Search Results (217)

Motivated by the need to reduce failure risks, enhance real-time situational awareness, and support data-driven decision-making, this article comprehensively reviews the latest progress in pipeline safety and intelligent technology, focusing on analyzing the effectiveness and challenges faced by integrity management technology in practical situations. A structured literature survey was conducted to outline the key role and significant achievements of smart technology in improving the efficiency and reliability of pipeline safety management. Using this methodology, the review synthesizes progress in pipeline integrity management and monitoring technology, including the application of distributed strain measurement technology, wireless sensor networks, and Internet of Things technology, as well as the practical effects of deep learning and machine learning in defect detection and incident recognition. Additionally, special attention is given to analyzing the latest achievements in applications of large model technology, distributed optical fiber sensing technology, and acoustic analysis technology in the field of leakage monitoring. Based on the reviewed research, the article identifies key technical challenges, including targeted monitoring technology solutions and management strategies for the challenges in the field of pipeline safety. The findings conclude that intelligent technologies substantially enhance the development trend of AI applications. Hence, next-generation pipeline safety will rely on tightly coupled AI–IoT ecosystems. It anticipates the future of pipeline safety management by providing theoretical reference and technical support for pipeline safety guarantees and intelligent operation and maintenance. Full article

Multiphoton endomicroscopy (MPEM) has recently become a key development in optical biomedical diagnostics, providing histologically relevant in vivo images that are eliminating both the need for tissue damage during biopsy sampling and the need for dye injections. Due to its ability to visualize structures at the epithelial, extracellular matrix, and subcellular levels, MPEM offers a promising diagnostic method for precancerous conditions and early forms of gastrointestinal (GI) cancer. The high specificity of multiphoton signals—the two-photon fluorescence response of endogenous fluorophores (NADH, FAD), the second-harmonic generation signal from collagen, and others—makes this method a promising alternative to both traditional histology and confocal endoscopy, enabling real-time assessment of metabolic status, intestinal epithelial cell status, and stromal remodeling. Despite the promising prospects of multiphoton microscopy, its practical implementation is progressing extremely slowly. The main factors here include the difficulty of delivering ultrashort pulses with high peak power, which is necessary for multiphoton excitation (MPE), and obtaining these pulses at the required wavelengths to activate the autofluorescence mechanism. One of the most promising solutions is the use of specialized multimode optical fibers that can both induce beam self-cleaning (BSC), which allows for the formation of a stable beam profile close to the fundamental mode, and significantly broaden the optical spectrum, which can ultimately cover the entire region of interest. This review presents the biophysical foundations of multiphoton microscopy of GI tissue, existing endoscopic architectures for MPE, and an analysis of the potential for using novel nonlinear effects in multimode optical fibers, such as the BSC effect and supercontinuum generation. It is concluded that the use of optical fibers in which the listed effects are realized in the tracts of multiphoton endomicroscopes can become a key step in the creation of a new generation of high-resolution instruments for the early detection of malignant neoplasms of the GI tract. Full article

The integration of complementary metal–oxide–semiconductor (CMOS) and micro-electromechanical systems (MEMSs) technologies for miniaturized biosensor fabrication enables unprecedented spatiotemporal resolution in monitoring the bioelectrical activity of the nervous system. Wafer-level CMOS technology incurs high costs, but multi-project wafer (MPW) runs mitigate this by allowing multiple users to share a single wafer. Still, monolithic CMOS biosensors require specialized surface materials or device geometries incompatible with standard CMOS processes. Performing MEMS post-processing on the few square millimeters available in MPW dies remains a significant challenge. In this paper, we present a MEMS post-processing workflow tailored for CMOS dies that supports both surface material modification and layout shaping for intracortical biosensing applications. To address lithographic limitations on small substrates, we optimized spray-coating photolithography methods that suppress edge effects and enable reliable patterning and lift-off of diverse materials. We fabricated a needle-like, 512-channel simultaneous neural recording active pixel sensor (SiNAPS) technology based neural probe designed for integration with optical fibers for optogenetic studies. To mitigate photoelectric effects induced by light stimulation, we incorporated a photoelectric shield through simple modifications to the photolithography mask. Optical bench testing demonstrated >96% light-shielding effectiveness at 3 mW of light power applied directly to the probe electrodes. In vivo experiments confirmed the probe’s capability for high-resolution electrophysiological measurements. 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

Antibiotics are persistent organic pollutants that pose a serious problem for water resources, ultimately having a detrimental effect on human and animal health. The most important aspect of controlling and preventing the spread of antibiotics and their degradation products is continuous screening and monitoring of environmental samples. Optical sensing technologies represent a large group of sensors that allow short-term detection of antibiotics in non-laboratory settings. This article reviews the advances in optical sensing systems (colorimetric, fluorescent, surface-enhanced Raman spectra-based, surface plasmon resonance-based, localized surface plasmon resonance-based, photonic crystal-based, fiber optic, molecularly imprinted polymer-based and electro-optical platforms) for the detection of antibacterial drugs in water. Special attention is paid to the evaluation of the analytic characteristics of optical sensors for the analysis of antibiotics. Particular attention is paid to electro-optical sensing and to the unique possibility of its use in antibiotic determination. Potential strategies are considered for amplifying the recorded signals and improving the performance of sensor systems. The main trends in optical sensing for antibiotic analysis and the prospects for the commercial application of optical sensors are described. Full article

This study presents the experimental optimization of an interferometric optical fiber sensor for acoustic emission (AE) detection. The system employs a simple and low-cost structure composed of sensing and reference fibers, enabling interference-based detection without specialized components such as fiber Bragg gratings or Fabry–Perot cavities. A narrowband laser source was selected through comparative experiments for its superior stability and interference performance. The influence of fiber-loop parameters, including the number of turns and the optical-path intensity ratio, was systematically evaluated to clarify their effects on AE sensitivity and frequency response. The experimental results demonstrate that detection performance and bandwidth can be flexibly tuned by optimizing the loop configuration. Finally, the sensor was validated using a tensile test, successfully detecting AE signals in the range of 20 kHz to 1 MHz. The proposed system provides a robust, EMI-resistant, and cost-effective interferometric solution for AE monitoring. Full article

Simulation-based laboratories have become an essential component of modern engineering education, particularly in courses where access to physical equipment is limited. This paper presents a structured methodology for teaching the fundamentals of optical fiber communication systems using OptiPerformer 18, i.e., a freely available optical communication simulation platform. The novelty of this work lies in integrating a complete set of parameter-driven laboratory exercises, covering eye-diagram analysis, chromatic dispersion and dispersion compensation, Gaussian pulse propagation, and BER/Q-factor evaluation, into both distance and face-to-face teaching, and validating their effectiveness across four academic years involving more than 200 students. Representative simulation results generated with OptiPerformer are provided to illustrate the learning process and to demonstrate how key transmission impairments and system-level behaviors can be visualized and quantitatively analyzed without specialized hardware. The pedagogical effectiveness of the approach is assessed through student surveys and final grades, showing consistently high learning outcomes and strong student engagement in both remote and in-person settings. These findings indicate that the proposed simulation-based laboratory framework offers a scalable, hardware-independent, and conceptually rich alternative to traditional fiber-optic laboratory classes, supporting deeper understanding of transmission physics and enhancing analytical and problem-solving skills essential in modern optical communication engineering. Full article

This review summarizes recent progress and emerging trends in multiparameter optical fiber sensing, emphasizing techniques that enable the simultaneous measurement of temperature, strain, acoustic waves, pressure, and other environmental quantities within a single sensing network. Such capabilities are increasingly important for structural health monitoring, environmental surveillance, industrial diagnostics, and geophysical observation, where multiple stimuli act on the fiber simultaneously. The paper outlines the physical principles and architectures underlying these systems and focuses on strategies for compensating and decoupling cross-sensitivity among measured parameters. Special attention is devoted to advanced distributed sensing schemes based on coherent optical frequency-domain reflectometry (C-OFDR), coherent phase-sensitive time-domain reflectometry (Φ-OTDR), and Brillouin optical time-domain reflectometry (BOTDR). Their theoretical foundations, their signal-processing algorithms, and the design modifications that improve parameter discrimination and accuracy are analyzed and compared. The review also highlights the roles of polarization and mode diversity and the growing application of machine-learning techniques in the interpretation and calibration of data. Finally, current challenges and promising directions for the next generation of fiber-optic multiparameter sensors are outlined, with a view toward high-resolution, low-cost, and field-deployable solutions for real-world monitoring applications. Full article

Fiber optic vibration identification has significant applications in engineering fields, like security surveillance and structural health assessment. However, present methods primarily depend on either temporal–frequency domain or image features simply, challenging the simultaneous consideration of both image attributes and the temporal dependencies of vibration signals. Consequently, the performance of fiber optic vibration recognition remains subject to improvement, and its effectiveness further diminishes under conditions of uneven data distribution. Therefore, this study integrates residual neural networks, long short-term memory networks, and diffusion denoising probabilistic models to propose a fiber optic vibration recognition method DR-LSTM, which incorporates both image and temporal features while ensuring high recognition accuracy across balanced and imbalanced data distributions. Firstly, features of the Mel spectrum image and temporal characteristics of fiber optic vibration events are extracted. Subsequently, specialized neural network models are developed for categories with scarce data to produce similar images for data augmentation. Finally, the retrieved composite characteristics are employed to train recognition models, thereby improving recognition accuracy. Experiments were performed on datasets from natural environment and anthropogenic vibration, including for both balanced and imbalanced data distributions. The results show that on the two balanced datasets, the proposed model achieves improvements in classification accuracy of at least 0.67% and 7.4% compared to conventional methods. In the two imbalanced datasets, the model’s accuracy exceeds that of conventional models by a minimum of 18.79% and 2.4%. This validates the effectiveness and feasibility of DR-LSTM in enhancing recognition accuracy and addressing issues with imbalanced data distribution. Full article

With the growing global deployment of Fiber-to-the-Home (FTTH) networks driven by the demand for ensuring high-capacity broadband services, mobile network operators (MNOs) face challenges of excessive energy consumption (EC) of wired optical access networks (OANs). This paper presents a comprehensive review of methods aimed at improving the energy efficiency (EE) of wired access passive optical networks (PONs) and active optical networks (AONs). The most important energy management and power-saving methods for Optical Line Terminals (OLTs) and Optical Network Units (ONUs), as key OAN components, are overviewed in the paper. Special attention in the paper is further given to analyzing the impact of a constant increase in the number of subscribers and average data rate per subscriber on global instantaneous power and annual energy consumption trends of FTTH Gigabit PONs (GPONs) and FTTH point-to-point (P-t-P) networks. The analysis combines the real ONU/OLT device-level power profiles and the number of installed OLT and ONU devices with data traffic and subscriber growth projections for the period 2025–2035. A comparative EE analysis is performed for different MNO FTTH OAN architectures and technologies, point-of-presence (PoP) subscriber capacities, and GPON-to-P-t-P subscriber distribution ratios. The findings indicate that different FTTH PON and AON architectures, FTTH technologies, and PON-to-AON subscriber distributions can yield significantly different EE gains in the future. This review paper can serve as a decision-making guide for MNOs in balancing performance and sustainability goals, and as a reference for researchers, engineers, and policymakers engaged in designing next-generation wired optical access networks with minimized environmental impact. Full article

This study examines the soliton dynamics in the time-fractional cubic–quintic nonlinear non-paraxial propagation model, applicable to optical signal processing, nonlinear optics, fiber-optic communication, and biomedical laser–tissue interactions. The fractional framework exhibits a wide range of nonlinear effects, such as self-phase modulation, wave mixing, and self-focusing, arising from the balance between cubic and quintic nonlinearities. By employing the Multivariate Generalized Exponential Rational Integral Function (MGERIF) method, we derive an extensive catalog of analytic solutions, multi-peakon structures, lump solitons, kinks, and bright and dark solitary waves, while periodic and singular solutions emerge as special cases. These outcomes are systematically constructed within a single framework and visualized through 2D, 3D, and contour plots under both anomalous and normal dispersion regimes. The analysis also addresses modulation instability (MI), interpreted as a sideband amplification of continuous-wave backgrounds that generates pulse trains and breather-type structures. Our results demonstrate that cubic–quintic contributions substantially affect MI gain spectrum, broadening instability bands and permitting MI beyond the anomalous-dispersion regime. These findings directly connect the obtained solution classes to experimentally observed routes for solitary wave shaping, pulse propagation, and instability and instability-driven waveform formation in optical communication devices, photonic platforms, and laser technologies. Full article

This review examines how diabetes affects the ganglion cells of the retina, including the axons that make up the optic nerve. Links between established changes in the morphology of retinal ganglion cells (RGCs) and vision loss, as well as other functions, such as the pupillary light reflex, are considered. RGC morphology and function are significantly altered in both animal models and humans with diabetes. Diabetes affects all parts of the RGC, including the dendrites, the cell body, the axons making up the nerve fiber layer, and the optic nerve. Subtypes of RGCs appear to be affected differently by diabetes, and the morphology and electrophysiological output are more significantly affected in ON-RGCs than in OFF cells, which may explain part of the mechanism underlying the widely documented diabetes-induced reduction in contrast sensitivity. Furthermore, the morphology of the specialized light-sensitive melanopsin-containing RGCs also appears to be affected by diabetes, which may explain deficits in circadian rhythm and the pupillary light reflex. Potential therapeutic approaches aimed at protecting RGCs in diabetes are also discussed. Overall, strong evidence supports the conclusion that diabetes impacts the form and function of RGCs and their axons within the optic nerve, resulting in deficient regulation of circadian rhythms and the pupillary light reflex, in addition to vision. Full article

With accelerating urbanization and the exponential growth in vehicle populations, high-precision traffic monitoring has become indispensable for intelligent transportation systems (ITSs). Conventional sensing technologies—including inductive loops, cameras, and radar—suffer from inherent limitations: restrictive spatial coverage, prohibitive installation costs, and vulnerability to adverse weather. Distributed Acoustic Sensing (DAS), leveraging Rayleigh backscattering to convert standard optical fibers into kilometer-scale, real-time vibration sensor networks, presents a transformative alternative. This paper provides a comprehensive review of the principles and system architecture of DAS for roadway traffic monitoring, with a focus on signal processing techniques, feature extraction methods, and recent advances in vehicle detection, classification, and speed/flow estimation. Special attention is given to the integration of deep learning approaches, which enhance noise suppression and feature recognition under complex, multi-lane traffic conditions. Real-world deployment cases on highways, urban roads, and bridges are analyzed to demonstrate DAS’s practical value. Finally, this paper delineates emerging research trends and technical hurdles, providing actionable insights for the scalable implementation of DAS-enhanced ITS infrastructures. Full article

Optical fiber-based biosensors have proven to be a powerful platform for chemical and biological analysis due to their compact size, fast response, high sensitivity, and immunity to electromagnetic interference. Among the various fiber designs, tapered optical fibers have gained prominence due to the increased evanescent fields that significantly improve light–analyte interactions, making them well-suited for advanced sensing applications. At the same time, advances in microfluidics have allowed for the precise control of small-volume fluids, supporting integration with optical fiber sensors to create compact and multifunctional optofluidic systems. This review explores recent developments in optical fiber optofluidic sensing, with a focus on two primary architectures: in-fiber and outside-fiber platforms. The advantages, limitations, and fabrication strategies for each are discussed, along with their compatibility with various sensing mechanisms. Special emphasis is placed on tapered optical fibers, focusing on design strategies, fabrication, and integration with microfluidics. While in-fiber systems offer compactness and extended interaction lengths, outside-fiber platforms offer greater mechanical stability, modularity, and ease of functionalization. The review highlights the growing interest in tapered fiber-based optofluidic biosensors and their potential to serve as the foundation for autonomous lab-on-a-fiber technologies. Future pathways for achieving self-contained, multiplexed, and reconfigurable sensing platforms are also discussed. Full article