Search Results (692)

Manual palpation serves as a conventional clinical method for assessing soft tissue stiffness; however, its results are susceptible to subjective factors and exhibit limited reliability. To achieve objective evaluation of pathological tissue stiffness, this study utilizes ultrasonic transducers to measure the time-of-flight (ToF) difference in ultrasound signals in silicone samples and ex vivo animal tissues under specific pressure gradients. A correlation model between the ToF difference and tissue stiffness was established, thereby enabling the detection of tissue stiffness. Based on this methodology, a wearable sensing system incorporating ultrasonic transducers was developed. The system applies fixed gradient pressure to human tissues via a pneumatic control unit and detects the corresponding ToF difference, allowing real-time monitoring of stiffness variations in the biceps brachii and thigh during relaxation and contraction, in the forearm during gripping and release actions, as well as in simulated lesions. This study provides a quantitative technological framework for wearable tissue stiffness monitoring, and its objective measurement characteristics offer support for clinical diagnostic decision-making. Full article

With the rapid development of Unmanned Aerial Vehicle (UAV) technology, it plays an increasingly important role in electrical power inspection. Automated approaches that generate inspection waypoints based on tower features have emerged in recent years. However, these solutions commonly rely on tower coordinates, which can be difficult to obtain at times. To address this issue, this study presents an autonomous inspection waypoint generation method based on object detection. The main contributions are as follows: (1) After acquiring and constructing the distribution tower dataset, we propose a lightweight object detector based on You Only Look Once (YOLOv8). The model integrates the Generalized Efficient Layer Aggregation Network (GELAN) module in the backbone to reduce model parameters and incorporates Powerful Intersection over Union (PIoU) to enhance the accuracy of bounding box regression. (2) Based on detection results, a three-stage waypoint generator is designed: Stage 1 estimates the initial tower’s coordinates and altitude; Stage 2 refines these estimates; and Stage 3 determines the positions of subsequent towers. The generator ultimately provides the target’s position and heading information, enabling the UAV to perform inspection maneuvers. Compared to classic models, the proposed model runs at 56 Frames Per Second (FPS) and achieves an approximate 2.1% improvement in mAP50:95. In addition, the proposed waypoint estimator achieves tower position estimation errors within 0.8 m and azimuth angle errors within 0.01 rad. Multiple consecutive tower inspection flights in actual environments further validate the effectiveness of the proposed method. The proposed method’s effectiveness is validated through actual flight tests involving multiple consecutive distribution towers. Full article

Rapid advancements in surveying technology have necessitated the development of more accurate and efficient tools. Leica Geosystems AG (Heerbrugg, Switzerland), a leading provider of measurement and surveying solutions, has initiated a study to enhance the capabilities of its GNSS INS-based surveying systems. This research focuses on integrating the Leica GS18 I GNSS receiver and the AP20 AutoPole with a Time of Flight (ToF) camera through sensor fusion. The primary objective is to leverage the unique strengths of each device to improve accuracy, efficiency, and usability in challenging surveying environments. Results indicate that the fused AP20 configuration achieves decimetre-level accuracy (2.7–4.4 cm on signalized points; 5.2–20.0 cm on natural features). In contrast, the GS18 I fused configuration shows significantly higher errors (17.5–26.6 cm on signalized points; 16.1–69.4 cm on natural features), suggesting suboptimal spatio-temporal fusion. These findings confirm that the fused AP20 configuration demonstrates superior accuracy in challenging GNSS conditions compared to the GS18 I setup with deviations within acceptable limits for most practical applications, while highlighting the need for further refinement of the GS18 I configuration. Full article

Monitoring groundcover diversity in vineyards is a complex task, often limited by the time and expertise required for accurate botanical identification. Remote sensing technologies and AI-based tools are still underutilized in this context, particularly for classifying herbaceous vegetation in inter-row areas. In this study, we introduce a novel approach to classify the groundcover into one of nine categories, in order to simplify this task. Using UAV images to train a convolutional neural network through a deep learning methodology, this study evaluates the effectiveness of different backbone structures applied to a UNet network for the classification of pixels into nine classes of groundcover: vine canopy, bare soil, and seven distinct cover crop community types. Our results demonstrate that the UNet model, especially when using an EfficientNetB0 backbone, significantly improves classification performance, achieving 85.4% accuracy, 59.8% mean Intersection over Union (IoU), and a Jaccard index of 73.0%. Although this study demonstrates the potential of integrating remote sensing and deep learning for vineyard biodiversity monitoring, its applicability is limited by the small image coverage, as data were collected from a single vineyard and only one drone flight. Future work will focus on expanding the model’s applicability to a broader range of vineyard systems, soil types, and geographic regions, as well as testing its performance on lower-resolution multispectral imagery to reduce data acquisition costs and time, enabling large-scale and cost-effective monitoring. Full article

Performance breakthroughs and safety assurance of aerospace equipment are critical to the advancement of modern aerospace technology. As a key protective system for the hot-end components of aeroengines, thermal barrier coatings (TBCs) play a vital role in ensuring the safe operation of aeroengines and overall flight safety. To address the core detection technology challenge for micro–nano damage precursors in aerospace TBCs, this study proposes an enhanced detection framework, namely YOLOv11-LADC. Specifically, the framework integrates the LSKA attention mechanism to construct the C2PSA-LA module, thereby enhancing the detection capability for micro–nano damage precursors and adaptability to complex small-sample datasets. Additionally, it introduces deformable convolutions (DeformConv) to build the C3k2-DeformCSP module, which dynamically adapts to the irregular deformations of micro–nano damage precursors while reducing computational complexity. A data augmentation strategy incorporating 19 transformations is employed to expand the dataset to 5140 images. A series of experimental results demonstrates that, compared with the YOLOv11 baseline model, the proposed model achieves a 1.6% improvement in precision (P) and a 2.0% increase in recall (R), while maintaining mAP50 and mAP50-95 at near-constant levels. Meanwhile, the computational complexity (GFLOPs) is reduced to 6.2, validating the superiority of the enhanced framework in terms of detection accuracy and training efficiency. This further confirms the feasibility and practicality of the YOLOv11-LADC algorithm for detecting multi-scale micro–nano damage precursors in aerospace TBCs. Overall, this study provides an effective solution for the intelligent, high-precision, and real-time detection of multi-scale micro–nano damage precursors in aerospace TBCs. Full article

Precision agriculture increasingly recognizes the transformative potential of unmanned aerial systems (UASs) for crop monitoring and field assessment, yet research consistently highlights significant usability barriers as the main constraints to widespread adoption. Complex mission planning processes, including detailed flight plan creation and way point management, pose substantial technical challenges that mainly affect non-expert operators. Farmers and their teams generally prefer user-friendly, straightforward tools, as evidenced by the rapid adoption of GPS guidance systems, which underscores the need for simpler mission planning in UAS operations. To enhance accessibility and safety in UAS control, especially for non-expert operators in agriculture and related fields, we propose a Secure UAS Control Framework (SAUCF): a comprehensive system for natural-language-driven UAS mission management with integrated dual-factor biometric authentication. The framework converts spoken user instructions into executable flight plans by leveraging a language-model-powered mission planner that interprets transcribed voice commands and generates context-aware operational directives, including takeoff, location monitoring, return-to-home, and landing operations. Mission orchestration is performed through a large language model (LLM) agent, coupled with a human-in-the-loop supervision mechanism that enables operators to review, adjust, or confirm mission plans before deployment. Additionally, SAUCF offers a manual override feature, allowing users to assume direct control or interrupt missions at any stage, ensuring safety and adaptability in dynamic environments. Proof-of-concept demonstrations on a UAS plat-form with on-board computing validated reliable speech-to-text transcription, biometric verification via voice matching and face authentication, and effective Sim2Real transfer of natural-language-driven mission plans from simulation environments to physical UAS operations. Initial evaluations showed that SAUCF reduced mission planning time, minimized command errors, and simplified complex multi-objective workflows compared to traditional waypoint-based tools, though comprehensive field validation remains necessary to confirm these preliminary findings. The integration of natural-language-based interaction, real-time identity verification, human-in-the-loop LLM orchestration, and manual override capabilities allows SAUCF to significantly lower the technical barrier to UAS operation while ensuring mission security, operational reliability, and operator agency in real-world conditions. These findings lay the groundwork for systematic field trials and suggest that prioritizing ease of operation in mission planning can drive broader deployment of UAS technologies. Full article

This paper investigates a UAV-assisted wireless communication system that integrates optical wireless communication (LiFi) with conventional RF links to enhance network capacity in crowd-gathering scenarios. While the unmanned aerial vehicle (UAV) serves as a flying base station providing downlink transmission to mobile ground users, the study places particular emphasis on the role of LiFi as a complementary physical layer technology within heterogeneous networks—an aspect closely connected to optical and photonics advancements. The proposed system is designed for environments such as theme parks and public events, where user groups move collectively toward points of interest (PoIs). To maintain quality of service (QoS) under dynamic mobility, we develop a joint optimization framework that simultaneously designs the UAV’s flight path and resource allocation over time. Given the problem’s non-convexity, a block coordinate descent (BCD) based approach is introduced, which decomposes the problem into power allocation and path planning subproblems. The power allocation step is solved using convex optimization techniques, while the path planning subproblem is handled via successive convex approximation (SCA). Simulation results demonstrate that the proposed algorithm achieves rapid convergence within 3–5 iterations while guaranteeing 100% heterogeneous QoS satisfaction, ultimately yielding nearly 15.00 bps/Hz system capacity enhancement over baseline approaches. These findings motivate the integration of coordinated three-dimensional trajectory planning for multi-UAV cooperation as a promising direction for further enhancement. Although LiFi is implemented in free-space optics rather than fiber-based sensing, this work highlights a relevant optical technology that may inspire future cross-domain applications, including those in optical sensing, where UAVs and reconfigurable optical links play a role. Full article

We investigate crosstalk effects in a dual-modality tactile–proximity sensing system based on Time-of-Flight (ToF) technology integrated within a flat poly(methyl methacrylate) (PMMA) light guide. Building on the OptoSkin framework, we employ two commercially available TMF8828 multi-zone ToF sensors, one configured for tactile detection via frustrated total internal reflection (FTIR) and the other for external proximity measurements through the same transparent substrate. Controlled experiments were conducted using a 2 cm² silicone pad for tactile interaction and an A4-sized diffuse white target for proximity detection. Additional measurements with a movable PMMA sheet were performed to quantify signal attenuation, peak broadening, and confidence degradation under transparent-substrate conditions. The results demonstrate that the TMF8828 can simultaneously resolve both contact-induced scattering and distant reflections, but that localized interference zones occur when sensor fields of view overlap within the substrate. Histogram analysis reveals the underlying multi-path contributions, providing diagnostic insight not available from black-box ToF devices. These findings highlight both the opportunities and limitations of integrating multiple ToF sensors into transparent waveguides and inform design strategies for scalable robotic skins, wearable interfaces, and multi-modal human–machine interaction systems. Full article

In this work, we present a novel compact particle identification (PID) detector concept based on Silicon Photomultipliers (SiPMs) optimized to perform combined Ring-Imaging Cherenkov (RICH) and Time-of-Flight (TOF) measurements using a common photodetector layer. The system consists of a Cherenkov radiator layer separated from a photosensitive surface equipped with SiPMs by an expansion gap. A thin glass slab, acting as a second Cherenkov radiator, is coupled to the SiPMs to perform Cherenkov-based charged particle timing measurements. We assembled a small-scale prototype instrumented with various Hamamatsu SiPM array sensors with pixel pitches ranging from 2 to 3 mm and coupled with 1 mm thick fused silica window. The RICH radiator consisted of a 2 cm thick aerogel tile with a refractive index of 1.03 at 400 nm. The prototype was successfully tested in beam test campaigns at the CERN PS T10 beam line with pions and protons. We measured a single-hit angular resolution of about 4 mrad at the Cherenkov angle saturation value and a time resolution better than 50 ps RMS for charged particles with Z = 1. The present technology makes the proposed SiPM-based PID system particularly attractive for space applications due to the limited detector volumes available. In this work, we present beam test results obtained with the detector prototype and we discuss possible configurations optimized for the identification of ions in space applications. Full article

Recent breakthroughs in machine learning, artificial intelligence, and the emergence of large datasets have made the integration of eye tracking increasingly feasible not only in computing but also in many other disciplines to accelerate innovation and scientific discovery. These transformative changes often depend on intelligently analyzing and interpreting gaze data, which demand a substantial technical background. Overcoming these technical barriers has remained an obstacle to the broader adoption of eye tracking technologies in certain communities. In an effort to increase accessibility that potentially empowers a broader community of researchers and practitioners to leverage eye tracking, this paper presents an open-source software platform: Beach Environment for the Analytics of Human Gaze (BEACH-Gaze), designed to offer comprehensive descriptive and predictive analytical support. Firstly, BEACH-Gaze provides sequential gaze analytics through window segmentation in its data processing and analysis pipeline, which can be used to achieve simulations of real-time gaze-based systems. Secondly, it integrates a range of established machine learning models, allowing researchers from diverse disciplines to generate gaze-enabled predictions without advanced technical expertise. The overall goal is to simplify technical details and to aid the broader community interested in eye tracking research and applications in data interpretation, and to leverage knowledge gained from eye gaze in the development of machine intelligence. As such, we further demonstrate three use cases that apply descriptive and predictive gaze analytics to support individuals with autism spectrum disorder during technology-assisted exercises, to dynamically tailor visual cues for an individual user via physiologically adaptive visualizations, and to predict pilots’ performance in flight maneuvers to enhance aviation safety. Full article

With the rapid advancement of aviation technology, modern aircraft cockpits are evolving toward high automation and intelligence, making pilot-cockpit interaction a critical factor influencing flight safety and efficiency. Pilot pose estimation and behavior recognition are critical for monitoring pilot state, preventing operational errors, and enabling adaptive human–machine interaction, thus playing an essential role in aviation safety assurance and intelligent cockpit development. However, existing methods face challenges in real-time performance, reliability, and computational complexity in practical applications. Traditional approaches, such as wearable sensors and image-processing-based algorithms, demonstrate certain effectiveness but still exhibit limitations in aviation environments. To address these issues, this paper proposes a lightweight pilot pose estimation and behavior recognition framework, integrating Vision Transformer with depth-wise separable convolution to optimize the accuracy and efficiency of keypoint detection. Additionally, a novel multimodal data fusion technique is introduced, along with a scientifically designed evaluation system, to enhance the robustness and security of the system in complex environments. Experimental results on a pilot keypoint detection dataset captured in a simulated cockpit environment show that the proposed method achieves 81.9 AP, while substantially reducing model parameters and notably improving inference efficiency compared with HRNet. This study provides new insights and methodologies for the design and evaluation of aviation human-machine interaction systems. Full article

The aim of this paper is to analyze the transfer performance of a spacecraft equipped with a TANDEM electric propulsion system in a classical interplanetary mission scenario targeting Mars, Venus, or a near-Earth asteroid. The TANDEM concept is a coaxial, two-channel Hall-effect thruster recently proposed under ESA’s Technology Development Element program. This innovative propulsion system, currently undergoing experimental characterization, is designed to operate at power levels between $3\mathrm{kW}$ and $25\mathrm{kW}$, delivering a maximum thrust of approximately $1\mathrm{N}$. Its architecture allows operation using a single channel (internal or external) or both channels simultaneously to achieve maximum thrust. This inherent flexibility enables the definition of advanced control strategies for future missions employing such a propulsion system. In the context of a heliocentric mission scenario, this paper adopts a simplified thrust model based on actual thruster characteristics and a semi-analytical model for spacecraft mass breakdown. Transfer performance is evaluated within an optimization framework in terms of time of flight and the corresponding propellant mass consumption as functions of the main spacecraft design parameters. Full article

Composite engine fan blades are critical aircraft engine components, and their failure can compromise the safe and reliable operation of the entire aircraft. To enhance aircraft availability and safety within a condition-based maintenance framework, effective methods are needed to identify damage and monitor the blades’ condition throughout manufacturing and operation. This paper presents a unique experimental framework for real-time monitoring of composite engine blades utilizing state-of-the-art structural health monitoring (SHM) technologies, discussing the associated benefits and challenges. A case study is conducted on a representative Foreign Object Damage (FOD) panel, a substructure of a LEAP (Leading Edge Aviation Propulsion) engine fan blade, which is a curved, 3D-woven Carbon Fiber Reinforced Polymer (CFRP) panel with a secondary bonded steel leading edge. The loading scheme involves incrementally increasing, cyclic 4-point bending (loading–unloading) to induce controlled damage growth, simulating in-operation conditions and allowing evaluation of flexural properties before and after degradation. External damage, simulating foreign object impact common during flight, is introduced using a drop tower apparatus either before or during testing. The panel’s condition is monitored in-situ and in real time by two types of SHM sensors: screen-printed piezoelectric sensors for guided ultrasonic wave propagation studies and surface-bonded Fiber Bragg Grating (FBG) strain sensors. Experiments are conducted until panel collapse, and degradation is quantified by the reduction in initial stiffness, derived from the experimental load-displacement curves. This paper aims to demonstrate this unique experimental setup and the resulting SHM data, highlighting both the potential and challenges of this SHM framework for monitoring complex composite structures, while an attempt is made at correlating SHM data with structural degradation. Full article

Advanced manufacturing places stringent demands on measurement technologies, requiring ultra-high precision, non-contact operation, high throughput, and real-time adaptability. Optical metrology, with its distinct advantages, has become a key enabler in this context. This paper reviews optical metrology techniques from the perspective of precision manufacturing applications, emphasizing precision positioning and surface topography measurement while noting the limitations of traditional contact-based methods. For positioning, interferometers, optical encoders, and time-of-flight methods enable accurate linear and angular measurements. For surface characterization, techniques such as interferometry, structured light profilometry, and confocal microscopy provide reliable evaluation across scales, from large structures to micro- and nano-scale features. By integrating these approaches, optical metrology is shown to play a central role in bridging macroscopic and nano-scale characterization, supporting both structural assessment and process optimization. This review highlights its essential contribution to advanced manufacturing, and offers a concise reference for future progress in high-precision and intelligent production. Full article

Icing on aircraft surfaces during operation poses a threat to flight safety. As a passive anti-icing technology, hydrophobic microstructure can achieve long-term anti-icing. In this work, a composite process combining hot-embossing of PVD-coated punches with a low surface energy fluoride-modification scheme is proposed to generate nanoscale cluster structures on hundreds of microns array channels to construct a superhydrophobic micro-nano composite structure. The droplet freezing and frosting behavior of the hydrophobic microstructures was analyzed, and it was found that the anti-icing and anti-frost properties of the microstructure surface improved with an increase in the microstructure period size (T). Compared with the original surface, the freezing time of the microstructure at T = 500 μm was delayed by 214.3% (7 s → 22 s), and the frost layer coverage time was delayed by 75.7% (70 s → 123 s). The maximum water contact angle of the superhydrophobic micro-nano composite structure was 153.3°, and the droplet freezing time was delayed to 95 s, which is a 1166.67% difference, indicating that the multi-stage micro-nano composite structure can significantly improve surface anti-icing performance. The main reason for this result is that the bottom of the microstructure can store air pockets, preventing droplet wetting and heat exchange. Full article