Search Results (2,385)

Metasurfaces have emerged as a powerful platform for subwavelength light manipulation, attracting widespread interest for their potential to replace bulky optical components. However, most metasurfaces are statically designed with fixed functionalities. Here, we demonstrate a high-efficiency tunable meta-waveplate by heterogeneously integrating a phase-change Sb₂Se₃ layer with a piezoelectric MEMS mirror. Leveraging the reversible amorphous–crystalline transition of Sb₂Se₃, combined with MEMS-enabled nanoscale air gap tuning, the metasurface achieves dynamic switching among zero-, half-, and quarter-waveplate functionalities at the communication wavelength of 1550 nm. The device exhibits stable polarization conversion performance under various rotation angles. Furthermore, we developed a nano-quarter-waveplate library on this platform, which provides extensive phase control over the reflected field and enables programmable beam deflection. This tunable architecture opens new avenues for adaptive photonics with dynamically switchable functionalities. Full article

This review paper presents a comprehensive synthesis of current scientific knowledge on the integration of low-emission technologies into airport operational models. Attention is also given to the role of artificial intelligence techniques in predicting environmental risks, optimizing energy system design, and enhancing operational safety. The primary objective of the study is to evaluate the synergy between renewable energy sources (solar and wind energy) and emerging propulsion technologies in aviation (hydrogen and electrification) from the perspective of safety and operational stability. The methodology is based on a systematic review of 78 scientific studies identified in the Scopus and Web of Science databases. The analysis identifies critical technical and operational barriers, including electromagnetic interference caused by wind turbines, optical hazards associated with photovoltaic systems, and stability challenges in airport microgrids under peak loads resulting from the charging of electric aircraft. Particular attention is given to the safety of hydrogen infrastructure, where findings from the literature indicate the need to revise separation distances and highlight the potential reduction of airport stand capacity by 5% to 16%. The study synthesizes these findings into a strategic framework for “Smart Green Airports”, proposing solutions such as adaptive infrastructure design, the deployment of predictive models based on artificial intelligence, and the implementation of inherently safe energy storage systems. The paper concludes that achieving airport energy self-sufficiency while maintaining the integrity of flight operations is feasible only through the holistic integration of technical measures, simulation-based planning, and strict compliance with updated safety regulations. Full article

Textile equipment monitoring and scheduling are constrained by device heterogeneity, stringent real-time requirements, and complex dynamic resource scheduling. To address these challenges, this study proposes a cloud-edge-end collaborative remote monitoring and scheduling system for textile equipment. The proposed system aims to overcome the limitations of traditional solutions in compatibility, real-time performance, and resource utilization. This work is positioned as an applied systems study, in which the scheduling modules are used as monitoring-driven service extensions rather than as standalone algorithmic contributions. We develop (i) an adaptive multi-protocol parsing mechanism, (ii) a collaborative hierarchical alerting framework, and (iii) monitoring-driven computing-resource and production-scheduling services. The system is implemented across the terminal device layer, edge computing layer, and central cloud layer. Embedded acquisition terminals were designed to support multiple industrial protocols, including Modbus RTU, OPC UA, and EtherCAT. Dynamic protocol adaptation was used to identify, parse, and map heterogeneous protocol frames into a unified information model at runtime. In the workshop deployment reported in this study, field validation was conducted on 120 air-jet looms connected through RS485-based Modbus RTU. Other interfaces were evaluated as prototype-supported communication options rather than as quantitatively validated workshop interfaces. A cloud-edge-end collaborative alerting framework is designed by combining an improved OPTICS algorithm with a graph neural network (GNN) model. It improves the redundant-alarm filtering rate by 42.1%, achieves 96.8% root-cause diagnosis accuracy, and keeps the end-to-end alert latency at or below 200 ms at the 99th percentile. A cross-layer resource scheduling strategy incorporating a fuzzy PID controller is proposed, accompanied by a weighted multi-criteria resource-optimization model. This strategy increases the average CPU utilization of edge nodes to 84.3 ± 3.6% and reduces burst-task response latency to 236 ± 48 ms. In addition, an adaptive particle-swarm optimization module based on a scalarized composite scheduling objective reduces the equipment idle rate to 6.5% and shortens the average order completion time by 28.4%. Overall, the proposed framework demonstrates the feasibility of cloud-edge-end collaborative monitoring and scheduling in the validated RS485/Modbus-RTU-based weaving-workshop scenario, while its application to other textile processes, machine types, and communication configurations requires further protocol-specific adaptation and field validation. Full article

Infrared focal plane array detectors produce column stripe noise due to inter-detector response variations. Existing single-frame correction methods operate exclusively on the degraded infrared image and cannot reliably distinguish column noise from genuine vertical scene structures. With the increasing availability of co-registered visible-light cameras in modern electro-optical/infrared payloads, we propose to exploit the visible image as a structural guide for infrared destriping. Through a cross-modal correlation analysis, we show that the structural correspondence between RGB and infrared images is spatially non-uniform, motivating a selective rather than uniform fusion strategy. Based on this observation, we propose CMSP (Cross-Modal Scene Prior), a lightweight single-frame denoising architecture that selectively applies RGB guidance where it is beneficial. The proposed AdaptiveSPADE module blends RGB-guided modulation with standard instance normalization through a learned per-pixel confidence map, while a dual-path output head separately estimates pixel-wise residuals and column-constant stripe patterns. Evaluated on three public RGB–IR datasets, CMSP achieves 51.91 dB PSNR on M3FD, outperforming the best baseline by 5.79 dB with only 638 K parameters. A downstream evaluation on real stripe noise demonstrates that CMSP not only removes artifacts but also preserves the fine structures critical for infrared small target detection. Ablation studies confirm that adaptive gating more than doubles the benefit of RGB guidance compared to uniform modulation, and prevents degradation when cross-modal alignment is weak. Full article

Temperature is a physical quantity that represents the degree of heat or cold of an object and has significant application value across various fields. Traditional contact temperature measurement technologies, such as thermocouples and infrared thermometers, suffer from limitations like poor environmental adaptability and low spatial resolution, which makes it difficult to meet the temperature measurement requirements for micro-/nano-devices and extreme environments. In recent years, non-contact optical temperature measurement technology based on the luminescence characteristics of rare earth ions has garnered widespread attention due to its high sensitivity, strong interference resistance, and good environmental adaptability. In addition to inorganic luminescent materials, lanthanide-based molecular and coordination-complex thermometers have also become an important branch of this field; however, this paper focuses on inorganic rare earth luminescence thermometry. This paper provides a systematic review of the mechanisms of temperature measurement using rare earth ion luminescence, including single-energy-level luminescence intensity measurement and luminescence intensity ratio measurement based on thermally coupled levels (TCLs) and non-thermally coupled levels (NTCLs). It analyzes the principles of various technologies, performance parameters (such as absolute sensitivity S_a, relative sensitivity S_r, and temperature resolution δT), and their application progress in fields such as biomedical imaging, high-temperature aerospace environments, and the integration of micro-/nano-devices. Special attention is paid to emerging research directions, including Stark sublevel engineering for enhanced sensitivity, negative thermal expansion (NTE) host design for anti-thermal quenching, multi-modal collaborative thermometry, and artificial intelligence (AI)-assisted material design and data processing. The article also discusses the challenges currently faced by the technology, such as high-temperature fluorescence quenching and signal interference, and looks forward to future development directions, including artificial intelligence-assisted material design and multi-modal cooperative temperature measurement, aiming to provide a reference for the research and application of rare earth luminescence temperature sensing technology. Full article

This paper presents a hybrid methodology for the creation of educational mobile robots, combining the efforts of developers in design, construction, and instrumentation with the use of “Vibe Coding” as an alternative programming approach. To achieve this objective, the methodology integrates electronics and algorithmic thinking to enable adaptive behavior across three operating modes for robotics competitions. The mobile robot features a compact and modular architecture (430 g, 22 cm length × 14 cm width) with support components manufactured using 3D printing. Instrumentation included an Arduino Uno^® development board, a Syb-170^® proto shield, a buzzer, an HC-SR04^® ultrasonic sensor, an SG90 RC^® servomotor, a SSD1315 display, three TCRT5000^® reflective optical sensors, two DC motors with integrated 48:1 gearboxes, an L298N motor driver, and two 18650^® rechargeable lithium-ion batteries. Programming and algorithmic implementation were carried out using Vibe Coding, leveraging its intuitive environment to accelerate the development of three independent operating modes: (1) line follower on a racetrack, (2) obstacle avoidance with various objects, and (3) Bluetooth control via the free MIT Application Inventor. The mobile robot successfully demonstrated all three tasks, validating its suitability for educational and competitive purposes. Furthermore, its architecture supports AI-assisted decision-making through Vibe Coding, enabling dynamic responses to environmental disturbances. The multimodal configuration enhances navigation by correcting trajectory deviations, thereby improving robustness, adaptability, and overall functionality. Full article

Fiber dispersion causes pulse broadening and signal distortion. Existing dispersion compensation approaches depend on standardized dispersion parameters at specific wavelengths (e.g., 1550 nm), which often mismatch actual fiber dispersion, leading to residual dispersion. We develop a Sagnac ring interferometry and electro-optic modulation system, combined with machine learning, to accurately characterize the C-band dispersion curve of a G.652D fiber, and inversely design a chirped fiber Bragg grating (CFBG) for tailored compensation. However, when attempting to quantify the residual dispersion numerically, conventional differentiation methods yield physically implausible results. Monte Carlo simulations confirm this fundamental unreliability, yielding a 95% confidence interval of 319,605 ps/(nm·km). To circumvent this limitation, we propose a joint evaluation method based on refractive index flatness and group delay uniformity. Within 1545–1555 nm, both indicators fluctuate by no more than 0.015% relative to their means, confirming that residual dispersion has been effectively suppressed. This approach provides a precise, personalized compensation mechanism applicable to optical fibers with individual dispersion characteristics, offering a controllable path for adaptive dispersion compensation in high-speed communication systems. Full article

The transport behavior of multiphase flow in porous media is governed by the cross-scale coupling between fluid properties and pore structure, and serves as the theoretical foundation for core processes in fields such as energy development, underground carbon storage, and environmental remediation. Accurately characterizing the intrinsic relationship between physical properties and seepage responses is crucial for enhancing engineering prediction capabilities and optimizing operational strategies. However, the inherent heterogeneity and multiscale nature of natural reservoirs, coupled with the limitations of traditional experimental methods in terms of optical opacity and spatiotemporal resolution, severely hinder a deep understanding of the mechanisms of multiphase flow at the pore-scale. This paper systematically reviews the methodological framework for characterizing physical properties and seepage mechanisms in multiphase flow systems, with a focus on cutting-edge breakthroughs in experimental measurement and visualization technologies over the past decade. Starting with classical and emerging testing methods for key physical properties such as saturation, relative permeability, capillary pressure, and interfacial tension, the paper evaluates the applicability, accuracy advantages, and inherent limitations of different techniques. The paper focuses on the latest advancements in pore-scale visualization technologies, covering microfluidic models, high-resolution X-ray CT, synchrotron rapid dynamic imaging, and multimodal, multiscale imaging fusion strategies; it also explores AI-enabled image processing and data analysis methods, as well as the application potential of cross-scale numerical coupling models in revealing transient seepage mechanisms and correlating them with macroscopic responses. Based on this, an integrated analytical framework of “physical property measurement—visualization characterization—theoretical modeling—engineering application” is established, and the core challenges and future pathways for advancing multiphase flow and seepage research toward “quantification of mechanisms, cross-scale correlation, and adaptation to in situ real-world conditions” are identified. Full article

This paper presents the design and development of an adaptive augmented reality (AR) assistance system for complex mechanical assembly tasks. Integrating a wrist-worn optical heart rate sensor to evaluate the user’s cognitive state, the system is intended to run as a standalone application on the Meta Quest 3 headset. The system displays instructions and visual cues directly overlaid on the user’s physical workspace and constantly monitors their heart rate variability through the sensor as an estimate of their cognitive load. When the system detects an overload, it dynamically adjusts the presentation of information—for example, it slows down pacing, simplifies instructions, or switches to a different interaction modality (audio)—as an attempt to reduce the overload. The paper makes three contributions: first, it provides a documented standalone integration of physiological sensing with adaptive interface logic on a mixed reality headset without external compute infrastructure; second, it provides a systematic characterization of platform-specific tracking incompatibilities on the Meta Quest 3, documenting the progression through four spatial registration strategies and the specific failure condition that triggered each transition; third, it reports spatial interface design observations from iterative developer testing in the current prototype configuration, including panel height ranges not previously reported in the AR interface literature at this level of specificity. The paper also discusses the within-subjects evaluation protocol that is planned for final system testing with actual users. The work is intended as an engineering and design contribution that establishes the foundation for subsequent empirical evaluation of adaptive AR guidance in industrial assembly contexts. Full article

In recent years, salient object detection in optical remote sensing images (ORSI-SOD) has garnered increasing research attention. However, in practical applications, issues such as blurred target edges under low-contrast and complex background interference continue to restrict the accuracy and robustness of detection. To address these problems, this paper proposes the Phase Congruency-Guided Cross-Scale Contextual Fusion Network (PCFNet). Specifically, we design a novel Phase Congruency Enhanced Module (PCE) to solve the problem of low-contrast between targets and backgrounds. It acquire phase features via Fourier decomposition and employs them to generate a weighting map to modulate the shallow features via element-wise multiplication, thereby highlighting structurally significant regions. Meanwhile, we adopt a tailored loss weighting mechanism to weight phase congruency learning for better PCE adaptation. To address complex background interference, we design a novel Dynamic Residual Fusion (DRF) Module. It leverages dynamic spatial attention to generate sample-specific kernels that perform convolution to spatially weight features and uses consecutive residual connection, thereby refining multi-scale features to accurately capture effective targets under complex background interference. Experiments on ORSSD, EORSSD, and ORSI4199 benchmarks demonstrate that PCFNet achieves nine best performances and three second-best performances across the twelve core evaluation metrics, outperforming 23 state-of-the-art methods. Notably, the $F_{\beta }$ score is 1.16% higher than HFCNet on ORSSD and 0.85% higher than MCPNet on EORSSD. Full article

Atmospheric turbulence originates from random fluctuations in the refractive index of the propagation medium that induce wavefront distortions and intensity scintillation. In application scenarios such as adaptive optics, rapid and accurate characterization of turbulence conditions is of critical importance. Existing turbulence-sensing approaches predominantly rely on intensity statistical analysis, wavefront measurements, and parameter estimation inferred from imaging degradation. However, these methods typically require complex reconstruction procedures, leading to increased system complexity and substantial computational overhead, which limits their applicability in scenarios demanding low-latency lightweight architectures, such as adaptive optics and ground-to-satellite laser communications. In this work, turbulence perception is reformulated from a conventional wavefront reconstruction problem into a measurement-operator design problem. We propose an all-optical turbulence perception framework based on a multilayer diffractive processor. The proposed approach maps the phase statistical characteristics induced by atmospheric turbulence into discriminative intensity-domain features, enabling direct perception of turbulence strength. The perception process is performed exclusively in the optical domain, without the need for numerical reconstruction. Numerical results demonstrate that the proposed diffractive processor can robustly distinguish different turbulence strength levels, with an overall classification accuracy of 79.50%, indicating its effectiveness as a new technological pathway for atmospheric turbulence perception. Full article

The transition toward smart grids and Industry 4.0 demands a fundamental shift in maintenance strategies, as manual inspection methods are increasingly being supplanted by automated monitoring systems. Among the advanced technologies for smart inspection, infrared imaging has advantages including non-contact operation, intuitive visualization, and predictive capabilities, which has become a cornerstone for autonomous inspection of critical power infrastructure. This review provides recent advancements in infrared imaging, with a specific focus on automated power system inspection. The discussion starts with an overview of the fundamental principles and system architectures, emphasizing the pivotal role of infrared detectors. A detailed analysis traces the technological evolution from traditional photon detectors to current uncooled microbolometers, and critically assesses emerging low-dimensional materials. The analysis highlights inherent performance trade-offs among sensitivity, operating temperature, and fabrication cost. Subsequently, the review explores advanced signal processing algorithms, such as real-time non-uniformity correction and adaptive noise suppression, which are typically implemented on FPGA platforms. Advanced optical configurations—encompassing computational imaging, lensless designs, and scattering suppression methods—are also discussed, demonstrating how their convergence enhances image fidelity and operational reliability in complex field environments. Representative application paradigms are surveyed, including drone-based transmission line inspections, patrol robots in substations, and fault diagnosis in photovoltaic plants; for each, operational efficacy and economic benefits are assessed. Despite considerable progress, several challenges persist, notably the performance–stability–cost trilemma in novel detector development, the substantial computational demands of end-to-end optimized systems, and a lack of standardization. Finally, the review outlines future research directions, such as high-performance uncooled arrays, AI-driven co-design of optics and algorithms, and the development of standardized, low-cost, intelligent inspection platforms. Full article

Orbit determination from optical observations remains a challenging problem due to the absence of direct range measurements and the presence of sparse, noisy, and irregularly sampled data. This work presents TRACER (Tracking, Recognition, and Analysis for Celestial Ephemerides Retrieval), a robust and fully automated framework for angles-only orbit determination. The proposed approach integrates probabilistic and deterministic strategies within a unified, decision-driven architecture. In particular, statistical ranging is employed for short-arc regimes to explore admissible solutions, while deterministic methods, including modified Gauss and Väisälä techniques, are used for longer arcs and refinement. Candidate solutions are evaluated through a unified scoring function that combines observational consistency with physically motivated penalties. A key contribution of TRACER is the introduction of a randomized subset-selection outer loop, which repeatedly solves the orbit determination problem on different observation subsets and validates solutions against the full dataset, enhancing robustness in challenging scenarios. Additional mechanisms for adaptive subarc selection, recovery from failure, and progressive data assimilation further improve reliability. The resulting framework enables fully autonomous orbit determination without manual intervention, bridging the gap between individual algorithms and operational pipelines for real-world astrometric data processing. Full article

To provide a ground-based experimental reference for free-space optical time–frequency synchronization in future space applications, this paper investigates the impact of beam drift and dynamic link-state variations on free-space laser links. A bidirectional free-space laser time–frequency synchronization and ranging system is established and the synchronization process is uniformly modeled. An improved Kalman filtering method based on innovation consistency is proposed in which a strong tracking mechanism enhances adaptability to model mismatch and abnormal observations; at the same time, an adaptive observation noise modeling strategy based on online statistical estimation characterizes the time-varying noise properties of free-space optical links. Experimental validation is conducted using an equivalent free-space laser link of approximately 321 m. The results show that the proposed method improves the time synchronization accuracy from 78.32 ps to 45.64 ps, corresponding to an enhancement of about 41%. In terms of time stability, the time deviation (TDEV) is reduced from $7.14\times {10}^{-11}$ s to $4.33\times {10}^{-11}$ s at an averaging time of $\tau =1$ s, and from $4.20\times {10}^{-12}$ s to $7.01\times {10}^{-13}$ s at $\tau =800$ s. For ranging performance, the system achieves an average measured distance of 321.56 m with a ranging standard deviation of 15.2 mm. These results demonstrate that the proposed approach enables high-precision and stable state estimation for integrated free-space laser time–frequency synchronization and ranging systems. Full article

Clear aligner therapy has become increasingly widespread in contemporary orthodontics, relying on composite resin attachments to enhance force transmission and improve the predictability of tooth movement. The physicochemical and mechanical properties of these biomaterials play a crucial role in attachment durability, dimensional stability, and esthetic performance during treatment. This systematic review aimed to evaluate how different composite resin types influence the mechanical, optical, and functional performances of orthodontic attachments used in clear aligner therapy. A systematic literature search was conducted in the PubMed, Scopus, and Cochrane databases for studies published between 2015 and 2025, following PRISMA guidelines. In vitro studies and clinical trials evaluating composite resins used for attachment fabrication were included. Fifteen studies met the eligibility criteria, including eleven laboratory investigations and four clinical studies. The evaluated outcomes comprised shear bond strength, wear resistance, surface roughness, microhardness, color stability, and accuracy of attachment reproduction. Overall, all evaluated composite resins demonstrated shear bond strength values within clinically acceptable ranges. However, significant differences were observed in the material performances depending on the resin composition and viscosity. Nanohybrid and high-viscosity composite resins were generally associated with improved mechanical resistance, reduced wear, and greater dimensional stability, although SBS outcomes should be interpreted in light of the bonding protocols used. In contrast, flowable composite resins showed improved handling and adaptation to attachment molds but presented higher susceptibility to surface degradation and discoloration. The findings suggest that the composition and properties of composite resins significantly influence the mechanical and optical behavior of orthodontic attachments. Optimizing material selection according to biomechanical demands and esthetic requirements may improve attachment longevity and treatment predictability in clear aligner therapy. Clinicians should prioritize nanohybrid or high-viscosity composite resins for high-load attachments and use flowable composite resins materials when adaptation and esthetics are critical. Full article