Search Results (469)

Optical solitons have emerged as a highly active research domain in nonlinear fiber optics, driving significant advancements and enabling a wide range of practical applications. This study investigates the dynamics of dark solitons in systems governed by the resonant nonlinear Schrödinger equation (RNLSE). For the RNLSE with third-order (3OD) and fourth-order (4OD) dispersions, the dark soliton solution of the equation in the (1+1)-dimensional case is derived using the F-expansion method, and the analytical study is extended to the (2+1)-dimensional case via the self-similar method. Subsequently, the nonlinear equation incorporating perturbation terms is further studied, with particular attention given to the dark soliton solutions in both one and two dimensions. The soliton dynamics are illustrated through graphical representations to elucidate their propagation characteristics. Finally, modulation instability analysis is conducted to evaluate the stability of the nonlinear system. These theoretical findings provide a solid foundation for experimental investigations of dark solitons within the systems governed by the RNLSE model. Full article

Driven by the rapid development of e-commerce and intelligent logistics, the volume of express delivery services has surged, making the efficient and accurate identification of shipping information a core requirement for automatic sorting systems. However, traditional Optical Character Recognition (OCR) technology struggles to meet the accuracy and real-time demands of complex logistics scenarios due to challenges such as image distortion, uneven illumination, and field overlap. This paper proposes a three-level collaborative recognition method based on deep learning that facilitates structured information extraction through regional normalization, dual-path parallel extraction, and a dynamic matching mechanism. First, the geometric distortion associated with contour detection and the lightweight direction classification model has been improved. Second, by integrating the enhanced YOLOv5s for key area localization with the upgraded PaddleOCR for full-text character extraction, a dual-path parallel architecture for positioning and recognition has been constructed. Finally, a dynamic space–semantic joint matching module has been designed that incorporates anti-offset IoU metrics and hierarchical semantic regularization constraints, thereby enhancing matching robustness through density-adaptive weight adjustment. Experimental results indicate that the accuracy of this method on a self-constructed dataset is 89.5%, with an F1 score of 90.1%, representing a 24.2% improvement over traditional OCR methods. The dynamic matching mechanism elevates the average accuracy of YOLOv5s from 78.5% to 89.7%, surpassing the Faster R-CNN benchmark model while maintaining a real-time processing efficiency of 76 FPS. This study offers a lightweight and highly robust solution for the efficient extraction of order information in complex logistics scenarios, significantly advancing the intelligent upgrading of sorting systems. Full article

This study presents a U-Net-based automatic segmentation framework for quantitative analysis of surface morphology in a PEEK-based composite following tribological testing. Controlled Pin-on-Disc tests were conducted to characterize tribological performance, worn surfaces were captured by laser scanning microscopy to acquire optical images and height maps, and the model produced pixel-level segmentation masks distinguishing different regions, enabling high-throughput, objective analysis of worn surface morphology. Sixty-three manually annotated image sets—with labels for fiber, third-body patch, and matrix regions—formed the training corpus. A 70-layer U-Net architecture with four-channel input was developed and rigorously evaluated using five-fold cross-validation. To enhance performance on the challenging patch and fiber classes, the top five model instances were ensembled through Bayesian-optimized weighted voting, achieving significant improvements in class-specific F1 metrics. Segmentation outputs on unseen data confirmed the method’s robustness and generalizability across complex surface topographies. This approach establishes a scalable, accurate tool for automated morphological analysis, with potential extensions to real-time monitoring and other composite systems. Full article

Electrochromic (EC) devices are gaining increasing attention for next-generation smart windows and low-power displays due to their reversible color modulation, low operating voltage, and flexible form factors. Recently, electrochromic energy storage devices (EESDs) have emerged as a promising class of multifunctional systems, enabling simultaneous energy storage and real-time visual monitoring. In this study, we report a flexible dual-functional EESD constructed using polyaniline (PANI) films doped with anthraquinone-1-sulfonic acid sodium salt (AQS), coupled with a redox-active PVA-based gel electrolyte also incorporating AQS. The incorporation of AQS into both the polymer matrix and the gel electrolyte introduces synergistic redox activity, facilitating bidirectional Faradaic reactions at the film–electrolyte interface and within the bulk gel phase. The resulting vertically aligned PANI-AQS nanoneedle films provide high surface area and efficient ion pathways, while the AQS-doped gel electrolyte contributes to enhanced ionic conductivity and electrochemical stability. The device exhibits rapid and reversible color switching from light green to deep black (within 2 s), along with a high areal capacitance of 194.2 mF·cm⁻² at 1 mA·cm⁻² and 72.1% capacitance retention over 5000 cycles—representing a 31.5% improvement over undoped systems. These results highlight the critical role of redox-functionalized gel electrolytes in enhancing both the energy storage and optical performance of EESDs, offering a scalable strategy for multifunctional, gel-based electrochemical systems in wearable and smart electronics. Full article

Low-finesse Fabry–Perot (F–P) cavities, widely applied in the field of micro-displacement measurement, offer significant advantages in reducing the influence of higher-order reflections and improving the accuracy of measurement systems. Generally, an F–P cavity finesse of 0.5 is required to achieve high-precision micro-displacement measurements. However, in optical design, low-finesse cavities impose strict requirements on reflectivity, and maintaining fine stability during cavity movement is challenging. Achieving ideal orthogonal interference with a finesse of 0.5 thus presents considerable difficulties. This study proposes a novel low-finesse F–P cavity design that employs a high-reflectivity spherical reflector and the end face of a fiber collimator as the reflective surfaces of the cavity. By utilizing beam divergence characteristics and geometric parameters, a structure with a finesse of approximately 0.5 is quantitatively designed, enabling a simplified implementation without the need for angular alignment. Compared with conventional approaches, this method eliminates the need for precise angular alignment of the reflective surfaces, significantly simplifying implementation. The experimental results show that, under fixed receiving field angles and beam radii of the fiber collimators, ideal orthogonal interference can be achieved by selecting the radius of the reflective sphere. Under varying working distances, the average finesse values of the interference spectra measured by Collimators 1 and 2 are 0.496 and 0.502, respectively, both close to the theoretical design value of 0.5, thereby meeting the design requirements. Full article

In this paper, we present the development of a new semi-distributed interferometer (SDI) biosensor with a Zn, Cu, and Co metal oxide nanopowder coating for the detection of a kidney disease biomarker as a model system. The combination of nanopowder coating with the SDI platform opens up unique opportunities for improving measurement reproducibility while maintaining high sensitivity. The fabrication of sensors is simple, which involves one splice and subsequent cutting at the end of an optical fiber. To ensure specific detection of the biomarker, a monoclonal antibody was immobilized on the surface of the probe. The biosensor has demonstrated an impressive ability to detect biomarkers in a wide range of concentrations, from 1 aM to 100 nM. The theoretical limit of detection was 126 fM, and the attomolar detection level was experimentally achieved. The sensors have achieved a maximum sensitivity of 190 dB/RIU and operate with improved stability and reduced dispersion. Quantitative analysis revealed that the sensor’s response gradually increases with increasing concentration. The signal varies from 0.05 dB at 1 aM to 0.81 dB at 100 nM, and the linear correlation coefficient was R² = 0.96. The sensor showed excellent specificity and reproducibility, maintaining detection accuracy at about 10⁻⁴ RIU. This opens up new horizons for reliable and highly sensitive biomarker detection, which can be useful for early disease diagnosis and monitoring using a cost-effective and reproducible sensor system. Full article

Optical tactile sensing is gaining traction as a foundational technology in collaborative and human-interactive robotics, where reliable touch and pressure feedback are critical. Traditional systems based on total internal reflection (TIR) and frustrated TIR (FTIR) often require complex infrared setups and lack adaptability to curved or flexible surfaces. To overcome these limitations, we developed OptoSkin—a novel tactile platform leveraging direct time-of-flight (ToF) LiDAR principles for robust contact and pressure detection. In this extended study, we systematically evaluate how key optical properties of waveguide materials affect ToF signal behavior and sensing fidelity. We examine a diverse set of materials, characterized by varying light transmission (82–92)%, scattering coefficients (0.02–1.1) cm⁻¹, diffuse reflectance (0.17–7.40)%, and refractive indices 1.398–1.537 at the ToF emitter wavelength of 940 nm. Through systematic evaluation, we demonstrate that controlled light scattering within the material significantly enhances ToF signal quality for both direct touch and near-proximity sensing. These findings underscore the critical role of material selection in designing efficient, low-cost, and geometry-independent optical tactile systems. Full article

The trade-off between resolution and contrast is a transcendental problem in optical imaging, spanning from artistic photography to technoscientific applications. To the latter, Fourier-optics-based filters, such as the 4f system, are well-known for their image-enhancement properties, removing high spatial frequencies from an optically Fourier-transformed light signal through simple aperture adjustment. Nonetheless, assessing the contrast–resolution balance in optical imaging remains a challenging task, often requiring complex mathematical treatment and controlled laboratory conditions to match theoretical predictions. With that in mind, we propose a simple yet robust analytical technique to determine the optimal aperture in a 4f imaging system for static and quasi-static objects. Our technique employs the mathematical formalism of the H-theorem, enabling us to directly access the information of an imaged object. By varying the aperture at the Fourier plane of the 4f system, we have empirically found an optimal aperture region where the imaging entropy is maximum, given that the object is fitted to the imaged area. At that region, the image is lit and well-resolved, and no further aperture decrease improves that, as information of the whole assembly (object plus imaging system) is maximum. With that analysis, we have also been able to investigate how the imperfections in an object affect the entropy during its imaging. Despite its simplicity, our technique is generally applicable and passable for automation, making it interesting for many imaging-based optical devices. Full article

The use of analog radio-over-fiber (RoF) systems combined with power-over-fiber (PoF) systems has been proposed in recent years for applications involving remote sensors used in hazardous environments or where electrical wiring may be impractical. This article presents a hybrid architecture topology that combines PoF and RoF, using Raman amplification to obtain RF gain. The first emphasis is placed on the use of two types of high-power laser sources (HPLSs) for the PoF system: a 1480 nm Raman-based HPLS and a 1550 nm HPLS that is based on an erbium-doped fiber amplifier (EDFA). The second emphasis of this paper is on how these two HPLSs simulate Raman scattering (SRS) in the fiber, considering different lengths of SMF 28 for the link. Thus, a comparative analysis is proposed considering the effects induced on the RF signal, mainly focused on its RF power gain ($G_{RF}$), noise figure (NF), and spurious-free dynamic range (SFDR). The obtained results show that the architecture using a PoF system based on the 1550 nm HPLS benefits from a lower noise figure degradation, even when the noise generated by the optical amplification is considered. Full article

Integrated photonics is a transformative technology for enhancing communication and computation in Cloud and Fog computing networks. Photonic integrated circuits (PICs) enable significant improvements in data-processing speed, energy-efficiency, scalability, and latency. In Cloud infrastructures, PICs support high-speed optical interconnects, energy-efficient switching, and compact wavelength division multiplexing (WDM), addressing growing data demands. Fog computing, with its edge-focused processing and analytics, benefits from the compactness and low latency of integrated photonics for real-time signal processing, sensing, and secure data transmission near IoT devices. PICs also facilitate the low-loss, high-speed modulation, transmission, and detection of RF signals in scalable Radio-over-Fiber (RoF) links, enabling seamless IoT integration with Cloud and Fog networks. This results in centralized processing, reduced latency, and efficient bandwidth use across distributed infrastructures. Overall, integrating photonic technologies into RoF, Fog and Cloud computing networks paves the way for ultra-efficient, flexible, and scalable next-generation network architectures capable of supporting diverse real-time and high-bandwidth applications. This paper provides a comprehensive review of the current state and emerging trends in integrated photonics for IoT sensors, RoF, Fog and Cloud computing systems. It also outlines open research opportunities in photonic devices and system-level integration, aimed at advancing performance, energy-efficiency, and scalability in next-generation distributed computing networks. Full article

Rockfalls on railways are considered a natural disaster under the topic of landslides. It is an event that varies regionally due to landforms and climate. In addition to traffic density, the Karabük–Yenice railway line also passes through mountainous areas, river crossings, and experiences heavy seasonal rainfall. These conditions necessitate the implementation of proactive measures to mitigate risks such as rockfalls, tree collapses, landslides, and other geohazards that threaten the railway line. Undetected environmental events pose a significant threat to railway operational safety. The study aims to provide early detection of environmental phenomena using vibrations emitted through fiber optic cables. This study presents a real-time hazard detection system that integrates Distributed Acoustic Sensing (DAS) with a hybrid ensemble learning model. Using fiber optic cables and the Luna OBR-4600 interrogator, the system captures environmental vibrations along a 6 km railway corridor in Karabük, Türkiye. CatBoosting, Support Vector Machine (SVM), LightGBM, Decision Tree, XGBoost, Random Forest (RF), and Gradient Boosting Classifier (GBC) algorithms were used to detect the incoming signals. However, the Voting Classifier hybrid model was developed using SVM, RF, XGBoost, and GBC algorithms. The signaling system on the railway line provides critical information for safety by detecting environmental factors. Major natural disasters such as rockfalls, tree falls, and landslides cause high-intensity vibrations due to environmental factors, and these vibrations can be detected through fiber cables. In this study, a hybrid model was developed with the Voting Classifier method to accurately detect and classify vibrations. The model leverages an ensemble of classification algorithms to accurately categorize various environmental disturbances. The system has proven its effectiveness under real-world conditions by successfully detecting environmental events such as rockfalls, landslides, and falling trees with 98% success for Precision, Recall, F1 score, and accuracy. Full article

Background/Objectives: Diabetic retinopathy (DR) remains one of the primary causes of preventable vision impairment worldwide, particularly among individuals with long-standing diabetes. The progressive damage of retinal microvasculature can lead to irreversible blindness if not detected and managed at an early stage. Therefore, the development of reliable, non-invasive, and automated screening tools has become increasingly vital in modern ophthalmology. With the evolution of medical imaging technologies, Optical Coherence Tomography (OCT) has emerged as a valuable modality for capturing high-resolution cross-sectional images of retinal structures. In parallel, machine learning has shown considerable promise in supporting early disease recognition by uncovering complex and often imperceptible patterns in image data. Methods: This study introduces a novel framework for the early detection of DR through multifractal analysis of OCT images. Multifractal features, extracted using a box-counting approach, provide quantitative descriptors that reflect the structural irregularities of retinal tissue associated with pathological changes. Results: A comparative evaluation of several machine learning algorithms was conducted to assess classification performance. Among them, the Multi-Layer Perceptron (MLP) achieved the highest predictive accuracy, with a score of $98.02\%$, along with precision, recall, and F1-score values of $98.24\%$, $97.80\%$, and $98.01\%$, respectively. Conclusions: These results highlight the strength of combining OCT imaging with multifractal geometry and deep learning methods to build robust and scalable systems for DR screening. The proposed approach could contribute significantly to improving early diagnosis, clinical decision-making, and patient outcomes in diabetic eye care. Full article

The spread of fake news on social media is complicated by the fact that fake information spreads extremely fast in both textual and visual formats. Traditional approaches to the detection of fake news focus mainly on text and image features, thereby missing valuable information contained within images and texts. In response to this, we propose a multimodal fake news detection method based on BERT, with an extension to text combined with the extracted text from images through Optical Character Recognition (OCR). Here, we consider extending feature analysis with BERT_base_uncased to process inputs for retrieving relevant text from images and determining a confidence score that suggests the probability of the news being authentic. We report extensive experimental results on the ISOT, WELFAKE, TRUTHSEEKER, and ISOT_WELFAKE_TRUTHSEEKER datasets. Our proposed model demonstrates better generalization on the TRUTHSEEKER dataset with an accuracy of 99.97%, achieving substantial improvements over existing methods with an F1-score of 0.98. Experimental results indicate a potential accuracy increment of +3.35% compared to the latest baselines. These results highlight the potential of our approach to serve as a strong resource for automatic fake news detection by effectively integrating both text and visual data streams. Findings suggest that using diverse datasets enhances the resilience of detection systems against misinformation strategies. Full article

In the mix-and-match approach and in binocular intraocular lens (IOL) systems, two different IOL models are implanted in each eye to achieve the desired binocular outcome. In the mix-and-match approach, the surgeon selects the IOL models to be combined according to the clinical situation, the patient’s needs, and personal preference. Combinations described in the literature include, among others, two bifocal IOLs, an extended-depth-of-focus (EDoF) IOL with a bifocal lens, a trifocal lens with an EDoF IOL or an enhanced monofocal IOL, and two EDoF models utilizing different optical principles. The outcomes depend on the selected combination of IOL models. Binocular IOL systems consist of a fixed combination of two lenses, developed to be complementary in binocular vision. Initial data indicate that they can achieve a depth of focus similar to that with a bilateral implantation of a trifocal IOL. However, comparative studies are needed to evaluate if the postoperative binocular outcome differs from that achieved with the conventional approach. The mix-and-match implantation and binocular IOL systems expand the options available to tailor the IOL selection to the patient’s needs. Full article

Photodetectors are critical components in modern optoelectronic systems due to their extensive applications in information conversion and image storage. Selenium (Se), an element with a low melting point, a broad spectral response, and rapid response speed, exhibits a disadvantage of high optical reflectivity, which leads to a reduction in response. Spiro-MeOTAD, featuring controllable energy bands and facile processing, has its practical application limited by inadequate thermal and environmental stability. In this study, Spiro-MeOTAD-1 with enhanced stability was prepared through the optimization of dopants (Zn(TFSI)₂ and CNT:TiO₂) within Spiro-MeOTAD, to create a Se-F/Spiro-MeOTAD-1 heterojunction photodetector by subsequently compositing with selenium foam (Se-F). The self-powered device demonstrates exceptional photovoltaic performance within the wavelength range of 350–800 nm at 0 V bias, exhibiting a maximum responsivity of 108 mA W⁻¹, a switching ratio of 5 × 10³, a specific detectivity of 2.96 × 10¹² Jones, and a response time of 20 ms/50 ms. The device also demonstrates elevated environmental stability pretreatment at 140 °C following a one-month period. The photodetection stability of the Se-F/Spiro-MeOTAD-1 flexible PD was demonstrated by its capacity to retain 76.3% of its initial light current when subjected to 70 bending cycles at 30°. This finding further substantiates the photodetection stability of the material under various bending conditions. Further verification of the applicability of Spiro-MeOTAD-1 in Se-based devices establishes a novel paradigm for designing photodetectors with enhanced performance and stability. Full article