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 3 | Viewed by 6447

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 2511

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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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

Viewed by 809

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

Viewed by 640

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 1 | Viewed by 1430

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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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

Viewed by 2148

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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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 798

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

Viewed by 2250

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 711

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

Viewed by 930

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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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 4 | Viewed by 1277

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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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 2578

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 917

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 2 | Viewed by 1142

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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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 1 | Viewed by 1363

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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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 624

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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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 1690

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 3 | Viewed by 6129

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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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

Viewed by 846

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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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 1435

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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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 5 | Viewed by 1485

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 1 | Viewed by 3250

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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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 1063

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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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 2176

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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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 774

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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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 1 | Viewed by 1518

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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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 10 | Viewed by 2469

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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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 809

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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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 4 | Viewed by 2028

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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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 1689

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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28 pages, 4405 KB

Open AccessEditor’s ChoiceArticle

Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models

by He-Sheng Wang, Dah-Jing Jwo and Zhi-Hang Gao

Sensors 2025, 25(3), 978; https://doi.org/10.3390/s25030978 - 6 Feb 2025

Cited by 1 | Viewed by 1822

Abstract

This paper addresses the critical challenge of understanding and interpreting deep learning models in Global Navigation Satellite System (GNSS) applications, specifically focusing on multipath effect detection and analysis. As GNSS systems become increasingly reliant on deep learning for signal processing, the lack of [...] Read more.

This paper addresses the critical challenge of understanding and interpreting deep learning models in Global Navigation Satellite System (GNSS) applications, specifically focusing on multipath effect detection and analysis. As GNSS systems become increasingly reliant on deep learning for signal processing, the lack of model interpretability poses significant risks for safety-critical applications. We propose a novel approach combining Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) cells with Layer-wise Relevance Propagation (LRP) to create an explainable framework for multipath detection. Our key contributions include: (1) the development of an interpretable LSTM architecture for processing GNSS observables, including multipath variables, carrier-to-noise ratios, and satellite elevation angles; (2) the adaptation of the LRP technique for GNSS signal analysis, enabling attribution of model decisions to specific input features; and (3) the discovery of a correlation between LRP relevance scores and signal anomalies, leading to a new method for anomaly detection. Through systematic experimental validation, we demonstrate that our LSTM model achieves high prediction accuracy across all GNSS parameters while maintaining interpretability. A significant finding emerges from our controlled experiments: LRP relevance scores consistently increase during anomalous signal conditions, with growth rates varying from 7.34% to 32.48% depending on the feature type. In our validation experiments, we systematically introduced signal anomalies in specific time segments of the data sequence and observed corresponding increases in LRP scores: multipath parameters showed increases of 7.34–8.81%, carrier-to-noise ratios exhibited changes of 12.50–32.48%, and elevation angle parameters increased by 16.10%. These results demonstrate the potential of LRP-based analysis for enhancing GNSS signal quality monitoring and integrity assessment. Our approach not only improves the interpretability of deep learning models in GNSS applications but also provides a practical framework for detecting and analyzing signal anomalies, contributing to the development of more reliable and trustworthy navigation systems. Full article

(This article belongs to the Special Issue Multi-Radio and/or Multi-Sensor Integrated Navigation System—2nd Edition)

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12 pages, 1901 KB

Open AccessEditor’s ChoiceArticle

Advancing Near-Infrared Probes for Enhanced Breast Cancer Assessment

by Mohammad Pouriayevali, Ryley McWilliams, Avner Bachar, Parmveer Atwal, Ramani Ramaseshan and Farid Golnaraghi

Sensors 2025, 25(3), 983; https://doi.org/10.3390/s25030983 - 6 Feb 2025

Cited by 1 | Viewed by 1435

Abstract

Breast cancer remains a leading cause of cancer-related deaths among women, emphasizing the critical need for early detection and monitoring techniques. Conventional imaging modalities such as mammography, MRI, and ultrasound have face sensitivity, specificity, cost, and patient comfort limitations. This study introduces a [...] Read more.

Breast cancer remains a leading cause of cancer-related deaths among women, emphasizing the critical need for early detection and monitoring techniques. Conventional imaging modalities such as mammography, MRI, and ultrasound have face sensitivity, specificity, cost, and patient comfort limitations. This study introduces a handheld Near-Infrared Diffuse Optical Tomography (NIR DOT) probe for breast cancer imaging. The NIRscan probe utilizes multi-wavelength light-emitting diodes (LEDs) and a linear charge-coupled device (CCD) sensor to acquire real-time optical data, reconstructing cross-sectional images of breast tissue based on scattering and absorption coefficients. With wavelengths optimized for the differential optical properties of tissue components, the probe enables functional imaging, distinguishing between healthy and malignant tissues. Clinical evaluations have demonstrated its potential for precise tumor localization and monitoring therapeutic responses, achieving a sensitivity of 94.7% and specificity of 84.2%. By incorporating machine learning algorithms and a modified diffusion equation (MDE), the system enhances the accuracy and speed of image reconstruction, supporting rapid, non-invasive diagnostics. This development represents a significant step forward in portable, cost-effective solutions for breast cancer detection, with potential applications in low-resource settings and diverse clinical environments. Full article

(This article belongs to the Special Issue Advanced Sensors for Detection of Cancer Biomarkers and Virus)

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32 pages, 3991 KB

Open AccessEditor’s ChoiceReview

Artificial Intelligence in IR Thermal Imaging and Sensing for Medical Applications

by Antoni Z. Nowakowski and Mariusz Kaczmarek

Sensors 2025, 25(3), 891; https://doi.org/10.3390/s25030891 - 1 Feb 2025

Cited by 5 | Viewed by 5668

Abstract

The state of the art in IR thermal imaging methods for applications in medical diagnostics is discussed. A review of advances in IR thermal imaging technology in the years 1960–2024 is presented. Recently used artificial intelligence (AI) methods in the analysis of thermal [...] Read more.

The state of the art in IR thermal imaging methods for applications in medical diagnostics is discussed. A review of advances in IR thermal imaging technology in the years 1960–2024 is presented. Recently used artificial intelligence (AI) methods in the analysis of thermal images are the main interest. IR thermography is discussed in view of novel applications of machine learning methods for improved diagnostic analysis and medical treatment. The AI approach aims to improve image quality by denoising thermal images, using applications of AI super-resolution algorithms, removing artifacts, object detection, face and characteristic features localization, complex matching of diagnostic symptoms, etc. Full article

(This article belongs to the Collection Medical Applications of Sensor Systems and Devices)

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19 pages, 5606 KB

Open AccessEditor’s ChoiceArticle

Static Calibration of a New Three-Axis Fiber Bragg Grating-Based Optical Accelerometer

by Abraham Perez-Alonzo, Luis Alvarez-Icaza and Gabriel E. Sandoval-Romero

Sensors 2025, 25(3), 835; https://doi.org/10.3390/s25030835 - 30 Jan 2025

Viewed by 2690

Abstract

Optical sensors are a promising technology in structural and health monitoring due to their high sensitivity and immunity to electromagnetic interference. Because of their high sensitivity, they can register the responses of buildings to a wide range of motions, including those induced by [...] Read more.

Optical sensors are a promising technology in structural and health monitoring due to their high sensitivity and immunity to electromagnetic interference. Because of their high sensitivity, they can register the responses of buildings to a wide range of motions, including those induced by ambient noise, or detect small structural changes caused by aging or environmental factors. In previous work, an FBG-based accelerometer was introduced that is suitable for use as an autonomous unit since it does not make use of any interrogator equipment. In this paper, we present the results of the characterization of this device, which yielded the best precision and accuracy. The results show the following: (i) improvements in the orthogonality of the sensor axes, which impact their cross-axis sensitivity; (ii) reductions in the electronic noise, which increase the signal-to-noise ratio. The results of our static characterization show that, in the worst case, we can obtain a correlation coefficient R² of 0.9999 when comparing the output voltage with the input acceleration for the X- and Y-axes of the sensor. We developed an analytical, non-iterative, 12-parameter matrix calibration approach based on the least-squares method, which allows compensation for different gains in its axes, offset, and cross-axis. To improve the accuracy of our sensor, we propose a table with correction terms that can be subtracted from the estimated acceleration. The mean error of each estimated acceleration component of the sensor is zero, with a maximum standard deviation of 0.018 m/s². The maximum RMSE for all tested positions is 6.7 × 10⁻³ m/s². Full article

(This article belongs to the Special Issue Intelligent Sensors for Structural Health Monitoring and Mechanical Fault Diagnosis)

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13 pages, 1543 KB

Open AccessEditor’s ChoiceArticle

SDR-Fi-Z: A Wireless Local Area Network-Fingerprinting-Based Indoor Positioning Method for E911 Vertical Accuracy Mandate

by Rahul Mundlamuri, Devasena Inupakutika and David Akopian

Sensors 2025, 25(3), 823; https://doi.org/10.3390/s25030823 - 30 Jan 2025

Cited by 1 | Viewed by 919

Abstract

The Enhanced 911 (E911) mandate of the Federal Communications Commission (FCC) drives the evolution of indoor three-dimensional (3D) location/positioning services for emergency calls. Many indoor localization systems exploit location-dependent wireless signaling signatures, often called fingerprints, and machine learning techniques for position estimation. In [...] Read more.

The Enhanced 911 (E911) mandate of the Federal Communications Commission (FCC) drives the evolution of indoor three-dimensional (3D) location/positioning services for emergency calls. Many indoor localization systems exploit location-dependent wireless signaling signatures, often called fingerprints, and machine learning techniques for position estimation. In particular, received signal strength indicators (RSSIs) and Channel State Information (CSI) in Wireless Local Area Networks (WLANs or Wi-Fi) have gained popularity and have been addressed in the literature. While RSSI signatures are easy to collect, the fluctuation of wireless signals resulting from environmental uncertainties leads to considerable variations in RSSIs, which poses a challenge to accurate localization on a single floor, not to mention multi-floor or even three-dimensional (3D) indoor localization. Considering recent E911 mandate attention to vertical location accuracy, this study aimed to investigate CSI from Wi-Fi signals to produce baseline Z-axis location data, which has not been thoroughly addressed. To that end, we utilized CSI measurements and two representative machine learning methods, an artificial neural network (ANN) and convolutional neural network (CNN), to estimate both 3D and vertical-axis positioning feasibility to achieve E911 accuracy compliance. Full article

(This article belongs to the Section Navigation and Positioning)

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23 pages, 6653 KB

Open AccessEditor’s ChoiceArticle

Monitoring Welfare of Individual Broiler Chickens Using Ultra-Wideband and Inertial Measurement Unit Wearables

by Imad Khan, Daniel Peralta, Jaron Fontaine, Patricia Soster de Carvalho, Ana Martos Martinez-Caja, Gunther Antonissen, Frank Tuyttens and Eli De Poorter

Sensors 2025, 25(3), 811; https://doi.org/10.3390/s25030811 - 29 Jan 2025

Cited by 1 | Viewed by 1945

Abstract

Monitoring animal welfare on farms and in research settings is attracting increasing interest, both for ethical reasons and for improving productivity through the early detection of stress or diseases. In contrast to video-based monitoring, which requires good light conditions and has difficulty tracking [...] Read more.

Monitoring animal welfare on farms and in research settings is attracting increasing interest, both for ethical reasons and for improving productivity through the early detection of stress or diseases. In contrast to video-based monitoring, which requires good light conditions and has difficulty tracking specific animals, recent advances in the miniaturization of wearable devices allow for the collection of acceleration and location data to track individual animal behavior. However, for broilers, there are several challenges to address when using wearables, such as coping with (i) the large numbers of chickens in commercial farms,(ii)the impact of their rapid growth, and (iii) the small weights that the devices must have to be carried by the chickens without any impact on their health or behavior. To this end, this paper describes a pilot study in which chickens were fitted with devices containing an Inertial Measurement Unit (IMU) and an Ultra-Wideband (UWB) sensor. To establish guidelines for practitioners who want to monitor broiler welfare and activity at different scales, we first compare the attachment methods of the wearables to the broiler chickens, taking into account their effectiveness (in terms of retention time) and their impact on the broiler’s welfare. Then, we establish the technical requirements to carry out such a study, and the challenges that may arise. This analysis involves aspects such as noise estimation, synergy between UWB and IMU, and the measurement of activity levels based on the monitoring of chicken activity. We show that IMU data can be used for detecting activity level differences between individual animals and environmental conditions. UWB data can be used to monitor the positions and movement patterns of up to 200 animals simultaneously with an accuracy of less than 20 cm. We also show that the accuracy depends on installation aspects and that errors are larger at the borders of the monitored area. Attachment with sutures had the longest mean retention of 19.5 days, whereas eyelash glue had the shortest mean retention of 3 days. To conclude the paper, we identify current challenges and future research lines in the field. Full article

(This article belongs to the Special Issue Flexible and Wearable Sensors and Sensing for Agriculture and Food)

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23 pages, 3741 KB

Open AccessEditor’s ChoiceArticle

Enhanced Pure Pursuit Path Tracking Algorithm for Mobile Robots Optimized by NSGA-II with High-Precision GNSS Navigation

by Xiongwen Jiang, Taiga Kuroiwa, Yu Cao, Linfeng Sun, Haohao Zhang, Takahiro Kawaguchi and Seiji Hashimoto

Sensors 2025, 25(3), 745; https://doi.org/10.3390/s25030745 - 26 Jan 2025

Cited by 1 | Viewed by 1900

Abstract

With the rapid development of automation and intelligent technology, mobile robots have shown wide application potential in many fields, and accurate navigation systems are the key to robots completing tasks. This paper proposes an enhanced pure pursuit path tracking algorithm for mobile robots, [...] Read more.

With the rapid development of automation and intelligent technology, mobile robots have shown wide application potential in many fields, and accurate navigation systems are the key to robots completing tasks. This paper proposes an enhanced pure pursuit path tracking algorithm for mobile robots, which is optimized using NSGA-II, with high-precision GNSS navigation for accurate positioning. The improved algorithm considers the dynamic characteristics and real–world operating conditions of the robot, optimizing steering decisions to enhance path tracking accuracy. Experimental results demonstrate the effectiveness of the algorithm: with a look–ahead distance of 0.5 and a maximum linear velocity of 3, the average absolute pose error (APE) is reduced by 14.63%, while a velocity of 4 reduces the APE by 55.94%. The enhanced algorithm significantly reduces path deviation and improves navigation performance. Full article

(This article belongs to the Special Issue Recent Advances in Integrated GNSSs towards Seamless Navigation Using Multi-sensor Fusion Technology)

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17 pages, 7941 KB

Open AccessEditor’s ChoiceArticle

Visual Localization Domain for Accurate V-SLAM from Stereo Cameras

by Eleonora Di Salvo, Sara Bellucci, Valeria Celidonio, Ilaria Rossini, Stefania Colonnese and Tiziana Cattai

Sensors 2025, 25(3), 739; https://doi.org/10.3390/s25030739 - 26 Jan 2025

Cited by 3 | Viewed by 1246

Abstract

Trajectory estimation from stereo image sequences remains a fundamental challenge in Visual Simultaneous Localization and Mapping (V-SLAM). To address this, we propose a novel approach that focuses on the identification and matching of keypoints within a transformed domain that emphasizes visually significant features. [...] Read more.

Trajectory estimation from stereo image sequences remains a fundamental challenge in Visual Simultaneous Localization and Mapping (V-SLAM). To address this, we propose a novel approach that focuses on the identification and matching of keypoints within a transformed domain that emphasizes visually significant features. Specifically, we propose to perform V-SLAM in a VIsual Localization Domain (VILD), i.e., a domain where visually relevant feature are suitably represented for analysis and tracking. This transformed domain adheres to information-theoretic principles, enabling a maximum likelihood estimation of rotation, translation, and scaling parameters by minimizing the distance between the coefficients of the observed image and those of a reference template. The transformed coefficients are obtained from the output of specialized Circular Harmonic Function (CHF) filters of varying orders. Leveraging this property, we employ a first-order approximation of the image-series representation, directly computing the first-order coefficients through the application of first-order CHF filters. The proposed VILD provides a theoretically grounded and visually relevant representation of the image. We utilize VILD for point matching and tracking across the stereo video sequence. The experimental results on real-world video datasets demonstrate that integrating visually-driven filtering significantly improves trajectory estimation accuracy compared to traditional tracking performed in the spatial domain. Full article

(This article belongs to the Special Issue Emerging Advances in Wireless Positioning and Location-Based Services)

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19 pages, 2273 KB

Open AccessEditor’s ChoiceArticle

Signal Preprocessing in Instrument-Based Electronic Noses Leads to Parsimonious Predictive Models: Application to Olive Oil Quality Control

by Luis Fernandez, Sergio Oller-Moreno, Jordi Fonollosa, Rocío Garrido-Delgado, Lourdes Arce, Andrés Martín-Gómez, Santiago Marco and Antonio Pardo

Sensors 2025, 25(3), 737; https://doi.org/10.3390/s25030737 - 25 Jan 2025

Viewed by 1502

Abstract

Gas sensor-based electronic noses (e-noses) have gained considerable attention over the past thirty years, leading to the publication of numerous research studies focused on both the development of these instruments and their various applications. Nonetheless, the limited specificity of gas sensors, along with [...] Read more.

Gas sensor-based electronic noses (e-noses) have gained considerable attention over the past thirty years, leading to the publication of numerous research studies focused on both the development of these instruments and their various applications. Nonetheless, the limited specificity of gas sensors, along with the common requirement for chemical identification, has led to the adaptation and incorporation of analytical chemistry instruments into the e-nose framework. Although instrument-based e-noses exhibit greater specificity to gasses than traditional ones, they still produce data that require correction in order to build reliable predictive models. In this work, we introduce the use of a multivariate signal processing workflow for datasets from a multi-capillary column ion mobility spectrometer-based e-nose. Adhering to the electronic nose philosophy, these workflows prioritized untargeted approaches, avoiding dependence on traditional peak integration techniques. A comprehensive validation process demonstrates that the application of this preprocessing strategy not only mitigates overfitting but also produces parsimonious models, where classification accuracy is maintained with simpler, more interpretable structures. This reduction in model complexity offers significant advantages, providing more efficient and robust models without compromising predictive performance. This strategy was successfully tested on an olive oil dataset, showcasing its capability to improve model parsimony and generalization performance. Full article

(This article belongs to the Special Issue Gas Recognition in E-Nose System)

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32 pages, 2512 KB

Open AccessEditor’s ChoiceReview

Mapping of Industrial IoT to IEC 62443 Standards

by Ivan Cindrić, Marko Jurčević and Tamara Hadjina

Sensors 2025, 25(3), 728; https://doi.org/10.3390/s25030728 - 25 Jan 2025

Cited by 3 | Viewed by 2659

Abstract

The increasing adoption of the Industrial Internet of Things (IIoT) has led to significant improvements in operational efficiency but has also brought new challenges for cybersecurity. To address these challenges, a number of standards have been introduced over the years. One of the [...] Read more.

The increasing adoption of the Industrial Internet of Things (IIoT) has led to significant improvements in operational efficiency but has also brought new challenges for cybersecurity. To address these challenges, a number of standards have been introduced over the years. One of the best-known series of standards for this purpose is ISA/IEC 62443. This paper examines the applicability of the ISA/IEC 62443 series of standards, traditionally used for securing industrial automation and control systems, to the IIoT environment. For each requirement described in the ISA/IEC 62443 standards, relevant research on that subject is reviewed and presented in a table-like manner. Based on this table, areas for future research are identified, including system hardening, asset inventory, safety instrumented system isolation, risk assessment methodologies, change management systems, data storage security, and incident response procedures. The focus on future improvement is performed for the area of system hardening, for which research and guidelines already exist but not for the specific area of IIoT environments. Full article

(This article belongs to the Section Industrial Sensors)

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32 pages, 9788 KB

Open AccessEditor’s ChoiceArticle

Experimental Assessment of OSNMA-Enabled GNSS Positioning in Interference-Affected RF Environments

by Alexandru Rusu-Casandra and Elena Simona Lohan

Sensors 2025, 25(3), 729; https://doi.org/10.3390/s25030729 - 25 Jan 2025

Viewed by 1063

Abstract

This article investigates the performance of the Galileo Open Service Navigation Message Authentication (OSNMA) system in real-life environments prone to RF interference (RFI), jamming, and/or spoofing attacks. Considering the existing data that indicate a relatively high number of RFI- and spoofing-related incidents reported [...] Read more.

This article investigates the performance of the Galileo Open Service Navigation Message Authentication (OSNMA) system in real-life environments prone to RF interference (RFI), jamming, and/or spoofing attacks. Considering the existing data that indicate a relatively high number of RFI- and spoofing-related incidents reported in Eastern Europe, this study details a data-collection campaign along various roads through urban, suburban, and rural settings, mostly in three border counties in East and South-East of Romania, and presents the results based on the data analysis. The key performance indicators are determined from the perspective of an end user relying only on Galileo OSNMA authenticated signals. The Galileo OSNMA signals were captured using one of the few commercially available GNSS receivers that can perform this OSNMA authentication algorithm incorporating the satellite signals. This work includes a presentation of the receiver’s operation and of the authentication results obtained during test runs that experienced an unusually high number of RFI-related incidents, followed by a detailed analysis of instances when such RFI impaired or fully prevented obtaining an authenticated position, velocity, and time (PVT) solution. The results indicate that Galileo OSNMA demonstrates significant robustness against interference in real-life RF-degraded environments, dealing with both accidental and intentional interference. Full article

(This article belongs to the Section Navigation and Positioning)

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24 pages, 2730 KB

Open AccessEditor’s ChoiceReview

The Future of Clinical Active Shoulder Range of Motion Assessment, Best Practice, and Its Challenges: Narrative Review

by Wolbert van den Hoorn, Arthur Fabre, Giacomo Nardese, Eric Yung-Sheng Su, Kenneth Cutbush, Ashish Gupta and Graham Kerr

Sensors 2025, 25(3), 667; https://doi.org/10.3390/s25030667 - 23 Jan 2025

Cited by 2 | Viewed by 7063

Abstract

Optimising outcomes after shoulder interventions requires objective shoulder range of motion (ROM) assessments. This narrative review examines video-based pose technologies and markerless motion capture, focusing on their clinical application for shoulder ROM assessment. Camera pose-based methods offer objective ROM measurements, though the accuracy [...] Read more.

Optimising outcomes after shoulder interventions requires objective shoulder range of motion (ROM) assessments. This narrative review examines video-based pose technologies and markerless motion capture, focusing on their clinical application for shoulder ROM assessment. Camera pose-based methods offer objective ROM measurements, though the accuracy varies due to the differences in gold standards, anatomical definitions, and deep learning techniques. Despite some biases, the studies report a high consistency, emphasising that methods should not be used interchangeably if they do not agree with each other. Smartphone cameras perform well in capturing 2D planar movements but struggle with that of rotational movements and forward flexion, particularly when thoracic compensations are involved. Proper camera positioning, orientation, and distance are key, highlighting the importance of standardised protocols in mobile phone-based ROM evaluations. Although 3D motion capture, per the International Society of Biomechanics recommendations, remains the gold standard, advancements in LiDAR/depth sensing, smartphone cameras, and deep learning show promise for reliable ROM assessments in clinical settings. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence in Gait and Posture Analysis)

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30 pages, 1550 KB

Open AccessEditor’s ChoiceReview

The Potential of Wearable Sensors for Detecting Cognitive Rumination: A Scoping Review

by Vitica X. Arnold and Sean D. Young

Sensors 2025, 25(3), 654; https://doi.org/10.3390/s25030654 - 23 Jan 2025

Cited by 2 | Viewed by 3253

Abstract

Cognitive rumination, a transdiagnostic symptom across mental health disorders, has traditionally been assessed through self-report measures. However, these measures are limited by their temporal nature and subjective bias. The rise in wearable technologies offers the potential for continuous, real-time monitoring of physiological indicators [...] Read more.

Cognitive rumination, a transdiagnostic symptom across mental health disorders, has traditionally been assessed through self-report measures. However, these measures are limited by their temporal nature and subjective bias. The rise in wearable technologies offers the potential for continuous, real-time monitoring of physiological indicators associated with rumination. This scoping review investigates the current state of research on using wearable technology to detect cognitive rumination. Specifically, we examine the sensors and wearable devices used, physiological biomarkers measured, standard measures of rumination used, and the comparative validity of specific biomarkers in identifying cognitive rumination. The review was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines on IEEE, Scopus, PubMed, and PsycInfo databases. Studies that used wearable devices to measure rumination-related physiological responses and biomarkers were included (n = 9); seven studies assessed one biomarker, and two studies assessed two biomarkers. Electrodermal Activity (EDA) sensors capturing skin conductance activity emerged as both the most prevalent sensor (n = 5) and the most comparatively valid biomarker for detecting cognitive rumination via wearable devices. Other commonly investigated biomarkers included electrical brain activity measured through Electroencephalogram (EEG) sensors (n = 2), Heart Rate Variability (HRV) measured using Electrocardiogram (ECG) sensors and heart rate fitness monitors (n = 2), muscle response measured through Electromyography (EMG) sensors (n = 1) and movement measured through an accelerometer (n = 1). The Empatica E4 and Empatica Embrace 2 wrist-worn devices were the most frequently used wearable (n = 3). The Rumination Response Scale (RRS), was the most widely used standard scale for assessing rumination. Experimental induction protocols, often adapted from Nolen-Hoeksema and Morrow’s 1993 rumination induction paradigm, were also widely used. In conclusion, the findings suggest that wearable technology offers promise in capturing real-time physiological responses associated with rumination. However, the field is still developing, and further research is needed to validate these findings and explore the impact of individual traits and contextual factors on the accuracy of rumination detection. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors for Medical Applications)

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17 pages, 6865 KB

Open AccessEditor’s ChoiceArticle

Improving Stroke Treatment Using Magnetic Nanoparticle Sensors to Monitor Brain Thrombus Extraction

by Dhrubo Jyoti, Daniel Reeves, Scott Gordon-Wylie, Clifford Eskey and John Weaver

Sensors 2025, 25(3), 672; https://doi.org/10.3390/s25030672 - 23 Jan 2025

Viewed by 1382

Abstract

(1) Background: Mechanical thrombectomy (MT) successfully treats ischemic strokes by extracting the thrombus, or clot, using a stent retriever to pull it through the blood vessel. However, clot slippage and/or fragmentation can occur. Real-time feedback to a clinician about attachment between the stent [...] Read more.

(1) Background: Mechanical thrombectomy (MT) successfully treats ischemic strokes by extracting the thrombus, or clot, using a stent retriever to pull it through the blood vessel. However, clot slippage and/or fragmentation can occur. Real-time feedback to a clinician about attachment between the stent and clot could enable more complete removal. We propose a system whereby antibody-targeted magnetic nanoparticles (NPs) are injected via a microcatheter to coat the clot, oscillating magnetic fields excite the particles, and a small coil attached to the catheter picks up a signal that determines the proximity of the clot to the stent. (2) Methods: We used existing simulation code to model the signal from NPs distributed on a hemispherical clot with three orthogonally applied magnetic fields. An in vitro apparatus was built that applied fields and read out signals from a 1.5 mm pickup coil at a variable distance and orientation angle from a sample of 100 nm iron oxide core/shell NPs. (3) Results: Our simulations suggest that the sum of the voltages induced in the pickup coil from three orthogonal applied fields could localize a clot to within 180 µm, regardless of the exact orientation of the pickup coil, with further precision added via rotation-correction formulae. Our experimental system validated simulations; we estimated an in vitro distance recovery precision of 41 µm with a pickup coil 1 mm from the clot. (4) Conclusions: Magnetic NP sensing could be a safe and real-time method to estimate whether a clot is attached to the stent retriever during MT. Full article

(This article belongs to the Special Issue Novel Nanoprobes for Biomedical Sensing, Disease Detection, and Theranostic Applications)

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21 pages, 9714 KB

Open AccessEditor’s ChoiceArticle

3D Metamaterials Facilitate Human Cardiac MRI at 21.0 Tesla: A Proof-of-Concept Study

by Bilguun Nurzed, Nandita Saha, Jason M. Millward and Thoralf Niendorf

Sensors 2025, 25(3), 620; https://doi.org/10.3390/s25030620 - 21 Jan 2025

Cited by 1 | Viewed by 1577

Abstract

The literature reports highlight the transmission field (B₁⁺) uniformity and efficiency constraints of cardiac magnetic resonance imaging (MRI) at ultrahigh magnetic fields (UHF). This simulation study proposes a 3D Metamaterial (MM) to address these challenges. The study proposes a [...] Read more.

The literature reports highlight the transmission field (B₁⁺) uniformity and efficiency constraints of cardiac magnetic resonance imaging (MRI) at ultrahigh magnetic fields (UHF). This simulation study proposes a 3D Metamaterial (MM) to address these challenges. The study proposes a 3D MM consisting of unit cells (UC) with split ring resonator (SRR) layers immersed in dielectric material glycerol. Implementing the proposed MM design aims to reduce the effective thickness and weight of the dielectric material while shaping B₁⁺ and improving the penetration depth. The latter is dictated by the chosen array size, where small local UC arrays can focus B₁⁺ and larger UC arrays can increase the field of view, at the cost of a lower penetration depth. Designing RF antennas that can effectively transmit at 21.0 T while maintaining patient safety and comfort is challenging. Using Self-Grounded Bow-Tie (SGBT) antennas in conjunction with the proposed MM demonstrated enhanced B₁⁺ efficiency and uniformity across the human heart without signal voids. The study employed dynamic parallel transmission with tailored kT points to homogenize the 3D flip angle over the whole heart. This proof-of-concept study provides the technical foundation for human cardiac MRI at 21.0 T. Such numerical simulations are mandatory precursors for the realization of whole-body human UHF MR instruments. Full article

(This article belongs to the Special Issue Electromagnetic Waves, Antennas and Sensor Technologies in Modern Biomedical and Environmental Applications)

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14 pages, 6733 KB

Open AccessEditor’s ChoiceArticle

Detailed Determination of Delamination Parameters in a Multilayer Structure Using Asymmetric Lamb Wave Mode

by Olgirdas Tumšys, Lina Draudvilienė and Egidijus Žukauskas

Sensors 2025, 25(2), 539; https://doi.org/10.3390/s25020539 - 18 Jan 2025

Cited by 1 | Viewed by 1072

Abstract

A signal-processing algorithm for the detailed determination of delamination in multilayer structures is proposed in this work. The algorithm is based on calculating the phase velocity of the Lamb wave A₀ mode and estimating this velocity dispersion. Both simulation and experimental studies [...] Read more.

A signal-processing algorithm for the detailed determination of delamination in multilayer structures is proposed in this work. The algorithm is based on calculating the phase velocity of the Lamb wave A₀ mode and estimating this velocity dispersion. Both simulation and experimental studies were conducted to validate the proposed technique. The delamination having a diameter of 81 mm on the segment of a wind turbine blade (WTB) was used for verification of the proposed technique. Four cases were used in the simulation study: defect-free, delamination between the first and second layers, delamination between the second and third layers, and defect (hole). The calculated phase velocity variation in the A₀ mode was used to determine the location and edge coordinates of the delaminations and defects. It has been found that in order to estimate the depth at which the delamination is, it is appropriate to calculate the phase velocity dispersion curves. The difference in the reconstructed phase velocity dispersion curves between the layers simulated at different depths is estimated to be about 60 m/s. The phase velocity values were compared with the delamination of the second and third layers and a hole drilled at the corresponding depth. The obtained simulation results confirmed that the drilled hole can be used as a defect corresponding to delamination. The WTB sample with a drilled hole of 81 mm was used in the experimental study. Using the proposed algorithm, detailed defect parameters were obtained. The results obtained using simulated and experimental signals indicated that the proposed new algorithm is suitable for the determination of delamination parameters in a multilayer structure. Full article

(This article belongs to the Special Issue Acoustic and Ultrasonic Sensing Technology in Non-Destructive Testing)

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14 pages, 6235 KB

Open AccessEditor’s ChoiceArticle

Integrating Machine Learning for Predictive Maintenance on Resource-Constrained PLCs: A Feasibility Study

by Riccardo Mennilli, Luigi Mazza and Andrea Mura

Sensors 2025, 25(2), 537; https://doi.org/10.3390/s25020537 - 17 Jan 2025

Cited by 4 | Viewed by 2759

Abstract

This study investigates the potential of deploying a neural network model on an advanced programmable logic controller (PLC), specifically the Finder Opta™, for real-time inference within the predictive maintenance framework. In the context of Industry 4.0, edge computing aims to process data directly [...] Read more.

This study investigates the potential of deploying a neural network model on an advanced programmable logic controller (PLC), specifically the Finder Opta™, for real-time inference within the predictive maintenance framework. In the context of Industry 4.0, edge computing aims to process data directly on local devices rather than relying on a cloud infrastructure. This approach minimizes latency, enhances data security, and reduces the bandwidth required for data transmission, making it ideal for industrial applications that demand immediate response times. Despite the limited memory and processing power inherent to many edge devices, this proof-of-concept demonstrates the suitability of the Finder Opta™ for such applications. Using acoustic data, a convolutional neural network (CNN) is deployed to infer the rotational speed of a mechanical test bench. The findings underscore the potential of the Finder Opta™ to support scalable and efficient predictive maintenance solutions, laying the groundwork for future research in real-time anomaly detection. By enabling machine learning capabilities on compact, resource-constrained hardware, this approach promises a cost-effective, adaptable solution for diverse industrial environments. Full article

(This article belongs to the Special Issue Advanced Sensors in Nondestructive Testing and Structural Health Monitoring)

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12 pages, 3243 KB

Open AccessEditor’s ChoiceArticle

Internal Integrated Temperature Sensor for Lithium-Ion Batteries

by Pengfei Yang, Kai Su, Shijie Weng, Jiang Han, Qian Zhang, Zhiqiang Li, Xiaoli Peng and Yong Xiang

Sensors 2025, 25(2), 511; https://doi.org/10.3390/s25020511 - 17 Jan 2025

Viewed by 3397

Abstract

Lithium-ion batteries represent a significant component of the field of energy storage, with a diverse range of applications in consumer electronics, portable devices, and numerous other fields. In view of the growing concerns about the safety of batteries, it is of the utmost [...] Read more.

Lithium-ion batteries represent a significant component of the field of energy storage, with a diverse range of applications in consumer electronics, portable devices, and numerous other fields. In view of the growing concerns about the safety of batteries, it is of the utmost importance to develop a sensor that is capable of accurately monitoring the internal temperature of lithium-ion batteries. External sensors are subject to the necessity for additional space and ancillary equipment. Moreover, external sensors cannot accurately measure internal battery temperature due to packaging material interference, causing a temperature discrepancy between the interior and surface. Consequently, this study presents an integrated temperature sensor within the battery, based on PT1000 resistance temperature detector (RTD). The sensor is integrated with the anode via a flexible printed circuit (FPC), simplifying the assembly process. The PT1000 RTD microsensor’s temperature is linearly related to resistance (R = 3.71T + 1003.86). It measures about 15 °C temperature difference inside/outside the battery. On short-circuit, the battery’s internal temperature rises to 27 °C in 10 s and 32 °C in 20 s, measured by the sensor. A battery with the PT1000 sensor retains 89.8% capacity under 2 C, similar to the normal battery. Furthermore, a PT1000 temperature array sensor was designed and employed to enable precise monitoring and localization of internal temperature variations. Full article

(This article belongs to the Section Industrial Sensors)

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33 pages, 1112 KB

Open AccessEditor’s ChoiceReview

A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis

by Xiaofeng Han, Diego Guffanti and Alberto Brunete

Sensors 2025, 25(2), 498; https://doi.org/10.3390/s25020498 - 16 Jan 2025

Cited by 6 | Viewed by 4555

Abstract

Analysis of the human gait represents a fundamental area of investigation within the broader domains of biomechanics, clinical research, and numerous other interdisciplinary fields. The progression of visual sensor technology and machine learning algorithms has enabled substantial developments in the creation of human [...] Read more.

Analysis of the human gait represents a fundamental area of investigation within the broader domains of biomechanics, clinical research, and numerous other interdisciplinary fields. The progression of visual sensor technology and machine learning algorithms has enabled substantial developments in the creation of human gait analysis systems. This paper presents a comprehensive review of the advancements and recent findings in the field of vision-based human gait analysis systems over the past five years, with a special emphasis on the role of vision sensors, machine learning algorithms, and technological innovations. The relevant papers were subjected to analysis using the PRISMA method, and 72 articles that met the criteria for this research project were identified. A detailing of the most commonly used visual sensor systems, machine learning algorithms, human gait analysis parameters, optimal camera placement, and gait parameter extraction methods is presented in the analysis. The findings of this research indicate that non-invasive depth cameras are gaining increasing popularity within this field. Furthermore, depth learning algorithms, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, are being employed with increasing frequency. This review seeks to establish the foundations for future innovations that will facilitate the development of more effective, versatile, and user-friendly gait analysis tools, with the potential to significantly enhance human mobility, health, and overall quality of life. This work was supported by [GOBIERNO DE ESPANA/PID2023-150967OB-I00]. Full article

(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation)

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26 pages, 21796 KB

Open AccessEditor’s ChoiceArticle

Design of a Cost-Effective Ultrasound Force Sensor and Force Control System for Robotic Extra-Body Ultrasound Imaging

by Yixuan Zheng, Hongyuan Ning, Eason Rangarajan, Aban Merali, Adam Geale, Lukas Lindenroth, Zhouyang Xu, Weizhao Wang, Philipp Kruse, Steven Morris, Liang Ye, Xinyi Fu, Kawal Rhode and Richard James Housden

Sensors 2025, 25(2), 468; https://doi.org/10.3390/s25020468 - 15 Jan 2025

Cited by 3 | Viewed by 2330

Abstract

Ultrasound imaging is widely valued for its safety, non-invasiveness, and real-time capabilities but is often limited by operator variability, affecting image quality and reproducibility. Robot-assisted ultrasound may provide a solution by delivering more consistent, precise, and faster scans, potentially reducing human error and [...] Read more.

Ultrasound imaging is widely valued for its safety, non-invasiveness, and real-time capabilities but is often limited by operator variability, affecting image quality and reproducibility. Robot-assisted ultrasound may provide a solution by delivering more consistent, precise, and faster scans, potentially reducing human error and healthcare costs. Effective force control is crucial in robotic ultrasound scanning to ensure consistent image quality and patient safety. However, existing robotic ultrasound systems rely heavily on expensive commercial force sensors or the integrated sensors of commercial robotic arms, limiting their accessibility. To address these challenges, we developed a cost-effective, lightweight, 3D-printed force sensor and a hybrid position–force control strategy tailored for robotic ultrasound scanning. The system integrates patient-to-robot registration, automated scanning path planning, and multi-sensor data fusion, allowing the robot to autonomously locate the patient, target the region of interest, and maintain optimal contact force during scanning. Validation was conducted using an ultrasound-compatible abdominal aortic aneurysm (AAA) phantom created from patient CT data and healthy volunteer testing. For the volunteer testing, during a 1-min scan, 65% of the forces were within the good image range. Both volunteers reported no discomfort or pain during the whole procedure. These results demonstrate the potential of the system to provide safe, precise, and autonomous robotic ultrasound imaging in real-world conditions. Full article

(This article belongs to the Special Issue Multi-sensor Fusion in Medical Imaging, Diagnosis and Therapy)

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