Sensors

20 pages, 6862 KiB

Open AccessArticle

Cross-Shaped Heat Tensor Network for Morphometric Analysis Using Zebrafish Larvae Feature Keypoints

by Xin Chai, Tan Sun, Zhaoxin Li, Yanqi Zhang, Qixin Sun, Ning Zhang, Jing Qiu and Xiujuan Chai

Sensors 2025, 25(1), 132; https://doi.org/10.3390/s25010132 (registering DOI) - 28 Dec 2024

Abstract

Deep learning-based morphometric analysis of zebrafish is widely utilized for non-destructively identifying abnormalities and diagnosing diseases. However, obtaining discriminative and continuous organ category decision boundaries poses a significant challenge by directly observing zebrafish larvae from the outside. To address this issue, this study [...] Read more.

Deep learning-based morphometric analysis of zebrafish is widely utilized for non-destructively identifying abnormalities and diagnosing diseases. However, obtaining discriminative and continuous organ category decision boundaries poses a significant challenge by directly observing zebrafish larvae from the outside. To address this issue, this study simplifies the organ areas to polygons and focuses solely on the endpoint positioning. Specifically, we introduce a deep learning-based feature endpoint detection method for quantitatively determining zebrafish larvae’s essential phenotype and organ features. We propose the cross-shaped heat tensor network (CSHT-Net), a feature point detection framework consisting of a novel keypoint training method named cross-shaped heat tensor and a feature extractor called combinatorial convolutional block. Our model alleviates the problem of the heatmap-based method that restricts attention to local regions around key points while enhancing the model’s ability to learn continuous, strip-like features. Moreover, we compiled a dataset of 4389 bright-field micrographs of zebrafish larvae at 120 h post-fertilization for the model training and algorithm evaluation of zebrafish phenotypic traits. The proposed framework achieves an average precision (AP) of 83.2% and an average recall (AR) of 85.8%, outperforming multiple widely adopted keypoint detection techniques. This approach enables robust phenotype extraction and reliable morphometric analysis for zebrafish larvae, fostering efficient hazard identification for chemicals and medical products. Full article

(This article belongs to the Section Intelligent Sensors)

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14 pages, 10328 KiB

Open AccessArticle

High-Impact Polystyrene Structured Light Components for Terahertz Imaging Applications

by Kasparas Stanaitis, Vladislovas Čižas, Augustė Bielevičiūtė, Ignas Grigelionis and Linas Minkevičius

Sensors 2025, 25(1), 131; https://doi.org/10.3390/s25010131 (registering DOI) - 28 Dec 2024

Abstract

Terahertz frequency range imaging has become more and more attractive for a wide range of practical applications; however, further component optimization is still required. The presented research introduces 3D-printed high-impact polystyrene (HIPS) beam-shaping components for the terahertz range. Gaussian, Bessel, and Airy beam-shaping [...] Read more.

Terahertz frequency range imaging has become more and more attractive for a wide range of practical applications; however, further component optimization is still required. The presented research introduces 3D-printed high-impact polystyrene (HIPS) beam-shaping components for the terahertz range. Gaussian, Bessel, and Airy beam-shaping structures are fabricated, and different combinations are employed to evaluate imaging system performance. The combination of the Gaussian element as focusing and the Bessel element as collecting is revealed to be similarly efficient and less sensitive to misalignment than the classical Gaussian–Gaussian element setup. The presented research paves the way for introducing cost-effective structured light beam-shaping elements into THz imaging systems. Full article

(This article belongs to the Special Issue Feature Papers in Sensing and Imaging 2024)

23 pages, 3243 KiB

Open AccessArticle

A Modular AI-Driven Intrusion Detection System for Network Traffic Monitoring in Industry 4.0, Using Nvidia Morpheus and Generative Adversarial Networks

by Beatrice-Nicoleta Chiriac, Florin-Daniel Anton, Anca-Daniela Ioniță and Bogdan-Valentin Vasilică

Sensors 2025, 25(1), 130; https://doi.org/10.3390/s25010130 (registering DOI) - 28 Dec 2024

Abstract

Every day, a considerable number of new cybersecurity attacks are reported, and the traditional methods of defense struggle to keep up with them. In the current context of the digital era, where industrial environments handle large data volumes, new cybersecurity solutions are required, [...] Read more.

Every day, a considerable number of new cybersecurity attacks are reported, and the traditional methods of defense struggle to keep up with them. In the current context of the digital era, where industrial environments handle large data volumes, new cybersecurity solutions are required, and intrusion detection systems (IDSs) based on artificial intelligence (AI) algorithms are coming up with an answer to this critical issue. This paper presents an approach for implementing a generic model of a network-based intrusion detection system for Industry 4.0 by integrating the computational advantages of the Nvidia Morpheus open-source AI framework. The solution is modularly built with two pipelines for data analysis. The pipelines use a pre-trained XGBoost (eXtreme Gradient Boosting) model that achieved an accuracy score of up to 90%. The proposed IDS has a fast rate of analysis, managing more than 500,000 inputs in almost 10 s, due to the application of the federated learning methodology. The classification performance of the model was improved by integrating a generative adversarial network (GAN) that generates polymorphic network traffic packets. Full article

(This article belongs to the Special Issue Data Protection and Privacy in Industry 4.0 Era)

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21 pages, 10689 KiB

Open AccessArticle

Human Occupancy Monitoring and Positioning with Speed-Responsive Adaptive Sliding Window Using an Infrared Thermal Array Sensor

by Yukai Lin and Qiangfu Zhao

Sensors 2025, 25(1), 129; https://doi.org/10.3390/s25010129 (registering DOI) - 28 Dec 2024

Abstract

In the current era of advanced IoT technology, human occupancy monitoring and positioning technology is widely used in various scenarios. For example, it can optimize passenger flow in public transportation systems, enhance safety in large shopping malls, and adjust smart home devices based [...] Read more.

In the current era of advanced IoT technology, human occupancy monitoring and positioning technology is widely used in various scenarios. For example, it can optimize passenger flow in public transportation systems, enhance safety in large shopping malls, and adjust smart home devices based on the location and number of occupants for energy savings. Additionally, in homes requiring special care, it can provide timely assistance. However, this technology faces limitations such as privacy concerns, environmental factors, and costs. Traditional cameras may not effectively address these issues, but infrared thermal sensors can offer similar applications while overcoming these challenges. Infrared thermal sensors detect the infrared heat emitted by the human body, protecting privacy and functioning effectively day and night with low power consumption, making them ideal for continuous monitoring scenarios like security systems or elderly care. In this study, we propose a system using the AMG8833, an 8 × 8 Infrared Thermal Array Sensor. The sensor data are processed through interpolation, adaptive thresholding, and blob detection, and the merged human heat signatures are separated. To enhance stability in human position estimation, a dynamic sliding window adjusts its size based on movement speed, effectively handling environmental changes and uncertainties. Full article

(This article belongs to the Special Issue Indoor Positioning Technologies for Internet-of-Things)

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28 pages, 2810 KiB

Open AccessArticle

Physics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral Sensing

by Po-Tong Wang, Chiu Wang Tseng and Li-Der Fang

Sensors 2025, 25(1), 128; https://doi.org/10.3390/s25010128 (registering DOI) - 28 Dec 2024

Abstract

The proliferation of sophisticated counterfeiting poses critical challenges to global security and commerce, with annual losses exceeding $2.2 trillion. This paper presents a novel physics-constrained deep learning framework for high-precision security ink colorimetry, integrating three key innovations: a physics-informed neural architecture achieving unprecedented [...] Read more.

The proliferation of sophisticated counterfeiting poses critical challenges to global security and commerce, with annual losses exceeding $2.2 trillion. This paper presents a novel physics-constrained deep learning framework for high-precision security ink colorimetry, integrating three key innovations: a physics-informed neural architecture achieving unprecedented color prediction accuracy (CIEDE2000(

Δ E_{00}

): 0.70 ± 0.08, p < 0.001), advanced attention mechanisms improving feature extraction efficiency by 58.3%, and a Bayesian optimization framework ensuring robust parameter tuning. Validated across 1500 industrial samples under varying conditions (±2 °C, 30–80% RH), this system demonstrates substantial improvements in production efficiency with a 50% reduction in rejections, a 35% decrease in calibration time, and 96.7% color gamut coverage. These achievements establish new benchmarks for security printing applications and provide scalable solutions for next-generation anti-counterfeiting technologies, offering a promising outlook for the future. Full article

(This article belongs to the Special Issue Signal Processing and Machine Learning for Sensor Systems)

17 pages, 2320 KiB

Open AccessArticle

Accuracy of Rhythm Diagnostic Systems’ MultiSense^® in Detection of Arterial Oxygen Saturation and Respiratory Rate During Hypoxia in Humans: Effects of Skin Color and Device Localization

by Charles Evrard, Amina El Attaoui, Cristina Pistea, Irina Enache, Mark Marriott, Louis Mayaud, Anne Charloux and Bernard Geny

Sensors 2025, 25(1), 127; https://doi.org/10.3390/s25010127 (registering DOI) - 28 Dec 2024

Abstract

The continuous monitoring of oxygen saturation (SpO₂) and respiratory rates (RRs) are major clinical issues in many cardio-respiratory diseases and have been of tremendous importance during the COVID-19 pandemic. The early detection of hypoxemia was crucial since it precedes significant complications, [...] Read more.

The continuous monitoring of oxygen saturation (SpO₂) and respiratory rates (RRs) are major clinical issues in many cardio-respiratory diseases and have been of tremendous importance during the COVID-19 pandemic. The early detection of hypoxemia was crucial since it precedes significant complications, and SpO₂ follow-up allowed early hospital discharge in patients needing oxygen therapy. Nevertheless, fingertip devices showed some practical limitations. In this study, we investigated the reliability of the new Multisense^® pulse oximetry system compared to a reference pulse oximeter (Vyntus CPX Pulse Oximeter) during hypoxia. In a population of sixteen healthy male subjects (mean age: 31.5 ± 7.0 years, BMI: 24.9 ± 3.6 kg/m², and 35% with darker skin tones), simultaneous SpO₂ and RR measurements were collected over 12.4 h, during which FiO₂ was progressively reduced from 21% to 10.5%. The average root mean square error (ARMS) of SpO₂ for Multisense^® placed on the back and chest was 2.94% and 2.98%, respectively, with permutation testing confirming a significant ARMS below 3.5% for both positions and no statistically significant difference in the ARMS between patch placements. Positive correlations and acceptable accuracy between devices were observed at both locations (r = 0.92, p < 0.001 and r = 0.90, p < 0.001 for back and chest placements, respectively). Bland–Altman analysis further indicated limits of agreement that support consistency across placements, with similar agreement levels noted across skin tones. Similar findings were obtained with the RR measurements. In conclusion, Multisense^® demonstrated robust accuracy in measuring SpO₂ and RRs during hypoxia in humans comparable to standard hospital-grade equipment. The effectiveness of the findings suggests that this wearable device is a valuable tool for the continuous monitoring of SpO₂ and RRs, potentially enhancing patient safety and optimizing hospital resource allocation. Nevertheless, to overcome study limitations and allow generalized use, further work on a larger population sample, including more subjects with a high phototype and desaturation below 80%, would be useful. Full article

(This article belongs to the Special Issue Technologies Related to Oxygen Supply to Tissue: Measurements of Oxygen Saturation, Tissue Blood Flow and Arterial Blood Pressure)

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20 pages, 3387 KiB

Open AccessArticle

Vibration Serviceability Assessment of Floor Structures: Simulation of Human–Structure–Environment Interactions Using Agent-Based Modeling

by Erfan Shahabpoor, Bernard Berari and Aleksandar Pavic

Sensors 2025, 25(1), 126; https://doi.org/10.3390/s25010126 (registering DOI) - 28 Dec 2024

Abstract

A rapidly growing body of experimental evidence in the literature shows that the effects of humans interacting with vibrating structures, other humans, and their surrounding environment can be critical for reliable estimation of structural vibrations. The Interaction-based Vibration Serviceability Assessment framework (I-VSA) was [...] Read more.

A rapidly growing body of experimental evidence in the literature shows that the effects of humans interacting with vibrating structures, other humans, and their surrounding environment can be critical for reliable estimation of structural vibrations. The Interaction-based Vibration Serviceability Assessment framework (I-VSA) was proposed by the authors in 2017 to address this, taking into account human-structure dynamic interactions (HSI) to simulate the structural vibrations experienced by each occupant/pedestrian. The I-VSA method, however, had limited provisions to simulate simultaneously multiple modes of structure in HSI, to simulate human-human and human-environment interactions, and the movement pattern of the occupants/pedestrians. This study proposes a new Agent-based Vibration Serviceability Assessment framework, termed AVSA, to address the following limitations: (a) allowing for multiple modes of structure to be simulated simultaneously, (b) to simulate effects of vibrations on gait parameters and walking pattern/routes, and (c) to simulate human-environment interactions, and movement patterns for any desired interior layout and use case. The AVSA framework was used to simulate the response and to assess the vibration serviceability of a lightweight floor under a combination of sitting and walking traffic, where three vertical modes of vibrations were engaged simultaneously. The results of the simulations show that for all tests, the experimental Cumulative Distribution Functions of the vibrations experienced by the participants are within the 95% confidence interval predicted by the AVSA method. The proposed method provides a generic and flexible framework to simulate simultaneously different interaction modalities, different human tasks and postures, and multiple modes of structure and the human body. Full article

(This article belongs to the Section Physical Sensors)

10 pages, 1466 KiB

Open AccessArticle

Assessing Changes in Reaction Time Following RAMP Warm-Up and Short-Term Repeated Volleyball Specific Exercise in Young Players

by Kacper Cieśluk, Dorota Sadowska and Justyna Krzepota

Sensors 2025, 25(1), 125; https://doi.org/10.3390/s25010125 (registering DOI) - 28 Dec 2024

Abstract

The assessment of the various skills of athletes is carried out in terms of their ability to perform sport-specific tasks. The cognitive abilities of the players have significance for their effectiveness. In volleyball, a player’s ability to react quickly appears to be crucial [...] Read more.

The assessment of the various skills of athletes is carried out in terms of their ability to perform sport-specific tasks. The cognitive abilities of the players have significance for their effectiveness. In volleyball, a player’s ability to react quickly appears to be crucial in responding to an opponent’s dynamic play. The aim of the study was to evaluate the changes in reaction time to light signals following warm-up and physical exercise. Eighteen volleyball players (15.58 ± 2.01 years) participated in the study. Four FITLIGHT Trainer^TM discs were placed on the wall facing the participant to conduct the reaction time test. The participant’s task was to react as quickly as possible to the illuminated disc by touching it with the palm of their hand. The procedure was repeated five times: before the warm-up, after the warm-up, and after each of the three agility tests. Friedman’s ANOVA showed statistically significant differences in reaction time between the tests (Chi² ANOVA = 61.23; p < 0.001). All tests performed after the warm-up according to the RAMP protocol showed statistically significantly better results than those before the warm-up (p ≤ 0.05). At the same time, no differences were observed between the tests performed after successive agility tests. The results indicated that a well-chosen warm-up plays an important role in shortening the time of visual-motor reaction to a light stimulus (RT). Subsequent studies should be expanded to include other research groups and assess other parameters. Full article

(This article belongs to the Special Issue Sensor Techniques and Methods for Sports Science)

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11 pages, 926 KiB

Open AccessArticle

Refinement of an Algorithm to Detect and Predict Freezing of Gait in Parkinson Disease Using Wearable Sensors

by Allison M. Haussler, Lauren E. Tueth, David S. May, Gammon M. Earhart and Pietro Mazzoni

Sensors 2025, 25(1), 124; https://doi.org/10.3390/s25010124 (registering DOI) - 28 Dec 2024

Abstract

Freezing of gait (FOG) is a debilitating symptom of Parkinson disease (PD). It is episodic and variable in nature, making assessment difficult. Wearable sensors used in conjunction with specialized algorithms, such as our group’s pFOG algorithm, provide objective data to better understand this [...] Read more.

Freezing of gait (FOG) is a debilitating symptom of Parkinson disease (PD). It is episodic and variable in nature, making assessment difficult. Wearable sensors used in conjunction with specialized algorithms, such as our group’s pFOG algorithm, provide objective data to better understand this phenomenon. While these methods are effective at detecting FOG retrospectively, more work is needed. The purpose of this paper is to explore how the existing pFOG algorithm can be refined to improve the detection and prediction of FOG. To accomplish this goal, previously collected data were utilized to assess the prediction ability of the current algorithm, the potency of each FOG assessment task(s) for eliciting FOG, and the maintenance of detection accuracy when modifying the sampling rate. Results illustrate that the algorithm was able to predict upcoming FOG episodes, but false positive rates were high. The Go Out and Turn-Dual Task was most potent for eliciting FOG, and the 360-Dual Task elicited the longest duration of FOG. The detection accuracy of the pFOG algorithm was maintained at a sampling rate of 60 Hz but significantly worse at 30 Hz. This work is an important step in refining the pFOG algorithm for improved clinical utility. Full article

(This article belongs to the Special Issue Sensors for Human Movement Recognition and Analysis)

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10 pages, 4282 KiB

Open AccessArticle

An Ultrasound Prototype for Remote Hand Movement Sensing: The Finger Tapping Case

by Stefano Franceschini, Maria Maddalena Autorino, Michele Ambrosanio, Vito Pascazio and Fabio Baselice

Sensors 2025, 25(1), 123; https://doi.org/10.3390/s25010123 (registering DOI) - 28 Dec 2024

Abstract

In the context of neurodegenerative diseases, finger tapping is a gold-standard test used by clinicians to evaluate the severity of the condition. The finger tapping test involves repetitive tapping between the index finger and thumb. Subjects affected by neurodegenerative diseases, such as Parkinson’s [...] Read more.

In the context of neurodegenerative diseases, finger tapping is a gold-standard test used by clinicians to evaluate the severity of the condition. The finger tapping test involves repetitive tapping between the index finger and thumb. Subjects affected by neurodegenerative diseases, such as Parkinson’s disease, often exhibit symptoms like bradykinesia, rigidity, and tremor. As a result, when these individuals perform the finger tapping task, instability in both the tap rate and finger displacement can be observed. Currently, clinicians assess bradykinesia by visually observing the patient’s finger tapping movements and qualitatively rating their severity. In this work, we present a novel ultrasound contactless system that provides quantitative measurements of finger tapping, including tap rate and finger displacements. The system functions as an ultrasound sonar capable of measuring the Doppler spectrum of waves reflected by the hand. From this spectrum, various characteristics of the hand movement can be extracted through appropriate processing techniques. Specifically, by performing time–frequency analysis and applying specialized data processing, tapping rates and finger displacements can be estimated. The system has been tested in real-world scenarios involving volunteer finger tapping sessions, demonstrating its potential for accurately measuring both tap rates and displacements. Full article

(This article belongs to the Special Issue Biomedical Sensors: New Technologies, Integration and Signal Processing)

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18 pages, 4506 KiB

Open AccessArticle

Three-Dimensionally Printed Mini Air Scrubbing Cartridges Based on Nano-Graphite for Air Pollution Monitoring

by Emiliano Zampetti, Mattia Ammiraglia, Marco Conti, Cassandra Montiroli, Paolo Papa, Daniele Bianconi and Antonella Macagnano

Sensors 2025, 25(1), 122; https://doi.org/10.3390/s25010122 (registering DOI) - 28 Dec 2024

Abstract

Ecosystems and environments are impacted by atmospheric pollution, which has significant effects on human health and climate. For these reasons, devices for developing portable and low-cost monitoring systems are required to assess human exposure during daily life. In the last decade, the advancements [...] Read more.

Ecosystems and environments are impacted by atmospheric pollution, which has significant effects on human health and climate. For these reasons, devices for developing portable and low-cost monitoring systems are required to assess human exposure during daily life. In the last decade, the advancements of 3D printing technology have pushed researchers to exploit, in different fields of applications, the advantages offered, such as rapid prototyping and low-cost replication of complex sample treatment devices. In this work, we present the fabrication and testing of 3D printed cartridges based on both commercial photopolymer and a modified version with the intrusion of nano graphite. The air scrubbing performances towards some volatile organic compounds have been investigated, inserting the cartridges into a low-cost monitoring system using a photoionization sensor. In particular, the cartridges were tested in the presence of concentrations of ethanol, benzene, and toluene to evaluate the abatement percentage with and without their use. Although the results have shown that all cartridges abated ethanol and toluene, the abatement of benzene increased 20 times in the case of cartridges based on modified resin with nano graphite. These results could enable their employment to reduce the concentration of interfering compounds in low-cost monitoring systems. Full article

(This article belongs to the Special Issue Sensors and Sensor Systems for Atmospheric and Environmental Pollution Monitoring)

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14 pages, 1529 KiB

Open AccessArticle

Luminescence Lifetime-Based Water Conductivity Sensing Using a Cationic Dextran-Supported Ru(II) Polypyridyl Complex

by Ya Jie Knöbl, Lauren M. Johnston, José Quílez-Alburquerque and Guillermo Orellana

Sensors 2025, 25(1), 121; https://doi.org/10.3390/s25010121 (registering DOI) - 28 Dec 2024

Abstract

Water conductivity sensing relies universally on electrical measurements, which are subject to corrosion of the electrodes and subsequent signal drift in prolonged in situ uses. Furthermore, they cannot provide contactless sensing or remote readout. To this end, a novel device for water conductivity [...] Read more.

Water conductivity sensing relies universally on electrical measurements, which are subject to corrosion of the electrodes and subsequent signal drift in prolonged in situ uses. Furthermore, they cannot provide contactless sensing or remote readout. To this end, a novel device for water conductivity monitoring has been developed by employing a microenvironment-sensitive ruthenium complex, [Ru(2,2′-bipyridine-4,4′-disulfonato)₃]⁴⁻, embedded into a quaternary ammonium functionalized cross-linked polymer support. The degree of swelling of the latter, which leads to a change in the emission lifetime, depends on the water conductivity. The sensor displays a reversible response (2 min ≤ t₉₀ ≤ 3 min) and has been shown to be stable for >65 h of continuous monitoring of 0.8–12.8 mS cm⁻¹ KCl solutions. Changes to the cation do not affect the sensor response, while changes to the anion type induce small effects. Variations in the dissolved O₂ or temperature require corrections of the response. The sensor can be interrogated alongside dissolved O₂ and pH luminescent sensors based on the same family of indicator dyes to exploit the definite advantages of luminescence lifetime-based detection. Full article

(This article belongs to the Special Issue Emerging Trends in Optical Sensing: Advanced Materials for Enhanced Light-Matter Interactions)

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24 pages, 6974 KiB

Open AccessArticle

Performance Improvement with Reduced Number of Channels in Motor Imagery BCI System

by Ali Özkahraman, Tamer Ölmez and Zümray Dokur

Sensors 2025, 25(1), 120; https://doi.org/10.3390/s25010120 (registering DOI) - 28 Dec 2024

Abstract

Classifying Motor Imaging (MI) Electroencephalogram (EEG) signals is of vital importance for Brain–Computer Interface (BCI) systems, but challenges remain. A key challenge is to reduce the number of channels to improve flexibility, portability, and computational efficiency, especially in multi-class scenarios where more channels [...] Read more.

Classifying Motor Imaging (MI) Electroencephalogram (EEG) signals is of vital importance for Brain–Computer Interface (BCI) systems, but challenges remain. A key challenge is to reduce the number of channels to improve flexibility, portability, and computational efficiency, especially in multi-class scenarios where more channels are needed for accurate classification. This study demonstrates that combining Electrooculogram (EOG) channels with a reduced set of EEG channels is more effective than relying on a large number of EEG channels alone. EOG channels provide useful information for MI signal classification, countering the notion that they only introduce eye-related noise. The study uses advanced deep learning techniques, including multiple 1D convolution blocks and depthwise-separable convolutions, to optimize classification accuracy. The findings in this study are tested on two datasets: dataset 1, the BCI Competition IV Dataset IIa (4-class MI), and dataset 2, the Weibo dataset (7-class MI). The performance for dataset 1, utilizing 3 EEG and 3 EOG channels (6 channels total), is of 83% accuracy, while dataset 2, with 3 EEG and 2 EOG channels (5 channels total), achieves an accuracy of 61%, demonstrating the effectiveness of the proposed channel reduction method and deep learning model. Full article

(This article belongs to the Section Biomedical Sensors)

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17 pages, 4791 KiB

Open AccessArticle

Photoreconfigurable Metasurface for Independent Full-Space Control of Terahertz Waves

by Zhengxuan Jiang, Guowen Ding, Xinyao Luo and Shenyun Wang

Sensors 2025, 25(1), 119; https://doi.org/10.3390/s25010119 (registering DOI) - 27 Dec 2024

Abstract

We present a novel photoreconfigurable metasurface designed for independent and efficient control of electromagnetic waves with identical incident polarization and frequency across the entire spatial domain. The proposed metasurface features a three-layer architecture: a top layer incorporating a gold circular split ring resonator [...] Read more.

We present a novel photoreconfigurable metasurface designed for independent and efficient control of electromagnetic waves with identical incident polarization and frequency across the entire spatial domain. The proposed metasurface features a three-layer architecture: a top layer incorporating a gold circular split ring resonator (CSRR) filled with perovskite material and dual C-shaped perovskite resonators; a middle layer of polyimide dielectric; and a bottom layer comprising a perovskite substrate with an oppositely oriented circular split ring resonator filled with gold. By modulating the intensity of a laser beam, we achieve autonomous manipulation of incident circularly polarized terahertz waves in both transmission and reflection modes. Simulation results demonstrate that the metasurface achieves a cross-polarized transmission coefficient of 0.82 without laser illumination and a co-polarization reflection coefficient of 0.8 under laser illumination. Leveraging the geometric phase principle, adjustments to the rotational orientation of the reverse split ring and dual C-shaped perovskite structures enable independent control of transmission and reflection phases. Furthermore, the proposed metasurface induces a +1 order orbital angular momentum in transmission and +2 order in reflection, facilitating beam deflection through metasurface convolution principles. Imaging using metasurface digital imaging technology showcases patterns “NUIST” in reflection and “LOONG” in transmission, illustrating the metasurface design principles via the proposed metasurface. The proposed metasurface’s capability for full-space control and reconfigurability presents promising applications in advanced imaging systems, dynamic beam steering, and tunable terahertz devices, highlighting its potential for future technological advancements. Full article

(This article belongs to the Special Issue Antennas, Metamaterials and Metasurfaces for Advanced Sensing Applications)

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11 pages, 848 KiB

Open AccessStudy Protocol

Do PROMs or Sensor-Based Monitoring Detect Improvements in Patients’ Knee Function After Total-Knee Arthroplasty?—A Study Protocol for a Prospective Controlled Study

by Lotanna Mba, Robert Prill, Jonathan Lettner, Nikolai Ramadanov, Robert Krause, Jan Reichmann and Roland Becker

Sensors 2025, 25(1), 118; https://doi.org/10.3390/s25010118 (registering DOI) - 27 Dec 2024

Abstract

Determining whether preoperative performance-based knee function predicts postoperative performance-based knee function and whether patient-reported outcome measures (PROMs) completed by participants can detect these changes could significantly enhance the planning of postoperative rehabilitation for patients following total knee arthroplasty (TKA). This study aims to [...] Read more.

Determining whether preoperative performance-based knee function predicts postoperative performance-based knee function and whether patient-reported outcome measures (PROMs) completed by participants can detect these changes could significantly enhance the planning of postoperative rehabilitation for patients following total knee arthroplasty (TKA). This study aims to collect data on performance-based knee function using inertial measurement units (IMUs) worn by participants both preoperatively and postoperatively. PROMs will be completed by the patients before and after surgery to assess their ability to detect the same changes in performance-based knee function measured by the sensors. Additionally, the study will investigate the correlation between the degree of knee alignment correction and postoperative performance-based knee function in participants after TKA. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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20 pages, 5338 KiB

Open AccessArticle

Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control

by Rodrigo Gutiérrez-Moreno, Rafael Barea, Elena López-Guillén, Felipe Arango, Fabio Sánchez-García and Luis M. Bergasa

Sensors 2025, 25(1), 117; https://doi.org/10.3390/s25010117 (registering DOI) - 27 Dec 2024

Abstract

The use of Deep Learning algorithms in the domain of Decision Making for Autonomous Vehicles has garnered significant attention in the literature in recent years, showcasing considerable potential. Nevertheless, most of the solutions proposed by the scientific community encounter difficulties in real-world applications. [...] Read more.

The use of Deep Learning algorithms in the domain of Decision Making for Autonomous Vehicles has garnered significant attention in the literature in recent years, showcasing considerable potential. Nevertheless, most of the solutions proposed by the scientific community encounter difficulties in real-world applications. This paper aims to provide a realistic implementation of a hybrid Decision Making module in an Autonomous Driving stack, integrating the learning capabilities from the experience of Deep Reinforcement Learning algorithms and the reliability of classical methodologies. Our Decision Making system is in charge of generating steering and velocity signals using the HD map information and sensors pre-processed data. This work encompasses the implementation of concatenated scenarios in simulated environments, and the integration of Autonomous Driving modules. Specifically, the authors address the Decision Making problem by employing a Partially Observable Markov Decision Process formulation and offer a solution through the use of Deep Reinforcement Learning algorithms. Furthermore, an additional control module to execute the decisions in a safe and comfortable way through a hybrid architecture is presented. The proposed architecture is validated in the CARLA simulator by navigating through multiple concatenated scenarios, outperforming the CARLA Autopilot in terms of completion time, while ensuring both safety and comfort. Full article

(This article belongs to the Special Issue Advances in Sensing, Imaging and Computing for Autonomous Driving)

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17 pages, 3187 KiB

Open AccessCommunication

Low-Coherence Integrated Optical Interferometer for Fibre Optic Sensors

by Petr Volkov, Alexander Bobrov, Oleg Vyazankin, Alexey Gorshkov, Alexander Goryunov, Glafira Lemeshevskaya, Andrey Lukyanov, Aleksey Nezhdanov, Daniil Semikov and Kirill Sidorenko

Sensors 2025, 25(1), 116; https://doi.org/10.3390/s25010116 (registering DOI) - 27 Dec 2024

Abstract

This paper proposes and implements a novel scheme for recording signals from fibre optic sensors based on tandem low-coherence interferometry with an integrated optical reference interferometer. The circuit allows precision control of the phase shift. Additionally, the paper illustrates the potential for detecting [...] Read more.

This paper proposes and implements a novel scheme for recording signals from fibre optic sensors based on tandem low-coherence interferometry with an integrated optical reference interferometer. The circuit allows precision control of the phase shift. Additionally, the paper illustrates the potential for detecting vibration and object deformation using fibre optic Fabry–Perot sensors connected to the registration system. Full article

(This article belongs to the Section Optical Sensors)

19 pages, 1203 KiB

Open AccessArticle

Resilient Privacy Preservation Through a Presumed Secrecy Mechanism for Mobility and Localization in Intelligent Transportation Systems

by Meshari D. Alanazi, Mohammed Albekairi, Ghulam Abbas, Turki M. Alanazi, Khaled Kaaniche, Gehan Elsayed and Paolo Mercorelli

Sensors 2025, 25(1), 115; https://doi.org/10.3390/s25010115 (registering DOI) - 27 Dec 2024

Abstract

An intelligent transportation system (ITS) offers commercial and personal movement through the smart city (SC) communication paradigms with hassle-free information sharing. ITS designs and architectures have improved via information and communication technologies in recent years. The information shared through the communication medium in [...] Read more.

An intelligent transportation system (ITS) offers commercial and personal movement through the smart city (SC) communication paradigms with hassle-free information sharing. ITS designs and architectures have improved via information and communication technologies in recent years. The information shared through the communication medium in SCs is exposed to adversary risk, resulting in privacy issues. Privacy issues impact the contingent mobility and localization of the ITS path. This paper introduces a novel resilient privacy preserving (RPP) method through presumed secrecy (PS) to provide a robust privacy measure. The privacy of the progressive communication sessions is preserved based on the previous security depletion levels. The interruptions in traffic data-related communication sessions are recurrently identified, and re-handoffs are recommended with dodged transfer learning. The empirical results indicate a 25% reduction in computational overhead and a 30% enhancement in privacy protection over conventional methods, demonstrating the model’s efficacy in secure ITS communication. Compared with existing methods, the proposed approach decreases security depletion rates by 15% across varying traffic densities, underscoring ITS resilience in high-interaction scenarios. Full article

(This article belongs to the Section Intelligent Sensors)

14 pages, 1701 KiB

Open AccessArticle

Room Temperature NO₂-Sensing Properties of N-Doped ZnO Nanoparticles Activated by UV-Vis Light

by Angelo Ferlazzo, Giovanni Neri, Andrea Donato, Giovanni Gugliandolo and Mariangela Latino

Sensors 2025, 25(1), 114; https://doi.org/10.3390/s25010114 (registering DOI) - 27 Dec 2024

Abstract

Zinc oxide nanoparticles (ZnO NPs) with varying levels of nitrogen (N) doping were synthesized using a straightforward sol–gel approach. The morphology and microstructure of the N-doped ZnO NPs were examined through techniques such as SEM, XRD, photoluminescence, and Raman spectroscopy. The characterization revealed [...] Read more.

Zinc oxide nanoparticles (ZnO NPs) with varying levels of nitrogen (N) doping were synthesized using a straightforward sol–gel approach. The morphology and microstructure of the N-doped ZnO NPs were examined through techniques such as SEM, XRD, photoluminescence, and Raman spectroscopy. The characterization revealed visible changes in the morphology and microstructure resulting from the incorporation of nitrogen into the ZnO lattice. These N-doped ZnO NPs were used in the fabrication of conductometric gas sensors designed to operate at room temperature (RT) for detecting low concentrations of NO₂ in the air, under LED UV-Vis irradiation (λ = 400 nm). The influence of nitrogen doping on sensor performance was systematically studied. The findings indicate that N-doping effectively enhances ZnO-based sensors’ ability to detect NO₂ at RT, achieving a notable response (S = R/R₀) of approximately 18 when exposed to 5 ppm of NO₂. These improvements in gas-sensing capabilities are attributed to the reduction in particle size and the narrowing of the optical band gap. Full article

(This article belongs to the Special Issue New Trend for Gas Sensor Development: From Sensing Materials to Metrological Evaluation)

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