Search Results (120)

The performance of grain storage silos is strongly influenced by discharge flow patterns, hopper geometry, and material properties such as moisture content and impurity levels. However, the combined effects of these factors on flow behavior, discharge rate, and segregation are not yet fully understood. This study experimentally investigated the integrated effects of moisture content, prismatic hopper geometry (hopper angle β), and impurity addition on flow behavior, segregation, and mass flow rate in reduced-scale silos. Experiments were conducted using three prismatic silos with hopper angles of β = 15°, 33°, and 45°, filled with maize at moisture contents of 13.6%, 20.2%, and 26.0% (wet basis), under both clean conditions and with the addition of 10% impurities (fraction passing through a 5 mm sieve). The discharge rate was determined by direct mass–time measurements, flow patterns were inferred from video analysis, and segregation was quantified based on the mass fraction of impurities in samples collected during discharge. The results indicate that moisture content was the most influential factor, reducing the discharge rate by up to 22.8% when increasing from 20.2% to 26.0% w.b. (p < 0.05). Hopper geometry also had a significant effect, with performance differences among configurations becoming more pronounced under high-moisture conditions. The addition of 10% impurities increased the discharge rate under all tested conditions, with gains of up to 29.0% at 26.0% w.b. and β = 15°. Segregation intensified with increasing moisture content, leading to a progressive accumulation of impurities toward the end of discharge. The stick–slip phenomenon was observed under a critical condition (26.0% w.b., β = 15°, with impurities), resulting in a 23.0% reduction in the average discharge rate compared to the equivalent stable condition. These findings demonstrate that granular flow behavior in silos is governed by the interaction between moisture, hopper geometry, and material composition. The results also suggest that operational strategies such as pre-cleaning should be evaluated in conjunction with expected moisture conditions, as pre-cleaning may adversely affect flow performance under high-moisture scenarios. Full article

This paper presents a metrology-oriented review of vision-based displacement and deformation measurement for civil structural health monitoring (SHM), with an emphasis on field robustness and uncertainty quantification (UQ). The review focuses on image- and video-based methods that convert visual information into quantitative physical measurements, such as displacement, strain, or derived dynamic indicators. The literature is organized according to the main stages of the measurement chain: image formation, image-plane motion estimation, and geometric conversion to metric motion. Within this framework, measurement pipelines are interpreted through three levels of geometric mapping, namely, a scalar scale-factor model, a planar homography-based model, and a full Jacobian-based model. The review synthesizes major method families, including marker-based and markerless tracking, feature-based tracking, optical flow, digital image correlation (DIC), phase-based motion magnification, edge-based estimators, fixed- and moving-camera configurations, UAV-based acquisition with ego-motion compensation, hybrid vision–sensor fusion, and deep-learning-enhanced pipelines. A structured taxonomy of uncertainty sources is then presented along the processing chain, covering camera geometry and calibration, imaging noise and blur, quantization, timing and synchronization, environmental disturbances, optical turbulence and heat haze, platform motion, algorithmic failure modes, and reference-sensor uncertainty. The paper also compares UQ practices, including GUM-aligned analytical propagation, Monte Carlo methods, DIC-specific error budgets, bootstrap and resampling strategies, and probabilistic deep learning. The main contribution of this review is to connect computer-vision-based displacement pipelines with metrological requirements by explicitly linking measurement models, uncertainty sources, UQ methods, and field-validation evidence within a unified framework. A practical uncertainty-budget template is compiled to support traceable reporting across different pipelines and deployment scenarios. The paper concludes with prioritized research gaps and future directions, including standardized benchmarks and datasets, traceable UQ for moving-camera systems, multi-sensor fusion with end-to-end uncertainty propagation, long-term drift characterization, optical-turbulence and adverse-weather modeling, validated subpixel limits at extreme range, probabilistic deep learning–metrology integration, and standardized reporting practices. Full article

Nystagmus, the involuntary rhythmic oscillation of the eyes, is a critical diagnostic marker in vestibular medicine, distinguishing life-threatening central disorders such as stroke from benign peripheral conditions including Benign Paroxysmal Positional Vertigo (BPPV). Despite its clinical importance, accurate nystagmus assessment has long been constrained by expensive infrared video-oculography equipment such as videonystagmography, specialist dependency, and the episodic nature of vestibular symptoms that are often resolved before a clinical encounter. This review synthesizes approximately 50 papers published between 1952 and 2026 across four thematic domains: AI-driven nystagmus analysis, clinical medicine, smartphone and portable hardware innovations, and telemedicine and remote monitoring. On the AI front, classical machine learning models achieve up to 98.77% nystagmus recognition accuracy using ensemble methods, while deep learning frameworks spanning CNNs, U-Nets, LSTMs, and optical flow networks demonstrate clinical-grade slow-phase velocity measurement equivalent to gold standard video-oculography on standard smartphone RGB video. Large language and vision models including GPT-4V and Gemini 2.0 show early-stage promise as zero-shot triage tools but currently fall well below specialist-level diagnostic accuracy. Concurrently, portable hardware innovations ranging from 3D-printed goggle systems to ARKit-based smartphone applications are narrowing the accessibility gap, while telemedicine frameworks enable ictal recording and cloud-based specialist review outside the clinic. Across all domains, the common barriers to clinical translation are dataset scarcity for rare BPPV subtypes, sensitivity to ambient conditions, and the absence of explainable AI mechanisms. This review maps the current state of the field and identifies multimodal data fusion, prospective clinical validation, and interpretable AI as the critical next steps toward equitable, specialist independent vestibular diagnostics. Full article

Background/Objectives: Single-case studies represent a sophisticated and rigorous methodological framework, widely established in clinical research for providing high-resolution data on individual functional responses. This study evaluates the clinical utility of integrating immersive Virtual Reality (VR) gaming as a novel “functional ingredient” within the Video Game Therapy (VGT) protocol. Given the exploratory single-case nature of this intervention, clinical state-modulations cannot be rigorously validated using standard aggregated group statistics. Therefore, the core objective of this paper is to investigate the therapeutic potential of the VR session on psychological state-modulation, introducing the Single-Case Normative Analytics (SCNA) framework as the mandatory statistical vehicle required to validate individual longitudinal shifts against normative data. Methods: The study treats individual VR exposures as independent, short-term clinical probes embedded within a real-world clinical journey. The SCNA framework was deployed by integrating Crawford’s modified t-tests with longitudinal percentile tracking against an empirical normative reference group (n = 20). Acute state-anxiety variations (STAI-Y1), psychological well-being (PGWBI), and flow dynamics were tracked across three distinct sessions to monitor the patient’s relative repositioning within the normative distribution. Results: The inferential analysis indicates that the immersive 20-min environment facilitated reliable, statistically significant changes in acute state anxiety and flow dimensions, systematically exceeding standard measurement error boundaries and successfully moving the patient’s psychometric profile toward healthy normative ranges. Conclusions: While these findings focus on individual, idiographic reactivity, they demonstrate the utility of the SCNA framework in providing clinicians with objective, evidence-based feedback on the clinical viability of specific VR-based functional units. This approach allows for a rigorous evaluation of standalone digital tools independently of a full, holistic VGT protocol, offering a structured alternative to traditional designs focused on identifying general patterns across groups. Full article

Headwater stream dynamics are vital for understanding hydrological and ecological processes in watersheds; however, traditional monitoring methods can be costly and time-consuming. This technical note documents the limitations and challenges encountered when deploying a low-cost camera system for continuous streamflow monitoring in a forested headwater stream in Ohio, USA. The study stream, with a channel width of less than 1 m and watershed of 0.4 km², is much smaller than previously studied streams. The camera system was constructed using inexpensive and easily accessible electronics, and it enabled application of large-scale particle image velocimetry (LSPIV) to videos collected at a frequency of 15 min. The application of LSPIV to much larger streams is well-established in previous studies; however, its application to extremely small headwater streams is understudied. Preliminary testing in a flume showed that this system was capable of providing accurate discharge measurements. In the field, however, a rating curve calibrated based on the LSPIV-derived flow estimates had an R² value of 0.70, which was weaker than relationships previously reported in the literature. The rating curve overestimated flows at lower channel stages and underestimated them at higher stages when compared to physical discharge measurements. Examination of the videos collected during field deployment revealed that unsteady flow conditions introduced significant variability in the rating curve analysis. Environmental noise from raindrops, illumination conditions, and leaf litter also caused erroneous flow measurements in the LSPIV results. This technical note presents a critical evaluation of the performance of LSPIV-based camera system in extremely small streams, and practitioners and researchers are advised to follow several best practices, offered as lessons learned from our study, to minimize specific sources of error during implementation. Full article

Accurate vehicle counts from traffic videos are fundamental to traffic measurement and to estimating roadway demand for infrastructure planning and maintenance. However, many vision-based traffic datasets and pretrained models under-represent vehicle types that are prevalent in developing countries, such as the keke (globally known as auto-rickshaw/three-wheeler), which can bias traffic composition estimates and downstream workload indicators. This paper presents a keke-aware vehicle detection and counting pipeline that combines fine-tuned YOLO-based detectors with BoT-SORT/ByteTrack tracking and ROI-based counting, together with a newly curated and publicly released traffic-video dataset that includes a dedicated keke class. The detectors are fine-tuned from pretrained weights on a six-class dataset (bicycle, bus, car, motorcycle, truck, keke) and evaluated on held-out roadside test videos with a manual counting baseline. On the validation split (2088 images; 8400 instances), the fine-tuned YOLO11l model achieves $P=0.752$, $R=0.696$, $\mathrm{mAP}@0.5=0.766$, and $\mathrm{mAP}@0.5\text{:}0.95=0.578$, with the keke class attaining $\mathrm{mAP}@0.5=0.772$, while YOLO26l achieves slightly higher overall precision ($P=0.766$) and stronger keke recall and $\mathrm{mAP}@0.5\text{:}0.95$. In system-level counting, the selected tuned ROI-based variants produce the most reliable results on the Yola Road downward flow, where keke counts remain close to the manual baseline, but performance is strongly direction- and scene-dependent, with substantially larger errors in the Yola upward flow and the more challenging Mubi Road scene. Flow-rate and ESAL-rate analyses further show that class misclassification can severely distort pavement-loading estimates even when total traffic flow appears close to baseline, underscoring the need for localized class ontologies and robust heavy-vehicle discrimination in mixed-traffic ITS deployments. The released dataset and baseline pipeline provide a practical reference for keke-aware traffic monitoring and for infrastructure-relevant traffic measurement in developing-country contexts. Full article

Remote photoplethysmography (rPPG) estimates heart rate by analyzing subtle blood-flow-induced color variations from camera videos; however, its performance is highly sensitive to illumination changes caused by variations in light intensity, position, and environmental conditions. To address this limitation, this study proposes a lightweight, training-free brightness normalization method that suppresses illumination-induced luminance fluctuations while preserving physiologically relevant color variations associated with blood perfusion. The proposed approach separates luminance and chrominance components from the frame-mean RGB vector and applies normalization only to the brightness component, thereby maintaining the intrinsic color direction essential for rPPG signal extraction and stabilizing temporal brightness without distorting chrominance relationships. Experimental evaluations show that channel-wise mean values vary only within $\pm 6$–$12\%$ with negligible changes in standard deviation, while dynamic range and temporal stability are significantly improved. Furthermore, when combined with an SNR-based signal selection strategy, the proposed method reduces the mean absolute error (MAE) of the CHROM algorithm on the DLCN dataset from approximately 18–19 BPM to 4.87 BPM under complex illumination scenarios, with consistent improvements also observed on the MR-NIRP dataset. These results suggest that the proposed preprocessing method helps preserve blood-flow-induced temporal color variations and improves the robustness of rPPG measurement under diverse illumination conditions. Full article

Quantifying river surface flow velocity is essential for hydrodynamic modelling, flood forecasting, and water resource management. Traditional in situ methods provide accurate point measurements but are costly and limited in spatial coverage. Unmanned aerial vehicles (UAVs) offer a flexible, non-contact alternative for high-resolution monitoring. Optical flow is a tracer-independent technique for deriving velocity fields from RGB video, making it well suited to UAV-based surveys. However, its operational use is hindered by the limited availability of annotated datasets and by instability under low-texture or noisy conditions. This study combines a Finite element method (FEM)-based physical flow model with UAV video to generate reference datasets and introduces a modified Recurrent All-Pairs Field Transforms (RAFT) architecture based on multiframe sequences. A Gated Recurrent Unit fusion module (Fuse-GRU) is incorporated prior to correlation computation, improving robustness to illumination changes and surface homogeneity while maintaining computational efficiency. The proposed model delivers stable, physically consistent velocity estimates across multiple rivers and flow conditions. Accuracy improves with higher spatial resolution and moderate temporal spacing. Compared to field measurements, the average angular difference ranged from 8 to 15°. The high error values were mainly caused by inaccuracies in the physical model and by complex river features. These findings confirm that multiframe optical flow can reproduce realistic river flow patterns with accuracy comparable to physically-based simulations, thereby supporting UAV-based hydrometric monitoring and model validation. Full article

The COVID-19 pandemic and the resulting mobility restrictions significantly disrupted urban traffic patterns. This study quantitatively assesses the impact of these restrictions on vehicle flow at a signalized central intersection in Cluj-Napoca, Romania, through an integrated methodology combining continuous radar-based traffic measurements and AI (Artificial Intelligence)-assisted video analysis. Traffic data were collected before the pandemic (November 2019) and during the lockdown period (April 2020), enabling a comparative evaluation of flow characteristics and vehicle arrival patterns. Under constrained observational conditions, vehicle arrivals were modeled using a probabilistic framework grounded in Poisson distribution. The findings indicate a dramatic contraction of mobility demand, with traffic volumes declining in 2020 to 9.55% of pre-pandemic levels. The probabilistic assessment highlights the predominance of free-flow regimes under reduced demand and confirms the adequacy of the Poisson model in low-density traffic scenarios. The obtained results contribute to a better understanding of urban traffic dynamics under extreme mobility disruptions and provide a transferable methodological framework for probabilistic traffic modeling, resilience-oriented urban mobility planning, and data-driven traffic management. Full article

This study investigates left-turn safety at urban intersections using surrogate safety measures derived from field video observations. Time-to-Collision (TTC) among motorized traffic and Post-Encroachment Time (PET) among pedestrian and motorized traffic were extracted for left-turn conflicts across five intersection types in Thessaloniki, Greece, and linked to geometric attributes, signal operations, and traffic conditions. Count-based models (Poisson, Negative Binomial) were estimated alongside machine-learning approaches (Random Forest, Gradient Boosting with Poisson loss). For PET events, the Poisson model had the best balance of parsimony and predictive accuracy, whereas the Negative Binomial model provided a superior fit for TTC events. Results indicate that PET-defined conflicts increased with pedestrian volume and the presence of shared and protected left-turn lanes, and decreased with higher opposing flow, greater average acceleration, and wider end-approach lanes. By contrast, TTC events were associated with lower average speeds, the presence of protected signal phasing for left turns, and the number of passenger cars. Machine-learning models underperformed relative to classical count models, reflecting limited sample size and the discrete event structure. The analysis indicates that the determinants of TTC and PET differ, with certain variables such as pedestrian activity and lane configuration having contrasting effects on the two surrogate safety measures. The analysis reveals that pedestrian demand and shared lane configurations significantly increase PET occurrences, whereas TTC events are more strongly associated with vehicle volumes, speeds, and signal phasing. This distinction underscores the importance of tailoring safety assessment and intervention strategies to the type of interaction being evaluated. Full article

This article examines differences between conventional manual measurements of tram operations and data extracted automatically using the REWIZOR program, based on the Yolo10s algorithm. The study addresses the broader question of how artificial intelligence can support analyses of passenger exchange processes in public transport and improve the efficiency of data collection. Measurements conducted in four Polish cities included tram types, stop times, and detailed boarding and alighting durations, while the REWIZOR software enabled automatic detection of stop times and passenger flows based on video recordings. The results show that, although both approaches yield consistent qualitative information regarding doors and passenger counts, significant quantitative discrepancies arise. These differences stem mainly from methodological inconsistencies and varying definitions of boarding, alighting, and stop times, as well as from software-related detection errors. The findings indicate that AI-based measurements require calibration against reference methods to allow reliable comparison with conventional datasets. As currently implemented, REWIZOR can be used effectively for internal analyses of passenger flows, if all compared data come from the same system. Further development—such as implementing simultaneous tracking of people and heads—may considerably improve accuracy and facilitate wider applicability in public transport studies. Full article

According to existing procedures for defining the velocity distribution across cross profile sections of watercourses (e.g., Entropy theory and Power Law theory), surface velocity is a key input parameter, together with cross-sectional bathymetry. Field measurements to obtain velocity values and their distributions are often difficult due to limited equipment, unreliable data, missing data, or hazardous conditions such as flooding and inaccessible locations. This creates a strong need for alternative approaches to measuring surface velocities in rivers. The application of unmanned aerial vehicles (UAVs), mobile phones, and traditional field instruments such as the Propeller Current Meter (PCM) can significantly improve measurement efficiency, especially in situations where conventional methods are not feasible. This paper presents an algorithm for comparing these measurement approaches and quantifying their differences. The methodology is demonstrated using a real case study on the Bednja River in Croatia, which flows through alluvial deposits. The results show that video-based surface velocity estimation using UAV and mobile phone imagery is feasible under real river conditions. Still, its accuracy depends strongly on flow conditions and surface characteristics. While UAV recordings provide reliable results in fast and turbulent flows, mobile phone videos yield more stable performance in smoother flow conditions, where additional surface texture is available from natural tracers. Full article

The optimization of fishway design relies on a deep understanding of fish swimming performance and behavioral traits. Traditional methods often underestimate fish swimming performance and overlook their behavior under high-flow conditions, particularly in the context of high-altitude species. This study, based on an open-channel flume system and combined with high-speed video tracking and Acoustic Doppler Velocity (ADV) measurements, constructs a Resource Selection Function-Generalized Additive Mixed Models (RSF-GAMMs) to quantify the swimming performance and behavior mechanisms of the high-altitude species, Schizothorax oconnori Lloyd, 1908 (S. oconnori), in high-velocity environments. The results show that S. oconnori significantly outperforms traditional swimming tests and exhibits strong dependence on movement modes. Endurance analysis reveals the breakpoints of endurance models, indicating the species’ high sensitivity to variations in exercise intensity, showcasing the unique physiological and behavioral characteristics of high-altitude fish. In high-velocity conditions, adult S. oconnori primarily aims to optimize energy conservation and stability, selectively choosing water bodies with varying disturbance levels depending on its movement mode and endurance state, thus optimizing path selection. This study presents a systematic method for quantifying the extreme swimming abilities and nonlinear behavioral responses of adult S. oconnori under complex flow conditions, providing scientific guidance for setting hydraulic thresholds and developing protection strategies for fishways. Full article

Translation-based Video Synthesis (TVS) has emerged as a transformative technology that enables sophisticated manipulation and generation of dynamic visual content. This comprehensive survey systematically examines the evolution of TVS methodologies, encompassing both image-to-video (I2V) and video-to-video (V2V) translation approaches. We analyze the progression from domain-specific facial animation techniques to generalizable diffusion-based frameworks, investigating architectural innovations that address fundamental challenges in temporal consistency and cross-domain adaptation. Our investigation categorizes V2V methods into paired approaches, including conditional GAN-based frameworks and world-consistent synthesis, and unpaired approaches organized into five distinct paradigms: 3D GAN-based processing, temporal constraint mechanisms, optical flow integration, content-motion disentanglement learning, and extended image-to-image frameworks. Through comprehensive evaluation across diverse datasets, we analyze the performance using spatial quality metrics, temporal consistency measures, and semantic preservation indicators. We present a qualitative analysis comparing methods evaluated on identical benchmarks, revealing critical trade-offs between visual quality, temporal coherence, and computational efficiency. Current challenges persist in long-term temporal coherence, with future research directions identified in long-range video generation, audio-visual synthesis for enhanced realism, and development of comprehensive evaluation metrics that better capture human perceptual quality. This survey provides a structured understanding of methodological foundations, evaluation frameworks, and future research opportunities in TVS. We identify pathways for advancing cross-domain generalization, improving computational efficiency, and developing enhanced evaluation metrics for practical deployment, contributing to the broader understanding of temporal video synthesis technologies. Full article

This study investigates the stochastic variation in droplet size generated within a microfluidic flow-focusing cross-junction. A commercial micro cross-junction was used to experimentally analyze droplet formation under fixed flow rate conditions. An in-house machine learning-based algorithm was developed to automatically detect and measure droplet dimensions from high-speed video recordings. Despite constant flow rates, the analysis revealed fluctuations in droplet size, attributed to velocity oscillations induced by syringe pumps. To explore this phenomenon, micro-Particle Image Velocimetry (micro-PIV) was employed to capture velocity profiles, which were then used to define time-dependent boundary conditions for numerical simulations. Simulations were conducted using the OpenFOAM solver interFoam and validated against experimental data. The results demonstrate good agreement and confirm that velocity fluctuations significantly influence droplet formation. This combined experimental and numerical approach provides an innovative, robust framework for understanding and predicting droplet behavior in microfluidic systems. Full article