Search Results (1,862)

A method for the second-harmonic generation (SHG) in optical fibers by exploiting the third-order nonlinearity under an external electric field is proposed. The analysis begins with the electric polarization vector of the SHG, and the analytical solution for the SHG is presented. When fiber birefringence is neglected, a mode-field matching condition is introduced. The nonlinearity-induced shift in propagation constant is provided based on Gaussian approximation. For a specific case, the power of SHG is calculated. The results show that the SHG power scales quadratically with the nonlinear coefficient. Reducing the effective area of the fiber and increasing the nonlinear coefficient can enhance the SHG power by 1–2 orders of magnitude. Since phase matching strongly affects the SHG process, optimizing the fiber design is crucial. Additionally, the polarization state of SHG is shown to have the same as the equivalent optical field of the injected fundamental wave. This work demonstrates potential for distributed sensing of electric fields and lightning events in high-voltage power grids using optical fibers. Full article

Optical fiber surface plasmon resonance (SPR) sensing, as a label-free, highly sensitive, rapid-response and in situ detection technology, has demonstrated significant utility in various physical, chemical and biological detection applications. This paper focuses on a fiber-integrated microscale spiral-grating tapered gold tip SPR sensor. We first introduce the working principle and sensing capability with high space–time resolution of this SPR microsensor. Then we provide a comprehensive description of its application in the study on the important fundamental scientific issue of liquid–liquid diffusion. Finally, we demonstrate the application of the spiral-grating tapered gold tip to plasmonic enhanced fluorescence and scanning near-field optical microscopy. By systematically summarizing the excellent multifunctional sensing performance of the microscale spiral-grating tapered gold tip, this paper aims to provide new optical schemes and tools for the study on complex physicochemical processes and light-matter interactions at microscale and nanoscale. Full article

To address the impact of environmental and equipment factors on signal identification in highway tunnel drainage pipeline blockage monitoring, this study aims to elucidate the influence patterns of pipeline flow rate, optical fiber deployment scheme, and fiber performance on blockage-induced acoustic signals. A full-scale concrete pipeline experimental platform was established. Data were acquired using a HIFI-DAS V2 sensing system. The time–frequency domain characteristics of acoustic signals under different flow rates (50 m³/h and 100 m³/h), fiber deployment schemes (inside the pipe, outside the pipe, and outside a soundproofing layer), and fiber materials (six typical types) were analyzed and compared. The degree of influence of each factor on signal amplitude and dominant frequency components was quantified. The experimental results indicate that: Compared to a flow rate of 50 m³/h, the amplitude characteristic value at the blockage channel exhibited a marked increase at 100 m³/h, accompanied by an increase in the number and amplitude of dominant frequency components. While the dominant frequency components of the acoustic signals were less stable across the three deployment schemes, the overall amplitude at the blockage channel was consistently higher than that at non-blockage channels. When the fiber was deployed farther from the fluid core (outside the soundproofing layer), the dominant frequencies essentially disappeared, with energy distributed in a broadband form. The peak amplitude and array energy of the sensitive vibration sensing fiber were 2 times and 3.6 times those of the worst-performing type, respectively. Furthermore, its physical properties are better suited to the tunnel environment, effectively enhancing signal acquisition stability and the signal-to-noise ratio. Comprehensive analysis demonstrates that deploying sensitive fibers inside the pipe is more conducive to the accurate identification of blockage events. Moreover, uniform dominant frequency components and threshold criteria are not recommended along the entire length of the drainage pipe. This research provides theoretical and experimental support for parameter optimization of DAS systems to achieve high-precision pipeline blockage monitoring in complex tunnel environments. Full article

The external color of smoked sausages is a critical indicator of quality and uniformity in processing. Commercial colorimeters are unsuitable for high-throughput sorting due to the challenges posed by the sausage’s curved cylindrical surface and the need for an inline application. This study introduces a novel non-contact sensing module (LEDs at 45°, fiber optic collection at 0°) to acquire spectral data (400–700 nm) and derive CIE LAB. First, a handheld prototype validated the accuracy of the sensing module against a benchtop spectrophotometer. It successfully categorized five color grades (‘Over light’, ‘Light’, ‘Standard’, ‘Dark’, and ‘Over dark’) with a clear distribution on the a*-L* diagram. This established acceptable color boundary conditions (44.2 < L* ≤ 61.3, 14.1 < a* < 23.9). Second, three sensing modules were integrated around a conveyor belt at 120° intervals, forming the core of an automated inline sorting system. Blind field tests (n = 150) achieved high sorting accuracies of 95.3–97.3% with an efficient inspection time of less than 2 s per sausage. This work realizes the standardization, digitalization, and automation of food color inspection, demonstrating strong potential for smart manufacturing in the processed meat industry. Full article

Rapid economic growth has directly driven up energy demand, and the gradual depletion of traditional fossil fuels has severely hindered sustainable development. Developing green and efficient geothermal exploitation technologies constitutes a crucial measure for tackling this sustainable development issue. This paper presents a field test associated with a clean energy system conducted in the Guanzhong Basin, China, with the core component of a coaxial casing deep geothermal well. A distributed temperature sensing system (DTS system) with over 3000 m-depth optical fiber installed and adopted to monitor near-wellbore formation temperature changes. Combining information on the inlet/outlet water temperature and flow rate monitored by an integrated temperature–pressure monitoring system, the heat transfer patterns during the operation of the deep geothermal well are deeply investigated. The research results demonstrate that a higher operation parameter of flow rates has a significant increasing effect on the heat transfer capacity of heat exchangers for coaxial casing deep geothermal wells. Although the increase in inlet temperature has minimal effect on the outlet temperature, it leads to a continuous decline in heat transfer capacity. In addition, as heat exchange duration extends, the geothermal gradient of the near-wellbore formation progressively declines. Full article

This paper proposes a large-scale, self-healing multipoint fiber Bragg grating (FBG) sensor network that employs reinforcement learning (RL) techniques to enhance the resilience and efficiency of optical wireless communication networks. The system features a mesh-structured, self-healing ring-mesh architecture employing 2 × 2 optical switches, enabling robust multipoint sensing and fault tolerance in the event of one or more link failures. To further extend network coverage and support distributed deployment scenarios, free-space optical (FSO) links are integrated as wireless optical backhaul between central offices and remote monitoring sites, including structural health, renewable energy, and transportation systems. These FSO links offer high-speed, line-of-sight connections that complement physical fiber infrastructure, particularly in locations where cable deployment is impractical. Additionally, RL-based artificial intelligence (AI) techniques are employed to enable intelligent path selection, optimize routing, and enhance network reliability. Experimental results confirm that the RL-based approach effectively identifies optimal sensing paths among multiple routing options, both wired and wireless, resulting in reduced energy consumption, extended sensor network lifespan, and improved transmission delay. The proposed hybrid FSO–fiber self-healing sensor system demonstrates high survivability, scalability, and low routing path loss, making it a strong candidate for future services and mission-critical applications. Full article

This study presents a robust deep learning-based approach for temperature sensing using Fiber Specklegram Sensors (FSS), leveraging an extended experimental framework to evaluate model generalization. A convolutional neural network (CNN), specifically a customized MobileNet architecture (MNet-reg), was trained on multiple experimental datasets to assess the impact of increasing data availability on sensing accuracy. Generalization is evaluated as cross-dataset performance under unseen experimental realizations, rather than under controlled intra-dataset splits. The experimental setup utilized a multi-mode optical fiber (MMF) (core diameter 62.5 µm) subjected to controlled thermal cycles via a PID-regulated heating system. The curated dataset comprises 24,528 specklegram images captured over a temperature range of 25.00 °C to 200.00 °C with increments of ~0.20 °C. The experimental results demonstrate that models trained with an increasing number of datasets (from 1 to 13) significantly improve accuracy, reducing Mean Absolute Error (MAE) from 13.39 to 0.69 °C, and achieving a Root Mean Square Error (RMSE) of 0.90 °C with an R² score of 0.99. Our systematic analysis establishes that scaling experimental data diversity—through training on multiple independent realizations—is the foundational strategy to overcome domain shift and enable robust cross-dataset generalization. Full article

Unmanned Aerial Vehicle (UAV)-based inspection is crucial for the maintenance and monitoring of high-voltage transmission lines, but detecting small objects in inspection images presents significant challenges, especially under complex backgrounds and varying lighting. These challenges are particularly evident when detecting the wire features of optical fiber composite overhead ground wire and conventional ground wires. Optical fiber composite overhead ground wire (OPGW) is a specialized cable designed to replace conventional shield wires on power utility towers. It contains one or more optical fibers housed in a protective tube, surrounded by layers of aluminum-clad steel and/or aluminum alloy wires, ensuring robust mechanical strength for grounding and high-bandwidth capabilities for remote sensing and control. Existing detection methods often struggle with low accuracy, insufficient performance, and high computational demands when dealing with small objects. To address these issues, this paper proposes an energy-efficient OPGW feature detection model for UAV-based inspection. The model incorporates a Feature Enhancement Module (FEM) to replace the C3K2 module in the sixth layer of the YOLO11 backbone, improving multi-scale feature extraction. A P2 shallow detection head is added to enhance the perception of small and edge features. Additionally, the traditional Intersection over Union (IoU) loss is replaced with Normalized Wasserstein Distance (NWD) loss function, which improves boundary regression accuracy for small objects. Experimental results show that the proposed method achieves a ${\mathrm{mAP}}_{50}$ of 78.3% and ${\mathrm{mAP}}_{50–95}$ of 52.0%, surpassing the baseline by 2.3% and 1.1%, respectively. The proposed model offers the advantages of high detection accuracy and low computational resource requirements, providing a practical solution for sustainable UAV-based inspections. Full article

We propose a vector bending sensor based on a tapered few-mode multi-core fiber (FM-MCF). A seven-core six-mode fiber is tapered with an optimized taper ratio, enabling bending sensing through power monitoring. When the tapered FM-MCF bends, coupling occurs between the central core and side cores in the tapered region. By monitoring the power of all cores and employing a power differential method, the bending direction and curvature can be reconstructed. The results show that within a curvature range of 2.5 m⁻¹ to 10 m⁻¹, the sensitivity of the ratio of the side core’s power to the middle core’s power with respect to curvature is not less than 0.14/m⁻¹. A deep fully connected feedforward neural network (DNN) is used to demodulate all power information and predict the bending shape of the optical fiber. The algorithm predicts the bending radius and rotation angle with mean absolute errors less than 0.038 m and 3.087°, respectively. This method is expected to achieve low-cost, high-sensitivity bending measurement applications with vector direction perception, providing an effective solution for scenarios with small curvatures that are challenging to detect using conventional sensing methods. Full article

We propose an external-cavity laser that combines wide tunability with narrow linewidth. The design utilizes a low-loss Si₃N₄ waveguide and a thermally tuned cascaded triple-ring resonator to enable continuous wavelength tuning. The numerical simulations indicate that the proposed laser exhibits a tuning range of 64 nm with a sub-kHz linewidth, an SMSR of more than 80 dB, an output power of 24 mW and a linewidth of 193 Hz at 1550 nm. Furthermore, we perform comparative system-level simulations using QPSK and 16QAM coherent optical fiber links at 50 Gbaud over 100 km. Under identical conditions, when the laser linewidth is reduced from 1 MHz level to 193 Hz, the BER of 16QAM decreases from 1.5 × 10⁻³ to 5.3 × 10⁻⁵. These results indicate that a narrow linewidth effectively mitigates phase noise degradation in high-order modulation formats. With its narrow linewidth, wide tuning range, high SMSR, and high output power, this laser serves as a promising on-chip light source for high-resolution sensing and coherent optical communications. Full article

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

Accurate measurement of luminous flux from solid-state light sources typically requires spectroradiometric equipment or integrating spheres. This work investigates a compact alternative based on a fiber-coupled RGB photodiode system and develops the optical, spectral, and geometric foundations required to obtain traceable flux estimates from reduced-channel measurements. The system under study comprises an LED with known spectral power distribution (SPD), optical head, optical fiber, a protective sensor window, and a photodiode matrix type sensor. A complete end-to-end analysis of the optical path is presented, including geometric coupling efficiency, fiber transmission and angular redistribution, Fresnel losses in the sensor window, and the mosaic structure of the sensor. Additional effects such as fiber–sensor alignment, fiber-facet tilt, air gaps, and LED placement tolerances are quantified and incorporated into a formal uncertainty budget. Using the manufacturer-supplied SPD of the reference LED together with the measured R, G, and B channel responsivity functions of the sensor, a calibration-based mapping is established to reconstruct photopic luminous flux from the three-channel outputs. These results demonstrate that, with appropriate modeling and calibration of all optical stages, a fiber-coupled RGB photodiode mosaic can provide practical and scientifically meaningful luminous-flux estimation for white LEDs, offering a portable and cost-effective alternative to conventional photometric instrumentation in mid-accuracy applications. Further optimization of computation speed can enable fully integrated measurement systems in resource-constrained environments. Full article

The simultaneous measurement of strain and temperature using Fiber Bragg Grating (FBG) sensors presents a significant challenge due to the intrinsic cross-sensitivity of the Bragg wavelength. While recent studies have increasingly employed “black-box” machine learning algorithms to address this ambiguity, such approaches often overlook the physical limitations of the sensor’s spectral response. This paper challenges the assumption that advanced algorithms alone can compensate for data that is physically ambiguous. We propose a “Sensor-Algorithm Co-Design” methodology, demonstrating that robust discrimination is achievable only when the sensor architecture exhibits a unique, orthogonal physical signature. Using a rigorous Transfer Matrix Method (TMM) and 4 × 4 polarization analysis, we evaluate three distinct architectures. Quantitative analysis reveals that a standard Quadratically Chirped FBG (QC-FBG) functions as an “ill-conditioned baseline” failing to distinguish measurands due to feature space collapse ($K_{cond}>4600$). Conversely, we validate two robust co-designs: (1) An Amplitude-Modulated Superstructure FBG (S-FBG) paired with an Artificial Neural Network (ANN), utilizing thermally induced duty-cycle variations to achieve high accuracy ($~3.4 °\mathrm{C}$ error) under noise; and (2) A Polarization-Diverse Inverse-Gaussian FBG (IG-FBG) paired with a 4 × 4 K-matrix, exploiting strain-induced birefringence ($K_{cond}\approx 64$). Furthermore, we address the data scarcity issue in AI-driven sensing by introducing a Physics-Informed Neural Network (PINN) strategy. By embedding TMM physics directly into the loss function, the PINN improves data efficiency by 2.2× compared to standard models, effectively bridging the gap between physical modeling and data-driven inference, addressing the critical data scarcity bottleneck identified in recent optical sensing roadmaps. Full article

A novel multi-wavelength differential absorption lidar operating at 1.5 μm band is proposed and theoretically analyzed for simultaneous remote sensing of vertical profiles of H₂¹⁶O, HD¹⁶O, and the isotopic ratio δD. The spectral band is compatible with mature, commercially available fiber-optic components, ensuring practical implementability. By employing the 1976 U.S. Standard atmosphere and considering the temperature dependence of H₂¹⁶O, the systematic error induced by a +1 K temperature uncertainty within the 2 km altitude is limited to 0.81% through appropriate absorption line selection. Simulations of atmospheric backscattered signals with a time resolution of 30 min and a range resolution of 120 m show that random error remains below 0.16% up to 2 km. The simultaneous retrieval errors of H₂¹⁶O and HD¹⁶O mixing ratio profiles at 2 km are 0.13 g/kg (3.19%) and 1.69 × 10⁻⁴ g/kg (18.02%), respectively, from which the δD is successfully and reliably retrieved. The results provide essential technical guidance for implementing high-resolution, isotopologue-resolved lidar observations in atmospheric science. Full article

This study proposes a cylindrical high-temperature-resistant fiber-optic composite sensor based on the EFPI-FBG hybrid structure for simultaneous temperature and pressure measurement, addressing the demand for high-performance monitoring in harsh environments. The sensor’s core consists of a cylindrical pressure chamber, a metal substrate, and an EFPI-FBG sensing structure fixed via resistance welding and high-temperature ceramic adhesive. The cylindrical pressure chamber converts pressure into axial deformation to modulate the EFPI cavity length, while the FBG with one end floating is exclusively used for temperature compensation, avoiding pressure interference. The EFPI cavity length exhibits a linear relationship with pressure, achieving a sensitivity of 0.171 μm/MPa and a linear correlation coefficient of 0.9986. Stable operation up to 600 °C and 20 MPa is demonstrated, with a decoupling matrix enabling accurate dual-parameter sensing. Full article