Search Results (4,817)

This study investigates the magneto-optical imaging (MOI) characteristics of weld defects under alternating magnetic field excitation. A magneto-optical sensor is employed to detect different types of weld defects, and the correlation between MOI features and magnetic field intensity is analyzed based on the Faraday magneto-optical effect. A finite element analysis (FEA) model integrated with a magnetic dipole model is established to explore the relationship between lift-off values and leakage magnetic field intensity, while clarifying the connection between magnetic flux leakage (MFL) signals and defect size as well as type. The results demonstrate that defects of varying sizes and types generate distinct MFL intensities. Meanwhile, in the MOI-based nondestructive testing (NDT) experiments, the gray values of MO images corresponding to defects of different sizes and types exhibit significant differences, indicating that the gray values of MO images can reflect the magnitude of leakage magnetic field defects. This research lays a theoretical foundation for industrial MOI nondestructive testing and provides clear engineering guidance for defect detection. Full article

Conventional High Dynamic Range (HDR) image fusion algorithms generally require two or more original images with different exposure times for synthesis, making them unsuitable for real-time processing scenarios such as video streams. Additionally, the synthesized HDR images have the same bit depth as the original images, which may lead to banding artifacts and limits their applicability in professional fields requiring high fidelity. This paper utilizes a Field Programmable Gate Array (FPGA) to support an image sensor operating in Clear HDR mode, which simultaneously outputs High Conversion Gain (HCG) and Low Conversion Gain (LCG) images. These two images share the same exposure duration and are captured at the same moment, making them well-suited for real-time HDR fusion. This approach provides a feasible solution for real-time processing of video streams. An adaptive adjustment algorithm is employed to address the requirement for high fidelity. First, the initial HCG and LCG images are fused under the initial fusion parameters to generate a preliminary HDR image. Subsequently, the gain of the high-gain images in the video stream is adaptively adjusted according to the brightness of the fused HDR image, enabling stable brightness under dynamic illumination conditions. Finally, by evaluating the read noise of the HCG and LCG images, the fusion parameters are adaptively optimized to synthesize an HDR image with higher bit depth. Experimental results demonstrate that the proposed method achieves a processing rate of 46 frames per second for 2688 × 1520 resolution video streams, enabling real-time processing. The bit depth of the image is enhanced from 12 bits to 16 bits, preserving more scene information and effectively addressing banding artifacts in HDR images. This improvement provides greater flexibility for subsequent image processing tasks. Consequently, the adaptive algorithm is particularly suitable for dynamically changing scenarios such as real-time surveillance and professional applications including industrial inspection. Full article

This study aims to develop and demonstrate a low-cost LiDAR-based 3D mapping approach for estimating tree carbon stock in university campuses. Unlike conventional field-based measurements, which are labor-intensive and error-prone, the proposed system integrates a 2D LiDAR sensor with a servo motor and odometry data to generate three-dimensional point clouds of trees. From these data, key biometric parameters such as diameter at breast height (DBH) and total height are automatically extracted and incorporated into species-specific and generalized allometric equations, in line with IPCC 2006/2019 guidelines, to estimate above-ground biomass, below-ground biomass, and total carbon storage. The experimental study is conducted over approximately 70,000 m² of green space at Gazi University, Ankara, where six dominant species have been identified, including Cedrus libani, Pinus nigra, Platanus orientalis, and Ailanthus altissima. Results revealed a total carbon stock of 16.82 t C, corresponding to 61.66 t CO₂eq. Among species, Cedrus libani (29,468.86 kg C) and Ailanthus altissima (13,544.83 kg C) showed the highest contributions, while Picea orientalis accounted for the lowest. The findings confirm that the proposed system offers a reliable, portable, cost-effective alternative to professional LiDAR scanners. This approach supports sustainable campus management and highlights the broader applicability of low-cost LiDAR technologies for urban carbon accounting and climate change mitigation strategies. Full article

This article provides an in-depth overview of mine gas emission monitoring practices in the Ostrava-Karviná Coalfield (OKR), one of the most significant regions in Central Europe affected by post-mining methane leakage. The study presents field measurement techniques, including atmogeochemical surveys, systematic methane screening in soil air, and surface emission rate monitoring using accumulation chambers. Over the course of several long-term projects, more than 43 km² of land were surveyed, and risk classification maps were developed based on measured methane concentrations and surface release rates. These data support land-use planning, the design of degasification measures, and the verification of their effectiveness. Results confirm that methane emissions persist even decades after mine closures and vary depending on atmospheric pressure and local geological conditions. The OKR methodology was also compared to international practices in Poland, Canada, and China. The article concludes with future research directions focused on automation, integration of sensor networks, and predictive modeling of gas migration in post-mining environments. Full article

This paper presents a pre-industrial, laboratory-stage version of an innovative sensor box designed to enable remote measurement of electrical currents. The proposed prototype functions as a drone-mounted payload that can be deployed onto overhead transmission lines. Utilizing Hall-effect sensors, electronic signal processing through filtering, and digital data transmission via Arduino and Bluetooth, the instantaneous line currents are visualized in MATLAB (R2023a) as time-based curves. The sensor box can also be remotely released from the transmission line once measurements are complete, allowing a fully autonomous mode of operation. Laboratory tests demonstrated promising results for real-world applications, with measurement efficiencies ranging from 92% to 98% under various test conditions, including stress tests involving harmonics and total harmonic distortion up to 40%. Future work will focus on implementing effective shielding against high electric fields to further enhance reliability and advance the sensor’s industrialization as a novel solution for power grid digitalization. Full article

This review provides a comprehensive analysis of modern strategies for the synthesis, functionalization, and application of carbon nanotubes (CNTs) and graphene for the development of high-performance polymer composites in the field of strain sensing. The paper systematically organizes key synthesis methods for CNTs and graphene (chemical vapor deposition (CVD), such as arc discharge, laser ablation, microwave synthesis, and flame synthesis, as well as approaches to their chemical and physical modification aimed at enhancing dispersion within polymer matrices and strengthening interfacial adhesion. A detailed examination is presented on the structural features of the nanofillers, such as the CNT aspect ratio, graphene oxide modification, and the formation of hybrid 3D networks and processing techniques, which enable the targeted control of the nanocomposite’s electrical conductivity, mechanical strength, and flexibility. Central focus is placed on the fundamental mechanisms of the piezoresistive response, analyzing the role of percolation thresholds, quantum tunneling effects, and the reconfiguration of conductive networks under mechanical load. The review summarizes the latest advancements in flexible and stretchable sensors capable of detecting both micro- and macro-strains for structural health monitoring, highlighting the achieved improvements in sensitivity, operational range, and durability of the composites. Ultimately, this analysis clarifies the interrelationship between nanofiller structure (CNTs and graphene), processing conditions, and sensor functionality, highlighting key avenues for future innovation in smart materials and wearable devices. Full article

The accurate prediction of human movement direction plays a crucial role in fields such as rehabilitation monitoring, sports science, and intelligent military systems. Based on plantar pressure and inertial sensor data, this study developed a hybrid deep learning model integrating a Convolutional Neural Network (CNN) and a Bidirectional Long Short-Term Memory (BiLSTM) network to enable joint spatiotemporal feature learning. Systematic comparative experiments involving four distinct deep learning models—CNN, BiLSTM, CNN-LSTM, and CNN-BiLSTM—were conducted to evaluate their convergence performance and prediction accuracy comprehensively. Results show that the CNN-BiLSTM model outperforms the other three models, achieving the lowest RMSE (0.26) and MAE (0.14) on the test set, with an R² of 0.86, which indicates superior fitting accuracy and generalization ability. The superior performance of the CNN-BiLSTM model is attributed to its ability to effectively capture local spatial features via CNN and model bidirectional temporal dependencies via BiLSTM, thus demonstrating strong adaptability for complex motion scenarios. This work focuses on the optimization and comparison of deep learning algorithms for spatiotemporal feature extraction, providing a reliable framework for real-time human motion prediction and offering potential applications in intelligent gait analysis, wearable monitoring, and adaptive human–machine interaction. Full article

Metal halide perovskites have emerged as promising photoactive materials for highly efficient photodetectors; however, the inherent instability of perovskite materials in oxygen and moisture limits their practical applications. In this study, the highly moisture-sensitive characteristics of the quasi-2D perovskite nanocrystals were used to fabricate a long-period grating (LPG) humidity sensor based on the perovskite/polyacrylonitrile (PAN) hybrid nanofibers film. The pure-bromide quasi-2D perovskite nanocrystals were in situ synthesized and encapsulated in the PAN matrix on the fiber grating via an electrospinning technique. Humidity-induced variation in the complex permittivity of perovskites can alter the evanescent field of the co-propagating cladding modes, resulting in changes in both resonant amplitude and wavelength in the transmission spectrum of the LPG. These effects yielded an intensity sensitivity of ~0.21 dB/%RH and a wavelength sensitivity of ~18.2 pm/%RH, respectively, in the relative humidity range of 50–80%RH. The proposed LPG sensor demonstrated a good performance, indicating its potential application in the humidity-sensing field. Full article

Cables in cable-supported bridges are critical structural components with exceptional tensile capacity, and their assessment is essential for the safety of both the cables themselves and the entire bridge. Microwave radar, a non-contact and efficient measurement technique, has emerged as a promising tool for bridge cable evaluation. This study demonstrates the deployment of microwave radar on bridge decks to efficiently measure the displacements of multiple cables, enabling coverage of all cables while effectively eliminating low-frequency components caused by deck deformation and radar motion using the LOWESS method. The measured cable displacements can be directly used to characterize vibrations, particularly for detecting vortex-induced vibrations (VIVs), without the need for numerical integration of accelerations. Furthermore, microwave radar is applied to free-decay testing for cable damping evaluation, providing an improved signal-to-noise ratio and eliminating the need for sensors installed via elevated platforms, thereby enhancing the reliability of damping assessments. The effectiveness of these approaches is validated through field testing on two cable-stayed bridges. Full article

This work presents an advanced electro-physical model for hydrogenated amorphous silicon (a-Si:H) Junction Field Effect Transistors (JFETs) to enable the design of devices with energy-efficient analog interface building blocks for Lab-on-Chip (LoC) systems. The presence of this device can support monolithic integration with thin-film sensors and circuit-level design through a validated compact formulation. The model accurately describes the behavior of a-Si:H JFETs addressing key physical phenomena, such as the channel thickness dependence on the gate-source voltage when the channel approaches full depletion. A comprehensive framework was developed, integrating experimental data and mathematical refinements to ensure robust predictions of JFET performance across operating regimes, including the transition toward full depletion and the associated current-limiting behavior. The model was validated through a broad set of fabricated devices, demonstrating excellent agreement with experimental data in both the linear and saturation regions. Specifically, the validation was carried out at 25 °C on 15 fabricated JFET configurations (12 nominally identical devices per configuration), using the mean characteristics of 9 devices with standard-deviation error bars. In the investigated bias range, the devices operate in a sub-µA regime (up to several hundred nA), which naturally supports µW-level dissipation for low-power interfaces. This work provides a compact, experimentally validated modeling basis for the design and optimization of a-Si:H JFET-based LoC front-end/readout circuits within technology-constrained and energy-efficient operating conditions. Full article

Nowadays, the population is subject to a lot of stress, being one of society’s most encountered problems affecting people all over the world. Being under a lot of stress for prolonged periods of time impacts the physical and mental health of individuals with effects on society as an economic burden. Cortisol is one of the main indicators of stress. Long-term exposure to this stress hormone can lead to severe medical conditions such as heart disease, lung issues, obesity, anxiety, or depression. In this context, the current review aims to provide a comprehensive overview of the most recent advances made in the development of versatile and efficient cortisol devices and biosensors capable of monitoring the cortisol levels in biofluids. Lately, both non-plasmonic (polymer-based sensors, optical sensors, electrochemical sensors) and plasmonic sensors (mono- and multiple-metallic nanoparticles-based sensors) have shown great results in cortisol detection. The work focuses on the advantages, remaining restrictions, and limitations in the field of cortisol biosensors from solution-based immunosensors to wearable and Lab-on-Skin monitoring devices, providing a better understanding of the fulfilled requirements and persisting challenges in the accurate detection and monitoring of the cortisol stress hormone. Full article

With advancements in Autonomous Underwater Vehicle (AUV) and sensor technologies, the operational paradigms for seabed survey are undergoing significant transformation. Compared to traditional towed or remotely operated platforms, AUV-based seabed survey systems demonstrate superior capabilities in data resolution, operational efficiency and stealth. Furthermore, propelled by progress in artificial intelligence, the technical approaches of AUV-based seabed exploration systems are also experiencing disruptive changes. Based on our observations, existing review articles predominantly focus on individual technologies within seabed survey operations, failing to reflect the systemic constraints and interdependencies among these discrete technological components. This review focuses on the scenario of seabed target detection within seabed survey operations, summarizing research progress aimed at enhancing the effectiveness of such systems across three key technical areas: image processing of side-scan sonar (SSS) systems, intelligent detection of seabed targets and autonomous path planning for survey missions, which is based on a representative system—AUV-mounted SSS system. Given the multi-faceted challenges still present in seabed exploration technology, this paper aims to provide directional guidance for new researchers entering this field. Full article

This study addresses the limitations of conventional highway visibility monitoring under rain–fog conditions, where fixed stations and visibility sensors provide limited spatial coverage and unstable accuracy. Considering that drivers’ visual fields are jointly affected by global fog and local spray-induced mist, a dynamic visibility recognition and risk assessment framework is proposed using roadside monocular CCTV (Closed-Circuit Television) imagery. The method integrates the Koschmieder scattering model with the dark channel prior to estimate atmospheric transmittance and derives visibility through lane-line calibration. A Monte Carlo-based coupling model simulates local visibility degradation caused by tire spray, while a safety potential field defines the low-visibility risk field force (LVRFF) combining dynamic visibility, relative speed, and collision distance. Results show that this approach achieves over 86% accuracy under heavy rain, effectively captures real-time visibility variations, and that LVRFF exhibits strong sensitivity to visibility degradation, outperforming traditional safety indicators in identifying high-risk zones. By enabling scalable, infrastructure-based visibility monitoring without additional sensing devices, the proposed framework reduces deployment cost and energy consumption while enhancing the long-term operational resilience of highway systems under adverse weather. From a sustainability perspective, the method supports safer, more reliable, and resource-efficient traffic management, contributing to the development of intelligent and sustainable transportation infrastructure. Full article

This study develops an immersive marine Information and Communication Technology (ICT) convergence framework designed to enhance coastal climate resilience by improving accessibility, visualization, and communication of scientific research on Dokdo (Dok Island) in the East Sea. High-resolution spatial datasets, multi-source marine observations, underwater imagery, and validated research outputs were integrated into an interactive virtual-reality (VR) and web-based three-dimensional (3D) platform that translates complex geophysical and ecological information into intuitive experiential formats. A geospatially accurate 3D virtual model of Dokdo was constructed from maritime and underwater spatial data and coupled with immersive VR scenarios depicting sea-level variability, coastal morphology, wave exposure, and ecological characteristics. To evaluate practical usability and pro environmental public engagement, a three-phase field survey (n = 174) and a System Usability Scale (SUS) assessment (n = 42) were conducted. The results indicate high satisfaction (88.5%), strong willingness to re-engage (97.1%), and excellent usability (mean SUS score = 80.18), demonstrating the effectiveness of immersive content for environmental education and science communication crucial for achieving Sustainable Development Goal 14 targets. The proposed platform supports stakeholder engagement, affective learning, early climate risk perception, conservation planning, and multidisciplinary science–policy dialogue. In addition, it establishes a foundation for a digital twin system capable of integrating real-time ecological sensor data for environmental monitoring and scenario-based simulation. Overall, this integrated ICT-driven framework provides a transferable model for visualizing marine research outputs, enhancing public understanding of coastal change, and supporting sustainable and adaptive decision-making in small island and coastal regions. Full article

With the rapid development of high-voltage DC (HVDC) power systems, accurate measurement of surface electrostatic potential on insulating components has become critical for electric field assessment and insulation reliability. This paper proposes an electrostatic potential sensor based on cantilever micro-vibration modulation, which employs piezoelectric actuators to drive high-frequency micro-vibration of cantilever-type shielding electrodes, converting the static electrostatic potential into an alternating induced charge signal. An electrostatic induction model is established to describe the sensing principle, and the influence of structural and operating parameters on sensitivity is analyzed. Multi-physics coupled simulations are conducted to optimize the cantilever geometry and modulation frequency, aiming to enhance modulation efficiency while maintaining a compact sensor structure. To validate the effectiveness of the proposed sensor, an electrostatic potential measurement platform for insulating components is constructed, obtaining response curves of the sensor at different potentials and establishing a compensation model for the working distance correction coefficient. The experimental results demonstrate that the sensor achieves a maximum measurement error of 0.92% and a linearity of 0.47% within the 1–10 kV range. Surface potential distribution measurements of a post insulator under DC voltage agreed well with simulation results, demonstrating the effectiveness and applicability of the proposed sensor for HVDC insulation monitoring. Full article