Sensors

19 pages, 4621 KB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 5 | Viewed by 2852

Abstract

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

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

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

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15 pages, 3600 KB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 1 | Viewed by 2937

Abstract

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

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

(This article belongs to the Section Biosensors)

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36 pages, 1195 KB

Open AccessEditor’s ChoiceReview

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

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

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

Cited by 7 | Viewed by 16853

Abstract

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

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

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

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16 pages, 1923 KB

Open AccessEditor’s ChoiceArticle

A High-Sensitivity, Low-Noise, and Low-Hysteresis Tunneling Magnetoresistance Sensor Based on Structural Optimization of Magnetic Tunnel Junctions

by Ran Bi, Ruiying Chen, Shilin Wu, Haoyu Ma, Huiquan Zhang, Xinting Liu, Jinliang He and Jun Hu

Sensors 2025, 25(6), 1730; https://doi.org/10.3390/s25061730 - 11 Mar 2025

Cited by 11 | Viewed by 2979

Abstract

Accurate measurement of magnetic fields holds immense significance across various disciplines, such as IC circuit measurement, geological exploration, and aerospace. The sensitivity and noise parameters of magnetic field sensors play a vital role in detecting minute fluctuations in magnetic fields. However, the current [...] Read more.

Accurate measurement of magnetic fields holds immense significance across various disciplines, such as IC circuit measurement, geological exploration, and aerospace. The sensitivity and noise parameters of magnetic field sensors play a vital role in detecting minute fluctuations in magnetic fields. However, the current detection capability of tunneling magnetoresistance (TMR) is insufficient to meet the requirements for weak magnetic field measurement. This study investigates the impact of structural and fabrication parameters on the performance of TMR sensors. We fabricated series-connected TMR sensors with varying long-axis lengths of the elliptical cross-section and adjusted their performance by modifying annealing magnetic fields and magnetic field bias along the easy axis. The results demonstrate that TMR sensitivity decreases with increasing long-axis length, increases initially and then decreases with an annealing magnetic field, and decreases with a higher bias magnetic field along the easy axis. The voltage noise level of TMR sensors decreases as the long-axis length increases. Notably, the detection capability of TMR sensors exhibits a non-monotonic dependence on long-axis length. Moreover, we optimized the hysteresis of TMR sensors by applying a magnetic field bias along the easy axis. When the bias along the easy axis reached 16 Oe or −40 Oe, the hysteresis level was reduced to below 0.5 Oe. After encapsulating the TMR devices into a full Wheatstone bridge structure, we achieved a detection capability of 17 nT/

\sqrt{Hz}

@1Hz. This study highlights that the detection capability of TMR devices is jointly influenced by fabrication parameters. By optimizing parameter configuration, this work provides theoretical guidance for further enhancing the performance of TMR devices in magnetic field measurements. Full article

(This article belongs to the Section Physical Sensors)

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13 pages, 2885 KB

Open AccessEditor’s ChoiceArticle

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

by Shamim Ahmed Hira, Jonathan Quintal and Aicheng Chen

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

Cited by 2 | Viewed by 2300

Abstract

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

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

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

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28 pages, 13455 KB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 1 | Viewed by 1899

Abstract

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

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

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

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22 pages, 1180 KB

Open AccessEditor’s ChoiceArticle

Implementation of an Internet of Things Architecture to Monitor Indoor Air Quality: A Case Study During Sleep Periods

by Afonso Mota, Carlos Serôdio, Ana Briga-Sá and Antonio Valente

Sensors 2025, 25(6), 1683; https://doi.org/10.3390/s25061683 - 8 Mar 2025

Cited by 4 | Viewed by 8086

Abstract

Most human time is spent indoors, and due to the pandemic, monitoring indoor air quality (IAQ) has become more crucial. In this study, an IoT (Internet of Things) architecture is implemented to monitor IAQ parameters, including CO₂ and particulate matter (PM). An [...] Read more.

Most human time is spent indoors, and due to the pandemic, monitoring indoor air quality (IAQ) has become more crucial. In this study, an IoT (Internet of Things) architecture is implemented to monitor IAQ parameters, including CO₂ and particulate matter (PM). An ESP32-C6-based device is developed to measure sensor data and send them, using the MQTT protocol, to a remote InfluxDBv2 database instance, where the data are stored and visualized. The Python 3.11 scripting programming language is used to automate Flux queries to the database, allowing a more in-depth data interpretation. The implemented system allows to analyze two measured scenarios during sleep: one with the door slightly open and one with the door closed. Results indicate that sleeping with the door slightly open causes CO₂ levels to ascend slowly and maintain lower concentrations compared to sleeping with the door closed, where CO₂ levels ascend faster and the maximum recommended values are exceeded. This demonstrates the benefits of ventilation in maintaining IAQ. The developed system can be used for sensing in different environments, such as schools or offices, so an IAQ assessment can be made. Based on the generated data, predictive models can be designed to support decisions on intelligent natural ventilation systems, achieving an optimized, efficient, and ubiquitous solution to moderate the IAQ. Full article

(This article belongs to the Section Internet of Things)

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23 pages, 4000 KB

Open AccessEditor’s ChoiceArticle

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

by Daniel Voipan, Andreea Elena Voipan and Marian Barbu

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

Cited by 15 | Viewed by 2851

Abstract

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

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

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

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22 pages, 16442 KB

Open AccessEditor’s ChoiceArticle

Assessing the Adhesiveness and Long-Term Behaviour of Piezoresistive Strain Sensor Materials for Application in Structural Health Monitored Structures

by Daniel Kimpfbeck, Herbert Enser, Jonas Wagner, Lukas Heinzlmeier, Boris Buchroithner, Pavel Kulha, Bettina Heise, Günther Hannesschläger, Christoph Kralovec and Martin Schagerl

Sensors 2025, 25(6), 1659; https://doi.org/10.3390/s25061659 - 7 Mar 2025

Cited by 1 | Viewed by 1381

Abstract

The durability of piezoresistive sensor materials is a core prerequisite for their implementation in structural health monitoring systems. In this work, three piezoresistive materials were subjected to extensive cyclic tensile loadings, and their behaviour was analysed before, after, and during testing. To this [...] Read more.

The durability of piezoresistive sensor materials is a core prerequisite for their implementation in structural health monitoring systems. In this work, three piezoresistive materials were subjected to extensive cyclic tensile loadings, and their behaviour was analysed before, after, and during testing. To this end, aluminium specimens were coated with three different industry-grade lacquers, and then piezoresistive materials were applied onto each specimen. Sensors made from carbon black displayed excellent linearity even after tensile loading cycles (

R^{2} > 0.88

). A decline in linearity of all sensors based on carbon allotropes was discovered, whereas the polymer-based sensors improved. Furthermore, their adhesion to the substrate is of great importance. Good adhesion ensures the strains in the underlying structure are correctly transmitted into the sensor materials. Based on contact angle measurements of liquids on sensor materials and on lacquers, their work of adhesion was determined. The findings were verified by tape adhesion tests. Full article

(This article belongs to the Section Sensor Materials)

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27 pages, 2191 KB

Open AccessEditor’s ChoiceArticle

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

by Agnieszka Duraj, Piotr S. Szczepaniak and Artur Sadok

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

Cited by 14 | Viewed by 7585

Abstract

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

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

F_{1 s c o r e}

is used. Full article

(This article belongs to the Section Intelligent Sensors)

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21 pages, 30213 KB

Open AccessEditor’s ChoiceArticle

Landsat Time Series Reconstruction Using a Closed-Form Continuous Neural Network in the Canadian Prairies Region

by Masoud Babadi Ataabadi, Darren Pouliot, Dongmei Chen and Temitope Seun Oluwadare

Sensors 2025, 25(5), 1622; https://doi.org/10.3390/s25051622 - 6 Mar 2025

Cited by 1 | Viewed by 1758

Abstract

The Landsat archive stands as one of the most critical datasets for studying landscape change, offering over 50 years of imagery. This invaluable historical record facilitates the monitoring of land cover and land use changes, helping to detect trends in and the dynamics [...] Read more.

The Landsat archive stands as one of the most critical datasets for studying landscape change, offering over 50 years of imagery. This invaluable historical record facilitates the monitoring of land cover and land use changes, helping to detect trends in and the dynamics of the Earth’s system. However, the relatively low temporal frequency and irregular clear-sky observations of Landsat data pose significant challenges for multi-temporal analysis. To address these challenges, this research explores the application of a closed-form continuous-depth neural network (CFC) integrated within a recurrent neural network (RNN) called CFC-mmRNN for reconstructing historical Landsat time series in the Canadian Prairies region from 1985 to present. The CFC method was evaluated against the continuous change detection (CCD) method, widely used for Landsat time series reconstruction and change detection. The findings indicate that the CFC method significantly outperforms CCD across all spectral bands, achieving higher accuracy with improvements ranging from 33% to 42% and providing more accurate dense time series reconstructions. The CFC approach excels in handling the irregular and sparse time series characteristic of Landsat data, offering improvements in capturing complex temporal patterns. This study underscores the potential of leveraging advanced deep learning techniques like CFC to enhance the quality of reconstructed satellite imagery, thus supporting a wide range of remote sensing (RS) applications. Furthermore, this work opens up avenues for further optimization and application of CFC in higher-density time series datasets such as MODIS and Sentinel-2, paving the way for improved environmental monitoring and forecasting. Full article

(This article belongs to the Special Issue Application of Satellite Remote Sensing in Geospatial Monitoring)

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17 pages, 4719 KB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 6 | Viewed by 4828

Abstract

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

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

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

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11 pages, 4983 KB

Open AccessEditor’s ChoiceArticle

High-Sensitivity Magnetic Field Sensor Based on an Optoelectronic Oscillator with a Mach–Zehnder Interferometer

by Mingjian Zhu, Pufeng Gao, Shiyi Cai, Naihan Zhang, Beilei Wu, Yan Liu, Bin Yin and Muguang Wang

Sensors 2025, 25(5), 1621; https://doi.org/10.3390/s25051621 - 6 Mar 2025

Cited by 3 | Viewed by 1686

Abstract

A high-sensitivity magnetic field sensor based on an optoelectronic oscillator (OEO) with a Mach–Zehnder interferometer (MZI) is proposed and experimentally demonstrated. The magnetic field sensor consists of a fiber Mach–Zehnder interferometer, with the lower arm of the interferometer wound around a magnetostrictive transducer. [...] Read more.

A high-sensitivity magnetic field sensor based on an optoelectronic oscillator (OEO) with a Mach–Zehnder interferometer (MZI) is proposed and experimentally demonstrated. The magnetic field sensor consists of a fiber Mach–Zehnder interferometer, with the lower arm of the interferometer wound around a magnetostrictive transducer. Due to the magnetostrictive effect, an optical phase shift induced by magnetic field variation is generated between two orthogonal light waves transmitted in the upper and lower arms of the MZI. The polarization-dependent property of a Mach–Zehnder modulator (MZM) is utilized to transform the magnetostrictive phase shift into the phase difference between the sidebands and optical carrier, which is mapped to the oscillating frequency upon the completion of an OEO loop. High-sensitivity magnetic field sensing is achieved by observing the frequency shift of the radio frequency (RF) signal. Temperature-induced cross-sensitivity is mitigated through precise length matching of the MZI arms. In the experiment, the high magnetic field sensitivity of 6.824 MHz/mT with a range of 25 mT to 25.3 mT is achieved and the sensing accuracy measured by an electrical spectrum analyzer (ESA) at “maxhold” mode is 0.002 mT. The proposed sensing structure has excellent magnetic field detection performance and provides a solution for temperature-insensitive magnetic field detection, which would have broad application prospects. Full article

(This article belongs to the Special Issue Advances in Microwave Photonics)

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27 pages, 2758 KB

Open AccessEditor’s ChoiceReview

A Review of SAW-Based Micro- and Nanoparticle Manipulation in Microfluidics

by Débora Amorim, Patrícia C. Sousa, Carlos Abreu and Susana O. Catarino

Sensors 2025, 25(5), 1577; https://doi.org/10.3390/s25051577 - 4 Mar 2025

Cited by 9 | Viewed by 4924

Abstract

Surface acoustic wave (SAW)-based microfluidics has emerged as a promising technology for precisely manipulating particles and cells at the micro- and nanoscales. Acoustofluidic devices offer advantages such as low energy consumption, high throughput, and label-free operation, making them suitable for particle manipulation tasks [...] Read more.

Surface acoustic wave (SAW)-based microfluidics has emerged as a promising technology for precisely manipulating particles and cells at the micro- and nanoscales. Acoustofluidic devices offer advantages such as low energy consumption, high throughput, and label-free operation, making them suitable for particle manipulation tasks including pumping, mixing, sorting, and separation. In this review, we provide an overview and discussion of recent advancements in SAW-based microfluidic devices for micro- and nanoparticle manipulation. Through a thorough investigation of the literature, we explore interdigitated transducer designs, materials, fabrication techniques, microfluidic channel properties, and SAW operational modes of acoustofluidic devices. SAW-based actuators are mainly based on lithium niobate piezoelectric transducers, with a plethora of wavelengths, microfluidic dimensions, and transducer configurations, applied for different fluid manipulation methods: mixing, sorting, and separation. We observed the accuracy of particle sorting across different size ranges and discussed different alternative device configurations to enhance sensitivity. Additionally, the collected data show the successful implementation of SAW devices in real-world applications in medical diagnostics and environmental monitoring. By critically analyzing different approaches, we identified common trends, challenges, and potential areas for improvement in SAW-based microfluidics. Furthermore, we discuss the current state-of-the-art and opportunities for further research and development in this field. Full article

(This article belongs to the Special Issue New Advances in Ultrasonic Sensors and Approaches for Nondestructive Testing and Imaging)

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24 pages, 3166 KB

Open AccessEditor’s ChoiceArticle

Human Activity Recognition Through Augmented WiFi CSI Signals by Lightweight Attention-GRU

by Hari Kang, Donghyun Kim and Kar-Ann Toh

Sensors 2025, 25(5), 1547; https://doi.org/10.3390/s25051547 - 2 Mar 2025

Cited by 9 | Viewed by 5854

Abstract

In this study, we investigate human activity recognition (HAR) using WiFi channel state information (CSI) signals, employing a single-layer gated recurrent unit (GRU) with an attention module. To overcome the limitations of existing state-of-the-art (SOTA) models, which, despite their good performance, have substantial [...] Read more.

In this study, we investigate human activity recognition (HAR) using WiFi channel state information (CSI) signals, employing a single-layer gated recurrent unit (GRU) with an attention module. To overcome the limitations of existing state-of-the-art (SOTA) models, which, despite their good performance, have substantial model sizes, we propose a lightweight model that incorporates data augmentation and pruning techniques. Our primary goal is to maintain high performance while significantly reducing model complexity. The proposed method demonstrates promising results across four different datasets, in particular achieving an accuracy of about 98.92%, outperforming an SOTA model on the ARIL dataset while reducing the model size from 252.10 M to 0.0578 M parameters. Additionally, our method achieves a reduction in computational cost from 18.06 GFLOPs to 0.01 GFLOPs for the same dataset, making it highly suitable for practical HAR applications. Full article

(This article belongs to the Special Issue Smart Sensing Technology for Human Activity Recognition)

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14 pages, 1602 KB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 8 | Viewed by 3164

Abstract

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

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

(This article belongs to the Section Sensing and Imaging)

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16 pages, 3031 KB

Open AccessEditor’s ChoiceArticle

Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain

by Damián Gulich, Myrian Tebaldi and Daniel Sierra-Sosa

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

Cited by 3 | Viewed by 2454

Abstract

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

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

(This article belongs to the Section Environmental Sensing)

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25 pages, 30298 KB

Open AccessEditor’s ChoiceReview

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

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

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

Cited by 14 | Viewed by 5513

Abstract

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

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

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

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23 pages, 1206 KB

Open AccessEditor’s ChoiceArticle

Air Pollution Monitoring Using Cost-Effective Devices Enhanced by Machine Learning

by Yanis Colléaux, Cédric Willaume, Bijan Mohandes, Jean-Christophe Nebel and Farzana Rahman

Sensors 2025, 25(5), 1423; https://doi.org/10.3390/s25051423 - 26 Feb 2025

Cited by 5 | Viewed by 4303

Abstract

Given the significant impact of air pollution on global health, the continuous and precise monitoring of air quality in all populated environments is crucial. Unfortunately, even in the most developed economies, current air quality monitoring networks are largely inadequate. The high cost of [...] Read more.

Given the significant impact of air pollution on global health, the continuous and precise monitoring of air quality in all populated environments is crucial. Unfortunately, even in the most developed economies, current air quality monitoring networks are largely inadequate. The high cost of monitoring stations has been identified as a key barrier to widespread coverage, making cost-effective air quality monitoring devices a potential game changer. However, the accuracy of the measurements obtained from low-cost sensors is affected by many factors, including gas cross-sensitivity, environmental conditions, and production inconsistencies. Fortunately, machine learning models can capture complex interdependent relationships in sensor responses and thus can enhance their readings and sensor accuracy. After gathering measurements from cost-effective air pollution monitoring devices placed alongside a reference station, the data were used to train such models. Assessments of their performance showed that models tailored to individual sensor units greatly improved measurement accuracy, boosting their correlation with reference-grade instruments by up to 10%. Nonetheless, this research also revealed that inconsistencies in the performance of similar sensor units can prevent the creation of a unified correction model for a given sensor type. Full article

(This article belongs to the Special Issue Current Advances in Sensor Design, Innovation, and Their Industry Applications)

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53 pages, 50379 KB

Open AccessEditor’s ChoiceReview

Sensing Techniques for Structural Health Monitoring: A State-of-the-Art Review on Performance Criteria and New-Generation Technologies

by Ali Mardanshahi, Abhilash Sreekumar, Xin Yang, Swarup Kumar Barman and Dimitrios Chronopoulos

Sensors 2025, 25(5), 1424; https://doi.org/10.3390/s25051424 - 26 Feb 2025

Cited by 42 | Viewed by 16036

Abstract

This systematic review examines the capabilities, challenges, and practical implementations of the most widely utilized and emerging sensing technologies in structural health monitoring (SHM) for infrastructures, addressing a critical research gap. While many existing reviews focus on individual methods, comprehensive cross-method comparisons have [...] Read more.

This systematic review examines the capabilities, challenges, and practical implementations of the most widely utilized and emerging sensing technologies in structural health monitoring (SHM) for infrastructures, addressing a critical research gap. While many existing reviews focus on individual methods, comprehensive cross-method comparisons have been limited due to the highly tailored nature of each technology. We address this by proposing a novel framework comprising five specific evaluation criteria—deployment suitability in SHM, hardware prerequisites, characteristics of the acquired signals, sensitivity metrics, and integration with Digital Twin environments—refined with subcriteria to ensure transparent and meaningful performance assessments. Applying this framework, we analyze both the advantages and constraints of established sensing technologies, including infrared thermography, electrochemical sensing, strain measurement, ultrasonic testing, visual inspection, vibration analysis, and acoustic emission. Our findings highlight critical trade-offs in scalability, environmental sensitivity, and diagnostic accuracy. Recognizing these challenges, we explore next-generation advancements such as self-sensing structures, unmanned aerial vehicle deployment, IoT-enabled data fusion, and enhanced Digital Twin simulations. These innovations aim to overcome existing limitations by enhancing real-time monitoring, data management, and remote accessibility. This review provides actionable insights for researchers and practitioners while identifying future research opportunities to advance scalable and adaptive SHM solutions for large-scale infrastructure. Full article

(This article belongs to the Special Issue Smart Sensors for Structural Health Monitoring and Nondestructive Evaluation: 2nd Edition)

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41 pages, 8971 KB

Open AccessEditor’s ChoiceReview

Scientific Machine Learning for Guided Wave and Surface Acoustic Wave (SAW) Propagation: PgNN, PeNN, PINN, and Neural Operator

by Nafisa Mehtaj and Sourav Banerjee

Sensors 2025, 25(5), 1401; https://doi.org/10.3390/s25051401 - 25 Feb 2025

Cited by 6 | Viewed by 5710

Abstract

The governing Partial Differential Equation (PDE) for wave propagation or the wave equation involves multi-scale and multi-dimensional oscillatory phenomena. Wave PDE challenges traditional computational methods due to high computational costs with rigid assumptions. The advent of scientific machine learning (SciML) presents a novel [...] Read more.

The governing Partial Differential Equation (PDE) for wave propagation or the wave equation involves multi-scale and multi-dimensional oscillatory phenomena. Wave PDE challenges traditional computational methods due to high computational costs with rigid assumptions. The advent of scientific machine learning (SciML) presents a novel paradigm by embedding physical laws within neural network architectures, enabling efficient and accurate solutions. This study explores the evolution of SciML approaches, focusing on PINNs, and evaluates their application in modeling acoustic, elastic, and guided wave propagation. PINN is a gray-box predictive model that offers the strong predictive capabilities of data-driven models but also adheres to the physical laws. Through theoretical analysis and problem-driven examples, the findings demonstrate that PINNs address key limitations of traditional methods, including discretization errors and computational inefficiencies, while offering robust predictive capabilities. Despite current challenges, such as optimization difficulties and scalability constraints, PINNs hold transformative potential for advancing wave propagation modeling. This comprehensive study underscores the transformative potential of PINN, followed by recommendations on why and how it could advance elastic, acoustic, and guided wave propagation modeling and sets the stage for future research in the field of Structural Health Monitoring (SHM)/Nondestructive Evaluation (NDE). Full article

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

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18 pages, 1858 KB

Open AccessEditor’s ChoiceArticle

The Design of a Low-Power Pipelined ADC for IoT Applications

by Junkai Zhang, Tao Sun, Zunkai Huang, Wei Tao, Ning Wang, Li Tian, Yongxin Zhu and Hui Wang

Sensors 2025, 25(5), 1343; https://doi.org/10.3390/s25051343 - 22 Feb 2025

Cited by 2 | Viewed by 3103

Abstract

This paper proposes a low-power 10-bit 20 MS/s pipelined analog-to-digital converter (ADC) designed for the burgeoning needs of low-data-rate communication systems, particularly within the Internet of Things (IoT) domain. To reduce power usage, multiple power-saving techniques are combined, such as sample-and-hold amplifier-less (SHA-less) [...] Read more.

This paper proposes a low-power 10-bit 20 MS/s pipelined analog-to-digital converter (ADC) designed for the burgeoning needs of low-data-rate communication systems, particularly within the Internet of Things (IoT) domain. To reduce power usage, multiple power-saving techniques are combined, such as sample-and-hold amplifier-less (SHA-less) architecture, capacitor scaling, and dynamic comparators. In addition, this paper presents a novel operational amplifier (op-amp) with gain boosting, featuring a dual-input differential pair that enables internal pipeline stage switching, effectively alleviating the crosstalk and memory effects inherent in conventional shared op-amp configurations, thereby further reducing power consumption. A prototype ADC was fabricated in a 180 nm CMOS process and the core size was 0.333 mm². The ADC implemented operated at a 20 MHz sampling rate under a 1.8 V supply voltage. It achieved a spurious-free dynamic range (SFDR) of 61.83 dB and a signal-to-noise-and-distortion ratio (SNDR) of 54.15 dB while demonstrating a maximum differential non-linearity (DNL) of 0.36 least significant bit (LSB) and a maximum integral non-linearity (INL) of 0.67 LSB. Notably, the ADC consumed less than 5 mW of power at the mentioned sampling frequency, showcasing excellent power efficiency. Full article

(This article belongs to the Section Electronic Sensors)

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19 pages, 8405 KB

Open AccessEditor’s ChoiceArticle

Effectiveness of Sound Field Corrections for High-Frequency Pressure Comparison Calibration of MEMS Microphones

by Fabio Saba, María Campo-Valera, Davide Paesante, Giovanni Durando, Mario Corallo and Diego Pugliese

Sensors 2025, 25(5), 1312; https://doi.org/10.3390/s25051312 - 21 Feb 2025

Cited by 1 | Viewed by 3848

Abstract

The calibration of Micro-Electro-Mechanical System (MEMS) microphones remains a critical challenge due to their miniaturized geometry and sensitivity to non-uniform acoustic fields. This study presents an advanced calibration methodology that integrates Finite Element Method (FEM) simulations with experimental corrections to improve the accuracy [...] Read more.

The calibration of Micro-Electro-Mechanical System (MEMS) microphones remains a critical challenge due to their miniaturized geometry and sensitivity to non-uniform acoustic fields. This study presents an advanced calibration methodology that integrates Finite Element Method (FEM) simulations with experimental corrections to improve the accuracy of pressure comparison calibrations using active couplers. A key innovation is the incorporation of asymmetric acoustic field analysis, which systematically quantifies and corrects discrepancies arising from cavity geometry, sensor positioning, and resonance effects peculiar of MEMS microphones. The proposed approach significantly reduces measurement uncertainties, especially in the high-frequency range above 5 kHz, where standard calibration techniques face challenges in taking into account localized pressure variations. Furthermore, the implementation of a measurement set-up, which includes the insert voltage technique, allows for an accurate assessment of the preamplifier gain and minimizes systematic errors. Experimental validation shows that the refined calibration methodology produces highly reliable correction values, ensuring a robust performance over a wide frequency range (20 Hz–20 kHz). These advances establish a rigorous framework for standardizing the calibration of MEMS microphones, strengthening their applicability in acoustic monitoring, sound source localization, and environmental sensing. Full article

(This article belongs to the Special Issue Metrology, Sensors and Instrumentation for Industry 4.0 and IoT)

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20 pages, 4945 KB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 3 | Viewed by 2790

Abstract

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

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

21 %

and

100 %

, with an average of

69 %

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

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

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19 pages, 11821 KB

Open AccessEditor’s ChoiceArticle

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

by Gen Fukuda and Nobuaki Kubo

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

Cited by 3 | Viewed by 4958

Abstract

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

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

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

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14 pages, 4128 KB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 1 | Viewed by 1951

Abstract

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

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

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

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17 pages, 5079 KB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 1 | Viewed by 1402

Abstract

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

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

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

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15 pages, 1845 KB

Open AccessEditor’s ChoiceArticle

Path Loss Modeling for RIS-Assisted Wireless Communication in Tunnel Scenarios

by Qi Yang, Yating Wu, Hengkai Zhao, Yichen Feng, Yanqiong Sun, Zhou Fang and Guoxin Zheng

Sensors 2025, 25(4), 1247; https://doi.org/10.3390/s25041247 - 18 Feb 2025

Cited by 7 | Viewed by 2789

Abstract

Aiming to address the problem of limited transmission distance in applying reconfigurable intelligent surface (RIS) technology, this study leverages a tunnel simulation platform to investigate RIS-assisted wireless communication systems. Through theoretical derivation, we propose a path loss model formula specifically applicable to tunnel [...] Read more.

Aiming to address the problem of limited transmission distance in applying reconfigurable intelligent surface (RIS) technology, this study leverages a tunnel simulation platform to investigate RIS-assisted wireless communication systems. Through theoretical derivation, we propose a path loss model formula specifically applicable to tunnel scenarios. Simulation results demonstrate that the proposed model accurately reflects the communication performance characteristics of RIS in tunnel scenarios, verifying the capability of RIS technology to enhance signal transmission distance within tunnels in rail transit engineering applications. This finding highlights the significant engineering potential and value of RIS technology. Full article

(This article belongs to the Section Communications)

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15 pages, 4374 KB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 3 | Viewed by 3622

Abstract

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

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

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

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29 pages, 21708 KB

Open AccessEditor’s ChoiceArticle

Design, Implementation and Practical Evaluation of an Opportunistic Communications Protocol Based on Bluetooth Mesh and libp2p

by Ángel Niebla-Montero, Iván Froiz-Míguez, Paula Fraga-Lamas and Tiago M. Fernández-Caramés

Sensors 2025, 25(4), 1190; https://doi.org/10.3390/s25041190 - 15 Feb 2025

Cited by 4 | Viewed by 1959

Abstract

The increasing proliferation of Internet of Things (IoT) devices has created a growing need for more efficient communication networks, especially in areas where continuous connectivity is unstable or unavailable. Opportunistic networks have emerged as a possible solution in such scenarios, allowing for intermittent [...] Read more.

The increasing proliferation of Internet of Things (IoT) devices has created a growing need for more efficient communication networks, especially in areas where continuous connectivity is unstable or unavailable. Opportunistic networks have emerged as a possible solution in such scenarios, allowing for intermittent and decentralized data sharing. This article presents a novel communication protocol that uses Bluetooth 5 and the libp2p framework to enable decentralized and opportunistic communications among IoT devices. The protocol provides dynamic peer discovery and decentralized management, resulting in a more flexible and robust IoT network infrastructure. The performance of the proposed architecture was evaluated through experiments in both controlled and industrial scenarios, with a particular emphasis on latency and on the impact of the presence of obstacles. The obtained results show that the protocol has the ability to improve data transfer in environments with limited connectivity, making it adequate for both urban and rural areas, as well as for challenging environments such as shipyards. Moreover, the presented findings conclude that the protocol works well in situations with minimal signal obstruction and short distances, like homes, where average latency values of about 8 s have been achieved with no losses. Furthermore, the protocol can also be used in industrial scenarios, even when metal obstacles increase signal attenuation, and over long distances, where average latency values of about 8.5 s have been obtained together with packet losses of less than 5%. Full article

(This article belongs to the Special Issue Applications of Sensors Based on Embedded Systems)

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18 pages, 5927 KB

Open AccessEditor’s ChoiceArticle

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

by Soumyadeep Saha, Manoj Sachdev and Sushanta K. Mitra

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

Cited by 2 | Viewed by 2770

Abstract

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

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

(This article belongs to the Section Biosensors)

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17 pages, 1391 KB

Open AccessEditor’s ChoiceArticle

Optimizing Sensor Data Interpretation via Hybrid Parametric Bootstrapping

by Victor V. Golovko

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

Cited by 3 | Viewed by 1344

Abstract

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

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

{}^{235}U

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

{}^{235}U

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

{}^{235}U

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

{}^{235}U

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

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

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28 pages, 10511 KB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 5 | Viewed by 3673

Abstract

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

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

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

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17 pages, 4470 KB

Open AccessEditor’s ChoiceArticle

Scene-Adaptive Loader Trajectory Planning and Tracking Control

by Yingnan Li, Wenwen Dong, Tianhao Zheng, Yakun Wang and Xuefei Li

Sensors 2025, 25(4), 1135; https://doi.org/10.3390/s25041135 - 13 Feb 2025

Cited by 8 | Viewed by 1599

Abstract

Wheel loaders play a crucial role in daily production and transportation. With the rapid development of intelligence in passenger vehicles, freeing loader operators from high-risk and repetitive tasks has become a pressing issue. This paper presents a novel and efficient path planning and [...] Read more.

Wheel loaders play a crucial role in daily production and transportation. With the rapid development of intelligence in passenger vehicles, freeing loader operators from high-risk and repetitive tasks has become a pressing issue. This paper presents a novel and efficient path planning and tracking framework tailored to the unique body structure and specific operating environment of loaders. We improve the Hybrid A* search algorithm based on the operational characteristics of loaders and integrate it with dynamically updated grid maps to enable the autonomous planning of loader operating paths in unstructured environments, meeting the efficiency requirements of production. Additionally, to address the challenge of poor trajectory tracking control accuracy caused by hydraulic articulated steering, we propose a new loader trajectory tracking controller based on the idea of hierarchical control. We use an extended state observer to compensate for unknown disturbances in the steering execution layer and employ fuzzy fractional-order PID to handle the nonlinearity of loaders. Field experiments using the proposed approach demonstrate that loaders can autonomously and in real-time complete tasks in dynamically changing operating scenarios. Full article

(This article belongs to the Section Physical Sensors)

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15 pages, 4481 KB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 9 | Viewed by 4352

Abstract

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

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

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

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44 pages, 9682 KB

Open AccessEditor’s ChoiceReview

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

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

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

Cited by 18 | Viewed by 10389

Abstract

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

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

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

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17 pages, 4402 KB

Open AccessEditor’s ChoiceArticle

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

by Colin Schaefer, Zeid Kootbally and Vinh Nguyen

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

Cited by 1 | Viewed by 1544

Abstract

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

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

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

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22 pages, 11164 KB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 21 | Viewed by 5850

Abstract

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

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

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

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16 pages, 3690 KB

Open AccessEditor’s ChoiceArticle

A Particle Swarm Optimization-Based Queue Scheduling and Optimization Mechanism for Large-Scale Low-Earth-Orbit Satellite Communication Networks

by Ziyong Zhang, Tao Dong, Jie Yin, Yue Xu, Zongyi Luo, Hao Jiang and Jing Wu

Sensors 2025, 25(4), 1069; https://doi.org/10.3390/s25041069 - 11 Feb 2025

Cited by 7 | Viewed by 1847

Abstract

The spatial topology of large-scale low-Earth-orbit satellite communication networks is dynamically time-variant, and the load on the output ports of network nodes is continuously changing. The lengths and numbers of output port queues at each network node can affect the packet loss rate [...] Read more.

The spatial topology of large-scale low-Earth-orbit satellite communication networks is dynamically time-variant, and the load on the output ports of network nodes is continuously changing. The lengths and numbers of output port queues at each network node can affect the packet loss rate and end-to-end latency of traffic flows. In order to provide high-quality satellite communication services, it is necessary to schedule and optimize the lengths and numbers of queues used for transmitting time-sensitive traffic flows at each node’s output port to achieve the best deterministic transmission performance. This paper introduces a queue scheduling optimization mechanism based on the Particle Swarm Optimization algorithm (PSO-QSO) for large-scale low-Earth-orbit satellite communication networks. This method analyzes the relevant parameters of various traffic flows transmitted through the network and calculates the maximum time-sensitive business load within network nodes. It applies the Particle Swarm Optimization algorithm to calculate the optimal solution for the length and number of queues at each node’s output port used for forwarding time-sensitive traffic flows. The mechanism proposed in this paper ensures the deterministic end-to-end transmission of time sensitive traffic in large-scale low-Earth-orbit satellite communication networks and can provide real-time satellite communication services. Full article

(This article belongs to the Special Issue 6G Space-Air-Ground Communication Networks and Key Technologies)

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22 pages, 5084 KB

Open AccessEditor’s ChoiceArticle

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

by Abdur R. Fayjie, Mathijs Lens and Patrick Vandewalle

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

Cited by 2 | Viewed by 3252

Abstract

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

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

(This article belongs to the Section Radar Sensors)

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27 pages, 17492 KB

Open AccessEditor’s ChoiceReview

Printed Two-Dimensional Materials for Flexible Photodetectors: Materials, Processes, and Applications

by Lingxian Kong, Shijie Wang, Qi Su, Zhiyong Liu, Guanglan Liao, Bo Sun and Tielin Shi

Sensors 2025, 25(4), 1042; https://doi.org/10.3390/s25041042 - 10 Feb 2025

Cited by 8 | Viewed by 3518

Abstract

With the rapid development of micro-nano technology and wearable devices, flexible photodetectors (PDs) have drawn widespread interest in areas such as healthcare, consumer electronics, and intelligence interfaces. Two-dimensional (2D) materials with layered structures have excellent optoelectronic properties and mechanical flexibility, which attract a [...] Read more.

With the rapid development of micro-nano technology and wearable devices, flexible photodetectors (PDs) have drawn widespread interest in areas such as healthcare, consumer electronics, and intelligence interfaces. Two-dimensional (2D) materials with layered structures have excellent optoelectronic properties and mechanical flexibility, which attract a great deal of attention in flexible applications. Although photodetectors based on mechanically exfoliated 2D materials have demonstrated superior performance compared to traditional Si-based PDs, large-scale manufacturing and flexible integration remain significant challenges for achieving industrial production. The emerging various printing technology provides a low-cost and highly effective method for integrated manufacturing. In this review, we comprehensively introduce the most recent progress on printed flexible 2D material PDs. We first reviewed the most recent research on flexible photodetectors, in which the discussion is focused on substrate materials, functional materials, and performance figures of merits. Furthermore, the solution processing for 2D materials coupled with printing functional film strategies to produce PDs are summarized. Subsequently, the various applications of flexible PDs, such as image sensors, healthcare, and wearable electronics, are also summarized. Finally, we point out the potential challenges of the printed flexible 2D material PDs and expect this work to inspire the development of flexible PDs and promote the mass manufacturing process. Full article

(This article belongs to the Special Issue Advances in Materials, Structures and Applications of Flexible Photodetectors)

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29 pages, 5837 KB

Open AccessEditor’s ChoiceArticle

Enhancing Clustering Efficiency in Heterogeneous Wireless Sensor Network Protocols Using the K-Nearest Neighbours Algorithm

by Abdulla Juwaied, Lidia Jackowska-Strumillo and Artur Sierszeń

Sensors 2025, 25(4), 1029; https://doi.org/10.3390/s25041029 - 9 Feb 2025

Cited by 11 | Viewed by 2951

Abstract

Wireless Sensor Networks are formed by tiny, self-contained, battery-powered computers with radio links that can sense their surroundings for events of interest and store and process the sensed data. Sensor nodes wirelessly communicate with each other to relay information to a central base [...] Read more.

Wireless Sensor Networks are formed by tiny, self-contained, battery-powered computers with radio links that can sense their surroundings for events of interest and store and process the sensed data. Sensor nodes wirelessly communicate with each other to relay information to a central base station. Energy consumption is the most critical parameter in Wireless Sensor Networks (WSNs). Network lifespan is directly influenced by the energy consumption of the sensor nodes. All sensors in the network send and receive data from the base station (BS) using different routing protocols and algorithms. These routing protocols use two main types of clustering: hierarchical clustering and flat clustering. Consequently, effective clustering within Wireless Sensor Network (WSN) protocols is essential for establishing secure connections among nodes, ensuring a stable network lifetime. This paper introduces a novel approach to improve energy efficiency, reduce the length of network connections, and increase network lifetime in heterogeneous Wireless Sensor Networks by employing the K-Nearest Neighbours (KNN) algorithm to optimise node selection and clustering mechanisms for four protocols: Low-Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP), Threshold-sensitive Energy Efficient sensor Network (TEEN), and Distributed Energy-efficient Clustering (DEC). Simulation results obtained using MATLAB (R2024b) demonstrate the efficacy of the proposed K-Nearest Neighbours algorithm, revealing that the modified protocols achieve shorter distances between cluster heads and nodes, reduced energy consumption, and improved network lifetime compared to the original protocols. The proposed KNN-based approach enhances the network’s operational efficiency and security, offering a robust solution for energy management in WSNs. Full article

(This article belongs to the Section Sensor Networks)

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16 pages, 4009 KB

Open AccessEditor’s ChoiceArticle

Curved Fabry-Pérot Ultrasound Detectors: Optical and Mechanical Analysis

by Barbara Rossi, Maria Alessandra Cutolo, Martino Giaquinto, Andrea Cusano and Giovanni Breglio

Sensors 2025, 25(4), 1014; https://doi.org/10.3390/s25041014 - 8 Feb 2025

Cited by 2 | Viewed by 2065

Abstract

Optical fiber-based acoustic detectors for ultrasound imaging in medical field feature plano-concave Fabry–Perot cavities integrated on fiber tips, realized via dip-coating. This technique imposes constraints on sensor geometry, potentially limiting performance. Lab-on-Fiber technology enables complex three-dimensional structures with precise control over geometric parameters, [...] Read more.

Optical fiber-based acoustic detectors for ultrasound imaging in medical field feature plano-concave Fabry–Perot cavities integrated on fiber tips, realized via dip-coating. This technique imposes constraints on sensor geometry, potentially limiting performance. Lab-on-Fiber technology enables complex three-dimensional structures with precise control over geometric parameters, such as the curvature radius. A careful investigation of the optical and mechanical aspects involved in the sensors’ performances is crucial for determining the design rules of such probes. In this study, we numerically analyzed the impact of curvature on the optical and acoustic properties of a plano-concave cavity using the Finite Element Method. Performance metrics, including sensitivity, bandwidth, and directivity, were compared to planar Fabry–Perot configurations. The results suggest that introducing curvature significantly enhances sensitivity by improving light confinement, especially for cavity thicknesses exceeding half the Rayleigh zone (∼45 μm), reaching an enhancement of 2.5 a L = 60 μm compared to planar designs. The curved structure maintains high spectral quality (FOM) despite 2% fabrication perturbations. A mechanical analysis confirms no disadvantages in acoustic response and bandwidth (∼40 MHz). These findings establish curved plano-concave structures as robust and reliable for high-sensitivity polymeric lab-on-fiber ultrasound detectors, offering improved performance and fabrication tolerance for MHz-scale bandwidth applications. Full article

(This article belongs to the Special Issue Feature Papers in Optical Sensors 2025)

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38 pages, 701 KB

Open AccessEditor’s ChoiceReview

Evolution of Bluetooth Technology: BLE in the IoT Ecosystem

by Grigorios Koulouras, Stylianos Katsoulis and Fotios Zantalis

Sensors 2025, 25(4), 996; https://doi.org/10.3390/s25040996 - 7 Feb 2025

Cited by 21 | Viewed by 14605

Abstract

The Internet of Things (IoT) has witnessed significant growth in recent years, with Bluetooth Low Energy (BLE) emerging as a key enabler of low-power, low-cost wireless connectivity. This review article provides an overview of the evolution of Bluetooth technology, focusing on the role [...] Read more.

The Internet of Things (IoT) has witnessed significant growth in recent years, with Bluetooth Low Energy (BLE) emerging as a key enabler of low-power, low-cost wireless connectivity. This review article provides an overview of the evolution of Bluetooth technology, focusing on the role of BLE in the IoT ecosystem. It examines the current state of BLE, including its applications, challenges, limitations, and recent advancements in areas such as security, power management, and mesh networking. The recent release of Bluetooth Low Energy version 6.0 by the Bluetooth Special Interest Group (SIG) highlights the technology’s ongoing evolution and growing importance within the IoT. However, this rapid development highlights a gap in the current literature, a lack of comprehensive, up-to-date reviews that fully capture the contemporary landscape of BLE in IoT applications. This paper analyzes the emerging trends and future directions for BLE, including the integration of artificial intelligence, machine learning, and audio capabilities. The analysis also considers the alignment of BLE features with the United Nations’ Sustainable Development Goals (SDGs), particularly energy efficiency, sustainable cities, and climate action. By examining the development and deployment of BLE technology, this article aims to provide insights into the opportunities and challenges associated with its adoption in various IoT applications, from smart homes and cities to industrial automation and healthcare. This review highlights the significance of the evolution of BLE in shaping the future of wireless communication and IoT, and provides a foundation for further research and innovation in this field. Full article

(This article belongs to the Special Issue Advances in Intelligent Sensors and IoT Solutions (2nd Edition))

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28 pages, 4405 KB

Open AccessEditor’s ChoiceArticle

Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models

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

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

Cited by 1 | Viewed by 3287

Abstract

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

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

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

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12 pages, 1901 KB

Open AccessEditor’s ChoiceArticle

Advancing Near-Infrared Probes for Enhanced Breast Cancer Assessment

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

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

Cited by 2 | Viewed by 2639

Abstract

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

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

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

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17 pages, 15387 KB

Open AccessEditor’s ChoiceArticle

Improving 3D Reconstruction Through RGB-D Sensor Noise Modeling

by Fahira Afzal Maken, Sundaram Muthu, Chuong Nguyen, Changming Sun, Jinguang Tong, Shan Wang, Russell Tsuchida, David Howard, Simon Dunstall and Lars Petersson

Sensors 2025, 25(3), 950; https://doi.org/10.3390/s25030950 - 5 Feb 2025

Cited by 5 | Viewed by 3753

Abstract

High-resolution RGB-D sensors are widely used in computer vision, manufacturing, and robotics. The depth maps from these sensors have inherently high measurement uncertainty that includes both systematic and non-systematic noise. These noisy depth estimates degrade the quality of scans, resulting in less accurate [...] Read more.

High-resolution RGB-D sensors are widely used in computer vision, manufacturing, and robotics. The depth maps from these sensors have inherently high measurement uncertainty that includes both systematic and non-systematic noise. These noisy depth estimates degrade the quality of scans, resulting in less accurate 3D reconstruction, making them unsuitable for some high-precision applications. In this paper, we focus on quantifying the uncertainty in the depth maps of high-resolution RGB-D sensors for the purpose of improving 3D reconstruction accuracy. To this end, we estimate the noise model for a recent high-precision RGB-D structured light sensor called Zivid when mounted on a robot arm. Our proposed noise model takes into account the measurement distance and angle between the sensor and the measured surface. We additionally analyze the effect of background light, exposure time, and the number of captures on the quality of the depth maps obtained. Our noise model seamlessly integrates with well-known classical and modern neural rendering-based algorithms, from KinectFusion to Point-SLAM methods using bilinear interpolation as well as 3D analytical functions. We collect a high-resolution RGB-D dataset and apply our noise model to improve tracking and produce higher-resolution 3D models. Full article

(This article belongs to the Special Issue Challenges and Future Trends of 3D Image Sensing, Visualization, and Processing)

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26 pages, 12669 KB

Open AccessEditor’s ChoiceReview

Recent Progress in Intrinsically Stretchable Sensors Based on Organic Field-Effect Transistors

by Mingxin Zhang, Mengfan Zhou, Jing Sun, Yanhong Tong, Xiaoli Zhao, Qingxin Tang and Yichun Liu

Sensors 2025, 25(3), 925; https://doi.org/10.3390/s25030925 - 4 Feb 2025

Cited by 4 | Viewed by 4633

Abstract

Organic field-effect transistors (OFETs) are an ideal platform for intrinsically stretchable sensors due to their diverse mechanisms and unique electrical signal amplification characteristics. The remarkable advantages of intrinsically stretchable sensors lie in their molecular tunability, lightweight design, mechanical robustness, solution processability, and low [...] Read more.

Organic field-effect transistors (OFETs) are an ideal platform for intrinsically stretchable sensors due to their diverse mechanisms and unique electrical signal amplification characteristics. The remarkable advantages of intrinsically stretchable sensors lie in their molecular tunability, lightweight design, mechanical robustness, solution processability, and low Young’s modulus, which enable them to seamlessly conform to three-dimensional curved surfaces while maintaining electrical performance under significant deformations. Intrinsically stretchable sensors have been widely applied in smart wearables, electronic skin, biological detection, and environmental protection. In this review, we summarize the recent progress in intrinsically stretchable sensors based on OFETs, including advancements in functional layer materials, sensing mechanisms, and applications such as gas sensors, strain sensors, stress sensors, proximity sensors, and temperature sensors. The conclusions and future outlook discuss the challenges and future outlook for stretchable OFET-based sensors. Full article

(This article belongs to the Special Issue Intelligent Biophotonic Sensing: AI-Enhanced OTFT-Based Technologies and Applications)

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15 pages, 10933 KB

Open AccessEditor’s ChoiceArticle

Timing-Optimised 3D Silicon Sensor with Columnar Electrode Geometry

by Angelo Loi, Adriano Lai, Jixing Ye and Gian-Franco Dalla Betta

Sensors 2025, 25(3), 926; https://doi.org/10.3390/s25030926 - 4 Feb 2025

Cited by 4 | Viewed by 1660

Abstract

Among various silicon sensor technologies, 3D silicon sensors demonstrate significant potential for applications requiring exceptional radiation hardness and intrinsic high time resolutions. Silicon pixel sensors with columnar-type electrodes are already operational within the ATLAS experiment, serving in the previous Inner B-Layer (IBL) and [...] Read more.

Among various silicon sensor technologies, 3D silicon sensors demonstrate significant potential for applications requiring exceptional radiation hardness and intrinsic high time resolutions. Silicon pixel sensors with columnar-type electrodes are already operational within the ATLAS experiment, serving in the previous Inner B-Layer (IBL) and the upcoming Inner Tracking (ITk) detectors. Concurrently, advancements driven by the next-generation LHCb VELO detector have led to the development of fast-timing 3D trench sensors within the INFN TimeSPOT project, achieving intrinsic time resolutions close to 10 ps. Remarkably, this performance is sustained even under irradiation levels far exceeding the expected limits for High Luminosity LHC operations. Despite these advantages, 3D trench sensors face challenges related to fabrication, as their production yields remain lower than those of the well-established columnar-type sensors. This highlights the necessity of designing a timing-optimized 3D sensor that leverages the robustness of a columnar electrode fabrication while achieving an intrinsic time resolution as close as possible to the trench-based designs. The design study addressed in this paper aimed to computationally compare the already designed and characterised TimeSPOT 3D trench sensor with alternative columnar electrode-based geometries, focusing particularly on configurations that approximate trench electrodes using parallel-oriented columnar designs. Different geometries and pixel sizes were designed, simulated, and compared. This work presents the entire design and selection effort as well as the preliminary layout of the selected pixel geometries, which are set to feature in FBK’s upcoming production run in 2025. Full article

(This article belongs to the Section Sensors Development)

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32 pages, 3991 KB

Open AccessEditor’s ChoiceReview

Artificial Intelligence in IR Thermal Imaging and Sensing for Medical Applications

by Antoni Z. Nowakowski and Mariusz Kaczmarek

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

Cited by 18 | Viewed by 11347

Abstract

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

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

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

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