by 1388
Abstract
Carbon fiber reinforced plastic (CFRP) is a composite material known for its high strength-to-weight ratio, stiffness, and corrosion and fatigue resistance, making it suitable for its use in structural components. However, CFRP can be subject to various types of damage, such as delamination, [...] Read more.
Carbon fiber reinforced plastic (CFRP) is a composite material known for its high strength-to-weight ratio, stiffness, and corrosion and fatigue resistance, making it suitable for its use in structural components. However, CFRP can be subject to various types of damage, such as delamination, matrix cracking, or fiber breakage, requiring nondestructive evaluation to ensure structural integrity. In this context, damage imaging algorithms are important for assessing the condition of this material. This paper presents signal and image processing methods for delamination characterization of thin CFRP plates using eddy current testing (ECT). The measurement system included an inductive ECT probe with three coil elements, which has the characteristic of allowing eddy currents to be induced in the specimen with two different configurations. In this study, the peak amplitude of the induced voltage in the receiver element and the phase shift between the excitation and receiver signals were considered as damage-sensitive features. Using the ECT probe, C-scans were performed in the vicinity of delamination defects of different sizes. The dimensions and shape of the ECT probe were considered by applying the erosion method in the damage imaging process. Different thresholding approaches were also investigated to extract the size of the defective areas. To evaluate the impact of this application, a comparison is made between the results obtained before and after thresholding using histogram analysis. The evaluation of damage imaging for three different delamination sizes is presented for quantitative analysis. Full article
(This article belongs to the Special Issue Sensors in Nondestructive Testing)
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13 pages, 3502 KiB  
Article
New, Optimized Skin Calorimeter Version for Measuring Thermal Responses of Localized Skin Areas during Physical Activity
by Miriam Rodríguez de Rivera and Pedro Jesús Rodríguez de Rivera
Sensors 2024, 24(18), 5927; https://doi.org/10.3390/s24185927 - 12 Sep 2024
Cited by 3 | Viewed by 1432
Abstract
We present an optimized version of the skin calorimeter for measuring localized skin thermal responses during physical activity. Enhancements include a new holding system, more sensitive thermopiles, and an upgraded spiked heat sink for improved efficiency. In addition, we used a new, improved [...] Read more.
We present an optimized version of the skin calorimeter for measuring localized skin thermal responses during physical activity. Enhancements include a new holding system, more sensitive thermopiles, and an upgraded spiked heat sink for improved efficiency. In addition, we used a new, improved calorimetric model that takes into account all the variables that influence the measurement process. Resolution in power measurement is 1 mW. Performance tests under air currents and movement disturbances showed that the device maintains high accuracy; the deviation produced by these significant disturbances is less than 5%. Human subject tests, both at rest and during exercise, confirmed its ability to accurately measure localized skin heat flux, heat capacity, and thermal resistance (less than 5% uncertainty). These findings highlight the calorimeter’s potential for applications in sports medicine and physiological studies. Full article
(This article belongs to the Special Issue Smart Wearable Health Monitoring Systems: Materials, Sensors, Nanogenerators and Self-Powered Applications)
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22 pages, 5266 KiB  
Article
Self-Supervised Dam Deformation Anomaly Detection Based on Temporal–Spatial Contrast Learning
by Yu Wang and Guohua Liu
Sensors 2024, 24(17), 5858; https://doi.org/10.3390/s24175858 - 9 Sep 2024
Cited by 3 | Viewed by 1672
Abstract
The detection of anomalies in dam deformation is paramount for evaluating structural integrity and facilitating early warnings, representing a critical aspect of dam health monitoring (DHM). Conventional data-driven methods for dam anomaly detection depend extensively on historical data; however, obtaining annotated data is [...] Read more.
The detection of anomalies in dam deformation is paramount for evaluating structural integrity and facilitating early warnings, representing a critical aspect of dam health monitoring (DHM). Conventional data-driven methods for dam anomaly detection depend extensively on historical data; however, obtaining annotated data is both expensive and labor-intensive. Consequently, methodologies that leverage unlabeled or semi-labeled data are increasingly gaining popularity. This paper introduces a spatiotemporal contrastive learning pretraining (STCLP) strategy designed to extract discriminative features from unlabeled datasets of dam deformation. STCLP innovatively combines spatial contrastive learning based on temporal contrastive learning to capture representations embodying both spatial and temporal characteristics. Building upon this, a novel anomaly detection method for dam deformation utilizing STCLP is proposed. This method transfers pretrained parameters to targeted downstream classification tasks and leverages prior knowledge for enhanced fine-tuning. For validation, an arch dam serves as the case study. The results reveal that the proposed method demonstrates excellent performance, surpassing other benchmark models. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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11 pages, 1730 KiB  
Article
Analytical Performance of the FreeStyle Libre 2 Glucose Sensor in Healthy Male Adults
by Eva Fellinger, Tom Brandt, Justin Creutzburg, Tessa Rommerskirchen and Annette Schmidt
Sensors 2024, 24(17), 5769; https://doi.org/10.3390/s24175769 - 5 Sep 2024
Cited by 2 | Viewed by 4952
Abstract
Continuous Glucose Monitoring (CGM) not only can be used for glycemic control in chronic diseases (e.g., diabetes), but is increasingly being utilized by individuals and athletes to monitor fluctuations in training and everyday life. However, it is not clear how accurately CGM reflects [...] Read more.
Continuous Glucose Monitoring (CGM) not only can be used for glycemic control in chronic diseases (e.g., diabetes), but is increasingly being utilized by individuals and athletes to monitor fluctuations in training and everyday life. However, it is not clear how accurately CGM reflects plasma glucose concentration in a healthy population in the absence of chronic diseases. In an oral glucose tolerance test (OGTT) with forty-four healthy male subjects (25.5 ± 4.5 years), the interstitial fluid glucose (ISFG) concentration obtained by a CGM sensor was compared against finger-prick capillary plasma glucose (CPG) concentration at fasting baseline (T0) and 30 (T30), 60 (T60), 90 (T90), and 120 (T120) min post OGTT to investigate differences in measurement accuracy. The overall mean absolute relative difference (MARD) was 12.9% (95%-CI: 11.8–14.0%). Approximately 100% of the ISFG values were within zones A and B in the Consensus Error Grid, indicating clinical accuracy. A paired t-test revealed statistically significant differences between CPG and ISFG at all time points (T0: 97.3 mg/dL vs. 89.7 mg/dL, T30: 159.9 mg/dL vs. 144.3 mg/dL, T60: 134.8 mg/dL vs. 126.2 mg/dL, T90: 113.7 mg/dL vs. 99.3 mg/dL, and T120: 91.8 mg/dL vs. 82.6 mg/dL; p < 0.001) with medium to large effect sizes (d = 0.57–1.02) and with ISFG systematically under-reporting the reference system CPG. CGM sensors provide a convenient and reliable method for monitoring blood glucose in the everyday lives of healthy adults. Nonetheless, their use in clinical settings wherein implications are drawn from CGM readings should be handled carefully. Full article
(This article belongs to the Special Issue (Bio)sensors for Physiological Monitoring)
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19 pages, 1200 KiB  
Article
Optimized Intrusion Detection for IoMT Networks with Tree-Based Machine Learning and Filter-Based Feature Selection
by Ghaida Balhareth and Mohammad Ilyas
Sensors 2024, 24(17), 5712; https://doi.org/10.3390/s24175712 - 2 Sep 2024
Cited by 9 | Viewed by 4119
Abstract
The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and [...] Read more.
The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient’s health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices. Consequently, concerns regarding security and privacy can arise due to the limited control over the transmitted information and reliance on wireless transmission, which leaves the network vulnerable to several kinds of attacks. Motivated by this, in this study, we aim to build and improve an efficient intrusion detection system (IDS) for IoMT networks. The proposed IDS leverages tree-based machine learning classifiers combined with filter-based feature selection techniques to enhance detection accuracy and efficiency. The proposed model is used for monitoring and identifying unauthorized or malicious activities within medical devices and networks. To optimize performance and minimize computation costs, we utilize Mutual Information (MI) and XGBoost as filter-based feature selection methods. Then, to reduce the number of the chosen features selected, we apply a mathematical set (intersection) to extract the common features. The proposed method can detect intruders while data are being transferred, allowing for the accurate and efficient analysis of healthcare data at the network’s edge. The system’s performance is assessed using the CICIDS2017 dataset. We evaluate the proposed model in terms of accuracy, F1 score, recall, precision, true positive rate, and false positive rate. The proposed model achieves 98.79% accuracy and a low false alarm rate 0.007 FAR on the CICIDS2017 dataset according to the experimental results. While this study focuses on binary classification for intrusion detection, we are planning to build a multi-classification approach for future work which will be able to not only detect the attacks but also categorize them. Additionally, we will consider using our proposed feature selection technique for different ML classifiers and evaluate the model’s performance empirically in real-world IoMT scenarios. Full article
(This article belongs to the Section Internet of Things)
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13 pages, 5707 KiB  
Article
Photonic Dipstick Immunosensor to Detect Adulteration of Ewe, Goat, and Donkey Milk with Cow Milk through Bovine κ-Casein Detection
by Dimitra Kourti, Michailia Angelopoulou, Eleni Makarona, Anastasios Economou, Panagiota Petrou, Konstantinos Misiakos and Sotirios Kakabakos
Sensors 2024, 24(17), 5688; https://doi.org/10.3390/s24175688 - 31 Aug 2024
Cited by 1 | Viewed by 1491
Abstract
The quality and authenticity of milk are of paramount importance. Cow milk is more allergenic and less nutritious than ewe, goat, or donkey milk, which are often adulterated with cow milk due to their seasonal availability and higher prices. In this work, a [...] Read more.
The quality and authenticity of milk are of paramount importance. Cow milk is more allergenic and less nutritious than ewe, goat, or donkey milk, which are often adulterated with cow milk due to their seasonal availability and higher prices. In this work, a silicon photonic dipstick sensor accommodating two U-shaped Mach–Zehnder Interferometers (MZIs) was employed for the label-free detection of the adulteration of ewe, goat, and donkey milk with cow milk. One of the two MZIs of the chip was modified with bovine κ-casein, while the other was modified with bovine serum albumin to serve as a blank. All assay steps were performed by immersion of the chip side where the MZIs are positioned into the reagent solutions, leading to a photonic dipstick immunosensor. Thus, the chip was first immersed in a mixture of milk with anti-bovine κ-casein antibody and then in a secondary antibody solution for signal enhancement. A limit of detection of 0.05% v/v cow milk in ewe, goat, or donkey milk was achieved in 12 min using a 50-times diluted sample. This fast, sensitive, and simple assay, without the need for sample pre-processing, microfluidics, or pumps, makes the developed sensor ideal for the detection of milk adulteration at the point of need. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2024)
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28 pages, 3667 KiB  
Review
Screen-Printed Electrodes as Low-Cost Sensors for Breast Cancer Biomarker Detection
by Yin Shen, Zhuang Sun, Shichao Zhao, Fei Chen, Peizheng Shi, Ningbin Zhao, Kaiqiang Sun, Chen Ye, Chengte Lin and Li Fu
Sensors 2024, 24(17), 5679; https://doi.org/10.3390/s24175679 - 31 Aug 2024
Cited by 3 | Viewed by 2772
Abstract
This review explores the emerging role of screen-printed electrodes (SPEs) in the detection of breast cancer biomarkers. We discuss the fundamental principles and fabrication techniques of SPEs, highlighting their adaptability and cost-effectiveness. The review examines various modification strategies, including nanomaterial incorporation, polymer coatings, [...] Read more.
This review explores the emerging role of screen-printed electrodes (SPEs) in the detection of breast cancer biomarkers. We discuss the fundamental principles and fabrication techniques of SPEs, highlighting their adaptability and cost-effectiveness. The review examines various modification strategies, including nanomaterial incorporation, polymer coatings, and biomolecule immobilization, which enhance sensor performance. We analyze the application of SPEs in detecting protein, genetic, and metabolite biomarkers associated with breast cancer, presenting recent advancements and innovative approaches. The integration of SPEs with microfluidic systems and their potential in wearable devices for continuous monitoring are explored. While emphasizing the promising aspects of SPE-based biosensors, we also address current challenges in sensitivity, specificity, and real-world applicability. The review concludes by discussing future perspectives, including the potential for early screening and therapy monitoring, and the steps required for clinical implementation. This comprehensive overview aims to stimulate further research and development in SPE-based biosensors for improved breast cancer management. Full article
(This article belongs to the Special Issue Advanced Low-Cost Sensing Technology for Exposure and Health Assessments)
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14 pages, 2945 KiB  
Article
Low-Cost CO2 NDIR Sensors: Performance Evaluation and Calibration Using Machine Learning Techniques
by Ravish Dubey, Arina Telles, James Nikkel, Chang Cao, Jonathan Gewirtzman, Peter A. Raymond and Xuhui Lee
Sensors 2024, 24(17), 5675; https://doi.org/10.3390/s24175675 - 31 Aug 2024
Cited by 9 | Viewed by 3842
Abstract
The study comprehensively evaluates low-cost CO2 sensors from different price tiers, assessing their performance against a reference-grade instrument and exploring the possibility of calibration using different machine learning techniques. Three sensors (Sunrise AB by Senseair, K30 CO2 by Senseair, and GMP [...] Read more.
The study comprehensively evaluates low-cost CO2 sensors from different price tiers, assessing their performance against a reference-grade instrument and exploring the possibility of calibration using different machine learning techniques. Three sensors (Sunrise AB by Senseair, K30 CO2 by Senseair, and GMP 343 by Vaisala) were tested alongside a reference instrument (Los Gatos precision greenhouse gas analyzer). The results revealed differences in sensor performance, with the higher cost Vaisala sensors exhibiting superior accuracy. Despite its lower price, the Sunrise sensors still demonstrated reasonable accuracy. Meanwhile, the K30 sensor measurements displayed higher variability and noise. Machine learning models, including linear regression, gradient boosting regression, and random forest regression, were employed for sensor calibration. In general, linear regression models performed best for extrapolating data, whereas decision tree-based models were generally more useful in handling non-linear datasets. Notably, a stack ensemble model combining these techniques outperformed the individual models and significantly improved sensor accuracy by approximately 65%. Overall, this study contributes to filling the gap in intercomparing CO2 sensors across different price categories and underscores the potential of machine learning for enhancing sensor accuracy, particularly in low-cost sensor applications. Full article
(This article belongs to the Section Environmental Sensing)
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25 pages, 2666 KiB  
Article
Wearable Sensor-Based Assessments for Remotely Screening Early-Stage Parkinson’s Disease
by Shane Johnson, Michalis Kantartjis, Joan Severson, Ray Dorsey, Jamie L. Adams, Tairmae Kangarloo, Melissa A. Kostrzebski, Allen Best, Michael Merickel, Dan Amato, Brian Severson, Sean Jezewski, Steve Polyak, Anna Keil, Josh Cosman and David Anderson
Sensors 2024, 24(17), 5637; https://doi.org/10.3390/s24175637 - 30 Aug 2024
Cited by 5 | Viewed by 3249
Abstract
Prevalence estimates of Parkinson’s disease (PD)—the fastest-growing neurodegenerative disease—are generally underestimated due to issues surrounding diagnostic accuracy, symptomatic undiagnosed cases, suboptimal prodromal monitoring, and limited screening access. Remotely monitored wearable devices and sensors provide precise, objective, and frequent measures of motor and non-motor [...] Read more.
Prevalence estimates of Parkinson’s disease (PD)—the fastest-growing neurodegenerative disease—are generally underestimated due to issues surrounding diagnostic accuracy, symptomatic undiagnosed cases, suboptimal prodromal monitoring, and limited screening access. Remotely monitored wearable devices and sensors provide precise, objective, and frequent measures of motor and non-motor symptoms. Here, we used consumer-grade wearable device and sensor data from the WATCH-PD study to develop a PD screening tool aimed at eliminating the gap between patient symptoms and diagnosis. Early-stage PD patients (n = 82) and age-matched comparison participants (n = 50) completed a multidomain assessment battery during a one-year longitudinal multicenter study. Using disease- and behavior-relevant feature engineering and multivariate machine learning modeling of early-stage PD status, we developed a highly accurate (92.3%), sensitive (90.0%), and specific (100%) random forest classification model (AUC = 0.92) that performed well across environmental and platform contexts. These findings provide robust support for further exploration of consumer-grade wearable devices and sensors for global population-wide PD screening and surveillance. Full article
(This article belongs to the Section Wearables)
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20 pages, 22057 KiB  
Article
Design and Evaluation of a Novel Venturi-Based Spirometer for Home Respiratory Monitoring
by Mariana Ferreira Nunes, Hugo Plácido da Silva, Liliana Raposo and Fátima Rodrigues
Sensors 2024, 24(17), 5622; https://doi.org/10.3390/s24175622 - 30 Aug 2024
Cited by 2 | Viewed by 2867
Abstract
The high cost and limited availability of home spirometers pose a significant barrier to effective respiratory disease management and monitoring. To address this challenge, this paper introduces a novel Venturi-based spirometer designed for home use, leveraging the Bernoulli principle. The device features a [...] Read more.
The high cost and limited availability of home spirometers pose a significant barrier to effective respiratory disease management and monitoring. To address this challenge, this paper introduces a novel Venturi-based spirometer designed for home use, leveraging the Bernoulli principle. The device features a 3D-printed Venturi tube that narrows to create a pressure differential, which is measured by a differential pressure sensor and converted into airflow rate. The airflow is then integrated over time to calculate parameters such as the Forced Vital Capacity (FVC) and Forced Expiratory Volume in one second (FEV1). The system also includes a bacterial filter for hygienic use and a circuit board for data acquisition and streaming. Evaluation with eight healthy individuals demonstrated excellent test-retest reliability, with intraclass correlation coefficients (ICCs) of 0.955 for FVC and 0.853 for FEV1. Furthermore, when compared to standard Pulmonary Function Test (PFT) equipment, the spirometer exhibited strong correlation, with Pearson correlation coefficients of 0.992 for FVC and 0.968 for FEV1, and high reliability, with ICCs of 0.987 for FVC and 0.907 for FEV1. These findings suggest that the Venturi-based spirometer could significantly enhance access to spirometry at home. However, further large-scale validation and reliability studies are necessary to confirm its efficacy and reliability for widespread use. Full article
(This article belongs to the Special Issue Biomedical Sensors: New Technologies, Integration and Signal Processing)
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15 pages, 3386 KiB  
Article
Open-Path Cavity Ring-Down Spectroscopy for Simultaneous Detection of Hydrogen Chloride and Particles in Cleanroom Environment
by Muhammad Bilal Khan, Christian L’Orange, Cheongha Lim, Deokhyeon Kwon and Azer P. Yalin
Sensors 2024, 24(17), 5611; https://doi.org/10.3390/s24175611 - 29 Aug 2024
Viewed by 1673
Abstract
The present study addresses advanced monitoring techniques for particles and airborne molecular contaminants (AMCs) in cleanroom environments, which are crucial for ensuring the integrity of semiconductor manufacturing processes. We focus on quantifying particle levels and a representative AMC, hydrogen chloride (HCl), having known [...] Read more.
The present study addresses advanced monitoring techniques for particles and airborne molecular contaminants (AMCs) in cleanroom environments, which are crucial for ensuring the integrity of semiconductor manufacturing processes. We focus on quantifying particle levels and a representative AMC, hydrogen chloride (HCl), having known detrimental effects on equipment longevity, product yield, and human health. We have developed a compact laser sensor based on open-path cavity ring-down spectroscopy (CRDS) using a 1742 nm near-infrared diode laser source. The sensor enables the high-sensitivity detection of HCl through absorption by the 2-0 vibrational band with an Allan deviation of 0.15 parts per billion (ppb) over 15 min. For quantifying particle number concentrations, we examine various detection methods based on statistical analyses of Mie scattering-induced ring-down time fluctuations. We find that the ring-down distributions’ 3rd and 4th standard moments allow particle detection at densities as low as ~105 m−3 (diameter > 1 μm). These findings provide a basis for the future development of compact cleanroom monitoring instrumentation for wafer-level monitoring for both AMC and particles, including mobile platforms. Full article
(This article belongs to the Special Issue New Trend for Gas Sensor Development: From Sensing Materials to Metrological Evaluation)
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14 pages, 3226 KiB  
Article
Identification of Beef Odors under Different Storage Day and Processing Temperature Conditions Using an Odor Sensing System
by Yuanchang Liu, Nan Peng, Jinlong Kang, Takeshi Onodera and Rui Yatabe
Sensors 2024, 24(17), 5590; https://doi.org/10.3390/s24175590 - 29 Aug 2024
Cited by 3 | Viewed by 4215
Abstract
This study used an odor sensing system with a 16-channel electrochemical sensor array to measure beef odors, aiming to distinguish odors under different storage days and processing temperatures for quality monitoring. Six storage days ranged from purchase (D0) to eight days (D8), with [...] Read more.
This study used an odor sensing system with a 16-channel electrochemical sensor array to measure beef odors, aiming to distinguish odors under different storage days and processing temperatures for quality monitoring. Six storage days ranged from purchase (D0) to eight days (D8), with three temperature conditions: no heat (RT), boiling (100 °C), and frying (180 °C). Gas chromatography–mass spectrometry (GC-MS) analysis showed that odorants in the beef varied under different conditions. Compounds like acetoin and 1-hexanol changed significantly with the storage days, while pyrazines and furans were more detectable at higher temperatures. The odor sensing system data were visualized using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). PCA and unsupervised UMAP clustered beef odors by storage days but struggled with the processing temperatures. Supervised UMAP accurately clustered different temperatures and dates. Machine learning analysis using six classifiers, including support vector machine, achieved 57% accuracy for PCA-reduced data, while unsupervised UMAP reached 49.1% accuracy. Supervised UMAP significantly enhanced the classification accuracy, achieving over 99.5% with the dimensionality reduced to three or above. Results suggest that the odor sensing system can sufficiently enhance non-destructive beef quality and safety monitoring. This research advances electronic nose applications and explores data downscaling techniques, providing valuable insights for future studies. Full article
(This article belongs to the Special Issue Electronic Nose and Artificial Olfaction)
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27 pages, 5737 KiB  
Review
Electrochemical Sensors for Antibiotic Detection: A Focused Review with a Brief Overview of Commercial Technologies
by Margaux Frigoli, Mikolaj P. Krupa, Geert Hooyberghs, Joseph W. Lowdon, Thomas J. Cleij, Hanne Diliën, Kasper Eersels and Bart van Grinsven
Sensors 2024, 24(17), 5576; https://doi.org/10.3390/s24175576 - 28 Aug 2024
Cited by 10 | Viewed by 5141
Abstract
Antimicrobial resistance (AMR) poses a significant threat to global health, powered by pathogens that become increasingly proficient at withstanding antibiotic treatments. This review introduces the factors contributing to antimicrobial resistance (AMR), highlighting the presence of antibiotics in different environmental and biological matrices as [...] Read more.
Antimicrobial resistance (AMR) poses a significant threat to global health, powered by pathogens that become increasingly proficient at withstanding antibiotic treatments. This review introduces the factors contributing to antimicrobial resistance (AMR), highlighting the presence of antibiotics in different environmental and biological matrices as a significant contributor to the resistance. It emphasizes the urgent need for robust and effective detection methods to identify these substances and mitigate their impact on AMR. Traditional techniques, such as liquid chromatography-mass spectrometry (LC-MS) and immunoassays, are discussed alongside their limitations. The review underscores the emerging role of biosensors as promising alternatives for antibiotic detection, with a particular focus on electrochemical biosensors. Therefore, the manuscript extensively explores the principles and various types of electrochemical biosensors, elucidating their advantages, including high sensitivity, rapid response, and potential for point-of-care applications. Moreover, the manuscript investigates recent advances in materials used to fabricate electrochemical platforms for antibiotic detection, such as aptamers and molecularly imprinted polymers, highlighting their role in enhancing sensor performance and selectivity. This review culminates with an evaluation and summary of commercially available and spin-off sensors for antibiotic detection, emphasizing their versatility and portability. By explaining the landscape, role, and future outlook of electrochemical biosensors in antibiotic detection, this review provides insights into the ongoing efforts to combat the escalating threat of AMR effectively. Full article
(This article belongs to the Special Issue Electronic, Mass-Sensitive and Thermal Transducers for Bio- and Chemosensing)
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23 pages, 5271 KiB  
Article
Robotic Valve Turning with a Wheeled Mobile Manipulator via Hybrid Passive/Active Compliance
by Hongjun Xing, Liang Ding, Jinbao Chen, Haibo Gao and Zongquan Deng
Sensors 2024, 24(17), 5559; https://doi.org/10.3390/s24175559 - 28 Aug 2024
Cited by 2 | Viewed by 1482
Abstract
This paper addresses the problems of valve-turning operation in rescue environments where a wheeled mobile manipulator (WMM) is employed, including the possible occurrence of large internal forces. Rather than attempting to obtain the exact position of the valve, this paper presents a solution [...] Read more.
This paper addresses the problems of valve-turning operation in rescue environments where a wheeled mobile manipulator (WMM) is employed, including the possible occurrence of large internal forces. Rather than attempting to obtain the exact position of the valve, this paper presents a solution to two main problems in robotic valve-turning operations: the radial position deviation between the rotation axes of the tool and the valve handle, which may cause large radial forces, and the possible axial displacement of the valve handle as the valve turns, which may lead to large axial forces. For the former problem, we designed a compliant end-effector with a tolerance of approximately 3.5° (angle) and 9.7 mm (position), and provided a hybrid passive/active compliance method. For the latter problem, a passivity-based force tracking algorithm was employed. Combining the custom-built compliant end-effector and the passivity-based control method can significantly reduce both the radial and the axial forces. Additionally, for valves with different installation types and WMMs with different configurations, we analyzed the minimum required number of actuators for valve turning. Simulation and experimental results are presented to show the effectiveness of the proposed approach. Full article
(This article belongs to the Section Sensors and Robotics)
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