Search Results (2,561)

This paper proposes an intelligent power adaptation framework for Free-Space Optics (FSO) communication systems operating under different weather conditions exploiting a deep learning (DL) analysis of received eye diagram images. The system incorporates two Convolutional Neural Network (CNN) architectures, LeNet and Wide Residual Network (Wide ResNet) algorithms to perform regression tasks that predict received signal quality metrics such as the Quality Factor (Q-factor) and Bit Error Rate (BER) from the received eye diagram. These models are evaluated using Mean Squared Error (MSE) and the coefficient of determination (R² score) to assess prediction accuracy. Additionally, a custom CNN‑based classifier is trained to determine whether the BER reading from the eye diagram exceeds a critical threshold of 10⁻⁴; this classifier achieves an overall accuracy of 99%, correctly detecting 194/195 “acceptable” and 4/5 “unacceptable” instances. Based on the predicted signal quality, the framework activates a dual-amplifier configuration comprising a pre-channel amplifier with a maximum gain of 25 dB and a post-channel amplifier with a maximum gain of 10 dB. The total gain of the amplifiers is adjusted to support the operation of the FSO system under all-weather conditions. The FSO system uses a 15 dBm laser source at 1550 nm. The DL models are tested on both internal and external datasets to validate their generalization capability. The results show that the regression models achieve strong predictive performance, and the classifier reliably detects degraded signal conditions, enabling the real-time gain control of the amplifiers to achieve the quality of transmission. The proposed solution supports robust FSO communication under challenging atmospheric conditions including dry snow, making it suitable for deployment in regions like Northern Europe, Canada, and Northern Japan. Full article

This study reports the synthesis and evaluation of iron molybdate (Fe₂(MoO₄)₃) and iron tungstate (FeWO₄) as photocatalysts for the degradation of a real industrial effluent from aluminum anodizing processes under visible light irradiation. The oxides were synthesized via a co-precipitation method in an aqueous medium, followed by microwave-assisted hydrothermal treatment. Structural and morphological characterizations were performed using X-ray diffraction, field-emission scanning electron microscopy, Raman spectroscopy, ultraviolet–visible (UV–vis), and photoluminescence (PL) spectroscopies. The effluent was characterized by means of ionic chromatography, total organic carbon (TOC) analysis, physicochemical parameters (pH and conductivity), and UV–vis spectroscopy. Both materials exhibited well-crystallized structures with distinct morphologies: Fe₂(MoO₄)₃ presented well-defined exposed (001) and (110) surfaces, while FeWO₄ showed a highly porous, fluffy texture with irregularly shaped particles. In addition to morphology, both materials exhibited narrow bandgaps—2.11 eV for Fe₂(MoO₄)₃ and 2.03 eV for FeWO₄. PL analysis revealed deep defects in Fe₂(MoO₄)₃ and shallow defects in FeWO₄, which can influence the generation and lifetime of reactive oxygen species. These combined structural, electronic, and morphological features significantly affected their photocatalytic performance. TOC measurements revealed degradation efficiencies of 32.2% for Fe₂(MoO₄)₃ and 45.3% for FeWO₄ after 120 min of irradiation. The results highlight the critical role of morphology, optical properties, and defect structures in governing photocatalytic activity and reinforce the potential of these simple iron-based oxides for real wastewater treatment applications. Full article

The exponential proliferation of the Internet of Things (IoT) and optical IoT (O-IoT) has introduced substantial challenges concerning computational capacity and energy efficiency. IoT devices generate vast volumes of aggregated data and require intensive processing, often resulting in elevated latency and excessive energy consumption. Task offloading has emerged as a viable solution; however, many existing strategies fail to adequately optimize both latency and energy usage. This paper proposes a novel task-offloading approach based on deep Q-network (DQN) learning, designed to intelligently and dynamically balance these critical metrics. The proposed framework continuously refines real-time task offloading decisions by leveraging the adaptive learning capabilities of DQN, thereby substantially reducing latency and energy consumption. To further enhance system performance, the framework incorporates optical networks into the IoT–fog–cloud architecture, capitalizing on their high-bandwidth and low-latency characteristics. This integration facilitates more efficient distribution and processing of tasks, particularly in data-intensive IoT applications. Additionally, we present a comparative analysis between the proposed DQN algorithm and the optimal strategy. Through extensive simulations, we demonstrate the superior effectiveness of the proposed DQN framework across various IoT and O-IoT scenarios compared to the BAT and DJA approaches, achieving improvements in energy consumption and latency of 35%, 50%, 30%, and 40%, respectively. These findings underscore the significance of selecting an appropriate offloading strategy tailored to the specific requirements of IoT and O-IoT applications, particularly with regard to environmental stability and performance demands. Full article

Lanthanide rare earth elements possess significant promise for material applications owing to their distinctive optical and magnetic characteristics, as well as their versatile coordination capabilities. This study introduced a lanthanide-functionalized magnetic nanocellulose composite (NNC@Fe₃O₄@La(OH)₃) for effective phosphorus/nitrogen (P/N) ligand separation. The hybrid material employs the adaptable coordination geometry and strong affinity for oxygen of La³⁺ ions to show enhanced DNA-binding capacity via multi-site coordination with phosphate backbones and bases. This study utilized cellulose as a carrier, which was modified through carboxylation and amination processes employing deep eutectic solvents (DES) and polyethyleneimine. Magnetic nanoparticles and La(OH)₃ were subsequently incorporated into the cellulose via in situ growth. NNC@Fe₃O₄@La(OH)₃ showed a specific surface area of 36.2 m²·g⁻¹ and a magnetic saturation intensity of 37 emu/g, facilitating the formation of ligands with accessible La³⁺ active sites, hence creating mesoporous interfaces that allow for fast separation. NNC@Fe₃O₄@La(OH)₃ showed a significant affinity for DNA, with adsorption capacities reaching 243 mg/g, mostly due to the multistage coordination binding of La³⁺ to the phosphate groups and bases of DNA. Simultaneously, kinetic experiments indicated that the binding process adhered to a pseudo-secondary kinetic model, predominantly dependent on chemisorption. This study developed a unique rare-earth coordination-driven functional hybrid material, which is highly significant for constructing selective separation platforms for P/N-containing ligands. Full article

Accurate estimation of upper-limb joint angles is essential in biomechanics, rehabilitation, and wearable robotics. While inertial measurement units (IMUs) offer portability and flexibility, systems requiring multiple inertial sensors can be intrusive and complex to deploy. In contrast, optical motion capture (MoCap) systems provide precise tracking but are constrained to controlled laboratory environments. This study presents a deep learning-based approach for estimating shoulder and elbow joint angles using only three IMU sensors positioned on the chest and both wrists, validated against reference angles obtained from a MoCap system. The input data includes Euler angles, accelerometer, and gyroscope data, synchronized and segmented into sliding windows. Two recurrent neural network architectures, Convolutional Neural Network with Long-short Term Memory (CNN-LSTM) and Bidirectional LSTM (BLSTM), were trained and evaluated using identical conditions. The CNN component enabled the LSTM to extract spatial features that enhance sequential pattern learning, improving angle reconstruction. Both models achieved accurate estimation performance: CNN-LSTM yielded lower Mean Absolute Error (MAE) in smooth trajectories, while BLSTM provided smoother predictions but underestimated some peak movements, especially in the primary axes of rotation. These findings support the development of scalable, deep learning-based wearable systems and contribute to future applications in clinical assessment, sports performance analysis, and human motion research. Full article

Background: Retinal diseases are becoming an important public health issue, with early diagnosis and timely intervention playing a key role in preventing vision loss. Optical coherence tomography (OCT) remains the leading non-invasive imaging technique for identifying retinal conditions. However, distinguishing between diabetic macular edema (DME) and macular edema resulting from retinal vein occlusion (RVO) can be particularly challenging, especially for clinicians without specialized training in retinal disorders, as both conditions manifest through increased retinal thickness. Due to the limited research exploring the application of deep learning methods, particularly for RVO detection using OCT scans, this study proposes a novel diagnostic approach based on stacked convolutional neural networks. This architecture aims to enhance classification accuracy by integrating multiple neural network layers, enabling more robust feature extraction and improved differentiation between retinal pathologies. Methods: The VGG-16, VGG-19, and ResNet50 models were fine-tuned using the Kermany dataset to classify the OCT images and afterwards were trained using a private OCT dataset. Four stacked models were then developed using these models: a model using the VGG-16 and VGG-19 networks, a model using the VGG-16 and ResNet50 networks, a model using the VGG-19 and ResNet50 models, and finally a model using all three networks. The performance metrics of the model includes accuracy, precision, recall, $F_2$-score, and area under of the receiver operating characteristic curve (AUROC). Results: The stacked neural network using all three models achieved the best results, having an accuracy of 90.7%, precision of 99.2%, a recall of 90.7%, and an $F_2$-score of 92.3%. Conclusions: This study presents a novel method for distinguishing retinal disease by using stacked neural networks. This research aims to provide a reliable tool for ophthalmologists to improve diagnosis accuracy and speed. Full article

Bruising in ‘Korla’ pears represents a prevalent phenomenon that leads to progressive fruit decay and substantial economic losses. The detection of early-stage bruising proves challenging due to the absence of visible external characteristics, and existing deep learning models have limitations in weak feature extraction under complex optical interference. To address the postharvest latent damage detection challenges in ‘Korla’ pears, this study proposes a collaborative detection framework integrating structured-illumination reflectance imaging (SIRI) with multi-order gated attention mechanisms. Initially, an SIRI optical system was constructed, employing 150 cycles·m⁻¹ spatial frequency modulation and a three-phase demodulation algorithm to extract subtle interference signal variations, thereby generating RT (Relative Transmission) images with significantly enhanced contrast in subsurface damage regions. To improve the detection accuracy of latent damage areas, the MOGA-UNet model was developed with three key innovations: 1. Integrate the lightweight VGG16 encoder structure into the feature extraction network to improve computational efficiency while retaining details. 2. Add a multi-order gated aggregation module at the end of the encoder to realize the fusion of features at different scales through a special convolution method. 3. Embed the channel attention mechanism in the decoding stage to dynamically enhance the weight of feature channels related to damage. Experimental results demonstrate that the proposed model achieves 94.38% mean Intersection over Union (mIoU) and 97.02% Dice coefficient on RT images, outperforming the baseline UNet model by 2.80% with superior segmentation accuracy and boundary localization capabilities compared with mainstream models. This approach provides an efficient and reliable technical solution for intelligent postharvest agricultural product sorting. Full article

This review examines the design of thermal control systems for state-of-the-art deep ultraviolet (DUV) and extreme ultraviolet (EUV) projection lithography tools. The lithographic system under investigation integrates several critical subsystems along the optical transmission chain, including the light source, reticle stage, projection optics (featuring DUV refractive lenses and EUV multilayer mirrors), immersion liquid, wafer stage, and metrology systems. Under high-power irradiation conditions with concurrent thermal perturbations, the degradation of thermal stability and gradient uniformity within these subsystems significantly compromises exposure precision. Through a systematic analysis of the thermal challenges specific to each subsystem, this review synthesizes established thermal control systems across two technical dimensions: thermal control structures and thermal control algorithms. Prospects for future advancements in lithographic thermal control are also discussed. Full article

This study focuses on the analysis of soliton solutions within the framework of the time-fractional cubic–quintic nonlinear Schrödinger equation (TFCQ-NLSE), a powerful model with broad applications in complex physical phenomena such as fiber optic communications, nonlinear optics, optical signal processing, and laser–tissue interactions in medical science. The nonlinear effects exhibited by the model—such as self-focusing, self-phase modulation, and wave mixing—are influenced by the combined impact of the cubic and quintic nonlinear terms. To explore the dynamics of this model, we apply a robust analytical technique known as the sub-ODE method, which reveals a diverse range of soliton structures and offers deep insight into laser pulse interactions. The investigation yields a rich set of explicit soliton solutions, including hyperbolic, rational, singular, bright, Jacobian elliptic, Weierstrass elliptic, and periodic solutions. These waveforms have significant real-world relevance: bright solitons are employed in fiber optic communications for distortion-free long-distance data transmission, while both bright and dark solitons are used in nonlinear optics to study light behavior in media with intensity-dependent refractive indices. Solitons also contribute to advancements in quantum technologies, precision measurement, and fiber laser systems, where hyperbolic and periodic solitons facilitate stable, high-intensity pulse generation. Additionally, in nonlinear acoustics, solitons describe wave propagation in media where amplitude influences wave speed. Overall, this work highlights the theoretical depth and practical utility of soliton dynamics in fractional nonlinear systems. Full article

Radio frequency (RF)-based wireless systems are broadly used in communication systems such as mobile networks, satellite links, and monitoring applications. These systems offer outstanding advantages over wired systems, particularly in terms of ease of installation. However, researchers are looking for safer alternatives as a result of worries about possible health problems connected to high-frequency radiofrequency transmission. Using the visible light spectrum is one promising approach; three cutting-edge technologies are emerging in this regard: Optical Camera Communication (OCC), Light Fidelity (Li-Fi), and Visible Light Communication (VLC). In this paper, we propose a Multiple-Input Multiple-Output (MIMO) modulation technology for Internet of Things (IoT) applications, utilizing an LED array and time-domain on-off keying (OOK). The proposed system is compatible with both rolling shutter and global shutter cameras, including commercially available models such as CCTV, webcams, and smart cameras, commonly deployed in buildings and industrial environments. Despite the compact size of the LED array, we demonstrate that, by optimizing parameters such as exposure time, camera focal length, and channel coding, our system can achieve up to 20 communication links over a 20 m distance with low bit error rate. Full article

This study proposes a deep learning-based method to improve overlay alignment precision in laser direct writing systems. Alignment errors arise from multiple sources in nanoscale processes, including optical aberrations, mechanical drift, and fiducial mark imperfections. A significant portion of the residual alignment error stems from the interpretation of mark coordinates by the vision system and algorithms. Here, we developed a convolutional neural network (CNN) model to predict the coordinates calculation error of 66,000 sets of computer-generated defective crosshair marks (simulating real fiducial mark imperfections). We compared 14 neural network architectures (8 CNN variants and 6 feedforward neural network (FNN) configurations) and found a well-performing, simple CNN structure achieving a mean squared error (MSE) of 0.0011 on the training sets and 0.0016 on the validation sets, demonstrating 90% error reduction compared to the FNN structure. Experimental results on test datasets showed the CNN’s capability to maintain prediction errors below 100 nm in both X/Y coordinates, significantly outperforming traditional FNN approaches. The proposed method’s success stems from the CNN’s inherent advantages in local feature extraction and translation invariance, combined with a simplified network architecture that prevents overfitting while maintaining computational efficiency. This breakthrough establishes a new paradigm for precision enhancement in micro–nano optical device fabrication. Full article

The spatial heterodyne spectrometer is an interferometric spectrometer specifically designed for particular detection targets, capable of achieving ultra-high spectral resolution within a designated spectral range. As the demand for signal detection accuracy continues to increase, the extraction of accurate target spectra from spatial heterodyne interferograms has become increasingly important. This paper applies a deep neural network to the spectral reconstruction of specific spatial heterodyne interferograms. The spectral reconstruction model, SRDNN, was trained using CO₂ data simulated by the SCIATRAN radiative transfer model and the principles of spatial heterodyne spectroscopy. The results indicate that SRDNN has excellent CO₂ spectral reconstruction performance, with an evaluation index $R^2$ of 0.9943 and an $MSE$ of 0.00021. The average difference between the reconstructed spectra and the target spectra is only 0.371%. Furthermore, the method was further validated using experimental data obtained from a spatial heterodyne spectrometer. The remarkable spectral reconstruction results and excellent evaluation indicators once again demonstrated the universality and effectiveness of the method. Finally, the robustness of the method was studied using noisy experimental data. The results demonstrate that the method can accurately reconstruct spectra from interferograms with slight noise without requiring additional processing, simplifying the spectral reconstruction process. This work is expected to provide novel methods and effective solutions for the spectral reconstruction of specific targets detected by spatial heterodyne spectrometers. 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

Background/Objectives: Diabetic macular edema (DME) is the primary cause of vision loss in people with diabetes, and if untreated, it can result in irreversible macular damage. Both fluorescein angiography (FA), the gold standard, and optical coherence tomography angiography (OCTA) are used for evaluation of this disease. The objective of this study was to compare the diagnostic value of both. Methods: We conducted a comparative analysis of 98 patients aged 18–80 years with significant DME and best-corrected visual acuity ≥0.1 according to the Snellen chart. Participants underwent glycated hemoglobin blood test (HbA1c) and ophthalmological examinations, including OCTA and FA. OCTA 3 × 3 mm scans of superficial (SCP) and deep capillary plexus (DCP) along with FA scans were exported to the Gimp computer program. Size of the foveal avascular zone (FAZ), the number of visible microaneurysms (MAs), and ETDRS report number 11 classification of the images were assessed. Results: FAZ size differed significantly in superficial plexus (0.41 mm²), deep plexus (0.43 mm²) OCTA, and FA (0.38 mm²) (p < 0.001). FAZ size in DCP OCTA closely correlated with that of FA (τ = 0.79, p < 0.001). The total number of MAs visualized in the OCTA was significantly lower than in FA (p < 0.001). ETDRS classification of scans revealed that the level of consistency between the examinations was moderate to very strong. Conclusions: OCTA may be useful in evaluating macular ischemia. It is less sensitive in detecting MAs in DME eyes. FAZ has sharper boundaries and is larger when measured in OCTA. Poor glycemic control results in higher incidence of MAs in macula. Full article

Nowadays, a single detection method is insufficient for comprehensively and clearly identifying both surface defects and inner defects in titanium alloys. To address this limitation, this paper proposes a titanium alloy defect detection method based on optical–acoustic image fusion. A detection system was developed to achieve comprehensive and precise inspection of titanium alloys by integrating advanced deep learning-based optical testing technology, reliable C-scan ultrasonic detection technology, and information fusion techniques. Furthermore, the PC software can output interactive fusion results and generate decision-level detection reports. The experimental results demonstrate that the surface defect detection algorithm achieves an accuracy of 99.0%, with a surface defect size measurement resolution of 0.01 mm, an internal defect size measurement resolution of 1 mm, and a positional error within 2 mm. It was found that the proposed method provides a potential solution for the practical application of inspecting surface defects and inner defects in the materials. Full article