Search Results (1,796)

The exponential growth of user-generated video content necessitates efficient summarization systems for improved accessibility, retrieval, and analysis. This study presents and benchmarks a multimodal video summarization framework that classifies segments as informative or non-informative using audio, visual, and fused features. Sixty hours of annotated video across ten diverse categories were analyzed. Audio features were extracted with pyAudioAnalysis, while visual features (colour histograms, optical flow, object detection, facial recognition) were derived using OpenCV. Six supervised classifiers—Naive Bayes, K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, and XGBoost—were evaluated, with hyperparameters optimized via grid search. Temporal coherence was enhanced using median filtering. Random Forest achieved the best performance, with 74% AUC on fused features and a 3% F1-score gain after post-processing. Spectral flux, grayscale histograms, and optical flow emerged as key discriminative features. The best model was deployed as a practical web service using TensorFlow and Flask, integrating informative segment detection with subtitle generation via beam search to ensure coherence and coverage. System-level evaluation demonstrated low latency and efficient resource utilization under load. Overall, the results confirm the strength of multimodal fusion and ensemble learning for video summarization and highlight their potential for real-world applications in surveillance, digital archiving, and online education. Full article

Accurate stockpile volume estimation is essential for industries that manage bulk materials across various stages of production. Conventional ground-based methods such as walking wheels, total stations, Global Navigation Satellite Systems (GNSSs), and Terrestrial Laser Scanners (TLSs) have been widely used, but often involve significant safety risks, particularly when accessing hard-to-reach or hazardous areas. Unmanned Aerial Systems (UASs) provide a safer and more efficient alternative for surveying irregularly shaped stockpiles. This study evaluates UAS-based methods for estimating the volume of coal stockpiles at a storage facility near Cadiz, Ohio. Two sensor platforms were deployed: a Freefly Alta X quadcopter equipped with a Real-Time Kinematic (RTK) Light Detection and Ranging (LiDAR, active sensor) and a WingtraOne UAS with Post-Processed Kinematic (PPK) multispectral imaging (optical, passive sensor). Three approaches were compared: (1) LiDAR; (2) Structure-from-Motion (SfM) photogrammetry with a Digital Surface Model (DSM) and Digital Terrain Model (DTM) (SfM–DTM); and (3) an SfM-derived DSM combined with a kriging-interpolated DTM (SfM–intDTM). An automated boundary detection workflow was developed, integrating slope thresholding, Near-Infrared (NIR) spectral filtering, and Canny edge detection. Volume estimates from SfM–DTM and SfM–intDTM closely matched LiDAR-based reference estimates, with Root Mean Square Error (RMSE) values of 147.51 m³ and 146.18 m³, respectively. The SfM–intDTM approach achieved a Mean Absolute Percentage Error (MAPE) of ~2%, indicating strong agreement with LiDAR and improved accuracy compared to prior studies. A sensitivity analysis further highlighted the role of spatial resolution in volume estimation. While RMSE values remained consistent (141–162 m³) and the MAPE below 2.5% for resolutions between 0.06 m and 5 m, accuracy declined at coarser resolutions, with the MAPE rising to 11.76% at 10 m. This emphasizes the need to balance the resolution with the study objectives, geographic extent, and computational costs when selecting elevation data for volume estimation. Overall, UAS-based SfM photogrammetry combined with interpolated DTMs and automated boundary extraction offers a scalable, cost-effective, and accurate approach for stockpile volume estimation. The methodology is well-suited for both the high-precision monitoring of individual stockpiles and broader regional-scale assessments and can be readily adapted to other domains such as quarrying, agricultural storage, and forestry operations. Full article

This study is the first to directly compare natural dynamic penumbra shadows with experimentally replicated constant-intensity shadows on photovoltaic modules, providing new insights into the limitations of conventional shadow approximations found in the existing body of knowledge. Neutral density filters were deemed the most appropriate method for replicating a constant-intensity shadow, as they reduce visible light relatively uniformly across the primary silicon wavelength range. Preliminary experiments established the intensity values for each neutral density filter chosen to be able to match with the 29 dynamic penumbra shadows being replicated by both the size of shadow and the averaged intensity. The results revealed that while constant-intensity shadows and dynamic penumbra shadows produced similar overall power loss magnitudes, the constant-intensity shadows consistently led to higher losses, averaging 9.65% more, despite having the same average intensity and shadow size. Regression modelling showed similar curvature trends for both shading types (Adjusted R² = 0.895 for constant-intensity shadows and Adjusted R² = 0.743 for dynamic-intensity shadows), but statistical analyses, including the Mann–Whitney U-test (p = 0.00229), confirmed a significant difference between the power loss output for the two penumbra shadow conditions. Consequently, the null hypothesis was rejected, confirming that the simplified constant-intensity shadows represented in the literature cannot accurately replicate the behaviour of dynamic-intensity penumbra on photovoltaic modules. Full article

Existing visual SLAM systems with neural representations excel in static scenes but fail in dynamic environments where moving objects degrade performance. To address this, we propose a robust dynamic SLAM framework combining classic geometric features for localization with learned photometric features for dense mapping. Our method first tracks objects using instance segmentation and a Kalman filter. We then introduce a cascaded, coarse-to-fine strategy for efficient motion analysis: a lightweight sparse optical flow method performs a coarse screening, while a fine-grained dense optical flow clustering is selectively invoked for ambiguous targets. By filtering features on dynamic regions, our system drastically improves camera pose estimation, reducing Absolute Trajectory Error by up to 95% on dynamic TUM RGB-D sequences compared to ORB-SLAM3, and generates clean dense maps. The 3D Gaussian Splatting backend, optimized with a Gaussian pyramid strategy, ensures high-quality reconstruction. Validations on diverse datasets confirm our system’s robustness, achieving accurate localization and high-fidelity mapping in dynamic scenarios while reducing motion analysis computation by 91.7% over a dense-only approach. Full article

Optical intrabody wireless nanosensor networks (OiWNSNs) enable groundbreaking biomedical applications via optical nanocommunication within biological tissues. Synchronization is critical for accurate data recovery in these energy- and size-constrained nanonetworks. In this study, we investigate timing synchronization in a highly dispersive and noisy intravascular optical channel, particularly under an on–off keying preamble comprising Gaussian optical pulses. We proposed a synchronization scheme based on the repetitive transmission of a preamble and noncoherent detection using continuous-time moving average filters with multiple integrator windows. The simulation results reveal that increasing the communication distance degrades the synchronization performance. To counter this degradation, we can increase the number of preamble repetitions, which markedly improves the system reliability and timing accuracy due to the averaging gain, although the performance saturates due to the dispersion floor inherent in the blood channel. Moreover, we found that low-resolution nanoreceivers with fewer integrators outperform high-resolution designs in dispersive environments, as they mitigate the energy-splitting problem due to pulse broadening. This tradeoff between temporal resolution and robustness highlights the importance of channel-aware receiver design. Overall, this study provides key insights into the physical layer design of OiWNSNs and offers practical guidelines for achieving robust synchronization under realistic biological conditions. Full article

This study simulates an open-cavity random distributed Raman amplifier for optimal performance across a 5 THz S-band spectrum (196.2–201.1 THz; 1490.76–1527.99 nm), evaluating its capacity via a 50-channel WDM grid with 100 GHz spacing. The primary Raman pump wavelength was tuned from 1318 to 1344 nm to identify the optimal point. A Fiber Bragg Grating (FBG), placed at the end of a 60 km single-mode fiber and upshifted 88 nm from the pump, enhances efficiency by transferring energy to the amplified signal, minimizing power variation. Results yield < 2 dB gain ripple across channels using raw Raman amplification without flattening filters with minor degradation from residual channels, confirming the DRA design’s viability for high-density S-band optical communication expansion. Full article

In mode-locked fiber lasers and optical sensors, in-line filters are essential components. Fiber-core Mach–Zehnder interferometer (MZI) technology has garnered a lot of research interest for the several manufacturing techniques for in-line MZI filters. Although multi-line inscription is frequently needed in existing methods to attain enough waveguide width, this approach adds complexity to production and may result in compromised waveguide quality. In this work, we present an improved single-line direct-writing method that attains similar MZI filtering results to multi-line scan. Additionally, the MZI filter created with the modified single-line direct-writing technique has a smaller insertion loss and requires less direct-writing energy than the previous single-line direct-writing technique. A 516 μm long MZI-based in-line filter was successfully constructed. The results of the characterization showed a central loss dip at 1089.82 nm, a free-spectral range (FSR) of 141.36 nm, an extinction ratio of 19.69 dB, and an insertion loss of 1.122 dB. This method decreased the insertion loss by a factor of 2.7 for an identical extinction ratio and improved the direct-writing efficiency by a factor of 9 for an equivalent FSR with multi-line scan. There was consistency between the experimental and simulation results. We also took measurements of the MZI’s temperature sensitivity. This work shows notable improvements in waveguide quality and ease of manufacture. This accomplishment lays the groundwork for further advancements in integrated mode-locked fiber laser technology. Full article

This study presents a reconfigurable planar photonic device capable of dynamically switching between optical filter and absorber functionalities by exploiting the phase transition properties of GeTe, a chalcogenide phase-change material. The device adopts a Metal–Dielectric–PCM architecture composed of silver (Ag), silicon dioxide (SiO₂), and GeTe layers, each playing a distinct role: the silver layer governs the transmission and absorption efficiency, the SiO₂ layer controls the resonance conditions, and the GeTe layer determines the device’s scattering behavior via its tunable optical losses. Numerical simulations revealed that the structure enables high RGB transmission in the amorphous state and broadband absorption in the crystalline state. By adjusting geometric parameters—especially the metallic thickness—the device exhibits finely tunable spectral responses under varying polarizations and incidence angles. These findings highlight the synergistic interplay between material functionality and layer configuration, positioning this platform as a compact and energy-efficient solution for applications in tunable photonics, optical sensing, and programmable metasurfaces. Full article

Optical frequency domain reflectometry (OFDR) is one of the key diagnostic tools for fiber optic components and circuits built on them. A low signal-to-noise ratio, resulting from the low intensity of backscattered signals, prevents the correct quantitative description of the medium parameters. Known methods of signal denoising, such as empirical mode decomposition, frequency filtering, and activation function dynamic averaging, make the signal smoother but introduce errors into its dynamic characteristics, changing the intensity of reflection peaks and distorting the backscattering level. We propose a method to reduce OFDR trace noise using elliptical arc fitting (EAF). The obtained results indicate that this algorithm efficiently processes both areas with and without contrasting back reflections, with zero distortion of Fresnel reflection peaks, and with zero attenuation error in regions without Fresnel reflections. At the same time, other methods distort reflection peaks by 14.2–42.6% and shift the correct level of Rayleigh scattering by 27.2–67.3%. Further work will be aimed at increasing the accuracy of the method and testing it with other types of data. Full article

Digital holography enables the reconstruction of both 2D and 3D object information from interference patterns captured by digital cameras. A major challenge in this field is speckle noise, which significantly degrades the quality of the reconstructed images. We propose a novel speckle noise reduction method based on 3D adaptive filtering. Our technique processes a stack of holograms, each with an uncorrelated speckle pattern, using an adapted 3D Frost filter. Unlike conventional filtering techniques, our approach exploits statistical adaptivity to enhance noise suppression while preserving fine image details in the reconstructed holograms. Both numerical simulations and optical experiments confirm that our 3D filtering technique significantly enhances reconstruction quality. Specifically, it reduces the normalized standard deviation by up to 40% and improves the structural similarity index by up to 60% compared to classical 2D, 3D median, BM3D, and BM4D filters. Optical experiments validate the method’s effectiveness in practical digital holography scenarios by local and global image quality estimation metrics. These results highlight adaptive 3D filtering as a promising approach for mitigating speckle noise while maintaining structural integrity in digital holography reconstructions. Full article

This study presents a novel methodology for mapping Fault- and Thrust-based Structural Lineaments (FT-SL) in the rugged and inaccessible Oued-Laou watershed of the Rif Belt, Morocco. Combining optical (Landsat-8 OLI, Sentinel-2 MSI) and radar (Sentinel-1 SAR) remote sensing data, the research employs manual, semi-automatic, and automatic extraction methods enhanced by spatial filtering (Sobel, Laplacian, Kuan). A Knowledge-Based System (KBS) integrated with Multi-Criteria Decision Analysis (MCDA) evaluates the effectiveness of these methods, focusing on lineament statistics, orientation, density distribution, and correlation with existing geological maps. The results highlight Sentinel-1 SAR’s superior performance in detecting subsurface structures, while manual extraction yields the highest accuracy. This study also demonstrates the potential for generalizing this approach to other Alpine orogenic regions, such as the Alps, due to shared geological characteristics. The findings provide a robust framework for structural lineament mapping in mountainous terrains, addressing challenges of accessibility and data scarcity. Full article

The TECSL has attracted much attention due to its wide tuning range, narrow linewidth, high output power, and excellent SMSR. It holds irreplaceable value in optical communication, spectroscopy analysis, and biomedical applications. The demand for a wide tuning range, high power, narrow linewidth, and a high SMSR has driven the development of high-performance TECSL structures. This paper comprehensively discusses five key TECSL structures: Littrow-type structures, Littman-type structures, filter-type structures, fiber-type structures, and waveguide-type structures, and elaborates on their structures and principles. This paper reviews the research process of different type-structure TECSLs, analyzes the advantages and disadvantages of different external cavity structures, and explores the future development trends of TECSLs. The review shows that the Littrow-type structure TECSL achieved an extremely wide tuning range using diffraction gratings, reaching up to 360 nm. The Littman-type structure TECSL demonstrated excellent spectral purity, achieving an SMSR of 71.03 dB. The filter-type structure TECSL was able to achieve flexible wavelength selection using tunable filters, achieving a linewidth of 570 Hz. The fiber-type structure TECSL has a linewidth of up to 600 Hz. The waveguide-type structure TECSL can achieve a linewidth as low as 0.252 kHz and a tuning range of up to 120.9 nm. Full article

Automated optical inspection (AOI) of wood surfaces is critical for ensuring product quality in the furniture and manufacturing industries; however, existing defect detection systems often struggle to generalize across complex grain patterns and diverse defect types. This study proposes a wood defect recognition model employing a Gabor Convolutional Network (GCN) that integrates convolutional neural networks (CNNs) with Gabor filters. To systematically optimize the network’s architecture and improve both detection accuracy and computational efficiency, the Taguchi method is employed to tune key hyperparameters, including convolutional kernel size, filter number, and Gabor parameters (frequency, orientation, and phase offset). Additionally, image tiling and augmentation techniques are employed to effectively increase the training dataset, thereby enhancing the model’s stability and accuracy. Experiments conducted on the MVTec Anomaly Detection dataset (wood category) demonstrate that the Taguchi-optimized GCN achieves an accuracy of 98.92%, outperforming a baseline Taguchi-optimized CNN by 2.73%. Results confirm that Taguchi-optimized GCNs enhance defect detection performance and computational efficiency, making them valuable for smart manufacturing. Full article

Single-pixel imaging enables two-dimensional image capture through a single-pixel detector, yet extending this to three-dimensional or higher-dimensional information capture in single-pixel optical imaging systems has remained a challenging problem. In this study, we present a single-pixel camera system for three-dimensional (3D) imaging based on compressed sensing (CS) with continuous wave (CW) pseudo-random volume structured illumination. An estimated image, which incorporates both spatial and depth information of a 3D scene, is reconstructed using an L1-norm minimization reconstruction algorithm. This algorithm employs prior knowledge of non-overlapping objects as a constraint in the target space, resulting in improved noise performance in both numerical simulations and physical experiments. Our simulations and experiments demonstrate the feasibility of the proposed 3D CS framework. This approach achieves compressive sensing in a 3D information capture system with a measurement ratio of 19.53%. Additionally, we show that our CS 3D capturing system can accurately reconstruct the color of a target using color filter modulation. Full article

Despite increasing evidence that cigarette smoke is a significant source of indoor fine particulate matter (PM_2.5), quantitative emission factors (EFs) for PM_2.5 and its toxic chemical composition in mainstream (MS) and sidestream (SS) smoke are still not well defined. In this study, we employed a custom-designed chamber to separately collect MS (intermittent puff) and SS (continuous sampling) smoke from eleven cigarette models, representing six brands and two product types, under controlled conditions. PM_2.5 was collected on quartz-fiber filters and analyzed for carbon fractions (using the thermal–optical IMPROVE-A protocol), nine water-soluble inorganic ions (by ion chromatography), and twelve trace elements (via ICP-MS). SS smoke exhibited significantly higher mass fractions of total analyzed species (84.7% vs. 65.9%), carbon components (50.6% vs. 44.2%), water-soluble ions (17.1% vs. 13.7%), and elements (17.0% vs. 7.0%) compared to MS smoke. MS smoke is characterized by a high proportion of pyrolytic organic carbon fractions (OC₁–OC₃) and specific elements such as vanadium (V) and arsenic (As), while SS smoke shows elevated levels of elemental carbon (EC1), water-soluble ions (NH₄⁺, NO₃⁻), and certain elements like zinc (Zn) and cadmium (Cd). The toxicity-weighted distribution indicates that MS smoke primarily induces membrane disruption and pulmonary inflammation through semi-volatile organics and elements, whereas SS smoke enhances oxidative stress and cardiopulmonary impairment via EC-mediated reactions and secondary aerosol formation. The mean OC/EC ratio of 132.4 in SS smoke is an order of magnitude higher than values reported for biomass or fossil-fuel combustion, indicative of extensive incomplete combustion unique to cigarettes and suggesting a high potential for oxidative stress generation. Emission factors (µg/g cigarette) revealed marked differences: MS delivered higher absolute EFs for PM_2.5 (422.1), OC (8.8), EC (5.0), Na⁺ (32.6), and V (29.2), while SS emitted greater proportions of NH₄⁺, NO₃⁻, Cl⁻, and carcinogenic metals (As, Cd, Zn). These findings provide quantitative source profiles suitable for receptor-oriented indoor source-apportionment models and offer toxicological evidence to support the prioritization of comprehensive smoke-free regulations. Full article