Search Results (31)

To address the limitations of traditional structural damage identification methods in terms of reliance on high-fidelity baseline models and sensitivity to minor damage, this paper proposes a novel physics-informed and data-driven approach based on the modal curvature variation coefficient. A damage-sensitive feature derived from the rate of change in the radius of curvature is established, providing a clear mathematical and physical interpretation to reduce model error interference and enhance local damage localization. The effectiveness of the proposed method is validated through a 1:20 scale model experiment of a main truss from a large stadium steel roof. A total of 33 experimental cases were designed, simulating single and multiple damage scenarios with varying severity levels (large, medium, and small). Multi-source monitoring techniques, including millimeter-wave radar interferometry, laser displacement sensors, high-resolution vision-based measurement, and accelerometers, were integrated. Modal parameters were extracted using the Stochastic Subspace Identification (SSI) method, and the finite element model was updated via a high-order response surface methodology. Numerical simulations and experimental results demonstrate that the proposed modal curvature variation coefficient is highly sensitive to local stiffness degradation and accurately locates both single and multiple large/medium damage regions. In cases involving multiple minor damages, the method effectively identifies the damaged areas but exhibits a risk of false positives in undamaged sections. The millimeter-wave radar measurements exhibit strong agreement with laser displacement data, confirming its viability for non-contact structural health monitoring. This research provides a robust technical framework and experimental foundation for condition assessment and early damage warning in large-scale engineering structures. Full article

Spinning tethered satellite systems represent a promising advancement in the design of spaceborne architectures for Earth and planetary observation. Leveraging the unique advantages of tether technology, such as mass efficiency in deploying large structures and fuel-free formation control, this study explores the feasibility and performance potential of CubeSat-scale spinning tethered formations. These systems consist of multiple spacecrafts connected by a tether, enabling easy dynamic adjustment of inter-satellite spacing and rotational velocity through conservation of angular momentum. Such flexibility facilitates precise, stable formations suitable for a range of remote sensing applications. In this paper, the authors present an overview of the dynamical modelling, deployment strategy, and operational advantages of spinning tether systems, focusing in particular on some key use cases: Earth, Moon and Mars surface observation. Three representative sensing modalities are analysed: (1) stereo imaging, where tethered platforms allow synchronized capture with tuneable baselines; (2) distributed radar sounding, which benefits from mechanically stabilized, spatially dispersed sensors to enhance resolution; and (3) Synthetic Aperture Radar (SAR) interferometry, where tether-induced baseline control improves accuracy and simplifies phase unwrapping. A performance assessment is provided for multiple orbital configurations around the Earth and the Moon. The results demonstrate that, while some issues still need to be explored in more detail, spinning tethered systems can offer competitive or superior observational performance in different mission scenarios compared to current technologies. The main challenges posed by this kind of architecture are discussed, alongside future research directions and development prospects. Full article

Dry eye disease (DED) is a multifactorial disorder of the ocular surface, characterised by tear film instability, hyperosmolarity, inflammation, and neurosensory abnormalities. Its clinical heterogeneity and the weak correlation between symptoms and signs complicate both diagnosis and management. Conventional assessments, such as patient-reported symptom questionnaires and basic clinical tests like the Schirmer test, are useful; however, their variability and limited sensitivity highlight the need for more reliable and objective diagnostic tools. This narrative review summarises and analyses current imaging approaches used for the diagnosis of DED. A comprehensive literature search was performed in the PubMed and Google Scholar databases to identify relevant studies published up to October 2025. In recent years, imaging technologies have revolutionised the approach to DED. Modalities such as in vivo confocal microscopy (IVCM), meibography, anterior segment optical coherence tomography (AS-OCT), interferometry, thermography, tear fluorescein clearance, impression cytology, and multifunctional imaging systems allow for non-invasive, high-resolution, and reproducible assessment of ocular surface structures and tear film dynamics. The integration of these techniques into clinical practice supports a more personalised management of DED. Future directions include further technological refinements and the application of artificial intelligence (AI) to imaging analysis, with the potential to enhance diagnostic precision and facilitate earlier intervention. While imaging cannot replace a thorough clinical examination, it has become an essential adjunct that significantly enriches the evaluation and management of patients with DED. Full article

Radar interferometry can provide important information for Structural Health Monitoring (SHM) of bridges and other transportation structures. In this article, joint communication and sensing (JCAS) telecommunication infrastructure is tested as a ground-based radar, offering advantages in terms of long-term costs, deployment and maintenance. This work specifically addresses the estimation of the radar support movement (i.e., pylon or mast), which represents a major challenge in this kind of measurements. Movements of the radar system combine with the true target motion and, if not correctly compensated, can compromise the accuracy of the results. A technique for estimating radar movements based on the displacement tracking of multiple permanent scatterers (PSs) in the scenario is presented. True target displacements can then be retrieved by applying linear regression methods to fixed PSs located at different viewing angles, accounting for both radar movements and atmospheric displacement components. The technique was validated using real data acquired during an experimental campaign on a bridge test site. First, results obtained for a target subject to known displacements are shown. A second measurement session was aimed at testing the method for bridge dynamic monitoring. Finally, the same technique was applied antenna mast monitoring in terms of modal analysis and vibration characterization. Full article

In order to accurately measure the low- and high-order modes of turbine blades, this study proposes a method that integrates piezoelectric ceramic excitation with time-averaged electronic speckle pattern interferometry (TA-ESPI). The piezoelectric exciter provides wideband and high-output excitation, effectively stimulating both low- and high-order blade modes. The TA-ESPI technique captures the vibration signals with high displacement sensitivity, enabling full-field mode shape measurement without the need for high-speed cameras. Additionally, an equivalent strain principle based on deflection curvature is introduced to extract strain modes from displacement modes for identifying potential fatigue failure areas (PFFAs). Experimental validation on an aero-engine turbine blade successfully identified the first eight natural frequencies (up to 7753 Hz), a significant advancement over the conventional method which identified only two. The measured first-order frequency showed a high agreement (deviation < 2%) with the impact hammer test, confirming accuracy. The extracted strain modes for the first six orders clearly revealed the PFFAs, which aligned well with actual failure regions. The proposed method proves to be an effective, practical, and efficient modal testing technique for turbine blades, offering a substantial improvement in bandwidth over established methods. Full article

This paper provides an overview of artificial intelligence (AI) applications in ophthalmology, with a focus on diagnosing dry eye disease (DED). We aim to synthesize studies that explicitly compare AI-based diagnostic models with clinical tests employed by ophthalmologists, examine results obtained using similar imaging modalities, and identify recurring limitations to propose recommendations for future work. We conducted a systematic literature search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines across four databases: Google Scholar, PubMed, ScienceDirect, and the Cochrane Library. We targeted studies published between 2020 and 2025 and applied predefined inclusion criteria to select 30 original peer-reviewed articles. We then analyzed each study based on the AI models used, development strategies, diagnostic performance, correlation with clinical parameters, and reported limitations. The imaging modalities covered include videokeratography, smartphone-based imaging, tear film interferometry, anterior segment optical coherence tomography, infrared meibography, in vivo confocal microscopy, and slit-lamp photography. Across modalities, deep learning models (e.g., U-shaped Convolutional Network (U-Net), Residual Network (ResNet), Densely Connected Convolutional Network (DenseNet), Generative Adversarial Networks (GANs), transformers) demonstrated promising performance, often matching or surpassing clinical assessments, with reported accuracies ranging from 82% to 99%. However, few studies performed external validations or addressed inter-expert variability. The findings confirm AI’s potential in DED diagnosis, but emphasize gaps in data diversity, clinical use, and reproducibility. It offers practical recommendations for future research to bridge these gaps and support AI deployment in routine eye care. Full article

Optical metrology and perception technologies employ light as an information carrier to enable non-contact, high-precision measurement of geometry, dynamics, and material properties. They are widely deployed in industrial and consumer domains, from nanoscale defect inspection in semiconductor manufacturing to environmental perception in autonomous driving and spatial tracking in AR/VR. However, existing reviews often treat individual modalities—such as interferometry, imaging, or spectroscopy—in isolation, overlooking the increasing cross-domain integration in emerging systems. This review proposes a hierarchical taxonomy encompassing four core systems: interferometry, imaging, spectroscopy, and hybrid/advanced methods. It introduces a “theory–application–innovation” framework to unify fundamental principles, application scenarios, and evolutionary trends, revealing synergies across modalities. By mapping technological progress to industrial and societal needs, including AI-driven optimization and quantum-enhanced sensing, this work provides a structured, evolving knowledge base. The framework supports both cross-disciplinary understanding and strategic decision-making, offering researchers and engineers a consolidated reference for navigating the rapidly expanding frontiers of optical metrology and perception. Full article

This paper presents advancements in structural health monitoring (SHM) techniques, with a particular focus on wave-screening methods for assessing prestress loss in a large-scale prestressed concrete (PC) bridge model under outdoor conditions. The wave-screening process utilizes low-frequency wave propagation obtained from seismic interferometry of structural free vibrations and high-frequency wave propagation obtained through ultrasonic transducers embedded in the structure. An adjustable post-tensioning system was employed in a series of experiments to simulate prestress loss. By comparing bridge vibrations under varying post-tensioning forces, the study investigated prestress loss and examined temperature-related effects using the coda wave interferometry (CWI) method. Local structural alterations were analyzed through wave velocity variations, demonstrating sensitivity to bridge temperature changes. The findings indicate that wave-based methods are more effective than traditional modal analysis for damage detection, highlighting the dual impacts of prestress loss and temperature, as well as damage localization. This study underscores the need for long-term measurements to account for temperature fluctuations when analyzing vibration measurements to investigate changes in prestressing force in PC structures. Full article

Phase wrapping is a common phenomenon in optical full-field imaging or measurement systems. It arises from large phase retardations and results in wrapped-phase maps that contain essential information about surface roughness and topology. However, these maps are often degraded by noise, such as speckle and Gaussian, which reduces the measurement accuracy and complicates phase reconstruction. Denoising such data is a fundamental problem in computer vision and plays a critical role in biomedical imaging modalities like Full-Field Optical Interferometry. In this paper, we propose WPD-Net (Wrapped-Phase Denoising Network), a lightweight deep learning-based neural network specifically designed to restore phase images corrupted by high noise levels. The network architecture integrates a shallow feature extraction module, a series of Residual Dense Attention Blocks (RDABs), and a dense feature fusion module. The RDABs incorporate attention mechanisms that help the network focus on critical features and suppress irrelevant noise, especially in high-frequency or complex regions. Additionally, WPD-Net employs a growth-rate-based feature expansion strategy to enhance multi-scale feature representation and improve phase continuity. We evaluate the model’s performance on both synthetic and experimentally acquired datasets and compare it with other state-of-the-art deep learning-based denoising methods. The results demonstrate that WPD-Net achieves superior noise suppression while preserving fine structural details even with mixed speckle and Gaussian noises. The proposed method is expected to enable fast image processing, allowing unwrapped biomedical images to be retrieved in real time. Full article

The present study encompasses a thorough analysis of the vibrations in a splash musical cymbal. The analysis is performed using a hybrid methodology that combines experimental measurements with parametric computer-aided design and finite element method simulations. Experimental measurements, including electronic speckle pattern interferometry, and impulse response measurements are conducted. The interferometric measurements are used as a reference for the evaluation of finite element method modal analysis results. The modal damping ratio is calculated via the impulse response measurements and is adopted by the corresponding simulations. Two different approximations are employed for the computer-aided design and finite element method models: one using three-point arcs and the other using lines to describe the non-smooth curvature introduced during manufacturing finishing procedures. The numerical models employing the latter approximation exhibit better agreement with experimental results. The numerical results demonstrate that the cymbal geometrical characteristics, such as the non-smooth curvature and thickness, greatly affect the vibrational behavior of the percussion instrument. These results are of valuable importance for the development of vibroacoustic numerical models that will accurately simulate the sound synthesis of cymbals. Full article

The paper presents theoretical analyses and experimental investigations of broadband differential interference in planar gradient waveguides made via K⁺-Na⁺ ion exchange in BK-7 glass. This technology, due to its large polarimetric dispersion, is especially useful for applications in differential interferometry. We discuss the influence of technological parameters on the operation characteristics of the structure in terms of sensor applications. The refractive index variation in the measured external surroundings affects the modal properties of TE and TM modes and the spectral distribution at the output of the differential interferometer. The optical system described in this work has been designed specifically for use in biological systems where variations in the index of refraction need to be measured. Full article

X-ray grating interferometry (XGI) can provide multiple image modalities. It does so by utilizing three different contrast mechanisms—attenuation, refraction (differential phase-shift), and scattering (dark-field)—in a single dataset. Combining all three imaging modalities could create new opportunities for the characterization of material structure features that conventional attenuation-based methods are unable probe. In this study, we proposed an image fusion scheme based on the non-subsampled contourlet transform and spiking cortical model (NSCT-SCM) to combine the tri-contrast images retrieved from XGI. It incorporated three main steps: (i) image denoising based on Wiener filtering, (ii) the NSCT-SCM tri-contrast fusion algorithm, and (iii) image enhancement using contrast-limited adaptive histogram equalization, adaptive sharpening, and gamma correction. The tri-contrast images of the frog toes were used to validate the proposed approach. Moreover, the proposed method was compared with three other image fusion methods by several figures of merit. The experimental evaluation results highlighted the efficiency and robustness of the proposed scheme, with less noise, higher contrast, more information, and better details. Full article

Recent backscattering interferometry studies utilise a single channel microfluidic system, typically approximately semicircular in cross-section. Here, we present a complete ray tracing model for on-chip backscattering interferometry with a semicircular cross-section, including the dependence upon polarisation and angle of incidence. The full model is validated and utilised to calculate the expected fringe patterns and sensitivities observed under both normal and oblique angles of incidence. Comparison with experimental data from approximately semicircular channels using the parameters stated shows that they cannot be explained using a semicircular geometry. The disagreement does not impact on the validity of the experimental data, but highlights that the optical mechanisms behind the various modalities of backscattering interferometry would benefit from clarification. From the analysis presented here, we conclude that for reasons of ease of analysis, data quality, and sensitivity for a given radius, capillary-based backscattering interferometry affords numerous benefits over on-chip backscattering interferometry. Full article

Backgound: The composition of stones formed in the urinary tract plays an important role in their management over time. The most common imaging method for the non-invasive evaluation of urinary stones is radiography and computed tomography (CT). However, CT is not very sensitive, and cannot differentiate between all critical stone types. In this study, we propose the application, and evaluate the potential, of a multi-modal (or multi-contrast) X-ray imaging technique called speckle-based imaging (SBI) to differentiate between various types of urinary stones. Methods: Three different stone samples were extracted from animal and human urinary tracts and examined in a laboratory-based speckle tracking setup. The results were discussed based on an X-ray diffraction analysis and a comparison with X-ray microtomography and grating-based interferometry. Results: The stones were classified through compositional analysis by X-ray diffraction. The multi-contrast images obtained using the SBI method provided detailed information about the composition of various urinary stone types, and could differentiate between them. X-ray SBI could provide highly sensitive and high-resolution characterizations of different urinary stones in the radiography mode, comparable to those by grating interferometry. Conclusions: This investigation demonstrated the capability of the SBI technique for the non-invasive classification of urinary stones through radiography in a simple and cost-effective laboratory setting. This opens the possibility for further studies concerning full-field in vivo SBI for the clinical imaging of urinary stones. Full article

Anthrax lethal factor (LF) is one of the enzymatic components of the anthrax toxin responsible for the pathogenic responses of the anthrax disease. The ability to screen multiplexed ligands against LF and subsequently estimate the effective kinetic rates ($k_{on}$ and $k_{off}$) and complementary binding behavior provides critical information useful in diagnostic and therapeutic development for anthrax. Tools such as biolayer interferometry (BLI) and surface plasmon resonance imaging (SPRi) have been developed for this purpose; however, these tools suffer from limitations such as signal jumps when the solution in the chamber is switched or low sensitivity. Here, we present multiplexed antibody affinity measurements obtained by the interferometric reflectance imaging sensor (IRIS), a highly sensitive, label-free optical biosensor, whose stability, simplicity, and imaging modality overcomes many of the limitations of other multiplexed methods. We compare the multiplexed binding results obtained with the IRIS system using two ligands targeting the anthrax lethal factor (LF) against previously published results obtained with more traditional surface plasmon resonance (SPR), which showed consistent results, as well as kinetic information previously unattainable with SPR. Additional exemplary data demonstrating multiplexed binding and the corresponding complementary binding to sequentially injected ligands provides an additional layer of information immediately useful to the researcher. Full article