Detected Research Articles

Interferometric Distributed Fiber Optic Vibration Sensor With Branching Sensing Optical Path Based on Time Delay Estimation for Disturbance Localization

Interferometric Distributed Fiber Optic Vibration Sensor With Branching Sensing Optical Path Based on Time Delay Estimation for Disturbance Localization

Zero and double-knotted droplet-shaped optical fiber sensor for rapid temperature variation monitoring

Compact, simple optical fiber sensors based on a macro-bend fiber configuration were fabricated and proposed for instantaneous temperature measurement. These fiber sensors were manufactured by straightforwardly mechanically bending two different pieces of single-mode fiber (SMF) into zero-knotted droplet-shaped and double-knotted droplet-shaped, separately, with radii of a few millimeters. The sharp bending excites mode interferences among the core mode and the stimulated modes transmitting in the cladding region of the silica SMF. Many resonant interference fringes were perceived in the transmission signal and were noticed to shift to a shorter spectral region with the rise of environmental temperature (ET). The resultant data prove the practicality of the fabricated optical fiber sensors with excellent temperature sensitivities of about −1.128 nm/°C and −1.904 nm/°C, for zero-knotted droplet-shaped and double-knotted droplet-shaped, respectively, which is several times larger than sensors based on straight-transmitted fiber constructions. The proposed sensors’ structures may contribute effectively to temperature variations monitoring for environmental or industrial uses due to their large thermo-optical coefficient of the silica core and the big RI difference between the core and cladding of the bending fiber section.

Impact of Moisture Absorption on Optical Fiber Sensors: New Bragg Law Formulation for Monitoring Composite Structures

In recent decades, the aviation industry has increasingly adopted composite materials for various aircraft components, due to their high strength-to-weight ratio and durability. To ensure the safety and reliability of these structures, Health and Usage Monitoring Systems (HUMSs) based on fiber optics (FO), particularly Fiber Bragg Grating (FBG) sensors, have been developed. However, both composite materials and optical fibers are susceptible to environmental factors such as moisture, in addition to the well-known effects of mechanical stress and thermal loads. Moisture absorption can lead to the degradation of mechanical properties, posing a risk to the structural integrity of aircraft components. This research aims to quantify and monitor the impact of moisture on composite materials. A new formulation of the Bragg equation is introduced, incorporating mechanical strain, thermal expansion, and hygroscopic swelling to accurately measure Bragg wavelength variations. Experimental validation was performed using both uncoated and polyimide-coated optical fibers subjected to controlled hygrothermal conditions in a climate chamber. The results demonstrate that uncoated fibers are insensitive to humidity, whereas coated fibers exhibit measurable wavelength shifts due to moisture absorption. The proposed model effectively predicts these shifts, with errors consistently below 2.6%. This approach is crucial for improving the performance and reliability of HUMSs in monitoring composite structures, ensuring long-term safety in extreme environmental conditions.

Improving Racing Drones Flight Analysis: A Data-Driven Approach Using Motion Capture Systems

The publication of the previous study, titled "Experimental Study on the Dynamic Behaviour of Drones Designed for Racing Competitions", highlighted the increasing interest in employing scientific methods for their design and analysis. That study examined the flight data of 15 racing drones within a large flight area, using Doppler-type sensors for data collection. Building on these findings and seeking to enhance them, the current work introduces an upgraded data acquisition system utilising optical sensors, thereby improving measurement accuracy. These enhanced flight data facilitate the development of updated quality indices and conclusions, offering a more precise and definitive analysis than was previously possible.

A Review of Emerging Sensor Technologies for Tank Inspection: A Focus on LiDAR and Hyperspectral Imaging and Their Automation and Deployment

This paper reviews various sensor technologies for tank inspection, focusing on Light Detection and Ranging (LiDAR) and Hyperspectral Imaging (HSI) as advanced solutions for corrosion detection. These technologies are evaluated alongside traditional methods such as ultrasonic, electromagnetic, and thermographic inspections. This review highlights their potential to enhance inspection accuracy, reduce the limitations of manual inspection, and support integrated data analysis for comprehensive asset management. Additionally, this paper proposes a pathway for automating these techniques to streamline inspection processes and improve implementation in practical applications.

Unlocking high-performance near-infrared photodetection: polaron-assisted organic integer charge transfer hybrids

Room temperature femtowatt sensitivity remains a sought-after attribute, even among commercial inorganic infrared (IR) photodetectors (PDs). While organic IR PDs are poised to emerge as a pivotal sensor technology in the forthcoming Fourth-Generation Industrial Era, their performance lags behind that of their inorganic counterparts. This discrepancy primarily stems from poor external quantum efficiencies (EQE), driven by inadequate exciton dissociation (high exciton binding energy) within organic IR materials, exacerbated by pronounced non-radiative recombination at narrow bandgaps. Here, we unveil a high-performance organic Near-IR (NIR) PD via integer charge transfer between Poly[2,5-bis(3-tetradecylthiophen-2-yl)thieno[3,2-b]thiophene] (C-14PBTTT) donor (D) and Tetrafluorotetracyanoquinodimethane (TCNQF4) acceptor (A) molecules, showcasing strong low-energy subgap absorptions up to 2.5 µm. We observe that specifically, polaron excitation in these radical and neutral D-A blended molecules enables bound charges to exceed the Coulombic attraction to their counterions, leading to an elevated EQE (polaron absorption region) compared to Frenkel excitons. As a result, our devices achieve a high EQE of ∼107%, femtowatt sensitivity (NEP) of ~0.12 fW Hz-1/2 along a response time of ~81 ms, at room temperature for a wavelength of 1.0 µm. Our innovative utilization of polarons highlights their potential as alternatives to Frenkel excitons in high-performance organic IR PDs.

Remote Sensing Technologies Quantify the Contribution of Ambient Air Pollution to Asthma Severity and Risk Factors in Greenness, Air Pollution, and Wildfire Ecological Settings: A Literature Review

Numerous epidemiologic studies have used remote sensing to quantify the contribution of greenness, air pollution, and wildfire smoke to asthma and other respiration outcomes. This is the first review paper to evaluate the influence of remote sensing exposures on specific outcome severity and risk factors in different ecological settings. Literature searches utilizing PubMed and Google Scholar identified 61 unique studies published between 2009 and 2023, with 198 specific outcomes. Respiration-specific outcomes were lower in greenness and higher in air pollution and wildfire ecological settings. Aerosol optical depth (AOD)-PM2.5 readings and specific outcomes were higher in economically developing than in economically developed countries. Prospective studies found prenatal and infant exposure to higher ambient AOD-PM2.5 concentration level readings contributed to higher childhood asthma incidence. Lung function was higher in greenness and lower in the other two ecological settings. Age, environment, gender, other, and total risk factors showed significant differences between health outcomes and ecological settings. Published studies utilized physiologic mechanisms of immune, inflammation, and oxidative stress to describe obtained results. Individual and total physiologic mechanisms differed between ecological settings. Study results were used to develop a descriptive physiologic asthma model and propose updated population-based asthma intervention program guidelines.

Machine‐Learning Enabled Biocompatible Capacitive‐Electromyographic Bimodal Flexible Sensor for Facial Expression Recognition

AbstractSingle‐mode sensors suffer from poor robustness and insufficient data features in facial expression recognition, so fusing multi‐sensor signals is the key to improving the accuracy of expression recognition systems. Here, a biocompatible capacitive‐electromyographic dual‐mode sensor (CEDS) is presented, consisting of a capacitive pressure sensing unit and dry electrodes for electrophysiological signal monitoring, assembled in a 3D stacking fashion. A double‐coupled microstructure is prepared and the electrical double‐layer effect is realized by doping ionic liquid, which significantly improves the capacitive performance of the sensor. The application of dry electrodes effectively solves the problems of hydrogel electrodes that are prone to water loss and skin irritation. Besides, the good biocompatibility and antimicrobial properties of CEDS are verified through cytotoxicity and bacteriostatic tests. Based on the sensing of a single signal, a fatigue driving monitoring system and a manipulator control system are constructed respectively. By further integrating the capacitive and electrophysiological signal monitoring functions of CEDS, a 1D convolutional neural network‐assisted facial expression recognition system is constructed, which effectively improves the accuracy of expression recognition and demonstrates the great potential of facial expression monitoring systems based on flexible sensor technology in practical applications.

Health Monitoring Technology for Hydraulic Engineering Gates Based on Multi-Sensor Data Fusion

An essential part of the hydraulic engineering systems, hydraulic gates regulate water flow in dams and other structures. To avoid structural problems, they must ensured to be consistent. Multi-sensor data fusion has become a key tool for health monitoring with advances in machine learning (ML) and sensor technologies. This study aims to provide a novel multi-sensor data fusion health monitoring system for hydraulic engineering gates. To provide more effective and precise real-time monitoring of hydraulic engineering gates, a unique Dynamic Satin Bowerbird search-driven Bidirectional Long Short-Term Memory (DSB-BiLSTM) model were proposed. The hydraulic monitoring dataset was collected from Kaggle website, comprising information and some attributes obtained from hydraulic engineering gates. To improve data quality, noise reduction, and data normalization are used. Time-series analysis techniques extract important information such as vibration patterns and pressure variations. The DSB-BiLSTM model leverages bidirectional LSTM to progress time-dependent data, and the Satin Bowerbird search optimizes sensor selection. The method shows better accuracy and efficiency in identifying potential faults compared to traditional models. When compared to four attributes such as pump, cooling, valve and accumulator with the metrics outcomes of 0.9901, 0.9925, 0.9950, and 0.9890 in accuracy, 0.9930, 0.9960, 0.9902, and 0.9889 in precision, 0.9920, 0.9960, 0.9911, and 0.9890 in recall, and 0.9915, 0.9955, 0.9905, and 0.9889 in F1-score, the DSB-BiLSTM model performs better than all other approaches that were examined. This suggests better overall hydraulic engineering gate health monitoring performance. The proposed DSB-BiLSTM model is a show potential solution for real-time health monitoring of hydraulic gates, provided that it enhances accuracy and reduces computational complexity.

Thermal analysis of mineral oil‐based nanofluids of distribution transformers exposed to simultaneous current and voltage harmonics

AbstractThe exact thermal evaluation of distribution transformers (DTs), which are critical and costly pieces of equipment for the power grids, may contribute to preventing the respective failures. Therefore, the present study non‐uniformly investigated DT for correct anticipation of hotspot temperature (HST). Optical fibre sensors (OFSs) were applied for assessing our newly developed non‐uniform 3D computational fluid dynamic (CFD)‐based modelling while performing the temperature rise test (TRT). It should be noted that this new 3D CFD‐based thermal analysis showed an error percentage of 0.11% (0.1°C) in comparison to the OFS measurement, reflecting the ideal efficiency and accuracy of the model. Moreover, thermography for both top‐oil temperature (TOT) and bottom‐oil temperature (BOT) was employed to validate the results from non‐uniform 3D (three‐dimensional) CFD‐based thermal evaluations. The results indicated an acceptable level of relationship between thermography and thermal analysis of 3D CFD at the specified two spots, with an error percentage of <0.65%, demonstrating the acceptable accuracy of the new non‐uniform 3D CFD‐based model. In the following, yet importantly, the new non‐uniform 3D model was subjected to the total harmonic distortions (THD) for the current and voltage of 5%, 10%, and 15%, which raised the HST more than the original model without harmonics by 3.3°C, 7.1°C, and 10.3°C, respectively. Ultimately, different mineral oil‐based nanofluids’, such as multi‐walled carbon nanotubes (MWCNTs) and diamond nanoparticles, influence on the HST decrement of DT in simultaneous current and voltage harmonics was investigated.

A self-supervised deep-driven model for automatic weather classification from remote sensing images

ABSTRACT The increasing use of Remote Sensing (RS) technology has resulted in the availability of large volumes of satellite imaging data, necessitating efficient, and scalable solutions for rapid analysis and classification in various interdisciplinary fields. This research focuses on addressing these challenges by examining different training and optimization techniques to achieve the highest classification accuracy on both labelled and unlabelled data from the Large-Scale Cloud Photos Dataset for Meteorology Research (LSCIDMR), which contains 10 high-resolution classes. A key aspect of this study is the implementation of a unique deep learning model based on the ResNet101 architecture, designed to handle the inter-class similarity problem through architectural improvements and imbalanced class distributions using data augmentation techniques. Furthermore, this model integrates a novel approach that combines self-learning with semi-supervised learning to process large amounts of unlabelled data. An iterative self-learning process is employed for continuous refinement, using pseudo-labels generated from the unlabelled data. To ensure higher quality and relevance, confidence-based selection of pseudo-labels is applied. Our model demonstrates superior performance in classification compared to recently reported deep learning algorithms, offering a robust solution for the automated categorization of satellite images. In this study, our ResNet101 model achieved a mean accuracy of 0.917 (±0.02), a mean precision of 0.917 (±0.01), with a mean F1 score of 0.9300 (±0.015) over five folds.

On-Site Electrochemical Sensor for Carbamazepine Monitoring in Aquatic Environments

Abstract This work analyzes the detection of carbamazepine (CBZ), a commonly used pharmaceutical that exhibits extended persistence in aquatic habitats, by the application of advanced sensor technologies. Owing to its chemical stability, CBZ is resistant to natural degradation, posing significant ecological risks in aquatic environments. Conventional techniques for CBZ detection, such as high-performance liquid chromatography and spectrophotometry, require intricate laboratory configurations and significant sample preparation, restricting their use for fast in situ monitoring. To tackle these issues, we created a portable electrochemical sensor using screen-printed electrodes on a flexible polyester-based substrate. Under optimized conditions, the sensor demonstrated high sensitivity in a linear detection range from 1 to 20 µM with a detection limit of 0.8 M by differential pulse voltammetry technique. Recovery range was found to be 93% to 107%, which further confirmed reliability, showing consistency in CBZ detection across varied concentration levels in wastewater. Preliminary results on continuous measurements have been carried out, demonstrating a promising application for prolonged analysis in real-settings.

Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.

Acute coronary syndrome (ACS) is a leading cause of death worldwide. Prompt and accurate diagnosis of acute myocardial infarction (AMI) or ACS is crucial for improved management and prognosis of patients. The rapid growth of machine learning (ML) research has significantly enhanced our understanding of ACS. Most studies have focused on applying ML to detect ACS, predict prognosis, manage treatment, identify risk factors, and discover potential biomarkers, particularly using data from electrocardiograms (ECGs), electronic medical records (EMRs), imaging, and omics as the main data modality. Additionally, integrating ML with smart devices such as wearables, smartphones, and sensor technology enables real-time dynamic assessments, enhancing clinical care for patients with ACS. This review provided an overview of the workflow and key concepts of ML as they relate to ACS. It then provides an overview of current ML algorithms used for ACS diagnosis, prognosis, identification of potential risk biomarkers, and management. Furthermore, we discuss the current challenges faced by ML algorithms in this field and how they might be addressed in the future, especially in the context of medicine.

Hydrogen sensing performance and stability of WO3–SiO2 composite film doped with Pt catalyst

AbstractHydrogen energy has attracted attention as a new energy carrier because it does not generate CO2 emissions during combustion. However, numerous problems face the establishment of a hydrogen infrastructure society. One problem is safety when using hydrogen. A fast sensing system for hydrogen at low concentrations will be needed for hydrogen to be used safely. WO3 is expected to be used as an optical hydrogen sensor element because it reacts with hydrogen and changes color. We prepared Pt-doped WO3 films by the sol–gel method using an ion-exchange technique under various experimental conditions and investigated the films’ response properties to hydrogen and their morphology. As a result, a Pt/WO3 film with SiO2 (Pt/WO3–SiO2 film) annealed at 200 °C showed the shortest coloring and bleaching time to 4 vol% hydrogen. The films also showed good reproducibility with respect to their hydrogen response and good long-term stability. In addition, the fast bleaching time led to a stable repeated response, enabling the films to be used in real-time monitoring applications. Moreover, the sensitivity of the Pt/WO3–SiO2 films depended on the hydrogen concentration, which suggested that quantitative sensing of hydrogen at concentrations below the lower explosive limit could be realized. Furthermore, the catalyst Pt active state and the difference in gas diffusivity due to the microstructure of the films were considered through analysis of the surface, cross-sectional structure, and elemental state of the films. Graphical Abstract

Proportional resonant controller design with anti-windup for a single-phase solar inverter

This paper introduces a novel control strategy for regulating the output voltage of a single-phase inverter equipped with an LC filter. The proposed method integrates a Proportional Resonant (PR) controller with an anti-windup compensator to address challenges in voltage regulation and system stability. Unlike conventional techniques, this control approach relies on a single measurement from an output voltage sensor, eliminating the need for complex frame transformations and significantly simplifying the control architecture. The innovative use of the PR controller ensures high performance by achieving zero steady-state error for sinusoidal references, making it particularly effective for applications requiring precision and reliability. The anti-windup compensator enhances system robustness by mitigating controller saturation and maintaining stability under varying load conditions. Extensive experimental tests were conducted to validate the proposed method, demonstrating its superior effectiveness and robustness compared to traditional control strategies. Results highlight its ability to achieve fast response times, minimal steady-state error, and consistent performance across a wide range of operating conditions. This control design offers a simplified yet efficient solution for inverter applications, paving the way for improved performance in renewable energy systems, power distribution networks, and industrial automation, where precise voltage regulation is critical.

Application of Anomaly Detection to Real World Monitoring Data of a Rail-Road Bridge

The growing importance of automatic bridge condition monitoring, driven by the need to sustain infrastructure and reduce construction-related CO2 emissions, has led to increased deployment of sensor technologies on bridges. However, the overwhelming volume of data generated necessitates innovative approaches. Machine learning (ML), particularly the Regression Based Thermal Response Prediction Method (RBTRPM), offers a promising solution. RBTRPM employs ML models to predict structural responses from temperature measurements. These predictions are compared with actual responses to identify anomalies. While introduced in 2014, its practical applicability has been limited. This paper successfully translates RBTRPM into practice, exploring key considerations. A measurement duration of at least six months is recommended, though a longer period would provide even better results. It is also important to emphasize the preference for using only structural influences in the response predictions. The study introduces the Peak over Threshold method for threshold determination applied on RBTRPM and employs moving metrics for concise result presentation, providing valuable insights for the implementation of RBTRPM in real-world bridge monitoring scenarios.

A Crop Water Stress Index for Hazelnuts Using Low-Cost Infrared Thermometers

Incorporating data-driven technologies into agriculture presents a promising approach to optimizing crop production, especially in regions dependent on irrigation, where escalating heat waves and droughts driven by climate change pose increasing challenges. Recent advancements in sensor technology have introduced diverse methods for assessing irrigation needs, including meteorological sensors for calculating reference evapotranspiration, belowground sensors for measuring plant available water, and plant sensors for direct water status measurements. Among these, infrared thermometry stands out as a non-destructive remote sensing method for monitoring transpiration, with significant potential for integration into drone- or satellite-based models. This study applies infrared thermometry to develop a crop water stress index (CWSI) model for European hazelnuts (Corylus avellana), a key crop in Oregon, the leading hazelnut-producing state in the United States. Utilizing low-cost, open-source infrared thermometers and data loggers, we aim to provide hazelnut farmers with a practical tool for improving irrigation efficiency and enhancing yields. The CWSI model was validated against plant water status metrics such as stem water potential and gas exchange measurements. Our results show that when stem water potential is below −6 bar, the CWSI remains under 0.2, indicating low plant stress, with corresponding leaf conductance rates ranging between 0.1 and 0.4 mol m2 s−1. Additionally, un-irrigated hazelnuts were stressed (CWSI > 0.2) from mid-July through the end of the season, while irrigated plants remained unstressed. The findings suggest that farmers can adopt a leaf conductance threshold of 0.2 mol m2 s−1 or a water potential threshold of −6 bar for irrigation management. This research introduces a new CWSI model for hazelnuts and highlights the potential of low-cost technology to improve agricultural monitoring and decision-making.

Technological applications to enhance independence in daily activities for older adults: a systematic review

IntroductionMonitoring daily activities in older adults using sensor technologies has grown significantly over the past two decades, evolving from simple tools to advanced systems that integrate Artificial Intelligence (AI) and the Internet of Things (IoT) for predictive monitoring. Despite these advances, there is still a need for a comprehensive review that addresses both technological progress and its impact on autonomous aging.ObjectiveTo conduct a systematic review of sensor technologies used to monitor the daily activities of independent older adults, focusing on sensor types, applications, usage contexts, and their evolution over time.MethodologyA search was conducted in PubMed, Scopus, Web of Science, PsycInfo, and Google Scholar databases, covering studies published between 2000 and 2024. The 37 selected studies were assessed in terms of methodological quality and organized into four chronological stages, allowing for an examination of the progressive development of these technologies. Each stage represents an advance in sensor type, technological application, and implementation context, ranging from basic sensors to intelligent systems in multi-resident homes.ResultsFindings indicate a clear progression in the accuracy and applicability of sensors, which evolved from fall detection to predictive interventions tailored to each user’s needs. Furthermore, the taxonomic classification of studies shows how sensors have been adapted to monitor physical, cognitive, and social dimensions, laying the groundwork for personalized care.ConclusionSensors represent a promising tool for promoting the independence and well-being of older adults, enabling proactive and personalized interventions in everyday settings. However, the lack of standardization in key parameters limits comparability between studies and highlights the need for consensus to facilitate the design of effective interventions that promote autonomous and healthy aging.

Bound States in the Continuum in Photonics and Metasurfaces: From Phenomena to Applications

AbstractBound states in the continuum (BICs) refer to nonradiative eigenmodes located within the radiation continuum, possessing infinitely high Q‐factor and enabling exceptionally strong light–matter interactions. BICs have found applications across various domains in photonics and metasurfaces, including nonlinear optical enhancement, vortex beam generation, sensor technology, microlasers, and other related areas. This work starts by classifying the phenomena of BICs and introducing the theoretical formation mechanisms and topological characteristics. Then, the current and advanced applications based on BIC‐devices are highlighted. Lastly, this work discusses the current challenges in studies related to BICs, such as structural precision, material selection, and measurement difficulties, and prospect the possible potentials in future developments. This review provides a theoretical background and application prospects of BIC‐engineered devices in optical and photonics fields, laying a solid foundation for future industrial applications.

Fully Automated and AI-Assisted Optical Fiber Sensing System for Multiplexed and Continuous Brain Monitoring.

Continuous and comprehensive brain monitoring is crucial for timely identification of changes or deterioration in brain function, enabling prompt intervention and personalized treatments. However, existing brain monitoring systems struggle to offer continuous and accurate monitoring of multiple brain biomarkers simultaneously. This study introduces a multiplexed optical fiber sensing system for continuous and simultaneous monitoring of six cerebrospinal fluid (CSF) biomarkers using tip-functionalized optical fibers and computational algorithms. Optimized machine learning models are developed and integrated for real-time spectra analysis, allowing for precise and continuous readout of biomarker concentrations. The developed machine learning-assisted fiber optic sensing system exhibits high sensitivity (0.04, 0.38, 0.67, 2.62, 0.0064, 0.33 I/I0 change per units of temperature, dissolved oxygen, glucose, pH, Na+, Ca2+, respectively), reversibility, and selectivity toward target biomarkers with a total diameter less than 2.5 mm. By monitoring brain metabolic and ionic dynamics, this system accurately identified brain physiology deterioration and recovery using ex vivo traumatic brain injury models. Additionally, the system successfully tracked biomarker fluctuations in clinical CSF samples with high accuracy (R2 > 0.93), demonstrating excellent sensitivity and selectivity in reflecting disease progression in real time. These findings underscore the enormous potential of automated and multiplexed optical fiber sensing systems for intraoperative and postoperative monitoring of brain physiologies.