Search Results (6,588)

This work presents a compact broadband in-fiber polarization filter using gold-deposited square-lattice photonic crystal fiber (PCF) numerically. The finite element method (FEM) is utilized to analyze the transmission characteristics of this PCF. The simulation results indicate that when the cladding hole diameter is 1.5 μm, the large hole diameter is 2.1 μm, the long axis of elliptical holes is 1.96 μm, the short axis of elliptical holes is 0.98 μm, the pitch is 2 μm, and the gold layer thickness is 50 nm, the x-polarized mode can interact with two plasmonic modes, and two surface plasmon resonance (SPR) processes at two common communication windows can be achieved. The length of this PCF filter is set as 0.5 mm, exhibiting the maximum extinction ratio (ER) of −51.4 dB at 1.31 μm and −47.3 dB at 1.55 μm, and the operating bandwidth of >860 nm. Additionally, the estimated splice losses are ~2.22 dB at 1.31 μm and ~1.42 dB at 1.55 μm. It is expected that this small-size PCF-SPR filter, characterized by its efficient filtering performance and wide bandwidth, will serve as a promising candidate for building integrated networks that combine optical fiber communication, sensing, and computing capabilities. Full article

The effect of structure on the properties of cotton fibers is yet to be fully understood even after many years of research. This is due to the presence of convolutions that occur at various intervals in cotton fibers. An attempt was made in this investigation to remove these convolutions using liquid ammonia treatment. The optical and X-ray orientation angles of two varieties of G. hirsutum cotton fibers were investigated at various stages of maturity, and results were compared. An American upland variety was also studied. Four-hour treatment of cotton fibers in liquid ammonia at a temperature of −50 °C ensures a complete change of the lattice structure from cellulose I polymorph to cellulose III polymorph. The cellulose I lattice structure is restored by boiling it in distilled water for 24 h. X-ray diffractograms confirm these conversions. Mature fibers after treatments are devoid of convolutions and are rounded in appearance with no central lumen. The scanning electron micrographs revealed these morphological structures. A close correlation exists between the optical and X-ray orientation measurements and are both strongly dependent on fiber maturity. In all the varieties studied, a maturity ratio of at least 0.8 is required for a cotton fiber to be of commercial value, in terms of strength and durability The progressive build-up of both the primary and secondary walls as the fiber matures shows a gradual decrease in helix angles and, hence, an increase in the orientation of the fibrils, conforming to the constant pitch model. The effect of convolutions on both the optical and X-ray orientation angle is found to be higher than 10%. Full article

Distributed optical fiber vibration sensing (DVS) has shown strong potential in perimeter security, pipeline leakage monitoring, transportation safety, and structural health diagnostics owing to its high sensitivity, long-range coverage, and immunity to electromagnetic interference. However, severe cross-scene distribution mismatch is often encountered in real-world deployments: indoor, outdoor, and pipeline environments exhibit markedly different noise patterns and time–frequency characteristics, thereby degrading the generalization ability of models trained in a single scene. To address this challenge, we propose a Progressive Multi-Strategy Domain Alignment (PMSDA) framework for label-disjoint cross-scene vibration recognition. PMSDA uses a compact expansion–compression encoder together with complementary alignment mechanisms—maximum mean discrepancy (MMD), correlation alignment (CORAL), and adversarial domain discrimination—to learn a scene-robust latent space from a labeled indoor source and two unlabeled target domains (outdoor and pipeline) within a single alternating-training model. Because the fine-grained source and target label spaces are disjoint, PMSDA is formulated as a representation-transfer framework rather than a standard label-shared unsupervised domain adaptation method; target-domain recognition is therefore performed through domain-specific prototype clustering in the aligned latent space. On three representative scenes with nine event classes in total, PMSDA achieved 89.5% accuracy, 86.7% macro-F1, and 0.93 AUC for Indoor→Outdoor, and 85.8%, 84.7%, and 0.87, respectively, for Indoor→Pipeline, outperforming traditional feature+SVM/RF pipelines, CNN/ResNet baselines, and representation-transfer baselines adapted from DANN/CDAN/SHOT under the same evaluation protocol. These results indicate that PMSDA is a promising and effective framework for offline cross-scene DVS evaluation under disjoint target event sets. Full article

Introduction: Optical coherence tomography (OCT) is the gold standard for retinal nerve fibre layer (RNFL) assessment; its high cost and limited accessibility hinder widespread use. This study aims to develop deep learning models that generate RNFL thickness maps from fundus images, providing a cost-effective alternative to OCT. Methods: A dataset of 5000 fundus-OCT image pairs from 5000 unique glaucoma patients was used to train and compare the following four U-Net-based deep learning models: ResU-Net, R2U-Net, Nested U-Net, and Dense U-Net. All models were trained for up to 1000 epochs with early stopping (patience = 50 epochs). Performance was evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Fréchet Inception Distance (FID). Results: ResU-Net demonstrated the best performance, achieving MSE = 0.00061, MAE = 0.01877, SSIM = 0.9163, PSNR = 32.19 dB, and FID = 30.08. These results represent a 108% improvement in SSIM and a 67% improvement in PSNR compared to previously published benchmark for this task. Conclusions: This study demonstrates that deep learning models, particularly ResU-Net, can generate high-fidelity RNFL thickness maps from fundus photographs, substantially outperforming prior published benchmarks. This approach represents a potential contribution toward accessible glaucoma assessment, contingent upon prospective clinical validation and regulatory evaluation. Full article

An all-silica-based sensor comprising a section of capillary fiber spliced between two singlemode fibers (SMFs) is proposed for the simultaneous measurement of strain and temperature. By intentionally introducing a controlled transversal offset at one of the fusion splice points, core and cladding modes are simultaneously excited in the capillary, enabling the coexistence of two distinct guiding mechanisms within the sensor. The resulting spectral response exhibits two superimposed modulations associated with antiresonance (AR) guidance and a Mach–Zehnder interferometer (MZI). A comprehensive numerical model is developed to describe the interaction between the two mechanisms as a function of the offset. The model is experimentally validated through characterization of the spectral response for increasing offsets, confirming the coexistence and evolution of the AR and MZI components through free spectral range and visibility analysis. The two interference components allow for independent tracking of their wavelength shifts, enabling simultaneous strain and temperature measurements with estimated resolutions of 11.9 με and 0.45 °C, respectively. Owing to the single-element, one-step fabrication process, and the entirely silica-based configuration, the proposed sensor offers a compact and cost-effective solution for localized multiparameter monitoring. Full article

Background and Objectives: Optic disc drusen (ODD) are deposits in the optic nerve head that can look like true swelling, and in some patients, slowly damage the optic nerve and cause visual field loss. We aimed to identify which eyes are most likely to worsen over time using common clinic tests. Methods: We studied 131 adults with OCT-confirmed ODD who also had OCT-angiography (a scan that measures small blood vessels around the optic nerve) and repeated visual field tests over at least 18 months. We measured (1) the size of the drusen (maximum drusen height), (2) blood vessel density around and inside the optic nerve, and (3) change in visual field performance over time. “Fast progression” was defined as visual field worsening of ≥0.5 dB per year. Results: Eyes with superficial ODD had larger drusen than buried ODD (382.6 ± 110.9 vs. 247.2 ± 92.8 µm; p < 0.001) and more frequent visual field defects (78.6% vs. 58.7%; p = 0.02). When blood vessel density around the optic nerve was low, fast progression was much more common (52.3%) than in the middle (16.3%) or highest groups (13.6%; p < 0.001). In the adjusted model, fast progression was more likely with superficial ODD (OR 6.3) and larger drusen (OR 2.0 per 100 µm), and less likely when the vessel density was higher (OR 0.8 per 1% increase). Adding the vessel measurements improved the prediction accuracy (AUC 0.8 → 0.9; p = 0.011). Conclusions: Combining drusen size and blood vessel measurements helps identify ODD patients at higher risk of faster visual field loss and may guide closer follow-up. Full article

Accurate absolute pressure measurement is of great importance in industrial control, environmental monitoring, and aerospace. Traditional fiber-optic Fabry–Perot (F-P) pressure sensors usually involve complex microfabrication and high-cost demodulation systems, while conventional diaphragm capsule sensors are limited in sensitivity and resolution. This work presents a low-cost, high-resolution fiber-optic F-P absolute pressure sensor. The sensor uses a vacuum capsule as one reflective surface and a partially reflective fiber collimator as the other, forming a low-finesse F-P interferometer. The cavity length is linearly modulated by the elastic deformation of the capsule under pressure, and high-precision demodulation is realized using frequency modulated continuous wave (FMCW) interferometry instead of conventional spectral methods. Static experiments from 10 to 110 kPa show that the sensor exhibits a high sensitivity of 15,105 nm/kPa and a resolution of 3.3 Pa. Furthermore, the sensor operates normally within the range of −20 °C to 70 °C, exhibiting a pressure–temperature cross-sensitivity of 0.081 kPa/°C and a cavity length drift of 496 nm/h. With the advantages of high performance, simple structure, low cost, and good scalability by selecting different capsules, the proposed sensor has promising potential for practical applications in pressure measurement fields. Full article

Ground subsidence and shaft lining deformation caused by compressed dewatered bottom aquifers in deep unconsolidated strata mining areas are critical engineering challenges, making the study of the seepage–soil deformation coupling mechanism during groundwater injection remediation vital. This study built a visual cylindrical model (1025 mm × 150 mm); formulated well-graded analogous materials based on the D₂₀ principle to simulate sandy gravel layers; embedded FBG sensors at 200/400/600 mm depths, combined with a dial indicator on the model top; and conducted two water injection–dewatering cycles. Results indicate: water injection generates excess pore water pressure, placing the entire model in a tensile stress state with top rebound; post-injection vertical stress redistributes (tension above the injection point, compression below, and an interlaced transitional band), validating the necessity of full-section injection; during the second injection–dewatering cycle, tensile strain at the upper monitoring point reaches 597.77 με, while compressive strain at lower depths reaches −253.90 με, internal deformation stabilizes within 6.5–10.0 days, injection improves the in situ stress state by reducing effective stress, and the deformation of the field strata remains in a stabilization period, with the stabilization time decreasing as the depth of the strata increases. This study clarifies the temporal evolution and representative spatial variation in internal strain at monitored depths during injection, providing theoretical and design references for optimizing water injection schemes to mitigate coal mine shaft damage. Full article

The shift toward lightweight powertrain architectures necessitates a detailed characterization of polymer gears to verify their efficiency and durability. This study investigated the effectiveness of non-contact structured-light 3D scanning for evaluating the surface topography and dimensional tolerance quality of polymer gears produced via distinct manufacturing technologies. A structured-light 3D scanner was used to capture dense point clouds (exceeding 6 million points) of gears produced by three methods: conventional hobbing (POM-C), Material Extrusion (MEX) with carbon fiber reinforcement, and Selective Laser Sintering (SLS). The manufactured parts were compared against the nominal Computer Aided Design (CAD) models to evaluate their geometrical deviations in accordance with DIN 3961 and surface roughness parameters per ISO 25178. The experimental results revealed a consistent ranking of manufacturing quality. The conventionally hobbed POM-C gear exhibited superior precision, achieving DIN quality grades of Q9–Q10 and the smoothest surface finish (S_a = 5.0 µm). Among additive manufacturing techniques, SLS-printed PA 12 showed intermediate quality (Q11, S_a = 12 µm), whereas MEX-printed PPS-CF exhibited significant deviations (exceeding Q12) and the highest surface irregularity (S_a = 25 µm) due to stair-stepping effects. These findings indicate that while additive manufacturing offers geometric flexibility, conventional hobbing retains a decisive advantage in dimensional precision. The optical scanning methodology demonstrated here constitutes an efficient metrological framework for gear quality control, with potential applications extending to the quality assurance of additively manufactured adaptive fixtures and assembly tooling, including automotive assembly operations. Full article

This work proposes a fiber-tethered UAV-enabled adaptive aerial passive optical network (AA-PON) framework for rapid extension of optical access and backhaul in hard-to-reach or temporarily disrupted environments. The proposed architecture supports two distinct operating modes: (i) an aerial base station (ABS) mode for wide-area service extension and (ii) a ground-to-air-to-ground (G2A2G) mode for targeted high-speed optical bridging to ground terminal units. Unlike conventional UAV relay approaches, the proposed framework is developed as a network-level optical access/backhaul architecture based on tether-assisted aerial nodes and reconfigurable optical topology formation. In the ABS mode, representative Bus, Ring, and Star topologies are analyzed to evaluate serviceability, outage, deployment latency, and scalability as the number of UAV nodes increases. In the G2A2G mode, a stochastic-geometry-based analysis is used to characterize blockage-limited optical serviceability and infrastructure-density trade-offs. To complement the analytical study, a 2 Gb/s proof-of-concept FSO link between two fiber-tethered UAVs is demonstrated as an initial feasibility validation of the end-to-end optical link. The results show that the proposed AA-PON provides a flexible aerial optical networking framework that combines reconfigurable topology support with localized high-capacity optical access extension. Full article

Partial-discharge (PD) monitoring is essential for assessing the insulation condition of generator stator bars. Conventional methods are susceptible to electromagnetic interference and are difficult to deploy in confined stator geometries. Fiber-optic acoustic detection technology offers strong immunity to electromagnetic interference and is suitable for the narrow and high-interference environment of stator bars, but it cannot directly provide discharge magnitude information. Therefore, in this study, fiber-optic acoustic detection technology was employed to acquire partial discharge acoustic signals from stator bars, and a mandrel-type fiber-optic acoustic sensor was developed, with PD tests performed on full-scale stator bars with internal defects. Meanwhile, considering the complex temporal characteristics of PD acoustic signals, a hybrid neural network—Transformer–convolutional neural network–long short-term memory (Transformer–CNN–LSTM)—was constructed for long-term time-series modeling to establish the mapping between acoustic signals and discharge magnitude intervals. The results indicate that fiber-optic acoustic detection enables sensitive and stable detection of weak PD acoustic signals. Phase-resolved PD (PRPD) patterns from the proposed system align with the discharge characteristics of internal defects, with the acoustic signal showing a phase lag relative to the electrical PD signal. The hybrid model achieved an overall interval estimation accuracy of 96.6%, outperforming CNN and CNN-LSTM models, with accuracies of 100% and 99.4% for discharge magnitude intervals below 100 pC and above 2000 pC, respectively. Full article

The growing demand for high-fidelity, multi-parameter, distributed sensing in critical domains such as structural health monitoring, oil and gas exploration, and secure perimeter surveillance is pushing traditional optical fiber sensors (OFS) to their performance limits. Although conventional multiplexing techniques such as time-division and wavelength-division multiplexing (TDM, WDM) have been commercially successful, they are rapidly approaching fundamental bottlenecks in sensor density, spatial resolution, and data capacity. This review argues that the synergistic convergence of space-division multiplexing (SDM) and artificial intelligence (AI) represents a paradigm shift, enabling a new generation of intelligent, high-dimensional sensing networks. We comprehensively survey the state of the art in SDM-based OFS, detailing the operating principles and applications of multi-core fibers (MCFs) for ultra-dense sensor arrays and 3D shape sensing, as well as few-mode fibers (FMFs) for mode-division multiplexing and enhanced multi-parameter discrimination. However, the unprecedented spatial parallelism provided by SDM introduces significant challenges, including inter-channel crosstalk, complex signal demultiplexing, and massive data volumes. This paper systematically explores how AI, particularly machine learning (ML) and deep learning (DL), is being leveraged not merely as a tool but as an indispensable core technology to mitigate these impairments. We critically analyze AI’s role in digital crosstalk suppression, intelligent mode demultiplexing, signal denoising, and solving complex inverse problems for parameter estimation. Furthermore, we highlight how this AI–SDM synergy enables capabilities beyond the reach of either technology alone, such as super-resolution sensing and predictive analytics. The discussion is extended to include the critical supporting pillars of this ecosystem, such as advanced interrogation techniques and the associated data management challenges. Finally, we provide a forward-looking perspective on the trajectory of the field, outlining a path toward cognitive sensing networks that are self-calibrating, adaptive, and capable of autonomous decision-making. This review is intended to serve as a foundational reference for researchers and engineers at the intersection of photonics and intelligent systems, illuminating the pathway toward tomorrow’s intelligent sensing infrastructure. Full article

With the increasing importance of securing quantum communication networks, practical and robust entity authentication is a critical requirement. Accordingly, we propose and experimentally validate a quantum entity authentication (QEA) protocol specifically designed for integration with BB84-type quantum key distribution (QKD) workflows and existing terminal architectures. We analyze the protocol’s security against intercept–resend man-in-the-middle (MitM) impersonation, showing that an unauthenticated adversary induces a characteristic 25% correlation error and that the rejection probability approaches unity as the number of detected authentication events increases. For practical realization, the protocol is deployed using weak coherent pulses (WCPs) with decoy-state estimation to bound single-photon contributions and mitigate photon-number-splitting (PNS)-enabled leakage. The system is demonstrated over a field-deployed fiber link of approximately 20 km with ~8 dB optical loss using signal/decoy intensities of ~0.5/~0.15 and sending probabilities 0.88/0.10/0.02 (signal/decoy/vacuum). Across both verification directions, stable operation is observed with quantum bit error rate (QBER) typically fluctuating between 1% and 4% while the sifted key rate remains constant over time. These results provide an experimental basis for integrating physical-layer entity authentication into deployed quantum communication networks. Full article

Background: In this study, we aimed to evaluate and compare the diagnostic performance of peripapillary retinal nerve fiber layer (RNFL) thickness, macular ganglion cell–inner plexiform layer (GCIPL) thickness, and GCIPL asymmetry parameters in differentiating healthy eyes from primary angle-closure glaucoma (PACG), primary open-angle glaucoma (POAG), and secondary open-angle glaucoma (SOAG). Methods: This retrospective study included 204 eyes of 204 patients categorized into four groups: healthy controls (n = 46), PACG (n = 53), POAG (n = 58), and SOAG (n = 47). All participants underwent spectral-domain optical coherence tomography (OCT). Peripapillary RNFL thickness, sectoral and average GCIPL thickness, and GCIPL-derived asymmetry ratios were analyzed. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis. Results: Diagnostic accuracy varied according to glaucoma subtype. In distinguishing POAG from healthy controls, the average RNFL thickness (area under the ROC curve [AUC] = 0.82) demonstrated the highest diagnostic performance, followed by the superotemporal, inferotemporal, and average GCIPL thickness parameters. In contrast, no parameter reached an AUC of ≥0.80 in the PACG or SOAG comparisons. GCIPL asymmetry ratios exhibited limited discriminative ability across most analyses. Subtype differentiation was modest; POAG versus SOAG comparisons yielded AUC values up to 0.66, whereas PACG versus SOAG comparisons demonstrated minimal discrimination (AUC range: 0.47–0.63). Conclusions: Peripapillary RNFL and localized temporal GCIPL thickness measurements provide the highest diagnostic accuracy for identifying POAG. Diagnostic performance is reduced in PACG and SOAG, and the OCT parameters show limited ability to differentiate between glaucoma subtypes. GCIPL asymmetry indices do not enhance diagnostic discrimination beyond direct thickness measurements. Full article

Lithium-ion batteries (LIBs) may experience thermal runaway (TR) under thermal abuse conditions, posing significant safety risks to energy storage systems, electric vehicles, and portable electronics. To ensure the safety of LIB-powered applications, developing an effective TR early warning method is crucial. This study employs polyimide-coated femtosecond fiber Bragg grating (FBG) sensors to investigate TR characteristics in 18,650 LIBs (LiNi_1/3Mn_1/3Co_1/3O₂/graphite), including TR onset temperature determination and the evolution of temperature and radial strain at different states of charge (SOCs). Compared with existing studies, the polyimide-coated femtosecond FBGs employed here offer superior breakage resistance and high-temperature tolerance, enabling more precise temperature and strain measurements. For radial strain monitoring obtained during high-temperature-induced LIBs thermal runaway experiments, temperature compensation was achieved using polyimide-coated femtosecond FBG temperature sensors, yielding higher-accuracy strain evolution profiles. Experimental results demonstrate that the higher-SOC LIBs exhibit more severe TR eruptions, with 1.76× higher peak temperatures and 1.3× greater mass loss than low-SOC LIBs. The proposed scheme pioneers an new approach to effective active safety warning of LIBs thermal runaway. Full article