Sensors

32 pages, 1912 KiB

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 1 | Viewed by 4297

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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27 pages, 5073 KiB

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 1600

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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22 pages, 4867 KiB

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 1096

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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14 pages, 4128 KiB

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 864

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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15 pages, 4481 KiB

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 2 | Viewed by 1655

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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28 pages, 4405 KiB

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 1483

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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23 pages, 6653 KiB

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 1701

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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19 pages, 9140 KiB

Open AccessEditor’s ChoiceArticle

Long-Gauge Fiber Optic Sensors: Strain Measurement Comparison for Reinforced Concrete Columns

by Haoran Lin, Zhaowen Xu, Wan Hong, Zhihong Yang, Yixin Wang and Bing Li

Sensors 2025, 25(1), 220; https://doi.org/10.3390/s25010220 - 2 Jan 2025

Cited by 2 | Viewed by 1247

Abstract

Long-gauge fiber optic sensors have proven to be valuable tools for structural health monitoring, especially in reinforced concrete (RC) beam structures. While their application in this area has been well-documented, their use in RC columns remains relatively unexplored. This suggests a promising avenue [...] Read more.

Long-gauge fiber optic sensors have proven to be valuable tools for structural health monitoring, especially in reinforced concrete (RC) beam structures. While their application in this area has been well-documented, their use in RC columns remains relatively unexplored. This suggests a promising avenue for further research and development. This paper presents a thorough comparison of long-gauge fiber optic sensors and traditional measurement tools when used to monitor RC columns under small eccentric compressive loading. The evaluation focuses on the stability and precision of each sensor type. A monitoring system was developed for laboratory testing to assess the performance of various sensor types under specific conditions. The system incorporated four measurement schemes, utilizing a combination of embedded and surface-mounted long-gauge fiber optic sensors, linear variable differential transformers (LVDTs), and point sensors (strain gauges). Long-gauge fiber optic sensors, securely mounted on the concrete surface near the tensile side, were found to accurately measure both large and small deformations, outperforming LVDTs. Compared to strain gauges and embedded optic sensors, the long-gauge fiber optic sensors demonstrated superior average strain measurement and minimal interference from protective covers. Full article

(This article belongs to the Special Issue Optical Sensors for Industrial Applications)

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11 pages, 8257 KiB

Open AccessEditor’s ChoiceArticle

Fibre Optic Method for Detecting Oil Fluorescence in Marine Sediments

by Emilia Baszanowska, Zbigniew Otremba and Maria Kubacka

Sensors 2025, 25(1), 173; https://doi.org/10.3390/s25010173 - 31 Dec 2024

Viewed by 942

Abstract

The aim of this study is to verify the possibility of detecting oil in the bottom sediment using a fibre optic system. The presence of oil is assessed on excitation–emission spectra obtained from spectral fluorescence signals of the sediment sample. A factory spectrofluorometer [...] Read more.

The aim of this study is to verify the possibility of detecting oil in the bottom sediment using a fibre optic system. The presence of oil is assessed on excitation–emission spectra obtained from spectral fluorescence signals of the sediment sample. A factory spectrofluorometer coupled with an experimental fibre optic measurement system was used. During the determination of spectra, the fibre optic system is set at a 45° angle to the sediment surface and placed above its surface. The light exciting the fluorescence and the light emitted by the sediment are transmitted in a combined bundle of fibre optic threads. The analysis of excitation–emission spectra of sediments contaminated with oil shows variability of the shapes of fluorescence spectra depending on the type and degree of oil contamination, which indicates the feasibility of the sensor design for detecting oil in the sediment in situ. Full article

(This article belongs to the Special Issue Sensor Technologies for Ocean Environments: Impact Assessment, Monitoring and Protection)

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16 pages, 8862 KiB

Open AccessEditor’s ChoiceArticle

Development of Automatic Method for Glucose Detection Based on Platinum Octaethylporphyrin Sol–Gel Film with Long-Term Stability

by Yujie Niu, Yongda Wang, Lu Li, Xiyu Zhang and Ting Liu

Sensors 2025, 25(1), 186; https://doi.org/10.3390/s25010186 - 31 Dec 2024

Cited by 2 | Viewed by 1181

Abstract

In this study, an approach has been proposed in response to the urgent need for a sensitive and stable method for glucose detection at low concentrations. Platinum octaethylporphyrin (PtOEP) was chosen as the probe and embedded into the matrix material to yield a [...] Read more.

In this study, an approach has been proposed in response to the urgent need for a sensitive and stable method for glucose detection at low concentrations. Platinum octaethylporphyrin (PtOEP) was chosen as the probe and embedded into the matrix material to yield a glucose-sensing film, i.e., Pt/TE-MTS, through a sol–gel process. The optical parameter (OP) was defined as the ratio of phosphorescence in the absence and presence of glucose, and the relationship between OP and glucose concentration (GC) was established in a theoretical way based on the Stern–Volmer equation and further obtained by photoluminescence measurement. OP exhibited a linear relationship with GC in a range of 0–720 μM. The time required by the photoluminescence of the film to reach equilibrium was measured to ensure the completion of the reaction, and it was found that the equilibrium time decreased as the GC increased. The photobleaching behavior and stabilization of the film were monitored, and the result showed that the film exhibited excellent resistance to photobleaching and was quite stable in an aqueous solution. Additionally, a LabVIEW-based GC-detection system was developed to achieve the practical application of the sensing film. In summary, the Pt/TE-MTS film exhibited high sensitivity in detecting the GC with excellent reproducibility, which is of high value in applications. Full article

(This article belongs to the Section Nanosensors)

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14 pages, 3937 KiB

Open AccessEditor’s ChoiceArticle

Concentration vs. Optical Density of ESKAPEE Bacteria: A Method to Determine the Optimum Measurement Wavelength

by Bruno Wacogne, Marine Belinger Podevin, Naïs Vaccari, Claudia Koubevi, Céline Codjiová, Emilie Gutierrez, Lucie Davoine, Marjorie Robert-Nicoud, Alain Rouleau and Annie Frelet-Barrand

Sensors 2024, 24(24), 8160; https://doi.org/10.3390/s24248160 - 21 Dec 2024

Cited by 4 | Viewed by 4639

Abstract

Optical density measurement has been used for decades to determine the microorganism concentration and more rarely for mammalian cells. Although this measurement can be carried out at any wavelength, studies report a limited number of measurement wavelengths, mainly around 600 nm, and no [...] Read more.

Optical density measurement has been used for decades to determine the microorganism concentration and more rarely for mammalian cells. Although this measurement can be carried out at any wavelength, studies report a limited number of measurement wavelengths, mainly around 600 nm, and no consensus seems to be emerging to propose an objective method for determining the optimum measurement wavelength for each microorganism. In this article, we propose a method for analyzing the absorbance spectra of ESKAPEE bacteria and determining the optimum measurement wavelength for each of them. The method is based on the analysis of the signal-to-noise ratio of the relationships between concentrations and optical densities when the measurement wavelength varies over the entire spectral range of the absorbance spectra measured for each bacterium. These optimum wavelengths range from 612 nm for Enterococcus faecium to 705 nm for Acinetobacter baumannii. The method can be directly applied to any bacteria, any culture method, and also to any biochemical substance with an absorbance spectrum without any particular feature such as an identified maximum. Full article

(This article belongs to the Special Issue Spectroscopy for Biochemical Imaging and Sensing)

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27 pages, 20405 KiB

Open AccessEditor’s ChoiceArticle

Implementation and Experimental Application of Industrial IoT Architecture Using Automation and IoT Hardware/Software

by David Calderón, Francisco Javier Folgado, Isaías González and Antonio José Calderón

Sensors 2024, 24(24), 8074; https://doi.org/10.3390/s24248074 - 18 Dec 2024

Cited by 14 | Viewed by 3074

Abstract

The paradigms of Industry 4.0 and Industrial Internet of Things (IIoT) require functional architectures to deploy and organize hardware and software taking advantage of modern digital technologies in industrial systems. In this sense, a lot of the literature proposes and describes this type [...] Read more.

The paradigms of Industry 4.0 and Industrial Internet of Things (IIoT) require functional architectures to deploy and organize hardware and software taking advantage of modern digital technologies in industrial systems. In this sense, a lot of the literature proposes and describes this type of architecture with a conceptual angle, without providing experimental validation or with scarce details about the involved equipment under real operation. Aiming at overcoming these limitations, this paper presents the experimental application of an IIoT architecture divided into four functional layers, namely, Sensing, Network, Middleware and Application layers. Automation and IoT hardware and software are used to implement and apply the architecture. Special attention is put on the software Grafana, chosen in the top layer to deploy graphical user interfaces that are remotely accessible via web. A pilot microgrid integrating photovoltaic energy and hydrogen served as scenario to test and prove the suitability of the architecture in four application cases. Full article

(This article belongs to the Special Issue Industry 4.0: Sensor-Based Broadband Advances towards the Industrial Internet of Things Ecosystem)

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31 pages, 2998 KiB

Open AccessEditor’s ChoiceReview

Remote Sensing Techniques for Water Quality Monitoring: A Review

by Swapna A. Jaywant and Khalid Mahmood Arif

Sensors 2024, 24(24), 8041; https://doi.org/10.3390/s24248041 - 17 Dec 2024

Cited by 7 | Viewed by 6397

Abstract

Freshwater resources are facing increasing challenges to water quality, due to factors such as population growth, human activities, climate change, and various human-made pressures. While on-site methods, as specified in the USGS water quality sampling handbook, are usually precise, they require more time, [...] Read more.

Freshwater resources are facing increasing challenges to water quality, due to factors such as population growth, human activities, climate change, and various human-made pressures. While on-site methods, as specified in the USGS water quality sampling handbook, are usually precise, they require more time, are costly, and provide data at specific points, which lacks the essential comprehensive geographic and temporal detail for water body assessment and management. Hence, conventional on-site monitoring methods are unable to provide a complete representation of freshwater systems. To address concerns regarding geographic and time-based coverage, remote sensing has developed into an effective solution, taking advantage of recent advancements in sensor technology and methodologies. The combination of GPS and GIS technologies, along with remotely sensed data, provides an efficient resource for continual monitoring and evaluation of water bodies. The use of remotely sensed data helps to establish a reliable geospatial database, serving as a standard for subsequent evaluations. The review emphasizes the contribution of remote sensing to water monitoring. It starts by exploring various space-borne and airborne sensors utilized for this purpose. Subsequently, the review explores remote sensing applications for water quality. Lastly, the review discusses the overall benefits and challenges related to remote sensing in water monitoring. Full article

(This article belongs to the Section Remote Sensors)

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21 pages, 4888 KiB

Open AccessEditor’s ChoiceArticle

Digital Twin-Assisted Lightpath Provisioning and Nonlinear Mitigation in C+L+S Multiband Optical Networks

by Sadegh Ghasrizadeh, Prasunika Khare, Nelson Costa, Marc Ruiz, Antonio Napoli, Joao Pedro and Luis Velasco

Sensors 2024, 24(24), 8054; https://doi.org/10.3390/s24248054 - 17 Dec 2024

Cited by 1 | Viewed by 1266

Abstract

Multiband (MB) optical transmission targets increasing the capacity of operators’ optical transport networks. However, nonlinear impairments (NLI) affect each optical channel in the C+L+S bands differently, and, therefore, the routing and spectrum assignment (RSA) problem needs to be complemented with fast and accurate [...] Read more.

Multiband (MB) optical transmission targets increasing the capacity of operators’ optical transport networks. However, nonlinear impairments (NLI) affect each optical channel in the C+L+S bands differently, and, therefore, the routing and spectrum assignment (RSA) problem needs to be complemented with fast and accurate tools to consider the quality of transmission (QoT) within the provisioning process. This paper proposes a digital twin-assisted approach for lightpath provisioning to provide a complete solution for the RSA problem that ensures the required QoT in MB optical networks. The OCATA time domain digital twin is proposed, not only to estimate the QoT of a selected path but also to support the QoT-based channel assignment process. OCATA is based on a Deep Neural Network (DNN) to model the propagation of the optical signal. However, because of the different impacts of nonlinear noise on each channel and the large number of channels that need to be considered in C+L+S MB scenarios, OCATA needs to be adapted to make it scalable, while keeping its high accuracy and fast QoT estimation characteristics. In consequence, a complete methodology is proposed in this work that limits the number of channels being modeled to just a few. Moreover, OCATA-MB helps to mitigate NLI noise by programming the receiver at the provisioning time and thus with very little complexity compared to its equivalent implemented during the operation. NLI noise mitigation can be applied in the case when a lightpath cannot be provisioned because none of the available channels can provide the required QoT, making it an advantageous tool for reducing connection blocking. Exhaustive simulation results demonstrate the remarkable accuracy of OCATA-MB in estimating the QoT for any channel. Interestingly, by utilizing the proposed OCATA-MB-assisted lightpath provisioning approach, a reduction of the blocking ratio exceeding 50% when compared to traditional approaches is shown when NLI noise mitigation is not applied. If NLI mitigation is implemented, an additional over 50% blocking reduction is achieved. Full article

(This article belongs to the Special Issue Sensing Technologies and Optical Communication)

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110 pages, 4916 KiB

Open AccessEditor’s ChoiceReview

Revolutionizing Free-Space Optics: A Survey of Enabling Technologies, Challenges, Trends, and Prospects of Beyond 5G Free-Space Optical (FSO) Communication Systems

by Isiaka A. Alimi and Paulo P. Monteiro

Sensors 2024, 24(24), 8036; https://doi.org/10.3390/s24248036 - 16 Dec 2024

Cited by 6 | Viewed by 9326

Abstract

As the demand for high-speed, low-latency communication continues to grow, free-space optical (FSO) communication has gained prominence as a promising solution for supporting the next generation of wireless networks, especially in the context of the 5G and beyond era. It offers high-speed, low-latency [...] Read more.

As the demand for high-speed, low-latency communication continues to grow, free-space optical (FSO) communication has gained prominence as a promising solution for supporting the next generation of wireless networks, especially in the context of the 5G and beyond era. It offers high-speed, low-latency data transmission over long distances without the need for a physical infrastructure. However, the deployment of FSO systems faces significant challenges, such as atmospheric turbulence, weather-induced signal degradation, and alignment issues, all of which can impair performance. This paper offers a comprehensive survey of the enabling technologies, challenges, trends, and future prospects for FSO communication in next-generation networks, while also providing insights into the current mitigation strategies. The survey explores the critical enabling technologies such as adaptive optics, modulation schemes, and error correction codes that are revolutionizing FSO communication and addressing the unique challenges of FSO links. Also, the integration of FSO with radio frequency, millimeter-wave, and Terahertz technologies is explored, emphasizing hybrid solutions that enhance reliability and coverage. Additionally, the paper highlights emerging trends, such as the integration of FSO with artificial intelligence-driven optimization techniques and the growing role of machine learning in enhancing FSO system performance for dynamic environments. By analyzing the current trends and identifying key challenges, this paper emphasizes the prospects of FSO communication in the evolving landscape of 5G and future networks. In this regard, it assesses the potential of FSO to meet the demands for high-speed, low-latency communication and offers insights into its scalability, reliability, and deployment strategies for 5G and beyond. The paper concludes by identifying the open challenges and future research directions critical to realizing the full potential of FSO in next-generation communication systems. Full article

(This article belongs to the Special Issue Advanced Optical Technologies for Communications, Perception, and Chips)

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25 pages, 4786 KiB

Open AccessEditor’s ChoiceArticle

Air Pollution Measurement and Dispersion Simulation Using Remote and In Situ Monitoring Technologies in an Industrial Complex in Busan, South Korea

by Naghmeh Dehkhoda, Juhyeon Sim, Juseon Shin, Sohee Joo, Sung Hwan Cho, Jeong Hun Kim and Youngmin Noh

Sensors 2024, 24(23), 7836; https://doi.org/10.3390/s24237836 - 7 Dec 2024

Cited by 2 | Viewed by 1914

Abstract

Rapid industrialization and the influx of human resources have led to the establishment of industrial complexes near urban areas, exposing residents to various air pollutants. This has led to a decline in air quality, impacting neighboring residential areas adversely, which highlights the urgent [...] Read more.

Rapid industrialization and the influx of human resources have led to the establishment of industrial complexes near urban areas, exposing residents to various air pollutants. This has led to a decline in air quality, impacting neighboring residential areas adversely, which highlights the urgent need to monitor air pollution in these areas. Recent advancements in technology, such as Solar Occultation Flux (SOF) and Sky Differential Optical Absorption Spectroscopy (SkyDOAS) used as remote sensing techniques and mobile extraction Fourier Transform Infrared Spectrometry (MeFTIR) used as an in situ technique, now offer enhanced precision in estimating the pollutant emission flux and identifying primary sources. In a comprehensive study conducted in 2020 in the Sinpyeong Jangrim Industrial Complex in Busan City, South Korea, a mobile laboratory equipped with SOF, SkyDOAS, and MeFTIR technologies was employed to approximate the emission flux of total alkanes, sulfur dioxide (SO₂), nitrogen dioxide (NO₂), formaldehyde (HCHO), and methane (CH₄). Using the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) diffusion model, pollutant dispersion to residential areas was simulated. The highest average daily emission flux was observed for total alkanes, with values of 69.9 ± 71.6 kg/h and 84.1 ± 85.8 kg/h in zones S₁ and S₂ of the Sinpyeong Jangrim Industrial Complex, respectively. This is primarily due to the prevalence of metal manufacturing and mechanical equipment industries in the area. The HYSPLIT diffusion model confirmed elevated pollution levels in residential areas located southeast of the industrial complex, underscoring the influence of the dominant northwesterly wind direction and wind speed on pollutant dispersion. This highlights the urgent need for targeted interventions to address and mitigate air pollution in downwind residential areas. The total annual emission fluxes were estimated at 399,984 kg/yr and 398,944 kg/yr for zones S₁ and S₂, respectively. A comparison with the Pollutant Release and Transfer Registers (PRTRs) survey system revealed that the total annual emission fluxes in this study were approximately 24.3 and 4.9 times higher than those reported by PRTRs. This indicates a significant underestimation of the impact of small businesses on local air quality, which was not accounted for in the PRTR survey system. Full article

(This article belongs to the Special Issue Remote Sensing in Atmospheric Measurements)

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18 pages, 2211 KiB

Open AccessEditor’s ChoiceArticle

Accuracy Evaluation of 3D Pose Reconstruction Algorithms Through Stereo Camera Information Fusion for Physical Exercises with MediaPipe Pose

by Sebastian Dill, Arjang Ahmadi, Martin Grimmer, Dennis Haufe, Maurice Rohr, Yanhua Zhao, Maziar Sharbafi and Christoph Hoog Antink

Sensors 2024, 24(23), 7772; https://doi.org/10.3390/s24237772 - 4 Dec 2024

Cited by 4 | Viewed by 5023

Abstract

In recent years, significant research has been conducted on video-based human pose estimation (HPE). While monocular two-dimensional (2D) HPE has been shown to achieve high performance, monocular three-dimensional (3D) HPE poses a more challenging problem. However, since human motion happens in a 3D [...] Read more.

In recent years, significant research has been conducted on video-based human pose estimation (HPE). While monocular two-dimensional (2D) HPE has been shown to achieve high performance, monocular three-dimensional (3D) HPE poses a more challenging problem. However, since human motion happens in a 3D space, 3D HPE offers a more accurate representation of the human, granting increased usability for complex tasks like analysis of physical exercise. We propose a method based on MediaPipe Pose, 2D HPE on stereo cameras and a fusion algorithm without prior stereo calibration to reconstruct 3D poses, combining the advantages of high accuracy in 2D HPE with the increased usability of 3D coordinates. We evaluate this method on a self-recorded database focused on physical exercise to research what accuracy can be achieved and whether this accuracy is sufficient to recognize errors in exercise performance. We find that our method achieves significantly improved performance compared to monocular 3D HPE (median RMSE of

30.1

compared to

56.3

, p-value below

10^{- 6}

) and can show that the performance is sufficient for error recognition. Full article

(This article belongs to the Special Issue Deep Learning Applications for Pose Estimation and Human Action Recognition)

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23 pages, 9811 KiB

Open AccessEditor’s ChoiceReview

Microwave Sensors and Their Applications in Permittivity Measurement

by Changjun Liu, Chongwei Liao, Yujie Peng, Weixin Zhang, Bo Wu and Peixiang Yang

Sensors 2024, 24(23), 7696; https://doi.org/10.3390/s24237696 - 1 Dec 2024

Cited by 7 | Viewed by 2767

Abstract

This paper reviews microwave sensors and their applications in permittivity measurement. The detection, diagnosis, classification, and monitoring without contact and invasion have been the subject of numerous studies based on permittivity characteristics tracking. This review illustrates many new types of research in recent [...] Read more.

This paper reviews microwave sensors and their applications in permittivity measurement. The detection, diagnosis, classification, and monitoring without contact and invasion have been the subject of numerous studies based on permittivity characteristics tracking. This review illustrates many new types of research in recent years. Firstly, the application background is briefly introduced, and several main measurement methods are presented. An overview of measurement technology in various applications is compiled and summarized based on numerous typical examples. Exciting applications are compared and presented separately, combining resonator sensors with strong electric fields. Furthermore, differential signals represent trends for future applications with strong environmental immunity, an alternative option to expensive measuring equipment. With the alternation of metamaterials, microfluidics technologies, cross-technology, algorithms, and so on, sensors play an exceptionally prominent role in practical and low-cost applications. Full article

(This article belongs to the Special Issue Advanced Microwave Sensors and Their Applications in Measurement)

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14 pages, 5456 KiB

Open AccessEditor’s ChoiceArticle

A Hybrid Photoplethysmography (PPG) Sensor System Design for Heart Rate Monitoring

by Farjana Akter Jhuma, Kentaro Harada, Muhamad Affiq Bin Misran, Hin-Wai Mo, Hiroshi Fujimoto and Reiji Hattori

Sensors 2024, 24(23), 7634; https://doi.org/10.3390/s24237634 - 29 Nov 2024

Cited by 4 | Viewed by 5166

Abstract

A photoplethysmography (PPG) sensor is a cost-effective and efficacious way of measuring health conditions such as heart rate, oxygen saturation, and respiration rate. In this work, we present a hybrid PPG sensor system working in a reflective mode with an optoelectronic module, i.e., [...] Read more.

A photoplethysmography (PPG) sensor is a cost-effective and efficacious way of measuring health conditions such as heart rate, oxygen saturation, and respiration rate. In this work, we present a hybrid PPG sensor system working in a reflective mode with an optoelectronic module, i.e., the combination of an inorganic light-emitting diode (LED) and a circular-shaped organic photodetector (OPD) surrounding the LED for efficient light harvest followed by the proper driving circuit for accurate PPG signal acquisition. The performance of the hybrid sensor system was confirmed by the heart rate detection process from the PPG using fast Fourier transform analysis. The PPG signal obtained with a 50% LED duty cycle and 250 Hz sampling rate resulted in accurate heart rate monitoring with an acceptable range of error. The effects of the LED duty cycle and the LED luminous intensity were found to be crucial to the heart rate accuracy and to the power consumption, i.e., indispensable factors for the hybrid sensor. Full article

(This article belongs to the Section Biosensors)

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15 pages, 4402 KiB

Open AccessEditor’s ChoiceArticle

Segmentation of Low-Grade Brain Tumors Using Mutual Attention Multimodal MRI

by Hiroyuki Seshimo and Essam A. Rashed

Sensors 2024, 24(23), 7576; https://doi.org/10.3390/s24237576 - 27 Nov 2024

Cited by 3 | Viewed by 2392

Abstract

Early detection and precise characterization of brain tumors play a crucial role in improving patient outcomes and extending survival rates. Among neuroimaging modalities, magnetic resonance imaging (MRI) is the gold standard for brain tumor diagnostics due to its ability to produce high-contrast images [...] Read more.

Early detection and precise characterization of brain tumors play a crucial role in improving patient outcomes and extending survival rates. Among neuroimaging modalities, magnetic resonance imaging (MRI) is the gold standard for brain tumor diagnostics due to its ability to produce high-contrast images across a variety of sequences, each highlighting distinct tissue characteristics. This study focuses on enabling multimodal MRI sequences to advance the automatic segmentation of low-grade astrocytomas, a challenging task due to their diffuse and irregular growth patterns. A novel mutual-attention deep learning framework is proposed, which integrates complementary information from multiple MRI sequences, including T2-weighted and fluid-attenuated inversion recovery (FLAIR) sequences, to enhance the segmentation accuracy. Unlike conventional segmentation models, which treat each modality independently or simply concatenate them, our model introduces mutual attention mechanisms. This allows the network to dynamically focus on salient features across modalities by jointly learning interdependencies between imaging sequences, leading to more precise boundary delineations even in regions with subtle tumor signals. The proposed method is validated using the UCSF-PDGM dataset, which consists of 35 astrocytoma cases, presenting a realistic and clinically challenging dataset. The results demonstrate that T2w/FLAIR modalities contribute most significantly to the segmentation performance. The mutual-attention model achieves an average Dice coefficient of 0.87. This study provides an innovative pathway toward improving segmentation of low-grade tumors by enabling context-aware fusion across imaging sequences. Furthermore, the study showcases the clinical relevance of integrating AI with multimodal MRI, potentially improving non-invasive tumor characterization and guiding future research in radiological diagnostics. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning in Image/Video Processing and Sensing)

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37 pages, 2256 KiB

Open AccessEditor’s ChoiceReview

Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review

by Bryan Nsoh, Abia Katimbo, Hongzhi Guo, Derek M. Heeren, Hope Njuki Nakabuye, Xin Qiao, Yufeng Ge, Daran R. Rudnick, Joshua Wanyama, Erion Bwambale and Shafik Kiraga

Sensors 2024, 24(23), 7480; https://doi.org/10.3390/s24237480 - 23 Nov 2024

Cited by 10 | Viewed by 7761

Abstract

This systematic review critically evaluates the current state and future potential of real-time, end-to-end smart, and automated irrigation management systems, focusing on integrating the Internet of Things (IoTs) and machine learning technologies for enhanced agricultural water use efficiency and crop productivity. In this [...] Read more.

This systematic review critically evaluates the current state and future potential of real-time, end-to-end smart, and automated irrigation management systems, focusing on integrating the Internet of Things (IoTs) and machine learning technologies for enhanced agricultural water use efficiency and crop productivity. In this review, the automation of each component is examined in the irrigation management pipeline from data collection to application while analyzing its effectiveness, efficiency, and integration with various precision agriculture technologies. It also investigates the role of the interoperability, standardization, and cybersecurity of IoT-based automated solutions for irrigation applications. Furthermore, in this review, the existing gaps are identified and solutions are proposed for seamless integration across multiple sensor suites for automated systems, aiming to achieve fully autonomous and scalable irrigation management. The findings highlight the transformative potential of automated irrigation systems to address global food challenges by optimizing water use and maximizing crop yields. Full article

(This article belongs to the Special Issue Sensor and AI Technologies in Intelligent Agriculture: 2nd Edition)

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18 pages, 4163 KiB

Open AccessEditor’s ChoiceArticle

Privacy-Preserving Synthetic Data Generation Method for IoT-Sensor Network IDS Using CTGAN

by Saleh Alabdulwahab, Young-Tak Kim and Yunsik Son

Sensors 2024, 24(22), 7389; https://doi.org/10.3390/s24227389 - 20 Nov 2024

Cited by 3 | Viewed by 3047

Abstract

The increased usage of IoT networks brings about new privacy risks, especially when intrusion detection systems (IDSs) rely on large datasets for machine learning (ML) tasks and depend on third parties for storing and training the ML-based IDS. This study proposes a privacy-preserving [...] Read more.

The increased usage of IoT networks brings about new privacy risks, especially when intrusion detection systems (IDSs) rely on large datasets for machine learning (ML) tasks and depend on third parties for storing and training the ML-based IDS. This study proposes a privacy-preserving synthetic data generation method using a conditional tabular generative adversarial network (CTGAN) aimed at maintaining the utility of IoT sensor network data for IDS while safeguarding privacy. We integrate differential privacy (DP) with CTGAN by employing controlled noise injection to mitigate privacy risks. The technique involves dynamic distribution adjustment and quantile matching to balance the utility–privacy tradeoff. The results indicate a significant improvement in data utility compared to the standard DP method, achieving a KS test score of 0.80 while minimizing privacy risks such as singling out, linkability, and inference attacks. This approach ensures that synthetic datasets can support intrusion detection without exposing sensitive information. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Sensors Cybersecurity)

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15 pages, 874 KiB

Open AccessEditor’s ChoiceArticle

Deep Reinforcement Learning-Driven Jamming-Enhanced Secure Unmanned Aerial Vehicle Communications

by Zhifang Xing, Yunhui Qin, Changhao Du, Wenzhang Wang and Zhongshan Zhang

Sensors 2024, 24(22), 7328; https://doi.org/10.3390/s24227328 - 16 Nov 2024

Cited by 1 | Viewed by 1296

Abstract

Despite its flexibility, unmanned aerial vehicle (UAV) communications are susceptible to eavesdropping due to the open nature of wireless channels and the broadcasting nature of wireless signals. This paper studies secure UAV communications and proposes a method to optimize the minimum secrecy rate [...] Read more.

Despite its flexibility, unmanned aerial vehicle (UAV) communications are susceptible to eavesdropping due to the open nature of wireless channels and the broadcasting nature of wireless signals. This paper studies secure UAV communications and proposes a method to optimize the minimum secrecy rate of the system by using interference technology to enhance it. To this end, the system not only deploys multiple UAV base stations (BSs) to provide services to legitimate users but also assigns dedicated UAV jammers to send interference signals to active or potential eavesdroppers to disrupt their eavesdropping effectiveness. Based on this configuration, we formulate the optimization process of parameters such as the user association variables, UAV trajectory, and output power as a sequential decision-making problem and use the single-agent soft actor-critic (SAC) algorithm and twin delayed deep deterministic policy gradient (TD3) algorithm to achieve joint optimization of the core parameters. In addition, for specific scenarios, we also use the multi-agent soft actor-critic (MASAC) algorithm to solve the joint optimization problem mentioned above. The numerical results show that the normalized average secrecy rate of the MASAC algorithm increased by more than 6.6% and 14.2% compared with that of the SAC and TD3 algorithms, respectively. Full article

(This article belongs to the Special Issue Novel Signal Processing Techniques for Wireless Communications)

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20 pages, 3171 KiB

Open AccessEditor’s ChoiceArticle

Multimodal Trajectory Prediction for Diverse Vehicle Types in Autonomous Driving with Heterogeneous Data and Physical Constraints

by Maoning Ge, Kento Ohtani, Ming Ding, Yingjie Niu, Yuxiao Zhang and Kazuya Takeda

Sensors 2024, 24(22), 7323; https://doi.org/10.3390/s24227323 - 16 Nov 2024

Cited by 1 | Viewed by 2945

Abstract

The accurate prediction of vehicle behavior is crucial for autonomous driving systems, impacting their safety and efficiency in complex urban environments. To address the challenge of multi-agent trajectory prediction, we propose a novel model integrating multiple input modalities, including historical trajectories, map data, [...] Read more.

The accurate prediction of vehicle behavior is crucial for autonomous driving systems, impacting their safety and efficiency in complex urban environments. To address the challenge of multi-agent trajectory prediction, we propose a novel model integrating multiple input modalities, including historical trajectories, map data, vehicle features, and interaction information. Our approach employs a Conditional Variational Autoencoder (CVAE) framework with a decoder that predicts control actions using the Gaussian Mixture Model (GMM) and then converts these actions into dynamically feasible trajectories through a bicycle model. Evaluated on the nuScenes dataset, the model achieves great performance across key metrics, including minADE₅ of 1.26 and minFDE₅ of 2.85, demonstrating robust performance across various vehicle types and prediction horizons. These results indicate that integrating multiple data sources, physical models, and probabilistic methods significantly improves trajectory prediction accuracy and reliability for autonomous driving. Our approach generates diverse yet realistic predictions, capturing the multimodal nature of future outcomes while adhering to Physical Constraints and vehicle dynamics. Full article

(This article belongs to the Special Issue AI-Driving for Autonomous Vehicles)

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37 pages, 3817 KiB

Open AccessEditor’s ChoiceReview

A Comprehensive Review of Biomarker Sensors for a Breathalyzer Platform

by Pardis Sadeghi, Rania Alshawabkeh, Amie Rui and Nian Xiang Sun

Sensors 2024, 24(22), 7263; https://doi.org/10.3390/s24227263 - 13 Nov 2024

Cited by 2 | Viewed by 3098

Abstract

Detecting volatile organic compounds (VOCs) is increasingly recognized as a pivotal tool in non-invasive disease diagnostics. VOCs are metabolic byproducts, mostly found in human breath, urine, feces, and sweat, whose profiles may shift significantly due to pathological conditions. This paper presents a thorough [...] Read more.

Detecting volatile organic compounds (VOCs) is increasingly recognized as a pivotal tool in non-invasive disease diagnostics. VOCs are metabolic byproducts, mostly found in human breath, urine, feces, and sweat, whose profiles may shift significantly due to pathological conditions. This paper presents a thorough review of the latest advancements in sensor technologies for VOC detection, with a focus on their healthcare applications. It begins by introducing VOC detection principles, followed by a review of the rapidly evolving technologies in this area. Special emphasis is given to functionalized molecularly imprinted polymer-based biochemical sensors for detecting breath biomarkers, owing to their exceptional selectivity. The discussion examines SWaP-C considerations alongside the respective advantages and disadvantages of VOC sensing technologies. The paper also tackles the principal challenges facing the field and concludes by outlining the current status and proposing directions for future research. Full article

(This article belongs to the Section Biosensors)

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16 pages, 5554 KiB

Open AccessEditor’s ChoiceArticle

Unmanned Aerial Vehicle Photogrammetry for Monitoring the Geometric Changes of Reclaimed Landfills

by Grzegorz Pasternak, Klaudia Pasternak, Eugeniusz Koda and Paweł Ogrodnik

Sensors 2024, 24(22), 7247; https://doi.org/10.3390/s24227247 - 13 Nov 2024

Cited by 2 | Viewed by 1441

Abstract

Monitoring reclaimed landfills is essential for ensuring their stability and monitoring the regularity of facility settlement. Insufficient recognition of the magnitude and directions of these changes can lead to serious damage to the body of the landfill (landslides, sinkholes) and, consequently, threaten the [...] Read more.

Monitoring reclaimed landfills is essential for ensuring their stability and monitoring the regularity of facility settlement. Insufficient recognition of the magnitude and directions of these changes can lead to serious damage to the body of the landfill (landslides, sinkholes) and, consequently, threaten the environment and the life and health of people near landfills. This study focuses on using UAV photogrammetry to monitor geometric changes in reclaimed landfills. This approach highlights the advantages of UAVs in expanding the monitoring and providing precise information critical for decision-making in the reclamation process. This study presents the result of annual photogrammetry measurements at the Słabomierz–Krzyżówka reclaimed landfill, located in the central part of Poland. The Multiscale Model to Model Cloud Comparison (M3C2) algorithm was used to determine deformation at the landfill. The results were simultaneously compared with the landfill’s reference (angular–linear) measurements. The mean vertical displacement error determined by the photogrammetric method was ±2.3 cm. The results showed that, with an appropriate measurement methodology, it is possible to decide on changes in geometry reliably. The collected 3D data also gives the possibility to improve the decision-making process related to repairing damage or determining the reclamation direction of the landfill, as well as preparing further development plans. Full article

(This article belongs to the Special Issue AI-Based Object Detection and Tracking in UAVs: Challenges and Research Directions)

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14 pages, 2896 KiB

Open AccessEditor’s ChoiceArticle

Sensor Arrays for Electrochemical Detection of PCR-Amplified Genes Extracted from Cells Suspended in Environmental Waters

by Hiroshi Aoki, Mai Kawaguchi, Yukiko Kumakura, Hiroki Kamo, Kazuki Miura, Yuki Hiruta, Siro Simizu and Daniel Citterio

Sensors 2024, 24(22), 7182; https://doi.org/10.3390/s24227182 - 8 Nov 2024

Viewed by 1493

Abstract

Ecological surveys of living things based on DNAs from environmental samples are attractive. However, despite simplicity of water sampling from the target environment, it is still necessary to transport the samples to the laboratory for DNA analysis based on skillful next-generation sequencers. To [...] Read more.

Ecological surveys of living things based on DNAs from environmental samples are attractive. However, despite simplicity of water sampling from the target environment, it is still necessary to transport the samples to the laboratory for DNA analysis based on skillful next-generation sequencers. To perform DNA-oriented surveys based on a simple protocol without any special training, we demonstrated, in this study, the detection of genes from cell-containing environmental waters using gene sensor arrays that require no DNA labeling and no external indicators. Cell-suspended PBS or river water were used as models of environmental waters containing living things, and DNA samples were prepared by PCR amplification. Ferrocene-terminated probes were synthesized and immobilized on an electrode array to develop a sensor array. The sensor array showed a large response to a target DNA complementary to the probe and no response to a mismatched DNA, indicating sequence-specific detection. For DNA samples prepared from the cells in PBS, they showed good responses similar to those for the target DNA. They also significantly detected DNA samples from the cells in river water at a general environmental concentration (38 cells mL⁻¹) with 28-fold larger responses than those for 0 cells mL⁻¹. Full article

(This article belongs to the Special Issue Electrochemical Sensor Applications for Environment Monitoring)

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28 pages, 3787 KiB

Open AccessEditor’s ChoiceReview

Plasmonic Sensors Based on a Metal–Insulator–Metal Waveguide—What Do We Know So Far?

by Muhammad A. Butt

Sensors 2024, 24(22), 7158; https://doi.org/10.3390/s24227158 - 7 Nov 2024

Cited by 8 | Viewed by 3086

Abstract

Metal–insulator–metal (MIM) waveguide-based plasmonic sensors are significantly important in the domain of advanced sensing technologies due to their exceptional ability to guide and confine light at subwavelength scales. These sensors exploit the unique properties of surface plasmon polaritons (SPPs) that propagate along the [...] Read more.

Metal–insulator–metal (MIM) waveguide-based plasmonic sensors are significantly important in the domain of advanced sensing technologies due to their exceptional ability to guide and confine light at subwavelength scales. These sensors exploit the unique properties of surface plasmon polaritons (SPPs) that propagate along the metal–insulator interface, facilitating strong field confinement and enhanced light–matter interactions. In this review, several critical aspects of MIM waveguide-based plasmonic sensors are thoroughly examined, including sensor designs, material choices, fabrication methods, and diverse applications. Notably, there exists a substantial gap between the numerical data and the experimental verification of these devices, largely due to the insufficient attention given to the hybrid integration of plasmonic components. This disconnect underscores the need for more focused research on seamless integration techniques. Additionally, innovative light-coupling mechanisms are suggested that could pave the way for the practical realization of these highly promising plasmonic sensors. Full article

(This article belongs to the Special Issue Waveguide-Based Sensors and Applications)

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11 pages, 2010 KiB

Open AccessEditor’s ChoiceArticle

Validation of a 3D Markerless Motion Capture Tool Using Multiple Pose and Depth Estimations for Quantitative Gait Analysis

by Mathis D’Haene, Frédéric Chorin, Serge S. Colson, Olivier Guérin, Raphaël Zory and Elodie Piche

Sensors 2024, 24(22), 7105; https://doi.org/10.3390/s24227105 - 5 Nov 2024

Cited by 5 | Viewed by 2839

Abstract

Gait analysis is essential for evaluating walking patterns and identifying functional limitations. Traditional marker-based motion capture tools are costly, time-consuming, and require skilled operators. This study evaluated a 3D Marker-less Motion Capture (3D MMC) system using pose and depth estimations with the gold-standard [...] Read more.

Gait analysis is essential for evaluating walking patterns and identifying functional limitations. Traditional marker-based motion capture tools are costly, time-consuming, and require skilled operators. This study evaluated a 3D Marker-less Motion Capture (3D MMC) system using pose and depth estimations with the gold-standard Motion Capture (MOCAP) system for measuring hip and knee joint angles during gait at three speeds (0.7, 1.0, 1.3 m/s). Fifteen healthy participants performed gait tasks which were captured by both systems. The 3D MMC system demonstrated good accuracy (LCC > 0.96) and excellent inter-session reliability (RMSE < 3°). However, moderate-to-high accuracy with constant biases was observed during specific gait events, due to differences in sample rates and kinematic methods. Limitations include the use of only healthy participants and limited key points in the pose estimation model. The 3D MMC system shows potential as a reliable tool for gait analysis, offering enhanced usability for clinical and research applications. Full article

(This article belongs to the Special Issue Biomedical Sensors for Diagnosis and Rehabilitation—2nd Edition)

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19 pages, 10067 KiB

Open AccessEditor’s ChoiceArticle

A Novel Panorama Depth Estimation Framework for Autonomous Driving Scenarios Based on a Vision Transformer

by Yuqi Zhang, Liang Chu, Zixu Wang, He Tong, Jincheng Hu and Jihao Li

Sensors 2024, 24(21), 7013; https://doi.org/10.3390/s24217013 - 31 Oct 2024

Cited by 1 | Viewed by 1986

Abstract

An accurate panorama depth estimation result is crucial to risk perception in autonomous driving practice. In this paper, an innovative framework is presented to address the challenges of imperfect observation and projection fusion in panorama depth estimation, enabling the accurate capture of distances [...] Read more.

An accurate panorama depth estimation result is crucial to risk perception in autonomous driving practice. In this paper, an innovative framework is presented to address the challenges of imperfect observation and projection fusion in panorama depth estimation, enabling the accurate capture of distances from surrounding images in driving scenarios. First, the Patch Filling method is proposed to alleviate the imperfect observation of panoramic depth in autonomous driving scenarios, which constructs a panoramic depth map based on the sparse distance data provided by the 3D point cloud. Then, in order to tackle the distortion challenge faced by outdoor panoramic images, a method for image context learning, ViT-Fuse, is proposed and specifically designed for equirectangular panoramic views. The experimental results show that the proposed ViT-Fuse reduces the estimation error by 9.15% on average in driving scenarios compared with the basic method and exhibits more robust and smoother results on the edge details of the depth estimation maps. Full article

(This article belongs to the Special Issue Large AI Models for Positioning and Perception in Autonomous Driving)

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17 pages, 978 KiB

Open AccessEditor’s ChoiceReview

TRPV4—A Multifunctional Cellular Sensor Protein with Therapeutic Potential

by Sanna Koskimäki and Sari Tojkander

Sensors 2024, 24(21), 6923; https://doi.org/10.3390/s24216923 - 29 Oct 2024

Cited by 2 | Viewed by 3038

Abstract

Transient receptor potential vanilloid (TRPV) channel proteins belong to the superfamily of TRP proteins that form cationic channels in the animal cell membranes. These proteins have various subtype-specific functions, serving, for example, as sensors for pain, pressure, pH, and mechanical extracellular stimuli. The [...] Read more.

Transient receptor potential vanilloid (TRPV) channel proteins belong to the superfamily of TRP proteins that form cationic channels in the animal cell membranes. These proteins have various subtype-specific functions, serving, for example, as sensors for pain, pressure, pH, and mechanical extracellular stimuli. The sensing of extracellular cues by TRPV4 triggers Ca²⁺-influx through the channel, subsequently coordinating numerous intracellular signaling cascades in a spatio-temporal manner. As TRPV channels play such a wide role in various cellular and physiological functions, loss or impaired TRPV protein activity naturally contributes to many pathophysiological processes. This review concentrates on the known functions of TRPV4 sensor proteins and their potential as a therapeutic target. Full article

(This article belongs to the Special Issue Feature Papers in Biosensors Section 2024)

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24 pages, 5390 KiB

Open AccessEditor’s ChoiceArticle

A Novel Single-Color FRET Sensor for Rho-Kinase Reveals Calcium-Dependent Activation of RhoA and ROCK

by Allison E. Mancini and Megan A. Rizzo

Sensors 2024, 24(21), 6869; https://doi.org/10.3390/s24216869 - 26 Oct 2024

Cited by 2 | Viewed by 1646

Abstract

Ras homolog family member A (RhoA) acts as a signaling hub in many cellular processes, including cytoskeletal dynamics, division, migration, and adhesion. RhoA activity is tightly spatiotemporally controlled, but whether downstream effectors share these activation dynamics is unknown. We developed a novel single-color [...] Read more.

Ras homolog family member A (RhoA) acts as a signaling hub in many cellular processes, including cytoskeletal dynamics, division, migration, and adhesion. RhoA activity is tightly spatiotemporally controlled, but whether downstream effectors share these activation dynamics is unknown. We developed a novel single-color FRET biosensor to measure Rho-associated kinase (ROCK) activity with high spatiotemporal resolution in live cells. We report the validation of the Rho-Kinase Activity Reporter (RhoKAR) biosensor. RhoKAR activation was specific to ROCK activity and was insensitive to PKA activity. We then assessed the mechanisms of ROCK activation in mouse fibroblasts. Increasing intracellular calcium with ionomycin increased RhoKAR activity and depleting intracellular calcium with EGTA decreased RhoKAR activity. We also investigated the signaling intermediates in this process. Blocking calmodulin or CaMKII prevented calcium-dependent activation of ROCK. These results indicate that ROCK activity is increased by calcium in fibroblasts and that this activation occurs downstream of CaM/CaMKII. Full article

(This article belongs to the Collection Recent Advances in Fluorescent Sensors)

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32 pages, 15095 KiB

Open AccessEditor’s ChoiceArticle

Multi-Sensor Soil Probe and Machine Learning Modeling for Predicting Soil Properties

by Sabine Grunwald, Mohammad Omar Faruk Murad, Stephen Farrington, Woody Wallace and Daniel Rooney

Sensors 2024, 24(21), 6855; https://doi.org/10.3390/s24216855 - 25 Oct 2024

Cited by 6 | Viewed by 6686

Abstract

We present a data-driven, in situ proximal multi-sensor digital soil mapping approach to develop digital twins for multiple agricultural fields. A novel Digital Soil Core^TM (DSC) Probe was engineered that contains seven sensors, each of a distinct modality, including sleeve friction, tip [...] Read more.

We present a data-driven, in situ proximal multi-sensor digital soil mapping approach to develop digital twins for multiple agricultural fields. A novel Digital Soil Core^TM (DSC) Probe was engineered that contains seven sensors, each of a distinct modality, including sleeve friction, tip force, dielectric permittivity, electrical resistivity, soil imagery, acoustics, and visible and near-infrared spectroscopy. The DSC System integrates the DSC Probe, DSC software (v2023.10), and deployment equipment components to sense soil characteristics at a high vertical spatial resolution (mm scale) along in situ soil profiles up to a depth of 120 cm in about 60 s. The DSC Probe in situ proximal data are harmonized into a data cube providing vertical high-density knowledge associated with physical–chemical–biological soil conditions. In contrast, conventional ex situ soil samples derived from soil cores, soil pits, or surface samples analyzed using laboratory and other methods are bound by a substantially coarser spatial resolution and multiple compounding errors. Our objective was to investigate the effects of the mismatched scale between high-resolution in situ proximal sensor data and coarser-resolution ex situ soil laboratory measurements to develop soil prediction models. Our study was conducted in central California soil in almond orchards. We collected DSC sensor data and spatially co-located soil cores that were sliced into narrow layers for laboratory-based soil measurements. Partial Least Squares Regression (PLSR) cross-validation was used to compare the results of testing four data integration methods. Method A reduced the high-resolution sensor data to discrete values paired with layer-based soil laboratory measurements. Method B used stochastic distributions of sensor data paired with layer-based soil laboratory measurements. Method C allocated the same soil analytical data to each one of the high-resolution multi-sensor data within a soil layer. Method D linked the high-density multi-sensor soil data directly to crop responses (crop performance and behavior metrics), bypassing costly laboratory soil analysis. Overall, the soil models derived from Method C outperformed Methods A and B. Soil predictions derived using Method D were the most cost-effective for directly assessing soil–crop relationships, making this method well suited for industrial-scale precision agriculture applications. Full article

(This article belongs to the Section Smart Agriculture)

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22 pages, 1654 KiB

Open AccessEditor’s ChoiceArticle

A New Scene Sensing Model Based on Multi-Source Data from Smartphones

by Zhenke Ding, Zhongliang Deng, Enwen Hu, Bingxun Liu, Zhichao Zhang and Mingyang Ma

Sensors 2024, 24(20), 6669; https://doi.org/10.3390/s24206669 - 16 Oct 2024

Viewed by 1283

Abstract

Smartphones with integrated sensors play an important role in people’s lives, and in advanced multi-sensor fusion navigation systems, the use of individual sensor information is crucial. Because of the different environments, the weights of the sensors will be different, which will also affect [...] Read more.

Smartphones with integrated sensors play an important role in people’s lives, and in advanced multi-sensor fusion navigation systems, the use of individual sensor information is crucial. Because of the different environments, the weights of the sensors will be different, which will also affect the method and results of multi-source fusion positioning. Based on the multi-source data from smartphone sensors, this study explores five types of information—Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), cellular networks, optical sensors, and Wi-Fi sensors—characterizing the temporal, spatial, and mathematical statistical features of the data, and it constructs a multi-scale, multi-window, and context-connected scene sensing model to accurately detect the environmental scene in indoor, semi-indoor, outdoor, and semi-outdoor spaces, thus providing a good basis for multi-sensor positioning in a multi-sensor navigation system. Detecting environmental scenes provides an environmental positioning basis for multi-sensor fusion localization. This model is divided into four main parts: multi-sensor-based data mining, a multi-scale convolutional neural network (CNN), a bidirectional long short-term memory (BiLSTM) network combined with contextual information, and a meta-heuristic optimization algorithm. Full article

(This article belongs to the Special Issue Smart Sensor Systems for Positioning and Navigation)

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32 pages, 10162 KiB

Open AccessEditor’s ChoiceReview

Advancements in Flexible and Stretchable Electronics for Resistive Hydrogen Sensing: A Comprehensive Review

by Kwonpil Park and Minsoo P. Kim

Sensors 2024, 24(20), 6637; https://doi.org/10.3390/s24206637 - 15 Oct 2024

Cited by 7 | Viewed by 3532

Abstract

Flexible and stretchable electronics have emerged as a groundbreaking technology with wide-ranging applications, including wearable devices, medical implants, and environmental monitoring systems. Among their numerous applications, hydrogen sensing represents a critical area of research, particularly due to hydrogen’s role as a clean energy [...] Read more.

Flexible and stretchable electronics have emerged as a groundbreaking technology with wide-ranging applications, including wearable devices, medical implants, and environmental monitoring systems. Among their numerous applications, hydrogen sensing represents a critical area of research, particularly due to hydrogen’s role as a clean energy carrier and its explosive nature at high concentrations. This review paper provides a comprehensive overview of the recent advancements in flexible and stretchable electronics tailored for resistive hydrogen sensing applications. It begins by introducing the fundamental principles underlying the operation of flexible and stretchable resistive sensors, highlighting the innovative materials and fabrication techniques that enable their exceptional mechanical resilience and adaptability. Following this, the paper delves into the specific strategies employed in the integration of these resistive sensors into hydrogen detection systems, discussing the merits and limitations of various sensor designs, from nanoscale transducers to fully integrated wearable devices. Special attention is paid to the sensitivity, selectivity, and operational stability of these resistive sensors, as well as their performance under real-world conditions. Furthermore, the review explores the challenges and opportunities in this rapidly evolving field, including the scalability of manufacturing processes, the integration of resistive sensor networks, and the development of standards for safety and performance. Finally, the review concludes with a forward-looking perspective on the potential impacts of flexible and stretchable resistive electronics in hydrogen energy systems and safety applications, underscoring the need for interdisciplinary collaboration to realize the full potential of this innovative technology. Full article

(This article belongs to the Special Issue Printed Flexible and Stretchable Electronics for Sensing Applications)

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44 pages, 14459 KiB

Open AccessEditor’s ChoiceReview

A Review: Laser Interference Lithography for Diffraction Gratings and Their Applications in Encoders and Spectrometers

by Linbin Luo, Shuonan Shan and Xinghui Li

Sensors 2024, 24(20), 6617; https://doi.org/10.3390/s24206617 - 14 Oct 2024

Cited by 9 | Viewed by 5045

Abstract

The unique diffractive properties of gratings have made them essential in a wide range of applications, including spectral analysis, precision measurement, optical data storage, laser technology, and biomedical imaging. With advancements in micro- and nanotechnologies, the demand for more precise and efficient grating [...] Read more.

The unique diffractive properties of gratings have made them essential in a wide range of applications, including spectral analysis, precision measurement, optical data storage, laser technology, and biomedical imaging. With advancements in micro- and nanotechnologies, the demand for more precise and efficient grating fabrication has increased. This review discusses the latest advancements in grating manufacturing techniques, particularly highlighting laser interference lithography, which excels in sub-beam generation through wavefront and amplitude division. Techniques such as Lloyd’s mirror configurations produce stable interference fringe fields for grating patterning in a single exposure. Orthogonal and non-orthogonal, two-axis Lloyd’s mirror interferometers have advanced the fabrication of two-dimensional gratings and large-area gratings, respectively, while laser interference combined with concave lenses enables the creation of concave gratings. Grating interferometry, utilizing optical interference principles, allows for highly precise measurements of minute displacements at the nanometer to sub-nanometer scale. This review also examines the application of grating interferometry in high-precision, absolute, and multi-degree-of-freedom measurement systems. Progress in grating fabrication has significantly advanced spectrometer technology, with integrated structures such as concave gratings, Fresnel gratings, and grating–microlens arrays driving the miniaturization of spectrometers and expanding their use in compact analytical instruments. Full article

(This article belongs to the Special Issue Feature Papers in Optical Sensors 2024)

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17 pages, 8226 KiB

Open AccessEditor’s ChoiceArticle

Design of a Capacitive Tactile Sensor Array System for Human–Computer Interaction

by Fei Fei, Zhenkun Jia, Changcheng Wu, Xiong Lu and Zhi Li

Sensors 2024, 24(20), 6629; https://doi.org/10.3390/s24206629 - 14 Oct 2024

Cited by 4 | Viewed by 1676

Abstract

This paper introduces a novel capacitive sensor array designed for tactile perception applications. Utilizing an all-in-one inkjet deposition printing process, the sensor array exhibited exceptional flexibility and accuracy. With a resolution of up to 32.7 dpi, the sensor array was capable of capturing [...] Read more.

This paper introduces a novel capacitive sensor array designed for tactile perception applications. Utilizing an all-in-one inkjet deposition printing process, the sensor array exhibited exceptional flexibility and accuracy. With a resolution of up to 32.7 dpi, the sensor array was capable of capturing the fine details of touch inputs, making it suitable for applications requiring high spatial resolution. The design incorporates two multiplexers to achieve a scanning rate of 100 Hz, ensuring the rapid and responsive data acquisition that is essential for real-time feedback in interactive applications, such as gesture recognition and haptic interfaces. To evaluate the performance of the capacitive sensor array, an experiment that involved handwritten number recognition was conducted. The results demonstrated that the sensor accurately captured fingertip inputs with a high precision. When combined with an Auxiliary Classifier Generative Adversarial Network (ACGAN) algorithm, the sensor system achieved a recognition accuracy of 98% for various handwritten numbers from “0” to “9”. These results show the potential of the capacitive sensor array for advanced human–computer interaction applications. Full article

(This article belongs to the Section Sensors Development)

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21 pages, 2895 KiB

Open AccessEditor’s ChoiceArticle

Traffic Flow Prediction in 5G-Enabled Intelligent Transportation Systems Using Parameter Optimization and Adaptive Model Selection

by Hanh Hong-Phuc Vo, Thuan Minh Nguyen, Khoi Anh Bui and Myungsik Yoo

Sensors 2024, 24(20), 6529; https://doi.org/10.3390/s24206529 - 10 Oct 2024

Cited by 3 | Viewed by 2136

Abstract

This study proposes a novel hybrid method, FVMD-WOA-GA, for enhancing traffic flow prediction in 5G-enabled intelligent transportation systems. The method integrates fast variational mode decomposition (FVMD) with optimization techniques, namely, the whale optimization algorithm (WOA) and genetic algorithm (GA), to improve the accuracy [...] Read more.

This study proposes a novel hybrid method, FVMD-WOA-GA, for enhancing traffic flow prediction in 5G-enabled intelligent transportation systems. The method integrates fast variational mode decomposition (FVMD) with optimization techniques, namely, the whale optimization algorithm (WOA) and genetic algorithm (GA), to improve the accuracy of overall traffic flow based on models tailored for each decomposed sub-sequence. The selected predictive models—long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU)—were considered to capture diverse temporal dependencies in traffic data. This research explored a multi-stage approach, where the decomposition, optimization, and selection of models are performed systematically to improve prediction performance. Experimental validation on two real-world traffic datasets further underscores the method’s efficacy, achieving root mean squared errors (RMSEs) of 152.43 and 7.91 on the respective datasets, which marks improvements of 3.44% and 12.87% compared to the existing methods. These results highlight the ability of the FVMD-WOA-GA approach to improve prediction accuracy significantly, reduce inference time, enhance system adaptability, and contribute to more efficient traffic management. Full article

(This article belongs to the Section Sensor Networks)

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9 pages, 3864 KiB

Open AccessEditor’s ChoiceCommunication

Photoelectric H₂S Sensing Based on Electrospun Hollow CuO-SnO₂ Nanotubes at Room Temperature

by Cheng Zou, Cheng Peng, Xiaopeng She, Mengqing Wang, Bo Peng and Yong Zhou

Sensors 2024, 24(19), 6420; https://doi.org/10.3390/s24196420 - 3 Oct 2024

Cited by 7 | Viewed by 1491

Abstract

Pure tin oxide (SnO₂) as a typical conductometric hydrogen sulfide (H₂S) gas-sensing material always suffers from limited sensitivity, elevated operation temperature, and poor selectivity. To overcome these hindrances, in this work, hollow CuO-SnO₂ nanotubes were successfully electrospun for [...] Read more.

Pure tin oxide (SnO₂) as a typical conductometric hydrogen sulfide (H₂S) gas-sensing material always suffers from limited sensitivity, elevated operation temperature, and poor selectivity. To overcome these hindrances, in this work, hollow CuO-SnO₂ nanotubes were successfully electrospun for room-temperature (25 °C) trace H₂S detection under blue light activation. Among all SnO₂-based candidates, a pure SnO₂ sensor showed no signal, even toward 10 ppm, while the 1% CuO-SnO₂ sensor achieved a limit of detection (LoD) value of 2.5 ppm, a large response of 4.7, and a short response/recovery time of 21/61 s toward 10 ppm H₂S, as well as nice repeatability, long-term stability, and selectivity. This excellent performance could be ascribed to the one-dimensional (1D) hollow nanostructure, abundant p-n heterojunctions, and the photoelectric effect of the CuO-SnO₂ nanotubes. The proposed design strategies cater to the demanding requirements of high sensitivity and low power consumption in future application scenarios such as Internet of Things and smart optoelectronic systems. Full article

(This article belongs to the Special Issue Electrospun Composite Nanofibers: Sensing and Biosensing Applications)

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18 pages, 5232 KiB

Open AccessEditor’s ChoiceArticle

Vehicle and Pedestrian Traffic Signal Performance Measures Using LiDAR-Derived Trajectory Data

by Enrique D. Saldivar-Carranza, Jairaj Desai, Andrew Thompson, Mark Taylor, James Sturdevant and Darcy M. Bullock

Sensors 2024, 24(19), 6410; https://doi.org/10.3390/s24196410 - 3 Oct 2024

Viewed by 1947

Abstract

Light Detection and Ranging (LiDAR) sensors at signalized intersections can accurately track the movement of virtually all objects passing through at high sampling rates. This study presents methodologies to estimate vehicle and pedestrian traffic signal performance measures using LiDAR trajectory data. Over 15,000,000 [...] Read more.

Light Detection and Ranging (LiDAR) sensors at signalized intersections can accurately track the movement of virtually all objects passing through at high sampling rates. This study presents methodologies to estimate vehicle and pedestrian traffic signal performance measures using LiDAR trajectory data. Over 15,000,000 vehicle and 170,000 pedestrian waypoints detected during a 24 h period at an intersection in Utah are analyzed to describe the proposed techniques. Sampled trajectories are linear referenced to generate Purdue Probe Diagrams (PPDs). Vehicle-based PPDs are used to estimate movement level turning counts, 85th percentile queue lengths (85QL), arrivals on green (AOG), highway capacity manual (HCM) level of service (LOS), split failures (SF), and downstream blockage (DSB) by time of day (TOD). Pedestrian-based PPDs are used to estimate wait times and the proportion of people that traverse multiple crosswalks. Although vehicle signal performance can be estimated from several days of aggregated connected vehicle (CV) data, LiDAR data provides the ability to measure performance in real time. Furthermore, LiDAR can measure pedestrian speeds. At the studied location, the 15th percentile pedestrian walking speed was estimated to be 3.9 ft/s. The ability to directly measure these pedestrian speeds allows agencies to consider alternative crossing times than those suggested by the Manual on Uniform Traffic Control Devices (MUTCD). Full article

(This article belongs to the Section Radar Sensors)

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26 pages, 542 KiB

Open AccessEditor’s ChoiceReview

WiFi-Based Human Identification with Machine Learning: A Comprehensive Survey

by Manal Mosharaf, Jae B. Kwak and Wooyeol Choi

Sensors 2024, 24(19), 6413; https://doi.org/10.3390/s24196413 - 3 Oct 2024

Cited by 2 | Viewed by 4610

Abstract

In the modern world of human–computer interaction, notable advancements in human identification have been achieved across fields like healthcare, academia, security, etc. Despite these advancements, challenges remain, particularly in scenarios with poor lighting, occlusion, or non-line-of-sight. To overcome these limitations, the utilization of [...] Read more.

In the modern world of human–computer interaction, notable advancements in human identification have been achieved across fields like healthcare, academia, security, etc. Despite these advancements, challenges remain, particularly in scenarios with poor lighting, occlusion, or non-line-of-sight. To overcome these limitations, the utilization of radio frequency (RF) wireless signals, particularly wireless fidelity (WiFi), has been considered an innovative solution in recent research studies. By analyzing WiFi signal fluctuations caused by human presence, researchers have developed machine learning (ML) models that significantly improve identification accuracy. This paper conducts a comprehensive survey of recent advances and practical implementations of WiFi-based human identification. Furthermore, it covers the ML models used for human identification, system overviews, and detailed WiFi-based human identification methods. It also includes system evaluation, discussion, and future trends related to human identification. Finally, we conclude by examining the limitations of the research and discussing how researchers can shift their attention toward shaping the future trajectory of human identification through wireless signals. Full article

(This article belongs to the Special Issue Wireless Sensors and Wireless Sensor Networks for Engineering Applications)

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12 pages, 3136 KiB

Open AccessEditor’s ChoiceArticle

Enhancing Time-of-Flight Diffraction (TOFD) Inspection through an Innovative Curved-Sole Probe Design

by Irati Sanchez Duo, Jose Luis Lanzagorta, Iratxe Aizpurua Maestre and Lander Galdos

Sensors 2024, 24(19), 6360; https://doi.org/10.3390/s24196360 - 30 Sep 2024

Viewed by 1896

Abstract

Time-of-Flight Diffraction (TOFD) is a method of ultrasonic testing (UT) that is widely established as a non-destructive technique (NDT) mainly used for the inspection of welds. In contrast to other established UT techniques, TOFD is capable of identifying discontinuities regardless of their orientation. [...] Read more.

Time-of-Flight Diffraction (TOFD) is a method of ultrasonic testing (UT) that is widely established as a non-destructive technique (NDT) mainly used for the inspection of welds. In contrast to other established UT techniques, TOFD is capable of identifying discontinuities regardless of their orientation. This paper proposes a redesign of the typical TOFD transducers, featuring an innovative curved sole aimed at enhancing their defect detection capabilities. This design is particularly beneficial for thick-walled samples, as it allows for deeper inspections without compromising the resolution near the surface area. During this research, an evaluation consisting in simulations of the ultrasonic beam distribution and experimental tests on a component with artificially manufactured defects at varying depths has been performed to validate the new design. The results demonstrate a 30 to 50% higher beam distribution area as well as an improvement in the signal-to-noise ratio (SNR) resulting in a 24% enhancement in the capability of defect detection compared to the traditional approach. Full article

(This article belongs to the Section Industrial Sensors)

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18 pages, 7421 KiB

Open AccessEditor’s ChoiceArticle

Enhanced Visual SLAM for Collision-Free Driving with Lightweight Autonomous Cars

by Zhihao Lin, Zhen Tian, Qi Zhang, Hanyang Zhuang and Jianglin Lan

Sensors 2024, 24(19), 6258; https://doi.org/10.3390/s24196258 - 27 Sep 2024

Cited by 9 | Viewed by 2259

Abstract

The paper presents a vision-based obstacle avoidance strategy for lightweight self-driving cars that can be run on a CPU-only device using a single RGB-D camera. The method consists of two steps: visual perception and path planning. The visual perception part uses ORBSLAM3 enhanced [...] Read more.

The paper presents a vision-based obstacle avoidance strategy for lightweight self-driving cars that can be run on a CPU-only device using a single RGB-D camera. The method consists of two steps: visual perception and path planning. The visual perception part uses ORBSLAM3 enhanced with optical flow to estimate the car’s poses and extract rich texture information from the scene. In the path planning phase, the proposed method employs a method combining a control Lyapunov function and control barrier function in the form of a quadratic program (CLF-CBF-QP) together with an obstacle shape reconstruction process (SRP) to plan safe and stable trajectories. To validate the performance and robustness of the proposed method, simulation experiments were conducted with a car in various complex indoor environments using the Gazebo simulation environment. The proposed method can effectively avoid obstacles in the scenes. The proposed algorithm outperforms benchmark algorithms in achieving more stable and shorter trajectories across multiple simulated scenes. Full article

(This article belongs to the Special Issue Intelligent Control Systems for Autonomous Vehicles)

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65 pages, 19918 KiB

Open AccessEditor’s ChoiceReview

Radiation Detectors and Sensors in Medical Imaging

by Christos Michail, Panagiotis Liaparinos, Nektarios Kalyvas, Ioannis Kandarakis, George Fountos and Ioannis Valais

Sensors 2024, 24(19), 6251; https://doi.org/10.3390/s24196251 - 26 Sep 2024

Cited by 6 | Viewed by 7162

Abstract

Medical imaging instrumentation design and construction is based on radiation sources and radiation detectors/sensors. This review focuses on the detectors and sensors of medical imaging systems. These systems are subdivided into various categories depending on their structure, the type of radiation they capture, [...] Read more.

Medical imaging instrumentation design and construction is based on radiation sources and radiation detectors/sensors. This review focuses on the detectors and sensors of medical imaging systems. These systems are subdivided into various categories depending on their structure, the type of radiation they capture, how the radiation is measured, how the images are formed, and the medical goals they serve. Related to medical goals, detectors fall into two major areas: (i) anatomical imaging, which mainly concerns the techniques of diagnostic radiology, and (ii) functional-molecular imaging, which mainly concerns nuclear medicine. An important parameter in the evaluation of the detectors is the combination of the quality of the diagnostic result they offer and the burden of the patient with radiation dose. The latter has to be minimized; thus, the input signal (radiation photon flux) must be kept at low levels. For this reason, the detective quantum efficiency (DQE), expressing signal-to-noise ratio transfer through an imaging system, is of primary importance. In diagnostic radiology, image quality is better than in nuclear medicine; however, in most cases, the dose is higher. On the other hand, nuclear medicine focuses on the detection of functional findings and not on the accurate spatial determination of anatomical data. Detectors are integrated into projection or tomographic imaging systems and are based on the use of scintillators with optical sensors, photoconductors, or semiconductors. Analysis and modeling of such systems can be performed employing theoretical models developed in the framework of cascaded linear systems analysis (LCSA), as well as within the signal detection theory (SDT) and information theory. Full article

(This article belongs to the Special Issue Multiple Sensor Signal and Image Processing for Clinical Application)

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10 pages, 945 KiB

Open AccessEditor’s ChoiceArticle

The Validity of Apple Watch Series 9 and Ultra 2 for Serial Measurements of Heart Rate Variability and Resting Heart Rate

by Ben O’Grady, Rory Lambe, Maximus Baldwin, Tara Acheson and Cailbhe Doherty

Sensors 2024, 24(19), 6220; https://doi.org/10.3390/s24196220 - 26 Sep 2024

Cited by 8 | Viewed by 13929

Abstract

The widespread use of wearable devices has enabled continuous monitoring of biometric data, including heart rate variability (HRV) and resting heart rate (RHR). However, the validity of these measurements, particularly from consumer devices like Apple Watch, remains underexplored. This study aimed to validate [...] Read more.

The widespread use of wearable devices has enabled continuous monitoring of biometric data, including heart rate variability (HRV) and resting heart rate (RHR). However, the validity of these measurements, particularly from consumer devices like Apple Watch, remains underexplored. This study aimed to validate HRV measurements obtained from Apple Watch Series 9 and Ultra 2 against the Polar H10 chest strap paired with the Kubios HRV software, which together served as the reference standard. A prospective cohort of 39 healthy adults provided 316 HRV measurements over a 14-day period. Generalized Estimating Equations were used to assess the difference in HRV between devices, accounting for repeated measures. Apple Watch tended to underestimate HRV by an average of 8.31 ms compared to the Polar H10 (p = 0.025), with a mean absolute percentage error (MAPE) of 28.88% and a mean absolute error (MAE) of 20.46 ms. The study found no significant impact of RHR discrepancies on HRV differences (p = 0.156), with RHR showing a mean difference of −0.08 bpm, an MAPE of 5.91%, and an MAE of 3.73 bpm. Equivalence testing indicated that the HRV measurements from Apple Watch did not fall within the pre-specified equivalence margin of ±10 ms. Despite accurate RHR measurements, these findings underscore the need for improved HRV algorithms in consumer wearables and caution in interpreting HRV data for clinical or performance monitoring. Full article

(This article belongs to the Collection Sensors for Gait, Human Movement Analysis, and Health Monitoring)

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17 pages, 6457 KiB

Open AccessEditor’s ChoiceArticle

A Cumulant-Based Method for Acquiring GNSS Signals

by He-Sheng Wang, Hou-Yu Wang and Dah-Jing Jwo

Sensors 2024, 24(19), 6234; https://doi.org/10.3390/s24196234 - 26 Sep 2024

Cited by 3 | Viewed by 1315

Abstract

Global Navigation Satellite Systems (GNSS) provide positioning, velocity, and time services for civilian applications. A critical step in the positioning process is the acquisition of visible satellites in the sky. Modern GNSS systems, such as Galileo—developed and maintained by the European Union—utilize a [...] Read more.

Global Navigation Satellite Systems (GNSS) provide positioning, velocity, and time services for civilian applications. A critical step in the positioning process is the acquisition of visible satellites in the sky. Modern GNSS systems, such as Galileo—developed and maintained by the European Union—utilize a new modulation technique known as Binary Offset Carrier (BOC). However, BOC signals introduce multiple side-peaks in their autocorrelation function, which can lead to significant errors during the acquisition process. In this paper, we propose a novel acquisition method based on higher-order cumulants that effectively eliminates these side-peaks. This method is capable of simultaneously acquiring both conventional ranging signals, such as GPS C/A code, and BOC-modulated signals. The effectiveness of the proposed method is demonstrated through the acquisition of simulated signals, with a comparison to traditional methods. Additionally, we apply the proposed method to real satellite signals to further validate its performance. Our results show that the proposed method successfully suppresses side-peaks, improves acquisition accuracy in weak signal environments, and demonstrates potential for indoor GNSS applications. The study concludes that while the method may increase computational load, its performance in challenging conditions makes it a promising approach for future GNSS receiver designs. Full article

(This article belongs to the Special Issue Advances in GNSS Signal Processing and Navigation)

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26 pages, 9754 KiB

Open AccessEditor’s ChoiceReview

Gas Sensing Properties of Indium–Oxide–Based Field–Effect Transistor: A Review

by Chengyao Liang, Zhongyu Cao, Jiongyue Hao, Shili Zhao, Yuanting Yu, Yingchun Dong, Hangyu Liu, Chun Huang, Chao Gao, Yong Zhou and Yong He

Sensors 2024, 24(18), 6150; https://doi.org/10.3390/s24186150 - 23 Sep 2024

Cited by 1 | Viewed by 3040

Abstract

Excellent stability, low cost, high response, and sensitivity of indium oxide (In₂O₃), a metal oxide semiconductor, have been verified in the field of gas sensing. Conventional In₂O₃ gas sensors employ simple and easy–to–manufacture resistive components as [...] Read more.

Excellent stability, low cost, high response, and sensitivity of indium oxide (In₂O₃), a metal oxide semiconductor, have been verified in the field of gas sensing. Conventional In₂O₃ gas sensors employ simple and easy–to–manufacture resistive components as transducers. However, the swift advancement of the Internet of Things has raised higher requirements for gas sensors based on metal oxides, primarily including lowering operating temperatures, improving selectivity, and realizing integrability. In response to these three main concerns, field–effect transistor (FET) gas sensors have garnered growing interest over the past decade. When compared with other metal oxide semiconductors, In₂O₃ exhibits greater carrier concentration and mobility. The property is advantageous for manufacturing FETs with exceptional electrical performance, provided that the off–state current is controlled at a sufficiently low level. This review presents the significant progress made in In₂O₃ FET gas sensors during the last ten years, covering typical device designs, gas sensing performance indicators, optimization techniques, and strategies for the future development based on In₂O₃ FET gas sensors. Full article

(This article belongs to the Special Issue Inorganic Nanostructure-Based Sensors: Design and Applications)

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23 pages, 8494 KiB

Open AccessEditor’s ChoiceReview

Advances in Group-10 Transition Metal Dichalcogenide PdSe₂-Based Photodetectors: Outlook and Perspectives

by Tawsif Ibne Alam, Kunxuan Liu, Sumaiya Umme Hani, Safayet Ahmed and Yuen Hong Tsang

Sensors 2024, 24(18), 6127; https://doi.org/10.3390/s24186127 - 22 Sep 2024

Cited by 4 | Viewed by 2512

Abstract

The recent advancements in low-dimensional material-based photodetectors have provided valuable insights into the fundamental properties of these materials, the design of their device architectures, and the strategic engineering approaches that have facilitated their remarkable progress. This review work consolidates and provides a comprehensive [...] Read more.

The recent advancements in low-dimensional material-based photodetectors have provided valuable insights into the fundamental properties of these materials, the design of their device architectures, and the strategic engineering approaches that have facilitated their remarkable progress. This review work consolidates and provides a comprehensive review of the recent progress in group-10 two-dimensional (2D) palladium diselenide (PdSe₂)-based photodetectors. This work first offers a general overview of the various types of PdSe₂ photodetectors, including their operating mechanisms and key performance metrics. A detailed examination is then conducted on the physical properties of 2D PdSe₂ material and how these metrics, such as structural characteristics, optical anisotropy, carrier mobility, and bandgap, influence photodetector device performance and potential avenues for enhancement. Furthermore, the study delves into the current methods for synthesizing PdSe₂ material and constructing the corresponding photodetector devices. The documented device performances and application prospects are thoroughly discussed. Finally, this review speculates on the existing trends and future research opportunities in the field of 2D PdSe₂ photodetectors. Potential directions for continued advancement of these optoelectronic devices are proposed and forecasted. Full article

(This article belongs to the Special Issue Feature Papers in Optical Sensors 2024)

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19 pages, 575 KiB

Open AccessEditor’s ChoiceArticle

Jointly Optimization of Delay and Energy Consumption for Multi-Device FDMA in WPT-MEC System

by Danxia Qiao, Lu Sun, Dianju Li, Huajie Xiong, Rina Liang, Zhenyuan Han and Liangtian Wan

Sensors 2024, 24(18), 6123; https://doi.org/10.3390/s24186123 - 22 Sep 2024

Cited by 2 | Viewed by 1975

Abstract

With the rapid development of mobile edge computing (MEC) and wireless power transfer (WPT) technologies, the MEC-WPT system makes it possible to provide high-quality data processing services for end users. However, in a real-world WPT-MEC system, the channel gain decreases with the transmission [...] Read more.

With the rapid development of mobile edge computing (MEC) and wireless power transfer (WPT) technologies, the MEC-WPT system makes it possible to provide high-quality data processing services for end users. However, in a real-world WPT-MEC system, the channel gain decreases with the transmission distance, leading to “double near and far effect” in the joint transmission of wireless energy and data, which affects the quality of the data processing service for end users. Consequently, it is essential to design a reasonable system model to overcome the “double near and far effect” and make reasonable scheduling of multi-dimensional resources such as energy, communication and computing to guarantee high-quality data processing services. First, this paper designs a relay collaboration WPT-MEC resource scheduling model to improve wireless energy utilization efficiency. The optimization goal is to minimize the normalization of the total communication delay and total energy consumption while meeting multiple resource constraints. Second, this paper imports a BK-means algorithm to complete the end terminals cluster to guarantee effective energy reception and adapts the whale optimization algorithm with adaptive mechanism (AWOA) for mobile vehicle path-planning to reduce energy waste. Third, this paper proposes an immune differential enhanced deep deterministic policy gradient (IDDPG) algorithm to realize efficient resource scheduling of multiple resources and minimize the optimization goal. Finally, simulation experiments are carried out on different data, and the simulation results prove the validity of the designed scheduling model and proposed IDDPG. Full article

(This article belongs to the Special Issue Integrated Sensing, Communication, and Computing Networks for IoT Applications)

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21 pages, 9876 KiB

Open AccessEditor’s ChoiceArticle

Estimation of Leaf Area Index across Biomes and Growth Stages Combining Multiple Vegetation Indices

by Fangyi Lv, Kaimin Sun, Wenzhuo Li, Shunxia Miao and Xiuqing Hu

Sensors 2024, 24(18), 6106; https://doi.org/10.3390/s24186106 - 21 Sep 2024

Cited by 2 | Viewed by 2130

Abstract

The leaf area index (LAI) is a key indicator of vegetation canopy structure and growth status, crucial for global ecological environment research. The Moderate Resolution Spectral Imager-II (MERSI-II) aboard Fengyun-3D (FY-3D) covers the globe twice daily, providing a reliable data source for large-scale [...] Read more.

The leaf area index (LAI) is a key indicator of vegetation canopy structure and growth status, crucial for global ecological environment research. The Moderate Resolution Spectral Imager-II (MERSI-II) aboard Fengyun-3D (FY-3D) covers the globe twice daily, providing a reliable data source for large-scale and high-frequency LAI estimation. VI-based LAI estimation is effective, but species and growth status impacts on the sensitivity of the VI–LAI relationship are rarely considered, especially for MERSI-II. This study analyzed the VI–LAI relationship for eight biomes in China with contrasting leaf structures and canopy architectures. The LAI was estimated by adaptively combining multiple VIs and validated using MODIS, GLASS, and ground measurements. Results show that (1) species and growth stages significantly affect VI–LAI sensitivity. For example, the EVI is optimal for broadleaf crops in winter, while the RDVI is best for evergreen needleleaf forests in summer. (2) Combining vegetation indices can significantly optimize sensitivity. The accuracy of multi-VI-based LAI retrieval is notably higher than using a single VI for the entire year. (3) MERSI-II shows good spatial–temporal consistency with MODIS and GLASS and is more sensitive to vegetation growth fluctuation. Direct validation with ground-truth data also demonstrates that the uncertainty of retrievals is acceptable (R² = 0.808, RMSE = 0.642). Full article

(This article belongs to the Section Remote Sensors)

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