Sensors

32 pages, 1912 KB

Open AccessEditor’s ChoiceReview

The IoT and AI in Agriculture: The Time Is Now—A Systematic Review of Smart Sensing Technologies

by Tymoteusz Miller, Grzegorz Mikiciuk, Irmina Durlik, Małgorzata Mikiciuk, Adrianna Łobodzińska and Marek Śnieg

Sensors 2025, 25(12), 3583; https://doi.org/10.3390/s25123583 - 6 Jun 2025

Cited by 16 | Viewed by 12187

Abstract

The integration of the Internet of Things (IoT) and artificial intelligence (AI) has reshaped modern agriculture by enabling precision farming, real-time monitoring, and data-driven decision-making. This systematic review, conducted in accordance with the PRISMA methodology, provides a comprehensive overview of recent advancements in [...] Read more.

The integration of the Internet of Things (IoT) and artificial intelligence (AI) has reshaped modern agriculture by enabling precision farming, real-time monitoring, and data-driven decision-making. This systematic review, conducted in accordance with the PRISMA methodology, provides a comprehensive overview of recent advancements in smart sensing technologies for arable crops and grasslands. We analyzed the peer-reviewed literature published between 2020 and 2024, focusing on the adoption of IoT-based sensor networks and AI-driven analytics across various agricultural applications. The findings reveal a significant increase in research output, particularly in the use of optical, acoustic, electromagnetic, and soil sensors, alongside machine learning models such as SVMs, CNNs, and random forests for optimizing irrigation, fertilization, and pest management strategies. However, this review also identifies critical challenges, including high infrastructure costs, limited interoperability, connectivity constraints in rural areas, and ethical concerns regarding transparency and data privacy. To address these barriers, recent innovations have emphasized the potential of Edge AI for local inference, blockchain systems for decentralized data governance, and autonomous platforms for field-level automation. Moreover, policy interventions are needed to ensure fair data ownership, cybersecurity, and equitable access to smart farming tools, especially in developing regions. This review is the first to systematically examine AI-integrated sensing technologies with an exclusive focus on arable crops and grasslands, offering an in-depth synthesis of both technological progress and real-world implementation gaps. Full article

(This article belongs to the Special Issue Smart Sensing Systems for Arable Crop and Grassland Management)

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30 pages, 1745 KB

Open AccessEditor’s ChoiceReview

The Human Voice as a Digital Health Solution Leveraging Artificial Intelligence

by Pratyusha Muddaloor, Bhavana Baraskar, Hriday Shah, Keerthy Gopalakrishnan, Divyanshi Sood, Prem C. Pasupuleti, Akshay Singh, Dipankar Mitra, Sumedh S. Hoskote, Vivek N. Iyer, Scott A. Helgeson and Shivaram P. Arunachalam

Sensors 2025, 25(11), 3424; https://doi.org/10.3390/s25113424 - 29 May 2025

Cited by 1 | Viewed by 4582

Abstract

The human voice is an important medium of communication and expression of feelings or thoughts. Disruption in the regulatory systems of the human voice can be analyzed and used as a diagnostic tool, labeling voice as a potential “biomarker”. Conversational artificial intelligence is [...] Read more.

The human voice is an important medium of communication and expression of feelings or thoughts. Disruption in the regulatory systems of the human voice can be analyzed and used as a diagnostic tool, labeling voice as a potential “biomarker”. Conversational artificial intelligence is at the core of voice-powered technologies, enabling intelligent interactions between machines. Due to its richness and availability, voice can be leveraged for predictive analytics and enhanced healthcare insights. Utilizing this idea, we reviewed artificial intelligence (AI) models that have executed vocal analysis and their outcomes. Recordings undergo extraction of useful vocal features to be analyzed by neural networks and machine learning models. Studies reveal machine learning models to be superior to spectral analysis in dynamically combining the huge amount of data of vocal features. Clinical applications of a vocal biomarker exist in neurological diseases such as Parkinson’s, Alzheimer’s, psychological disorders, DM, CHF, CAD, aspiration, GERD, and pulmonary diseases, including COVID-19. The primary ethical challenge when incorporating voice as a diagnostic tool is that of privacy and security. To eliminate this, encryption methods exist to convert patient-identifiable vocal data into a more secure, private nature. Advancements in AI have expanded the capabilities and future potential of voice as a digital health solution. Full article

(This article belongs to the Special Issue AI/ML in RF and Microwave Sensors for Medicine and Biomedical Applications)

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25 pages, 9541 KB

Open AccessEditor’s ChoiceReview

Review on Multispectral Photoacoustic Imaging Using Stimulated Raman Scattering Light Sources

by Yuon Song, Sang Min Park, Yongjae Jeong, Jeesu Kim and Hwidon Lee

Sensors 2025, 25(11), 3325; https://doi.org/10.3390/s25113325 - 25 May 2025

Cited by 1 | Viewed by 2195

Abstract

Photoacoustic imaging is an advanced biomedical imaging technique that has been widely developed and applied in diverse biomedical studies. By generating optical-absorption-based signals with ultrasound resolution, it enables in vivo visualization of molecular functional information in biological tissues. Extensive research has been conducted [...] Read more.

Photoacoustic imaging is an advanced biomedical imaging technique that has been widely developed and applied in diverse biomedical studies. By generating optical-absorption-based signals with ultrasound resolution, it enables in vivo visualization of molecular functional information in biological tissues. Extensive research has been conducted to develop the multispectral light sources required for functional photoacoustic imaging. Among the various approaches, multispectral light sources generated using stimulated Raman scattering have shown considerable promise, particularly in photoacoustic microscopy, where achieving multispectral illumination remains challenging. This review summarizes photoacoustic imaging systems that employ stimulated Raman scattering for multispectral light sources and delves into their configurations and applications in the functional analyses of biological tissues. In addition, the review discusses the future directions of multispectral light sources by comparing different technologies based on key factors such as wavelength tunability, repetition rate, and power, which critically affect the accuracy and quality of multispectral photoacoustic imaging. Full article

(This article belongs to the Special Issue Feature Review Papers in Optical Sensors)

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18 pages, 4439 KB

Open AccessEditor’s ChoiceArticle

Combining Infrared Thermography with Computer Vision Towards Automatic Detection and Localization of Air Leaks

by Ângela Semitela, João Silva, André F. Girão, Samuel Verdasca, Rita Futre, Nuno Lau, José P. Santos and António Completo

Sensors 2025, 25(11), 3272; https://doi.org/10.3390/s25113272 - 22 May 2025

Cited by 1 | Viewed by 1547

Abstract

This paper proposes an automated system integrating infrared thermography (IRT) and computer vision for air leak detection and localization in end-of-line (EOL) testing stations. This system consists of (1) a leak tester for detection and quantification of leaks, (2) an infrared camera for [...] Read more.

This paper proposes an automated system integrating infrared thermography (IRT) and computer vision for air leak detection and localization in end-of-line (EOL) testing stations. This system consists of (1) a leak tester for detection and quantification of leaks, (2) an infrared camera for real-time thermal image acquisition; and (3) an algorithm for automatic leak localization. The python-based algorithm acquires thermal frames from the camera’s streaming video, identifies potential leak regions by selecting a region of interest, mitigates environmental interferences via image processing, and pinpoints leaks by employing pixel intensity thresholding. A closed circuit with an embedded leak system simulated relevant leakage scenarios, varying leak apertures (ranging from 0.25 to 3 mm), and camera–leak system distances (0.2 and 1 m). Results confirmed that (1) the leak tester effectively detected and quantified leaks, with larger apertures generating higher leak rates; (2) the IRT performance was highly dependent on leak aperture and camera–leak system distance, confirming that shorter distances improve localization accuracy; and (3) the algorithm localized all leaks in both lab and industrial environments, regardless of the camera–leak system distance, mostly achieving accuracies higher than 0.7. Overall, the combined system demonstrated great potential for long-term implementation in EOL leakage stations in the manufacturing sector, offering an effective and cost-effective alternative for manual inspections. Full article

(This article belongs to the Special Issue Sensing Technology and Applications for Industrial Maintenance and Automation)

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20 pages, 4672 KB

Open AccessEditor’s ChoiceArticle

Industrial-Grade Graphene Films as Distributed Temperature Sensors

by Francesco Siconolfi, Gabriele Cavaliere, Sarah Sibilia, Francesco Cristiano, Gaspare Giovinco and Antonio Maffucci

Sensors 2025, 25(10), 3227; https://doi.org/10.3390/s25103227 - 21 May 2025

Cited by 1 | Viewed by 1123

Abstract

This paper investigates the feasibility of a multi-purpose use of thin films of industrial-grade graphene, adopted initially to realize advanced coatings for thermal management or electromagnetic shielding. Indeed, it is demonstrated that such coatings can be conveniently used as distributed temperature sensors based [...] Read more.

This paper investigates the feasibility of a multi-purpose use of thin films of industrial-grade graphene, adopted initially to realize advanced coatings for thermal management or electromagnetic shielding. Indeed, it is demonstrated that such coatings can be conveniently used as distributed temperature sensors based on the sensitivity of their electrical resistance to temperature. The study is carried out by characterizing three nanomaterials differing in the percentage of graphene nanoplatelets in the temperature range from −40 °C to +60 °C. The paper demonstrates the presence of a reproducible and linear negative temperature coefficient behavior, with a temperature coefficient of the resistance of the order of

- 1.5 \cdot 10^{- 3}

{° C}^{- 1}

. A linear sensor model is then developed and validated through an uncertainty-based approach, yielding a temperature prediction uncertainty of approximately ±2 °C. Finally, the robustness of the sensor concerning moderate environmental variations is verified, as the errors introduced by relative humidity values in the range from 40% to 60% are included in the model’s uncertainty bounds. These results suggest the realistic possibility of adding temperature-sensing capabilities to these graphene coatings with minimal increase in complexity and cost. Full article

(This article belongs to the Section Nanosensors)

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33 pages, 678 KB

Open AccessEditor’s ChoiceReview

Internet of Medical Things Systems Review: Insights into Non-Functional Factors

by Giovanni Donato Gallo and Daniela Micucci

Sensors 2025, 25(9), 2795; https://doi.org/10.3390/s25092795 - 29 Apr 2025

Cited by 2 | Viewed by 2672

Abstract

Internet of Medical Things (IoMT) is a rapidly evolving field with the potential to bring significant changes to healthcare. While several surveys have examined the structure and operation of these systems, critical aspects such as interoperability, sustainability, security, runtime self-adaptation [...] Read more.

Internet of Medical Things (IoMT) is a rapidly evolving field with the potential to bring significant changes to healthcare. While several surveys have examined the structure and operation of these systems, critical aspects such as interoperability, sustainability, security, runtime self-adaptation, and configurability are sometimes overlooked. Interoperability is essential for integrating data from various devices and platforms to provide a comprehensive view of a patient’s health. Sustainability addresses the environmental impact of IoMT technologies, crucial in the context of green computing. Security ensures the protection of sensitive patient data from breaches and manipulation. Runtime self-adaptation allows systems to adjust to changing patient conditions and environments. Configurability enables IoMT frameworks to monitor diverse patient conditions and manage different treatment paths. This article reviews current techniques addressing these aspects and highlights areas requiring further research. Full article

(This article belongs to the Section Biomedical Sensors)

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18 pages, 3089 KB

Open AccessEditor’s ChoiceArticle

Quantitative Estimation of Organic Pollution in Inland Water Using Sentinel-2 Multispectral Imager

by Jiayi Li, Ruru Deng, Yu Guo, Cong Lei, Zhenqun Hua and Junying Yang

Sensors 2025, 25(9), 2737; https://doi.org/10.3390/s25092737 - 26 Apr 2025

Cited by 2 | Viewed by 798

Abstract

Organic pollution poses a significant threat to water security, making the monitoring of organic pollutants in water environments essential for the protection of water resources. Remote sensing technology, with its wide coverage, continuous monitoring capability, and cost-efficiency, overcomes the limitations of traditional methods, [...] Read more.

Organic pollution poses a significant threat to water security, making the monitoring of organic pollutants in water environments essential for the protection of water resources. Remote sensing technology, with its wide coverage, continuous monitoring capability, and cost-efficiency, overcomes the limitations of traditional methods, which are often time-consuming, labor-intensive, and spatially restricted. As a result, it has become an effective tool for monitoring organic pollution in water environments. In this study, we propose a physically constrained remote sensing algorithm for the quantitative estimation of organic pollution in inland waters based on radiative transfer theory. The algorithm was applied to the Feilaixia Basin using Sentinel-2 data. Accuracy assessment results demonstrate good performance in the quantitative assessment of organic pollution, with a coefficient of determination (R²) of 0.79, a mean absolute percentage error (MAPE) of 13.03%, and a root mean square error (RMSE) of 0.39 mg/L. Additionally, a seasonal variation map of organic pollutant concentrations in the Feilaixia Basin was generated, providing valuable scientific support for regional water quality monitoring and management. Full article

(This article belongs to the Section Environmental Sensing)

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14 pages, 9227 KB

Open AccessEditor’s ChoiceArticle

In-Motion Alignment with MEMS-IMU Using Multilocal Linearization Detection

by Yulu Zhong, Xiyuan Chen, Ning Gao and Zhiyuan Jiao

Sensors 2025, 25(9), 2645; https://doi.org/10.3390/s25092645 - 22 Apr 2025

Cited by 1 | Viewed by 2555

Abstract

In-motion alignment is a critical step in obtaining the initial state of an integrated navigation system. This article considers the in-motion initial alignment problem using the multilocal linearization detection method. In contrast to the OBA-based method, which fully relies on satellite signals to [...] Read more.

In-motion alignment is a critical step in obtaining the initial state of an integrated navigation system. This article considers the in-motion initial alignment problem using the multilocal linearization detection method. In contrast to the OBA-based method, which fully relies on satellite signals to estimate the initial state of the Kalman filter, the proposed method utilizes the designed quasi-uniform quaternion generation method to estimate several possible initial states. Then, the proposed method selects the most probable result based on the generalized Schweppe likelihood ratios among multiple hypotheses. The experiment result of the proposed method demonstrates the advantage of estimation performance within poor-quality measurement conditions for the long-duration coarse alignment using MEMS-IMU compared with the OBA-based method. The proposed method has potential applications in alignment tasks for low-cost, small-scale vehicle navigation systems. Full article

(This article belongs to the Special Issue INS/GNSS Integrated Navigation Systems)

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18 pages, 3766 KB

Open AccessEditor’s ChoiceArticle

Self-Supervised Multiscale Contrastive and Attention-Guided Gradient Projection Network for Pansharpening

by Qingping Li, Xiaomin Yang, Bingru Li and Jin Wang

Sensors 2025, 25(8), 2560; https://doi.org/10.3390/s25082560 - 18 Apr 2025

Cited by 2 | Viewed by 874

Abstract

Pansharpening techniques are crucial in remote sensing image processing, with deep learning emerging as the mainstream solution. In this paper, the pansharpening problem is formulated as two optimization subproblems with a solution proposed based on multiscale contrastive learning combined with attention-guided gradient projection [...] Read more.

Pansharpening techniques are crucial in remote sensing image processing, with deep learning emerging as the mainstream solution. In this paper, the pansharpening problem is formulated as two optimization subproblems with a solution proposed based on multiscale contrastive learning combined with attention-guided gradient projection networks. First, an efficient and generalized Spectral–Spatial Universal Module (SSUM) is designed and applied to spectral and spatial enhancement modules (SpeEB and SpaEB). Then, the multiscale high-frequency features of PAN and MS images are extracted using discrete wavelet transform (DWT). These features are combined with contrastive learning and residual connection to progressively balance spectral and spatial information. Finally, high-resolution multispectral images are generated through multiple iterations. Experimental results verify that the proposed method outperforms existing approaches in both visual quality and quantitative evaluation metrics. Full article

(This article belongs to the Section Sensor Networks)

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36 pages, 10690 KB

Open AccessEditor’s ChoiceArticle

Novel Amperometric Sensor Based on Glassy Graphene for Flow Injection Analysis

by Ramtin Eghbal Shabgahi, Alexander Minkow, Michael Wild, Dietmar Kissinger and Alberto Pasquarelli

Sensors 2025, 25(8), 2454; https://doi.org/10.3390/s25082454 - 13 Apr 2025

Cited by 2 | Viewed by 1112

Abstract

Flow injection analysis (FIA) is widely used in drug screening, neurotransmitter detection, and water analysis. In this study, we investigated the electrochemical sensing performance of glassy graphene electrodes derived from pyrolyzed positive photoresist films (PPFs) via rapid thermal annealing (RTA) on SiO₂ [...] Read more.

Flow injection analysis (FIA) is widely used in drug screening, neurotransmitter detection, and water analysis. In this study, we investigated the electrochemical sensing performance of glassy graphene electrodes derived from pyrolyzed positive photoresist films (PPFs) via rapid thermal annealing (RTA) on SiO₂/Si and polycrystalline diamond (PCD). Glassy graphene films fabricated at 800, 900, and 950 °C were characterized using Raman spectroscopy, scanning electron microscopy (SEM), and atomic force microscopy (AFM) to assess their structural and morphological properties. Electrochemical characterization in phosphate-buffered saline (PBS, pH 7.4) revealed that annealing temperature and substrate type influence the potential window and double-layer capacitance. The voltammetric response of glassy graphene electrodes was further evaluated using the surface-insensitive [Ru(NH₃)₆]^3+/2+ redox marker, the surface-sensitive [Fe(CN)₆]^3−/4− redox couple, and adrenaline, demonstrating that electron transfer efficiency is governed by annealing temperature and substrate-induced microstructural changes. FIA with amperometric detection showed a linear electrochemical response to adrenaline in the 3–300 µM range, achieving a low detection limit of 1.05 µM and a high sensitivity of 1.02 µA cm⁻²/µM. These findings highlight the potential of glassy graphene as a cost-effective alternative for advanced electrochemical sensors, particularly in biomolecule detection and analytical applications. Full article

(This article belongs to the Special Issue Electrochemical Sensors and Biosensors: Materials, Methods and Applications)

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27 pages, 5073 KB

Open AccessEditor’s ChoiceReview

A Comprehensive Review of Deep Learning in Computer Vision for Monitoring Apple Tree Growth and Fruit Production

by Meng Lv, Yi-Xiao Xu, Yu-Hang Miao and Wen-Hao Su

Sensors 2025, 25(8), 2433; https://doi.org/10.3390/s25082433 - 12 Apr 2025

Cited by 1 | Viewed by 3427

Abstract

The high nutritional and medicinal value of apples has contributed to their widespread cultivation worldwide. Unfavorable factors in the healthy growth of trees and extensive orchard work are threatening the profitability of apples. This study reviewed deep learning combined with computer vision for [...] Read more.

The high nutritional and medicinal value of apples has contributed to their widespread cultivation worldwide. Unfavorable factors in the healthy growth of trees and extensive orchard work are threatening the profitability of apples. This study reviewed deep learning combined with computer vision for monitoring apple tree growth and fruit production processes in the past seven years. Three types of deep learning models were used for real-time target recognition tasks: detection models including You Only Look Once (YOLO) and faster region-based convolutional network (Faster R-CNN); classification models including Alex network (AlexNet) and residual network (ResNet); segmentation models including segmentation network (SegNet), and mask regional convolutional neural network (Mask R-CNN). These models have been successfully applied to detect pests and diseases (located on leaves, fruits, and trunks), organ growth (including fruits, apple blossoms, and branches), yield, and post-harvest fruit defects. This study introduced deep learning and computer vision methods, outlined in the current research on these methods for apple tree growth and fruit production. The advantages and disadvantages of deep learning were discussed, and the difficulties faced and future trends were summarized. It is believed that this research is important for the construction of smart apple orchards. Full article

(This article belongs to the Section Smart Agriculture)

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25 pages, 12941 KB

Open AccessEditor’s ChoiceArticle

Dynamic Multibody Modeling of Spherical Roller Bearings with Localized Defects for Large-Scale Rotating Machinery

by Luca Giraudo, Luigi Gianpio Di Maggio, Lorenzo Giorio and Cristiana Delprete

Sensors 2025, 25(8), 2419; https://doi.org/10.3390/s25082419 - 11 Apr 2025

Cited by 3 | Viewed by 1120

Abstract

Early fault detection in rotating machinery is crucial for optimizing maintenance and minimizing downtime costs, especially in medium-to-large-scale industrial applications. This study presents a multibody model developed in the Simulink^® Simscape environment to simulate the dynamic behavior of medium-sized spherical bearings. The [...] Read more.

Early fault detection in rotating machinery is crucial for optimizing maintenance and minimizing downtime costs, especially in medium-to-large-scale industrial applications. This study presents a multibody model developed in the Simulink^® Simscape environment to simulate the dynamic behavior of medium-sized spherical bearings. The model includes descriptions of the six degrees of freedoms of each subcomponent, and was validated by comparison with experimental measurements acquired on a test rig capable of applying heavy radial loads. The results show a good fit between experimental and simulated signals in terms of identifying characteristic fault frequencies, which highlights the model’s ability to reproduce vibrations induced by localized defects on the inner and outer races. Amplitude differences can be attributed to simplifications such as neglected housing compliancies and lubrication effects, and do not alter the model’s effectiveness in detecting fault signatures. In conclusion, the developed model represents a promising tool for generating useful datasets for training diagnostic and prognostic algorithms, thereby contributing to the improvement of predictive maintenance strategies in industrial settings. Despite some amplitude discrepancies, the model proves useful for generating fault data and supporting condition monitoring strategies for industrial machinery. Full article

(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2025)

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19 pages, 1385 KB

Open AccessEditor’s ChoiceArticle

Optimizing Sensor Placement for Event Detection: A Case Study in Gaseous Chemical Detection

by Priscile Fogou Suawa and Christian Herglotz

Sensors 2025, 25(8), 2397; https://doi.org/10.3390/s25082397 - 10 Apr 2025

Cited by 1 | Viewed by 1562

Abstract

In dynamic industrial environments, strategic sensor placement is key to accurately monitoring equipment and detecting critical events. Despite progress in Industry 4.0 and the Internet of Things, research on optimal sensor placement remains limited. This study addresses this gap by analyzing how sensor [...] Read more.

In dynamic industrial environments, strategic sensor placement is key to accurately monitoring equipment and detecting critical events. Despite progress in Industry 4.0 and the Internet of Things, research on optimal sensor placement remains limited. This study addresses this gap by analyzing how sensor placement impacts event detection, using chemical detection as a case study with an open dataset. Detecting gases is challenging due to their dispersion. Effective algorithms and well-planned sensor locations are required for reliable results. Using deep convolutional neural networks (DCNNs) and decision tree (DT) methods, we implemented and tested detection models on a public dataset of chemical substances collected at five locations. In addition, we also implemented a multi-objective optimization approach based on the non-dominated sorting genetic algorithm II (NSGA-II) to identify optimal sensor configurations that balance high detection accuracy with cost efficiency in sensor deployment. Using the refined sensor placement, the DCNN model achieved 100% accuracy using only 30% of the available sensors. Full article

(This article belongs to the Special Issue Innovative Technologies and Applications in Engineering Sensing Through Deep and Machine Learning)

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24 pages, 23606 KB

Open AccessEditor’s ChoiceArticle

Improved RRT*-Connect Manipulator Path Planning in a Multi-Obstacle Narrow Environment

by Xueyi He, Yimin Zhou, Haonan Liu and Wanfeng Shang

Sensors 2025, 25(8), 2364; https://doi.org/10.3390/s25082364 - 8 Apr 2025

Cited by 4 | Viewed by 2532

Abstract

This paper proposes an improved RRT*-Connect algorithm (IRRT*-Connect) for robotic arm path planning in narrow environments with multiple obstacles. A heuristic sampling strategy is adopted with the integration of the ellipsoidal subset sampling and goal-biased sampling strategies, which can continuously compress the sampling [...] Read more.

This paper proposes an improved RRT*-Connect algorithm (IRRT*-Connect) for robotic arm path planning in narrow environments with multiple obstacles. A heuristic sampling strategy is adopted with the integration of the ellipsoidal subset sampling and goal-biased sampling strategies, which can continuously compress the sampling space to enhance the sampling efficiency. During the node expansion process, an adaptive step-size method is introduced to dynamically adjust the step size based on the obstacle information, while a node rejection strategy is used to accelerate the search process so as to generate a near-optimal collision-free path. A pruning optimization strategy is also proposed to eliminate the redundant nodes from the path. Furthermore, a cubic non-uniform B-spline interpolation algorithm is applied to smooth the generated path. Finally, simulation experiments of the IRRT*-Connect algorithm are conducted in Python and ROS, and physical experiments are performed on a UR5 robotic arm. By comparing with the existing algorithms, it is demonstrated that the proposed method can achieve shorter planning times and lower path costs of the manipulator operation. Full article

(This article belongs to the Special Issue Advances in Bionic Tactile Systems for Sensor Application and Flexible Operation)

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16 pages, 4785 KB

Open AccessEditor’s ChoiceArticle

Fabrication and Characterization of a Flexible Non-Enzymatic Electrochemical Glucose Sensor Using a Cu Nanoparticle/Laser-Induced Graphene Fiber/Porous Laser-Induced Graphene Network Electrode

by Taeheon Kim and James Jungho Pak

Sensors 2025, 25(7), 2341; https://doi.org/10.3390/s25072341 - 7 Apr 2025

Cited by 5 | Viewed by 1877

Abstract

We demonstrate a flexible electrochemical biosensor for non-enzymatic glucose detection under different bending conditions. The novel flexible glucose sensor consists of a Cu nanoparticle (NP)/laser-induced graphene fiber (LIGF)/porous laser-induced graphene (LIG) network structure on a polyimide film. The bare LIGF/LIG electrode fabricated using [...] Read more.

We demonstrate a flexible electrochemical biosensor for non-enzymatic glucose detection under different bending conditions. The novel flexible glucose sensor consists of a Cu nanoparticle (NP)/laser-induced graphene fiber (LIGF)/porous laser-induced graphene (LIG) network structure on a polyimide film. The bare LIGF/LIG electrode fabricated using an 8.9 W laser power shows a measured sheet resistance and thickness of 6.8 Ω/□ and ~420 μm, respectively. In addition, a conventional Cu NP electroplating method is used to fabricate a Cu/LIGF/LIG electrode-based glucose sensor that shows excellent glucose detection characteristics, including a sensitivity of 1438.8 µA/mM∙cm², a limit of detection (LOD) of 124 nM, and a broad linear range at an applied potential of +600 mV. Significantly, the Cu/LIGF/LIG electrode-based glucose sensor exhibits a relatively high sensitivity, low LOD, good linear detection range, and long-term stability at bending angles of 0°, 45°, 90°, 135°, and 180°. Full article

(This article belongs to the Section Chemical Sensors)

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46 pages, 2791 KB

Open AccessEditor’s ChoiceReview

YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11

by Makara Mao and Min Hong

Sensors 2025, 25(7), 2270; https://doi.org/10.3390/s25072270 - 3 Apr 2025

Cited by 20 | Viewed by 6032

Abstract

Automated fabric defect detection is crucial for improving quality control, reducing manual labor, and optimizing efficiency in the textile industry. Traditional inspection methods rely heavily on human oversight, which makes them prone to subjectivity, inefficiency, and inconsistency in high-speed manufacturing environments. This review [...] Read more.

Automated fabric defect detection is crucial for improving quality control, reducing manual labor, and optimizing efficiency in the textile industry. Traditional inspection methods rely heavily on human oversight, which makes them prone to subjectivity, inefficiency, and inconsistency in high-speed manufacturing environments. This review systematically examines the evolution of the You Only Look Once (YOLO) object detection framework from YOLO-v1 to YOLO-v11, emphasizing architectural advancements such as attention-based feature refinement and Transformer integration and their impact on fabric defect detection. Unlike prior studies focusing on specific YOLO variants, this work comprehensively compares the entire YOLO family, highlighting key innovations and their practical implications. We also discuss the challenges, including dataset limitations, domain generalization, and computational constraints, proposing future solutions such as synthetic data generation, federated learning, and edge AI deployment. By bridging the gap between academic advancements and industrial applications, this review is a practical guide for selecting and optimizing YOLO models for fabric inspection, paving the way for intelligent quality control systems. Full article

(This article belongs to the Special Issue AI-Based Computer Vision Sensors & Systems)

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13 pages, 3659 KB

Open AccessEditor’s ChoiceArticle

A Non-Contact Privacy Protection Bed Angle Estimation Method Based on LiDAR

by Yezhao Ju, Yuanji Li, Haiyang Zhang, Le Xin, Changming Zhao and Ziyi Xu

Sensors 2025, 25(7), 2226; https://doi.org/10.3390/s25072226 - 2 Apr 2025

Viewed by 2879

Abstract

Accurate bed angle monitoring is crucial in healthcare settings, particularly in Intensive Care Units (ICUs), where improper bed positioning can lead to severe complications such as ventilator-associated pneumonia. Traditional camera-based solutions, while effective, often raise significant privacy concerns. This study proposes a non-intrusive [...] Read more.

Accurate bed angle monitoring is crucial in healthcare settings, particularly in Intensive Care Units (ICUs), where improper bed positioning can lead to severe complications such as ventilator-associated pneumonia. Traditional camera-based solutions, while effective, often raise significant privacy concerns. This study proposes a non-intrusive bed angle detection system based on LiDAR technology, utilizing the Intel RealSense L515 sensor. By leveraging time-of-flight principles, the system enables real-time, privacy-preserving monitoring of head-of-bed elevation angles without direct visual surveillance. Our methodology integrates advanced techniques, including coordinate system transformation, plane fitting, and a deep learning framework combining YOLO-X with an enhanced A2J algorithm. Customized loss functions further improve angle estimation accuracy. Experimental results in ICU environments demonstrate the system’s effectiveness, with an average angle detection error of less than 3 degrees. Full article

(This article belongs to the Section Radar Sensors)

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18 pages, 6709 KB

Open AccessEditor’s ChoiceArticle

Effects of Dust and Moisture Surface Contaminants on Automotive Radar Sensor Frequencies

by Jeongmin Kang, Oskar Hamidi, Karl Vanäs, Tobias Eidevåg, Emil Nilsson and Ross Friel

Sensors 2025, 25(7), 2192; https://doi.org/10.3390/s25072192 - 30 Mar 2025

Cited by 2 | Viewed by 1505

Abstract

Perception and sensing of the surrounding environment are crucial for ensuring the safety of autonomous driving systems. A key issue is securing sensor reliability from sensors mounted on the vehicle and obtaining accurate raw data. Surface contamination in front of a sensor typically [...] Read more.

Perception and sensing of the surrounding environment are crucial for ensuring the safety of autonomous driving systems. A key issue is securing sensor reliability from sensors mounted on the vehicle and obtaining accurate raw data. Surface contamination in front of a sensor typically occurs due to adverse weather conditions or particulate matter on the road, which can degrade system reliability depending on sensor placement and surrounding bodywork geometry. Moreover, the moisture content of dust contaminants can cause surface adherence, making it more likely to persist on a vertical sensor surface compared to moisture only. In this work, a 76–81 GHz radar sensor, a 72–82 GHz automotive radome tester, a 60–90 GHz vector network analyzer system, and a 76–81 GHz radar target simulator setup were used in combination with a representative polypropylene plate that was purposefully contaminated with a varying range of water and ISO standard dust combinations; this was used to determine any signal attenuation and subsequent impact on target detection. The results show that the water content in dust contaminants significantly affects radar signal transmission and object detection performance, with higher water content levels causing increased signal attenuation, impacting detection capability across all tested scenarios. Full article

(This article belongs to the Section Radar Sensors)

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24 pages, 6826 KB

Open AccessEditor’s ChoiceArticle

Preparation of NiO NWs by Thermal Oxidation for Highly Selective Gas-Sensing Applications

by Marwa Ben Arbia, Sung-Ho Kim, Jun-Bo Yoon and Elisabetta Comini

Sensors 2025, 25(7), 2075; https://doi.org/10.3390/s25072075 - 26 Mar 2025

Cited by 3 | Viewed by 1763

Abstract

This paper presents a novel approach for fabricating porous NiO films decorated with nanowires, achieved through sputtering followed by thermal oxidation of a metallic layer. Notably, we successfully fabricate NiO nanowires using this simple and cost-effective method, demonstrating its potential applicability in the [...] Read more.

This paper presents a novel approach for fabricating porous NiO films decorated with nanowires, achieved through sputtering followed by thermal oxidation of a metallic layer. Notably, we successfully fabricate NiO nanowires using this simple and cost-effective method, demonstrating its potential applicability in the gas-sensing field. Furthermore, by using the film of our nanowires, we are able to easily prepare NiO sensors and deposit the required Pt electrodes directly on the film. This is a key advantage, as it simplifies the fabrication process and makes it easier to integrate the sensors into practical gas-sensing devices without the need for nanostructure transfer or intricate setups. Scanning electron microscopy (SEM) reveals the porous structure and nanowire formation, while X-ray diffraction (XRD) confirms the presence of the NiO phase. As a preliminary investigation, the gas-sensing properties of NiO films with varying thicknesses were evaluated at different operating temperatures. The results indicate that thinner layers exhibit superior performances. Gas measurements confirm the p-type nature of the NiO samples, with sensors showing high responsiveness and selectivity toward NO₂ at an optimal temperature of 200 °C. However, incomplete recovery is observed due to the high binding energy of NO₂ molecules. At higher temperatures, sufficient activation energy enables a full sensor recovery but with reduced response. The paper discusses the adsorption–desorption reaction mechanisms on the NiO surface, examines how moisture impacts the enhanced responsiveness of Pt-NiO (2700%) and Au-NiO (400%) sensors, and highlights the successful fabrication of NiO nanowires through a simple and cost-effective method, presenting a promising alternative to more complex approaches. Full article

(This article belongs to the Special Issue Nanomaterials for Chemical Sensors 2023)

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25 pages, 40263 KB

Open AccessEditor’s ChoiceArticle

Autonomous Navigation of Mobile Robots: A Hierarchical Planning–Control Framework with Integrated DWA and MPC

by Zhongrui Wang, Shuting Wang, Yuanlong Xie, Tifan Xiong and Chao Wang

Sensors 2025, 25(7), 2014; https://doi.org/10.3390/s25072014 - 23 Mar 2025

Cited by 3 | Viewed by 1176

Abstract

In human–robot collaborative environments, the inherent complexity of shared operational spaces imposes dual requirements on process safety and task execution efficiency. To address the limitations of conventional approaches that decouple planning and control modules, we propose a hierarchical planning–control framework. The proposed framework [...] Read more.

In human–robot collaborative environments, the inherent complexity of shared operational spaces imposes dual requirements on process safety and task execution efficiency. To address the limitations of conventional approaches that decouple planning and control modules, we propose a hierarchical planning–control framework. The proposed framework explicitly incorporates path tracking constraints during path generation while simultaneously considering path characteristics in the control process. The framework comprises two principal components: (1) an enhanced Dynamic Window Approach (DWA) for the local path planning module, introducing adaptive sub-goal selection method and improved path evaluation functions; and (2) a modified Model Predictive Control (MPC) for the path tracking module, with a curvature-based reference state online changing strategy. Comprehensive simulation and real-world experiments demonstrate the framework’s operational advantages over conventional methods. Full article

(This article belongs to the Special Issue Toward Embodied Intelligence: State-of-the-Art in Sensing, Decision-Making and Control Technologies for Autonomous Robots (2nd Edition))

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22 pages, 4867 KB

Open AccessEditor’s ChoiceArticle

Integrating Proximal and Remote Sensing with Machine Learning for Pasture Biomass Estimation

by Bernardo Cândido, Ushasree Mindala, Hamid Ebrahimy, Zhou Zhang and Robert Kallenbach

Sensors 2025, 25(7), 1987; https://doi.org/10.3390/s25071987 - 22 Mar 2025

Cited by 2 | Viewed by 1956

Abstract

This study tackles the challenge of accurately estimating pasture biomass by integrating proximal sensing, remote sensing, and machine learning techniques. Field measurements of vegetation height collected using the PaddockTrac ultrasonic sensor were combined with vegetation indices (e.g., NDVI, MSAVI2) derived from Landsat 7 [...] Read more.

This study tackles the challenge of accurately estimating pasture biomass by integrating proximal sensing, remote sensing, and machine learning techniques. Field measurements of vegetation height collected using the PaddockTrac ultrasonic sensor were combined with vegetation indices (e.g., NDVI, MSAVI2) derived from Landsat 7 and Sentinel-2 satellite data. We applied the Boruta algorithm for feature selection to identify influential biophysical predictors and evaluated four machine learning models—Linear Regression, Decision Tree, Random Forest, and XGBoost—for biomass prediction. XGBoost consistently performed the best, achieving an R² of 0.86, an MAE of 414 kg ha⁻¹, and an RMSE of 538 kg ha⁻¹ using Landsat 7 data across multiple years. Sentinel-2’s red-edge indices did not substantially improve predictions, suggesting a limited benefit from finer spectral resolutions in this homogenous pasture context. Nonetheless, these indices may offer value in more complex vegetation scenarios. The findings emphasize the effectiveness of combining detailed ground-based measurements with advanced machine learning and remote sensing data, providing a scalable and accurate approach to biomass estimation. This integrated framework provides practical insights for precision agriculture and optimized pasture management, significantly advancing efficient and sustainable rangeland monitoring. Full article

(This article belongs to the Special Issue Remote Sensing of Plants, Soil, and Atmosphere for Agricultural Monitoring)

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29 pages, 4979 KB

Open AccessEditor’s ChoiceArticle

Land Cover Classification Model Using Multispectral Satellite Images Based on a Deep Learning Synergistic Semantic Segmentation Network

by Abdorreza Alavi Gharahbagh, Vahid Hajihashemi, José J. M. Machado and João Manuel R. S. Tavares

Sensors 2025, 25(7), 1988; https://doi.org/10.3390/s25071988 - 22 Mar 2025

Cited by 2 | Viewed by 2848

Abstract

Land cover classification (LCC) using satellite images is one of the rapidly expanding fields in mapping, highlighting the need for updating existing computational classification methods. Advances in technology and the increasing variety of applications have introduced challenges, such as more complex classes and [...] Read more.

Land cover classification (LCC) using satellite images is one of the rapidly expanding fields in mapping, highlighting the need for updating existing computational classification methods. Advances in technology and the increasing variety of applications have introduced challenges, such as more complex classes and a demand for greater detail. In recent years, deep learning and Convolutional Neural Networks (CNNs) have significantly enhanced the segmentation of satellite images. Since the training of CNNs requires sophisticated and expensive hardware and significant time, using pre-trained networks has become widespread in the segmentation of satellite image. This study proposes a hybrid synergistic semantic segmentation method based on the Deeplab v3+ network and a clustering-based post-processing scheme. The proposed method accurately classifies various land cover (LC) types in multispectral satellite images, including Pastures, Other Built-Up Areas, Water Bodies, Urban Areas, Grasslands, Forest, Farmland, and Others. The post-processing scheme includes a spectral bag-of-words model and K-medoids clustering to refine the Deeplab v3+ outputs and correct possible errors. The simulation results indicate that combining the post-processing scheme with deep learning improves the Matthews correlation coefficient (MCC) by approximately 5.7% compared to the baseline method. Additionally, the proposed approach is robust to data imbalance cases and can dynamically update its codewords over different seasons. Finally, the proposed synergistic semantic segmentation method was compared with several state-of-the-art segmentation methods in satellite images of Italy’s Lake Garda (Lago di Garda) region. The results showed that the proposed method outperformed the best existing techniques by at least 6% in terms of MCC. Full article

(This article belongs to the Special Issue Advancing Land Monitoring through Synergistic Harmonization of Optical, Radar and Lidar Satellite Technologies)

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42 pages, 14097 KB

Open AccessEditor’s ChoiceReview

Microfluidic Biosensors: Enabling Advanced Disease Detection

by Siyue Wang, Xiaotian Guan and Shuqing Sun

Sensors 2025, 25(6), 1936; https://doi.org/10.3390/s25061936 - 20 Mar 2025

Cited by 8 | Viewed by 8001

Abstract

Microfluidic biosensors integrate microfluidic and biosensing technologies to achieve the miniaturization, integration, and automation of disease diagnosis, and show great potential for application in the fields of cancer liquid biopsy, pathogenic bacteria detection, and POCT. This paper reviews the recent advances related to [...] Read more.

Microfluidic biosensors integrate microfluidic and biosensing technologies to achieve the miniaturization, integration, and automation of disease diagnosis, and show great potential for application in the fields of cancer liquid biopsy, pathogenic bacteria detection, and POCT. This paper reviews the recent advances related to microfluidic biosensors in the field of laboratory medicine, focusing on their applications in the above three areas. In cancer liquid biopsy, microfluidic biosensors facilitate the isolation, enrichment, and detection of tumor markers such as CTCs, ctDNA, miRNA, exosomes, and so on, providing support for early diagnosis, precise treatment, and prognostic assessment. In terms of pathogenic bacteria detection, microfluidic biosensors can achieve the rapid, highly sensitive, and highly specific detection of a variety of pathogenic bacteria, helping disease prevention and control as well as public health safety. Pertaining to the realm of POCT, microfluidic biosensors bring the convenient detection of a variety of diseases, such as tumors, infectious diseases, and chronic diseases, to primary health care. Future microfluidic biosensor research will focus on enhancing detection throughput, lowering costs, innovating new recognition elements and signal transduction methods, integrating artificial intelligence, and broadening applications to include home health care, drug discovery, food safety, and so on. Full article

(This article belongs to the Special Issue Recent Advances in Microfluidic Sensing Devices)

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30 pages, 6268 KB

Open AccessEditor’s ChoiceArticle

Cooperative Hybrid Modelling and Dimensionality Reduction for a Failure Monitoring Application in Industrial Systems

by Morgane Suhas, Emmanuelle Abisset-Chavanne and Pierre-André Rey

Sensors 2025, 25(6), 1952; https://doi.org/10.3390/s25061952 - 20 Mar 2025

Cited by 4 | Viewed by 1002

Abstract

Failure monitoring of industrial systems is imperative in order to ensure their reliability and competitiveness. This paper presents an innovative hybrid modelling approach applied to DC electric motors, specifically the Kollmorgen AKM42 servomotor. The proposed Cooperative Hybrid Model for Classification (CHMC) combines physics-based [...] Read more.

Failure monitoring of industrial systems is imperative in order to ensure their reliability and competitiveness. This paper presents an innovative hybrid modelling approach applied to DC electric motors, specifically the Kollmorgen AKM42 servomotor. The proposed Cooperative Hybrid Model for Classification (CHMC) combines physics-based and data-driven models to improve fault detection and extrapolation to new usage profiles. The integration of physical knowledge of the healthy behaviour of the motor into a recurrent neural network enhances the accuracy of bearing fault detection by identifying three health states: healthy, progressive fault and stabilised fault. Additionally, Singular Value Decomposition (SVD) is employed for the purposes of feature extraction and dimensionality reduction, thereby enhancing the model’s capacity to generalise with limited training data. The findings of this study demonstrate that a reduction in the input data of 90% preserves the essential information, with an analysis of the first harmonics revealing a narrow frequency range. This elucidates the reason why the first 20 components are sufficient to explain the data variability. The findings reveal that, for usage profiles analogous to the training data, both the CHMC and NHMC models demonstrate comparable performance without reduction. However, the CHMC model exhibits superior performance in detecting true negatives (90% vs. 89%) and differentiating between healthy and failure states. The NHMC model encounters greater difficulty in distinguishing failure states (83.92% vs. 86.56% for progressive failure). When exposed to new usage profiles with increased frequency and amplitude, the CHMC model adapts better, showing superior performance in detecting true positives and handling new data, highlighting its superior extrapolation capabilities. The integration of SVD further reduces input data complexity, and the CHMC model consistently outperforms the NHMC model in these reduced data scenarios, demonstrating the efficacy of combining physical models and dimensionality reduction in enhancing the model’s generalisation, fault detection, and adaptability. This approach has the advantage of reducing the need for retraining, which makes the CHMC model a cost-effective solution for motor fault classification in industrial settings. In conclusion, the CHMC model offers a generalisable method with significant advantages in fault detection, model adaptation, and predictive maintenance performance across varying usage profiles and on unseen operational scenarios. Full article

(This article belongs to the Section Industrial Sensors)

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17 pages, 2010 KB

Open AccessEditor’s ChoiceArticle

Gaze Estimation Network Based on Multi-Head Attention, Fusion, and Interaction

by Changli Li, Fangfang Li, Kao Zhang, Nenglun Chen and Zhigeng Pan

Sensors 2025, 25(6), 1893; https://doi.org/10.3390/s25061893 - 18 Mar 2025

Cited by 1 | Viewed by 1499

Abstract

Gaze is an externally observable indicator of human visual attention, and thus, recording the gaze position can help to solve many problems. Existing gaze estimation models typically utilize separate neural network branches to process data streams from both eyes and the face, failing [...] Read more.

Gaze is an externally observable indicator of human visual attention, and thus, recording the gaze position can help to solve many problems. Existing gaze estimation models typically utilize separate neural network branches to process data streams from both eyes and the face, failing to fully exploit their feature correlations. This study presents a gaze estimation network that integrates multi-head attention mechanisms, fusion, and interaction strategies to fuse facial features with eye features, as well as features from both eyes, separately. Specifically, multi-head attention and channel attention are used to fuse features from both eyes, and a face and eye interaction module is designed to highlight the most important facial features guided by the eye features; in addition, the channel attention in the Convolutional Block Attention Module (CBAM) is replaced with minimum pooling instead of maximum pooling, and a shortcut connection is added to enhance the network’s attention to eye region details. Comparative experiments on three public datasets—Gaze360, MPIIFaceGaze, and EYEDIAP—validate the superiority of the proposed method. Full article

(This article belongs to the Section Intelligent Sensors)

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21 pages, 6799 KB

Open AccessEditor’s ChoiceArticle

Spatial–Temporal Dynamics of Vegetation Indices in Response to Drought Across Two Traditional Olive Orchard Regions in the Iberian Peninsula

by Nazaret Crespo, Luís Pádua, Paula Paredes, Francisco J. Rebollo, Francisco J. Moral, João A. Santos and Helder Fraga

Sensors 2025, 25(6), 1894; https://doi.org/10.3390/s25061894 - 18 Mar 2025

Cited by 1 | Viewed by 1373

Abstract

This study investigates the spatial–temporal dynamics of vegetation indices in olive orchards across two traditionally rainfed regions of the Iberian Peninsula, namely the “Trás-os-Montes” (TM) agrarian region in Portugal and the Badajoz (BA) province in Spain, in response to drought conditions. Using satellite-derived [...] Read more.

This study investigates the spatial–temporal dynamics of vegetation indices in olive orchards across two traditionally rainfed regions of the Iberian Peninsula, namely the “Trás-os-Montes” (TM) agrarian region in Portugal and the Badajoz (BA) province in Spain, in response to drought conditions. Using satellite-derived vegetation indices, derived from the Harmonized Landsat Sentinel-2 project (HLSL30), such as the Normalized Difference Moisture Index (NDMI) and Soil-Adjusted Vegetation Index (SAVI), this study evaluates the impact of drought periods on olive tree growing conditions. The Mediterranean Palmer Drought Severity Index (MedPDSI), specifically developed for olive trees, was selected to quantify drought severity, and impacts on vegetation dynamics were assessed throughout the study period (2015–2023). The analysis reveals significant differences between the regions, with BA experiencing more intense drought conditions, particularly during the warm season, compared to TM. Seasonal variability in vegetation dynamics is clearly linked to MedPDSI, with lagged responses stronger in the previous two-months. Both the SAVI and the NDMI show vegetation vigour declines during dry seasons, particularly in the years of 2017 and 2022. The findings reported in this study highlight the vulnerability of rainfed olive orchards in BA to long-term drought-induced stress, while TM appears to have slightly higher resilience. The study underscores the value of combining satellite-derived vegetation indices with drought indicators for the effective monitoring of olive groves and to improve water use management practices in response to climate change. These insights are crucial for developing adaptation measures that ensure the sustainability, resiliency, and productivity of rainfed olive orchards in the Iberian Peninsula, particularly under climate change scenarios. Full article

(This article belongs to the Special Issue Advancing Land Monitoring through Synergistic Harmonization of Optical, Radar and Lidar Satellite Technologies)

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15 pages, 3600 KB

Open AccessEditor’s ChoiceArticle

Aptamer-Functionalized Platform for Selective Bacterial Isolation and Rapid RNA Purification Using Capture Pins

by Md Aminul Islam, Rebecca Giorno and Gergana G. Nestorova

Sensors 2025, 25(6), 1774; https://doi.org/10.3390/s25061774 - 13 Mar 2025

Viewed by 2056

Abstract

Efficient bacterial lysis and RNA purification are essential for molecular diagnostics and biosensing applications. This study presents a piezoelectric platform integrated with gold-plated RNA capture pins (RCPs) functionalized with synthetic oligonucleotides to extract and enrich E. coli 16S ribosomal RNA (rRNA). The 3D-printed [...] Read more.

Efficient bacterial lysis and RNA purification are essential for molecular diagnostics and biosensing applications. This study presents a piezoelectric platform integrated with gold-plated RNA capture pins (RCPs) functionalized with synthetic oligonucleotides to extract and enrich E. coli 16S ribosomal RNA (rRNA). The 3D-printed device enables selective bacterial capture using E. coli-specific aptamers and incorporates a piezoelectric transducer operating at 60 kHz to facilitate bacterial cell wall disruption. The platform demonstrated high specificity for E. coli over B. cereus, confirming aptamer selectivity. E. coli viability assessment demonstrated that positioning the piezoelectric plate in contact with the bacterial suspension significantly improved the bacterial lysis, reducing viability to 33.68% after 15 min. RNA quantification confirmed an increase in total RNA released by lysed E. coli, resulting in 10,913 ng after 15 min, compared to 4310 ng obtained via conventional sonication. RCP-extracted RNA has a threefold enrichment of 16S rRNA relative to 23S rRNA. RT-qPCR analysis indicated that the RCPs recovered, on average, 2.3 ng of 16S RNA per RCP from bacterial suspensions and 0.1 ng from aptamer-functionalized surfaces. This integrated system offers a rapid, selective, and label-free approach for bacterial lysis, RNA extraction, and enrichment for specific types of RNA with potential applications in clinical diagnostics and microbial biosensing. Full article

(This article belongs to the Section Biosensors)

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19 pages, 4621 KB

Open AccessEditor’s ChoiceArticle

Highly Selective Room-Temperature Blue LED-Enhanced NO₂ Gas Sensors Based on ZnO-MoS₂-TiO₂ Heterostructures

by Soraya Y. Flores, Elluz Pacheco, Carlos Malca, Xiaoyan Peng, Yihua Chen, Badi Zhou, Dalice M. Pinero, Liz M. Diaz-Vazquez, Andrew F. Zhou and Peter X. Feng

Sensors 2025, 25(6), 1781; https://doi.org/10.3390/s25061781 - 13 Mar 2025

Cited by 1 | Viewed by 2137

Abstract

This study presents the fabrication and characterization of highly selective, room-temperature gas sensors based on ternary zinc oxide–molybdenum disulfide–titanium dioxide (ZnO-MoS₂-TiO₂) nanoheterostructures. Integrating two-dimensional (2D) MoS₂ with oxide nano materials synergistically combines their unique properties, significantly enhancing gas [...] Read more.

This study presents the fabrication and characterization of highly selective, room-temperature gas sensors based on ternary zinc oxide–molybdenum disulfide–titanium dioxide (ZnO-MoS₂-TiO₂) nanoheterostructures. Integrating two-dimensional (2D) MoS₂ with oxide nano materials synergistically combines their unique properties, significantly enhancing gas sensing performance. Comprehensive structural and chemical analyses, including scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), Raman spectroscopy, and Fourier transform infrared spectroscopy (FTIR), confirmed the successful synthesis and composition of the ternary nanoheterostructures. The sensors demonstrated excellent selectivity in detecting low concentrations of nitrogen dioxide (NO₂) among target gases such as ammonia (NH₃), methane (CH₄), and carbon dioxide (CO₂) at room temperature, achieving up to 58% sensitivity at 4 ppm and 6% at 0.1 ppm for NO₂. The prototypes demonstrated outstanding selectivity and a short response time of approximately 0.51 min. The impact of light-assisted enhancement was examined under 1 mW/cm² weak ultraviolet (UV), blue, yellow, and red light-emitting diode (LED) illuminations, with the blue LED proving to deliver the highest sensor responsiveness. These results position ternary ZnO-MoS₂-TiO₂ nanoheterostructures as highly sensitive and selective room-temperature NO₂ gas sensors that are suitable for applications in environmental monitoring, public health, and industrial processes. Full article

(This article belongs to the Special Issue New Sensors Based on Inorganic Material)

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36 pages, 1195 KB

Open AccessEditor’s ChoiceReview

A Comprehensive Review of Home Sleep Monitoring Technologies: Smartphone Apps, Smartwatches, and Smart Mattresses

by Bhekumuzi M. Mathunjwa, Randy Yan Jie Kor, Wanida Ngarnkuekool and Yeh-Liang Hsu

Sensors 2025, 25(6), 1771; https://doi.org/10.3390/s25061771 - 12 Mar 2025

Cited by 5 | Viewed by 9027

Abstract

The home is an ideal setting for long-term sleep monitoring. This review explores a range of home-based sleep monitoring technologies, including smartphone apps, smartwatches, and smart mattresses, to assess their accuracy, usability, limitations, and how well they integrate with existing healthcare systems. This [...] Read more.

The home is an ideal setting for long-term sleep monitoring. This review explores a range of home-based sleep monitoring technologies, including smartphone apps, smartwatches, and smart mattresses, to assess their accuracy, usability, limitations, and how well they integrate with existing healthcare systems. This review evaluates 21 smartphone apps, 16 smartwatches, and nine smart mattresses through systematic data collection from academic literature, manufacturer specifications, and independent studies. Devices were assessed based on sleep-tracking capabilities, physiological data collection, movement detection, environmental sensing, AI-driven analytics, and healthcare integration potential. Wearables provide the best balance of accuracy, affordability, and usability, making them the most suitable for general users and athletes. Smartphone apps are cost-effective but offer lower accuracy, making them more appropriate for casual sleep tracking rather than clinical applications. Smart mattresses, while providing passive and comfortable sleep tracking, are costlier and have limited clinical validation. This review offers essential insights for selecting the most appropriate home sleep monitoring technology. Future developments should focus on multi-sensor fusion, AI transparency, energy efficiency, and improved clinical validation to enhance reliability and healthcare applicability. As these technologies evolve, home sleep monitoring has the potential to bridge the gap between consumer-grade tracking and clinical diagnostics, making personalized sleep health insights more accessible and actionable. Full article

(This article belongs to the Special Issue eHealth Platforms and Sensors for Health and Human Activity Monitoring)

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13 pages, 2885 KB

Open AccessEditor’s ChoiceArticle

Sensitive Electrochemical Determination of Vanillin Using a Bimetallic Hydroxide and Reduced Graphene Oxide Nanocomposite

by Shamim Ahmed Hira, Jonathan Quintal and Aicheng Chen

Sensors 2025, 25(6), 1694; https://doi.org/10.3390/s25061694 - 9 Mar 2025

Viewed by 1834

Abstract

Vanillin (VAN) is an organic compound which not only functions as a flavoring and fragrance enhancer in some foods but also has antioxidant, anti-inflammatory, anti-cancer, and anti-depressant effects. However, the excessive use of VAN can be associated with negative side effects on human [...] Read more.

Vanillin (VAN) is an organic compound which not only functions as a flavoring and fragrance enhancer in some foods but also has antioxidant, anti-inflammatory, anti-cancer, and anti-depressant effects. However, the excessive use of VAN can be associated with negative side effects on human health. As a result, it is crucial to find a reliable method for the rapid determination of VAN to enhance food safety. Herein, we developed a sensor using Ni and Co bimetallic hydroxide and reduced graphene oxide nanostructure (NiCo(OH)₂.rGO). Our prepared material was characterized using various physico-chemical techniques. The electrocatalytic efficiency of the NiCo(OH)₂.rGO-modified glassy carbon electrode was investigated using cyclic and square wave voltammetry. The developed sensor showed a limit of detection of 6.1 nM and a linear range of 5–140 nM. The synergistic effect of NiCo(OH)₂ and rGO improved the active sites and enhanced its catalytic efficiency. The practical applicability of the prepared sensor was investigated for the determination of VAN in food samples such as biscuits and chocolates, showing promise in practical applications. Full article

(This article belongs to the Special Issue Electrochemical Sensors: Technologies and Applications)

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28 pages, 13455 KB

Open AccessEditor’s ChoiceArticle

DUIncoder: Learning to Detect Driving Under the Influence Behaviors from Various Normal Driving Data

by Haoran Zhou, Alexander Carballo, Masaki Yamaoka, Minori Yamataka, Keisuke Fujii and Kazuya Takeda

Sensors 2025, 25(6), 1699; https://doi.org/10.3390/s25061699 - 9 Mar 2025

Cited by 1 | Viewed by 1345

Abstract

Driving Under the Influence (DUI) has emerged as a significant threat to public safety in recent years. Despite substantial efforts to effectively detect DUI, the inherent risks associated with acquiring DUI-related data pose challenges in meeting the data requirements for training. To address [...] Read more.

Driving Under the Influence (DUI) has emerged as a significant threat to public safety in recent years. Despite substantial efforts to effectively detect DUI, the inherent risks associated with acquiring DUI-related data pose challenges in meeting the data requirements for training. To address this issue, we propose DUIncoder, which is an unsupervised framework designed to learn exclusively from normal driving data across diverse scenarios to detect DUI behaviors and provide explanatory insights. DUIncoder aims to address the challenge of collecting DUI data by leveraging diverse normal driving data, which can be readily and continuously obtained from daily driving. Experiments on simulator data show that DUIncoder achieves detection performance superior to that of supervised learning methods which require additional DUI data. Moreover, its generalization capabilities and adaptability to incremental data demonstrate its potential for enhanced real-world applicability. Full article

(This article belongs to the Special Issue Advanced Sensing and Analysis Technology in Transportation Safety)

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23 pages, 4000 KB

Open AccessEditor’s ChoiceArticle

Evaluating Machine Learning-Based Soft Sensors for Effluent Quality Prediction in Wastewater Treatment Under Variable Weather Conditions

by Daniel Voipan, Andreea Elena Voipan and Marian Barbu

Sensors 2025, 25(6), 1692; https://doi.org/10.3390/s25061692 - 8 Mar 2025

Cited by 8 | Viewed by 2078

Abstract

Maintaining effluent quality in wastewater treatment plants (WWTPs) comes with significant challenges under variable weather conditions, where sudden changes in flow rate and increased pollutant loads can affect treatment performance. Traditional physical sensors became both expensive and susceptible to failure under extreme conditions. [...] Read more.

Maintaining effluent quality in wastewater treatment plants (WWTPs) comes with significant challenges under variable weather conditions, where sudden changes in flow rate and increased pollutant loads can affect treatment performance. Traditional physical sensors became both expensive and susceptible to failure under extreme conditions. In this study, we evaluate the performance of soft sensors based on artificial intelligence (AI) to predict the components underlying the calculation of the effluent quality index (EQI). We thus focus our study on three ML models: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer. Using the Benchmark Simulation Model no. 2 (BSM2) as the WWTP, we were able to obtain datasets for training the ML models and to evaluate their performance in dry weather scenarios, rainy episodes, and storm events. To improve the classification of networks according to the type of weather, we developed a Random Forest (RF)-based meta-classifier. The results indicate that for dry weather conditions the Transformer network achieved the best performance, while for rain episodes and storm scenarios the GRU was able to capture sudden variations with the highest accuracy. LSTM performed normally in stable conditions but struggled with rapid fluctuations. These results support the decision to integrate AI-based predictive models in WWTPs, highlighting the top performances of both a recurrent network (GRU) and a feed-forward network (Transformer) in obtaining effluent quality predictions under different weather conditions. Full article

(This article belongs to the Special Issue Soft Sensors and Sensing Techniques)

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17 pages, 4719 KB

Open AccessEditor’s ChoiceArticle

Synergistic Enhancement of Chemiresistive NO₂ Gas Sensors Using Nitrogen-Doped Reduced Graphene Oxide (N-rGO) Decorated with Nickel Oxide (NiO) Nanoparticles: Achieving sub-ppb Detection Limit

by Chiheb Walleni, Mounir Ben Ali, Mohamed Faouzi Ncib and Eduard Llobet

Sensors 2025, 25(5), 1631; https://doi.org/10.3390/s25051631 - 6 Mar 2025

Cited by 2 | Viewed by 3965

Abstract

Detecting low nitrogen dioxide concentrations (NO₂) is crucial for environmental monitoring. In this paper, we report the synergistic effect of decorating nitrogen-doped reduced graphene oxide (N-rGO) with nickel oxide (NiO) nanoparticles for developing highly selective and sensitive chemiresistive NO₂ gas [...] Read more.

Detecting low nitrogen dioxide concentrations (NO₂) is crucial for environmental monitoring. In this paper, we report the synergistic effect of decorating nitrogen-doped reduced graphene oxide (N-rGO) with nickel oxide (NiO) nanoparticles for developing highly selective and sensitive chemiresistive NO₂ gas sensors. The N-rGO/NiO sensor was synthesized straightforwardly, ensuring uniform decoration of NiO nanoparticles on the N-rGO surface. Comprehensive characterization using SEM, TEM, XRD, and Raman spectroscopy confirmed the successful integration of NiO nanoparticles with N-rGO and revealed key structural and morphological features contributing to its enhanced sensing performance. As a result, the NiO/N-rGO nanohybrids demonstrate a significantly enhanced response five orders of magnitude higher than that of N-rGO toward low NO₂ concentrations (<1 ppm) at 100 °C. Moreover, the present device has an outstanding performance, high sensitivity, and low limit of detection (<1 ppb). The findings pave the way for integrating these sensors into advanced applications, including environmental monitoring and IoT-enabled air quality management systems. Full article

(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications)

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27 pages, 2191 KB

Open AccessEditor’s ChoiceArticle

Detection of Anomalies in Data Streams Using the LSTM-CNN Model

by Agnieszka Duraj, Piotr S. Szczepaniak and Artur Sadok

Sensors 2025, 25(5), 1610; https://doi.org/10.3390/s25051610 - 6 Mar 2025

Cited by 6 | Viewed by 4983

Abstract

This paper presents a comparative analysis of selected deep learning methods applied to anomaly detection in data streams. The anomaly detection results obtained on the popular Yahoo! Webscope S5 dataset are used for the computational experiments. The two commonly used and recommended models [...] Read more.

This paper presents a comparative analysis of selected deep learning methods applied to anomaly detection in data streams. The anomaly detection results obtained on the popular Yahoo! Webscope S5 dataset are used for the computational experiments. The two commonly used and recommended models in the literature, which are the basis for this analysis, are the following: the LSTM and its more complicated variant, the LSTM autoencoder. Additionally, the usefulness of an innovative LSTM-CNN approach is evaluated. The results indicate that the LSTM-CNN approach can successfully be applied for anomaly detection in data streams as its performance compares favorably with that of the two mentioned standard models. For the performance evaluation, the

F_{1 s c o r e}

is used. Full article

(This article belongs to the Section Intelligent Sensors)

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14 pages, 1602 KB

Open AccessEditor’s ChoiceArticle

DepthLux: Employing Depthwise Separable Convolutions for Low-Light Image Enhancement

by Raul Balmez, Alexandru Brateanu, Ciprian Orhei, Codruta O. Ancuti and Cosmin Ancuti

Sensors 2025, 25(5), 1530; https://doi.org/10.3390/s25051530 - 1 Mar 2025

Cited by 5 | Viewed by 2464

Abstract

Low-light image enhancement is an important task in computer vision, often made challenging by the limitations of image sensors, such as noise, low contrast, and color distortion. These challenges are further exacerbated by the computational demands of processing spatial dependencies under such conditions. [...] Read more.

Low-light image enhancement is an important task in computer vision, often made challenging by the limitations of image sensors, such as noise, low contrast, and color distortion. These challenges are further exacerbated by the computational demands of processing spatial dependencies under such conditions. We present a novel transformer-based framework that enhances efficiency by utilizing depthwise separable convolutions instead of conventional approaches. Additionally, an original feed-forward network design reduces the computational overhead while maintaining high performance. Experimental results demonstrate that this method achieves competitive results, providing a practical and effective solution for enhancing images captured in low-light environments. Full article

(This article belongs to the Section Sensing and Imaging)

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16 pages, 3031 KB

Open AccessEditor’s ChoiceArticle

Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain

by Damián Gulich, Myrian Tebaldi and Daniel Sierra-Sosa

Sensors 2025, 25(5), 1483; https://doi.org/10.3390/s25051483 - 28 Feb 2025

Cited by 2 | Viewed by 1713

Abstract

Quantifying atmospheric turbulence intensity is a challenging task, particularly when assessing real-world scenarios. In this paper, we propose a deep learning method for quantifying atmospheric turbulence intensity based on the space-time domain analysis from videos depicting different turbulence levels. We capture videos of [...] Read more.

Quantifying atmospheric turbulence intensity is a challenging task, particularly when assessing real-world scenarios. In this paper, we propose a deep learning method for quantifying atmospheric turbulence intensity based on the space-time domain analysis from videos depicting different turbulence levels. We capture videos of a static image under controlled air turbulence intensities using an inexpensive camera, and then, by slicing these videos in the space-time domain, we extract spatio-temporal representations of the turbulence dynamics. These representations are then fed into a Convolutional Neural Network for classification. This network effectively learns to discriminate between different turbulence regimes based on the spatio-temporal features extracted from a real-world experiment captured in video slices. Full article

(This article belongs to the Section Environmental Sensing)

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25 pages, 30298 KB

Open AccessEditor’s ChoiceReview

Topological Photonic Crystal Sensors: Fundamental Principles, Recent Advances, and Emerging Applications

by Israa Abood, Sayed El. Soliman, Wenlong He and Zhengbiao Ouyang

Sensors 2025, 25(5), 1455; https://doi.org/10.3390/s25051455 - 27 Feb 2025

Cited by 5 | Viewed by 3794

Abstract

Topological photonic sensors have emerged as a breakthrough in modern optical sensing by integrating topological protection and light confinement mechanisms such as topological states, quasi-bound states in the continuum (quasi-BICs), and Tamm plasmon polaritons (TPPs). These devices exhibit exceptional sensitivity and high-Q [...] Read more.

Topological photonic sensors have emerged as a breakthrough in modern optical sensing by integrating topological protection and light confinement mechanisms such as topological states, quasi-bound states in the continuum (quasi-BICs), and Tamm plasmon polaritons (TPPs). These devices exhibit exceptional sensitivity and high-Q resonances, making them ideal for high-precision environmental monitoring, biomedical diagnostics, and industrial sensing applications. This review explores the foundational physics and diverse sensor architectures, from refractive index sensors and biosensors to gas and thermal sensors, emphasizing their working principles and performance metrics. We further examine the challenges of achieving ultrahigh-Q operation in practical devices, limitations in multiparameter sensing, and design complexity. We propose physics-driven solutions to overcome these barriers, such as integrating Weyl semimetals, graphene-based heterostructures, and non-Hermitian photonic systems. This comparative study highlights the transformative impact of topological photonic sensors in achieving ultra-sensitive detection across multiple fields. Full article

(This article belongs to the Special Issue Feature Review Papers in Optical Sensors)

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19 pages, 11821 KB

Open AccessEditor’s ChoiceArticle

Bias Estimation for Low-Cost IMU Including X- and Y-Axis Accelerometers in INS/GPS/Gyrocompass

by Gen Fukuda and Nobuaki Kubo

Sensors 2025, 25(5), 1315; https://doi.org/10.3390/s25051315 - 21 Feb 2025

Viewed by 3260

Abstract

Inertial navigation systems (INSs) provide autonomous position estimation capabilities independent of global navigation satellite systems (GNSSs). However, the high cost of traditional sensors, such as fiber-optic gyroscopes (FOGs), limits their widespread adoption. In contrast, micro-electromechanical system (MEMS)-based inertial measurement units (IMUs) offer a [...] Read more.

Inertial navigation systems (INSs) provide autonomous position estimation capabilities independent of global navigation satellite systems (GNSSs). However, the high cost of traditional sensors, such as fiber-optic gyroscopes (FOGs), limits their widespread adoption. In contrast, micro-electromechanical system (MEMS)-based inertial measurement units (IMUs) offer a low-cost alternative; however, their lower accuracy and sensor bias issues, particularly in maritime environments, remain considerable obstacles. This study proposes an improved method for bias estimation by comparing the estimated values from a trajectory generator (TG)-based acceleration and angular-velocity estimation system with actual measurements. Additionally, for X- and Y-axis accelerations, we introduce a method that leverages the correlation between altitude differences derived from an INS/GNSS/gyrocompass (IGG) and those obtained during the TG estimation process to estimate the bias. Simulation datasets from experimental voyages validate the proposed method by evaluating the mean, median, normalized cross-correlation, least squares, and fast Fourier transform (FFT). The Butterworth filter achieved the smallest angular-velocity bias estimation error. For X- and Y-axis acceleration bias, altitude-based estimation achieved differences of 1.2 × 10⁻² m/s² and 1.0 × 10⁻⁴ m/s², respectively, by comparing the input bias using 30 min data. These methods enhance the positioning and attitude estimation accuracy of low-cost IMUs, providing a cost-effective maritime navigation solution. Full article

(This article belongs to the Special Issue INS/GNSS Integrated Navigation Systems)

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20 pages, 4945 KB

Open AccessEditor’s ChoiceArticle

At-Home Stroke Neurorehabilitation: Early Findings with the NeuroExo BCI System

by Juan José González-España, Lianne Sánchez-Rodríguez, Maxine Annel Pacheco-Ramírez, Jeff Feng, Kathryn Nedley, Shuo-Hsiu Chang, Gerard E. Francisco and Jose L. Contreras-Vidal

Sensors 2025, 25(5), 1322; https://doi.org/10.3390/s25051322 - 21 Feb 2025

Cited by 3 | Viewed by 1820

Abstract

Background: Democratized access to safe and effective robotic neurorehabilitation for stroke survivors requires innovative, affordable solutions that can be used not only in clinics but also at home. This requires the high usability of the devices involved to minimize costs associated with support [...] Read more.

Background: Democratized access to safe and effective robotic neurorehabilitation for stroke survivors requires innovative, affordable solutions that can be used not only in clinics but also at home. This requires the high usability of the devices involved to minimize costs associated with support from physical therapists or technicians. Methods: This paper describes the early findings of the NeuroExo brain–machine interface (BMI) with an upper-limb robotic exoskeleton for stroke neurorehabilitation. This early feasibility study consisted of a six-week protocol, with an initial training and BMI calibration phase at the clinic followed by 60 sessions of neuromotor therapy at the homes of the participants. Pre- and post-assessments were used to assess users’ compliance and system performance. Results: Participants achieved a compliance rate between

21 %

and

100 %

, with an average of

69 %

, while maintaining adequate signal quality and a positive perceived BMI performance during home usage with an average Likert scale score of four out of five. Moreover, adequate signal quality was maintained for four out of five participants throughout the protocol. These findings provide valuable insights into essential components for comprehensive rehabilitation therapy for stroke survivors. Furthermore, linear mixed-effects statistical models showed a significant reduction in trial duration (p-value < 0.02) and concomitant changes in brain patterns (p-value < 0.02). Conclusions: the analysis of these findings suggests that a low-cost, safe, simple-to-use BMI system for at-home stroke rehabilitation is feasible. Full article

(This article belongs to the Special Issue Advancements in Healthcare Robotics: Control, Sensing, and Biomedical Applications)

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14 pages, 4128 KB

Open AccessEditor’s ChoiceArticle

A Portable High-Resolution Snapshot Multispectral Imaging Device Leveraging Spatial and Spectral Features for Non-Invasive Corn Nitrogen Treatment Classification

by Xuan Li, Zhongzhong Niu, Ana Gabriela Morales-Ona, Ziling Chen, Tianzhang Zhao, Daniel J. Quinn and Jian Jin

Sensors 2025, 25(5), 1320; https://doi.org/10.3390/s25051320 - 21 Feb 2025

Viewed by 1418

Abstract

Spectral imaging has been widely applied in plant phenotyping to assess corn leaf nitrogen status. Recent studies indicate that spatial variations within a single leaf’s multispectral image provide stronger signals for corn nitrogen estimation. However, current technologies for corn multispectral imaging cannot capture [...] Read more.

Spectral imaging has been widely applied in plant phenotyping to assess corn leaf nitrogen status. Recent studies indicate that spatial variations within a single leaf’s multispectral image provide stronger signals for corn nitrogen estimation. However, current technologies for corn multispectral imaging cannot capture a large corn leaf segment with high-resolution and simple operation, limiting their efficiency and accuracy in nitrogen estimation. To address this gap, this study developed a proximal multispectral imaging device that can capture high-resolution snapshot multispectral images of a large segment of a single corn leaf. This device uses airflow to autonomously position and flatten the leaf to minimize the noise in images due to leaf curvature and simplify operation. Moreover, this device adopts a transmittance imaging regime by clamping the corn leaf between the camera and the lighting source to block the environmental lights and supply uniform lighting to capture high-resolution and high-precision leaf images within six seconds. A field assay was conducted to validate the effectiveness of the multispectral images captured by this device in assessing nitrogen status by classifying the nitrogen treatments applied to corn. Six nitrogen treatments were applied to 12 plots of corn fields, and 10 images were collected at each plot. By using the average vegetative index of the whole image, only one treatment was significantly different from the other five treatments, and no significant difference was observed among any other groups. However, by extracting the spatial and spectral features from the images and combining these features, the accuracy of nitrogen treatment classification improved compared to using the average index. In another analysis, by applying spatial–spectral analysis methods to the images, the nitrogen treatment classification accuracy has improved compared to using the average index. These results demonstrated the advantages of this high-resolution and high-throughput imaging device for distinguishing nitrogen treatments by facilitating spatial–spectral combined analysis for more precise classification. Full article

(This article belongs to the Special Issue Proximal Sensing in Precision Agriculture)

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17 pages, 5079 KB

Open AccessEditor’s ChoiceArticle

Holey Carbon Nanohorns-Based Nanohybrid as Sensing Layer for Resistive Ethanol Sensor

by Bogdan-Catalin Serban, Niculae Dumbravescu, Octavian Buiu, Marius Bumbac, Mihai Brezeanu, Cristina Pachiu, Cristina-Mihaela Nicolescu, Oana Brancoveanu and Cornel Cobianu

Sensors 2025, 25(5), 1299; https://doi.org/10.3390/s25051299 - 20 Feb 2025

Cited by 1 | Viewed by 1005

Abstract

The study presents the ethanol vapor sensing performance of a resistive sensor that utilizes a quaternary nanohybrid sensing layer composed of holey carbon nanohorns (CNHox), graphene oxide (GO), SnO₂, and polyvinylpyrrolidone (PVP) in an equal mass ratio of 1:1:1:1 (w [...] Read more.

The study presents the ethanol vapor sensing performance of a resistive sensor that utilizes a quaternary nanohybrid sensing layer composed of holey carbon nanohorns (CNHox), graphene oxide (GO), SnO₂, and polyvinylpyrrolidone (PVP) in an equal mass ratio of 1:1:1:1 (w/w/w/w). The sensing device includes a flexible polyimide substrate and interdigital transducer (IDT)-like electrodes. The sensing film is deposited by drop-casting on the sensing structure. The morphology and composition of the sensitive film are analyzed using scanning electron microscopy (SEM), Energy Dispersive X-ray (EDX) Spectroscopy, and Raman spectroscopy. The manufactured resistive device presents good sensitivity to concentrations of alcohol vapors varying in the range of 0.008–0.16 mg/cm³. The resistance of the proposed sensing structure increases over the entire range of measured ethanol concentration. Different types of sensing mechanisms are recognized. The decrease in the hole concentration in CNHox, GO, and CNHox due to the interaction with ethanol vapors, which act as electron donors, and the swelling of the PVP are plausible and seem to be the prevalent sensing pathway. The hard–soft acid-base (HSAB) principle strengthens our analysis. Full article

(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications)

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15 pages, 4374 KB

Open AccessEditor’s ChoiceArticle

An Artificial Intelligence Model for Sensing Affective Valence and Arousal from Facial Images

by Hiroki Nomiya, Koh Shimokawa, Shushi Namba, Masaki Osumi and Wataru Sato

Sensors 2025, 25(4), 1188; https://doi.org/10.3390/s25041188 - 15 Feb 2025

Cited by 2 | Viewed by 2302

Abstract

Artificial intelligence (AI) models can sense subjective affective states from facial images. Although recent psychological studies have indicated that dimensional affective states of valence and arousal are systematically associated with facial expressions, no AI models have been developed to estimate these affective states [...] Read more.

Artificial intelligence (AI) models can sense subjective affective states from facial images. Although recent psychological studies have indicated that dimensional affective states of valence and arousal are systematically associated with facial expressions, no AI models have been developed to estimate these affective states from facial images based on empirical data. We developed a recurrent neural network-based AI model to estimate subjective valence and arousal states from facial images. We trained our model using a database containing participant valence/arousal states and facial images. Leave-one-out cross-validation supported the validity of the model for predicting subjective valence and arousal states. We further validated the effectiveness of the model by analyzing a dataset containing participant valence/arousal ratings and facial videos. The model predicted second-by-second valence and arousal states, with prediction performance comparable to that of FaceReader, a commercial AI model that estimates dimensional affective states based on a different approach. We constructed a graphical user interface to show real-time affective valence and arousal states by analyzing facial video data. Our model is the first distributable AI model for sensing affective valence and arousal from facial images/videos to be developed based on an empirical database; we anticipate that it will have many practical uses, such as in mental health monitoring and marketing research. Full article

(This article belongs to the Special Issue Emotion Recognition Based on Sensors (3rd Edition))

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17 pages, 1391 KB

Open AccessEditor’s ChoiceArticle

Optimizing Sensor Data Interpretation via Hybrid Parametric Bootstrapping

by Victor V. Golovko

Sensors 2025, 25(4), 1183; https://doi.org/10.3390/s25041183 - 14 Feb 2025

Cited by 2 | Viewed by 864

Abstract

The Chalk River Laboratories (CRL) site in Ontario, Canada, has long been a hub for nuclear research, which has resulted in the accumulation of legacy nuclear waste, including radioactive materials such as uranium, plutonium, and other radionuclides. Effective management of this legacy requires [...] Read more.

The Chalk River Laboratories (CRL) site in Ontario, Canada, has long been a hub for nuclear research, which has resulted in the accumulation of legacy nuclear waste, including radioactive materials such as uranium, plutonium, and other radionuclides. Effective management of this legacy requires precise contamination and risk assessments, with a particular focus on the concentration levels of fissile materials such as

{}^{235}U

. These assessments are essential for maintaining nuclear criticality safety. This study estimates the upper bounds of

{}^{235}U

concentrations. We investigated the use of a hybrid parametric bootstrapping method and robust statistical techniques to analyze datasets with outliers, then compared these outcomes with those derived from nonparametric bootstrapping. This study underscores the significance of measuring

{}^{235}U

for ensuring safety, conducting environmental monitoring, and adhering to regulatory compliance requirements at nuclear legacy sites. We used publicly accessible

{}^{235}U

data from the Eastern Desert of Egypt to demonstrate the application of these statistical methods to small datasets, providing reliable upper limit estimates that are vital for remediation and decommissioning efforts. This method seeks to enhance the interpretation of sensor data, ultimately supporting safer nuclear waste management practices at legacy sites such as CRL. Full article

(This article belongs to the Special Issue Sensors and Extreme Environments)

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18 pages, 5927 KB

Open AccessEditor’s ChoiceArticle

Design and Optimization of a Gold and Silver Nanoparticle-Based SERS Biosensing Platform

by Soumyadeep Saha, Manoj Sachdev and Sushanta K. Mitra

Sensors 2025, 25(4), 1165; https://doi.org/10.3390/s25041165 - 14 Feb 2025

Cited by 2 | Viewed by 1851

Abstract

This study investigates the design and optimization of a nanoparticle-based surface-enhanced Raman scattering (SERS) biosensing platform using COMSOL Multiphysics simulations. The primary goal is to enhance the sensitivity and specificity of SERS biosensors, which are crucial for the precise detection and quantification of [...] Read more.

This study investigates the design and optimization of a nanoparticle-based surface-enhanced Raman scattering (SERS) biosensing platform using COMSOL Multiphysics simulations. The primary goal is to enhance the sensitivity and specificity of SERS biosensors, which are crucial for the precise detection and quantification of biomolecules. The simulation study explores the use of gold and silver nanoparticles in various arrangements, including single, multiple, and periodic nanospheres. The effects of polarization and the phenomenon of local hotspot switching in trimer and tetramer nanosphere systems are analyzed. To validate the simulation results, a SERS biosensing platform is fabricated by self-assembling gold nanoparticles on a silicon substrate, with methylene blue used as the Raman probe molecule. The findings demonstrate the feasibility of optimizing SERS biochips through simulation, which can be extended to various nanostructures. This work contributes to the advancement of highly sensitive and specific SERS biosensors for diagnostic and analytical applications. Full article

(This article belongs to the Section Biosensors)

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28 pages, 10511 KB

Open AccessEditor’s ChoiceArticle

Weather-Adaptive Regenerative Braking Strategy Based on Driving Style Recognition for Intelligent Electric Vehicles

by Marwa Ziadia, Sousso Kelouwani, Ali Amamou and Kodjo Agbossou

Sensors 2025, 25(4), 1175; https://doi.org/10.3390/s25041175 - 14 Feb 2025

Cited by 2 | Viewed by 2287

Abstract

This paper examines the energy efficiency of smart electric vehicles equipped with regenerative braking systems under challenging weather conditions. While Advanced Driver Assistance Systems (ADAS) are primarily designed to enhance driving safety, they often overlook energy efficiency. This study proposes a Weather-Adaptive Regenerative [...] Read more.

This paper examines the energy efficiency of smart electric vehicles equipped with regenerative braking systems under challenging weather conditions. While Advanced Driver Assistance Systems (ADAS) are primarily designed to enhance driving safety, they often overlook energy efficiency. This study proposes a Weather-Adaptive Regenerative Braking Strategy (WARBS) system, which leverages onboard sensors and data processing capabilities to enhance the energy efficiency of regenerative braking across diverse weather conditions while minimizing unnecessary alerts. To achieve this, we develop driving style recognition models that integrate road conditions, such as weather and road friction, with different driving styles. Next, we propose an adaptive deceleration plan that aims to maximize the conversion of kinetic energy into electrical energy for the vehicle’s battery under varying weather conditions, considering vehicle dynamics and speed constraints. Given that the potential for energy recovery through regenerative braking is diminished on icy and snowy roads compared to dry ones, our approach introduces a driving context recognition system to facilitate effective speed planning. Both simulation and experimental validation indicate that this approach can significantly enhance overall energy efficiency. Full article

(This article belongs to the Special Issue Connected and Autonomous Vehicles: Trends, Applications and Security Challenges)

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17 pages, 4402 KB

Open AccessEditor’s ChoiceArticle

Quality Evaluation for Colored Point Clouds Produced by Autonomous Vehicle Sensor Fusion Systems

by Colin Schaefer, Zeid Kootbally and Vinh Nguyen

Sensors 2025, 25(4), 1111; https://doi.org/10.3390/s25041111 - 12 Feb 2025

Cited by 1 | Viewed by 1277

Abstract

Perception systems for autonomous vehicles (AVs) require various types of sensors, including light detection and ranging (LiDAR) and cameras, to ensure their robustness in driving scenarios and weather conditions. The data from these sensors are fused together to generate maps of the surrounding [...] Read more.

Perception systems for autonomous vehicles (AVs) require various types of sensors, including light detection and ranging (LiDAR) and cameras, to ensure their robustness in driving scenarios and weather conditions. The data from these sensors are fused together to generate maps of the surrounding environment and provide information for the detection and tracking of objects. Hence, evaluation methods are necessary to compare existing and future sensor systems through quantifiable measurements given the wide range of sensor models and design choices. This paper presents an evaluation method to compare colored point clouds, a common fused data type, among two LiDAR–camera fusion systems and a stereo camera setup. The evaluation approach uses a test artifact measured by the fusion system’s colored point cloud through the spread, area coverage, and color difference of the colored points within the computed space. The test results showed the evaluation approach was able to rank the sensor fusion systems based on its metrics and complement the experimental observations. The proposed evaluation methodology is, therefore, suitable towards the comparison of generated colored point clouds by sensor fusion systems. Full article

(This article belongs to the Special Issue Connected and Autonomous Vehicles: Trends, Applications and Security Challenges)

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22 pages, 11164 KB

Open AccessEditor’s ChoiceArticle

Acoustic Emission-Based Pipeline Leak Detection and Size Identification Using a Customized One-Dimensional DenseNet

by Faisal Saleem, Zahoor Ahmad, Muhammad Farooq Siddique, Muhammad Umar and Jong-Myon Kim

Sensors 2025, 25(4), 1112; https://doi.org/10.3390/s25041112 - 12 Feb 2025

Cited by 13 | Viewed by 3804

Abstract

Effective leak detection and leak size identification are essential for maintaining the operational safety, integrity, and longevity of industrial pipelines. Traditional methods often suffer from high noise sensitivity, limited adaptability to non-stationary signals, and excessive computational costs, which limits their feasibility for real-time [...] Read more.

Effective leak detection and leak size identification are essential for maintaining the operational safety, integrity, and longevity of industrial pipelines. Traditional methods often suffer from high noise sensitivity, limited adaptability to non-stationary signals, and excessive computational costs, which limits their feasibility for real-time monitoring applications. This study presents a novel acoustic emission (AE)-based pipeline monitoring approach, integrating Empirical Wavelet Transform (EWT) for adaptive frequency decomposition with customized one-dimensional DenseNet architecture to achieve precise leak detection and size classification. The methodology begins with EWT-based signal segmentation, which isolates meaningful frequency bands to enhance leak-related feature extraction. To further improve signal quality, adaptive thresholding and denoising techniques are applied, filtering out low-amplitude noise while preserving critical diagnostic information. The denoised signals are processed using a DenseNet-based deep learning model, which combines convolutional layers and densely connected feature propagation to extract fine-grained temporal dependencies, ensuring the accurate classification of leak presence and severity. Experimental validation was conducted on real-world AE data collected under controlled leak and non-leak conditions at varying pressure levels. The proposed model achieved an exceptional leak detection accuracy of 99.76%, demonstrating its ability to reliably differentiate between normal operation and multiple leak severities. This method effectively reduces computational costs while maintaining robust performance across diverse operating environments. Full article

(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2025)

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15 pages, 4481 KB

Open AccessEditor’s ChoiceArticle

A Novel Time Domain Reflectometry (TDR) System for Water Content Estimation in Soils: Development and Application

by Alessandro Comegna, Simone Di Prima, Shawcat Basel Mostafa Hassan and Antonio Coppola

Sensors 2025, 25(4), 1099; https://doi.org/10.3390/s25041099 - 12 Feb 2025

Cited by 6 | Viewed by 2973

Abstract

Nowadays, there is a particular need to estimate soil water content accurately over space and time scales in various applications. For example, precision agriculture, as well as the fields of geology, ecology, and hydrology, necessitate rapid, onsite water content measurements. The time domain [...] Read more.

Nowadays, there is a particular need to estimate soil water content accurately over space and time scales in various applications. For example, precision agriculture, as well as the fields of geology, ecology, and hydrology, necessitate rapid, onsite water content measurements. The time domain reflectometry (TDR) technique is a geophysical method that allows, in a time-varying electric field, the determination of dielectric permittivity and electrical conductivity for a wide class of porous materials. Measuring the volumetric water content in soils is the most frequent application of TDR in soil science and soil hydrology. TDR has grown in popularity over the last 40 years because it is a practical and non-destructive technique that provides laboratory and field-scale measurements. However, a significant limitation of this technique is the relatively high cost of TDR devices, despite the availability of a range of commercial systems with varying prices. This paper aimed to design and implement a low-cost, compact TDR device tailored for classical hydrological applications. A series of laboratory experiments were carried out on soils of different textures to calibrate and validate the proposed measuring system. The results show that the device can be used to obtain predictions for monitoring soil water status with acceptable accuracy (R² = 0.95). Full article

(This article belongs to the Special Issue Sensors in Smart Irrigation Systems)

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44 pages, 9682 KB

Open AccessEditor’s ChoiceReview

Mid-Infrared Photonic Sensors: Exploring Fundamentals, Advanced Materials, and Cutting-Edge Applications

by Muhammad A. Butt, Marcin Juchniewicz, Mateusz Słowikowski, Łukasz Kozłowski and Ryszard Piramidowicz

Sensors 2025, 25(4), 1102; https://doi.org/10.3390/s25041102 - 12 Feb 2025

Cited by 8 | Viewed by 7023

Abstract

Mid-infrared (MIR) photonic sensors are revolutionizing optical sensing by enabling precise chemical and biological detection through the interrogation of molecules’ unique vibrational modes. This review explores the core principles of MIR photonics, emphasizing the light–matter interactions within the 2–20 µm wavelength range. Additionally, [...] Read more.

Mid-infrared (MIR) photonic sensors are revolutionizing optical sensing by enabling precise chemical and biological detection through the interrogation of molecules’ unique vibrational modes. This review explores the core principles of MIR photonics, emphasizing the light–matter interactions within the 2–20 µm wavelength range. Additionally, it examines innovative sensor architectures, such as integrated photonic platforms and optical fibers, that enhance sensitivity, specificity, and device miniaturization. The discussion extends to groundbreaking applications in environmental monitoring, medical diagnostics, industrial processes, and security, highlighting the transformative impact of these technologies. This comprehensive overview aims to illuminate the current state-of-the-art while inspiring future developments in MIR photonic sensing. Full article

(This article belongs to the Special Issue New Trends and Progress in Plasmonic Sensors and Sensing Technology)

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22 pages, 5084 KB

Open AccessEditor’s ChoiceArticle

Few-Shot Segmentation of 3D Point Clouds Under Real-World Distributional Shifts in Railroad Infrastructure

by Abdur R. Fayjie, Mathijs Lens and Patrick Vandewalle

Sensors 2025, 25(4), 1072; https://doi.org/10.3390/s25041072 - 11 Feb 2025

Cited by 2 | Viewed by 2371

Abstract

Industrial railway monitoring systems require precise understanding of 3D scenes, typically achieved using deep learning models for 3D point cloud segmentation. However, real-world applications demand these models to rapidly adapt to infrastructure upgrades and diverse environmental conditions across regions. Conventional deep learning models, [...] Read more.

Industrial railway monitoring systems require precise understanding of 3D scenes, typically achieved using deep learning models for 3D point cloud segmentation. However, real-world applications demand these models to rapidly adapt to infrastructure upgrades and diverse environmental conditions across regions. Conventional deep learning models, which rely on large-scale annotated datasets for training and are evaluated on test sets that are drawn independently and identically from the training distribution, often fail to account for such real-world changes, leading to overestimated model performance. Recent advancements in few-shot learning, which aim to develop generalizable models with minimal annotations, have shown promise. Motivated by this potential, the paper investigates the application of few-shot learning to railway monitoring by formalizing three types of distributional shifts that are commonly encountered in such systems: (a) in-domain shifts caused by sensor noise, (b) in-domain out-of-distribution shifts arising from infrastructure changes, and (c) cross-domain out-of-distribution shifts driven by geographical variations. A systematic evaluation of few-shot learning’s adaptability to these shifts is conducted using three performance metrics and a predictive uncertainty estimation metric. Extensive experimentation demonstrates that few-shot learning outperforms fine-tuning and maintains strong generalization under in-domain shifts with only ~1% performance deviation. However, it experiences a significant drop in performance under both in-domain and cross-domain out-of-distribution shifts, pronounced when dealing with previously unseen infrastructure classes. Additionally, we show that incorporating predictive uncertainty estimation enhances few-shot learning applicability by quantifying the model’s sensitivity to distributional shifts, offering valuable insights into the model’s reliability for safety-critical applications. Full article

(This article belongs to the Section Radar Sensors)

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