Search Results (108)

3D digitization of museum artifacts is essential for both their virtual presentation and re-exhibition in the event of damage or loss. Given the number of artifacts that can be exhibited in a museum, the effectiveness of single-digitization practices under designed conditions is limited in terms of realism. In this study, a highly detailed sine quadrant object was digitized in a museum environment using photogrammetry and structured-light scanning (SLS) techniques. 3D models were generated from point clouds derived in photogrammetry and directly obtained from SLS. In the qualitative assessment based on the distinguishability of linear and edge details, the photogrammetric technique was found to be better; in the quantitative assessment based on the reference length values on the artifact, SLS was better, while photogrammetry was also found to be adequate. The maximum difference values for photogrammetry and SLS were 0.40 and 0.27 cm, respectively, while the average difference values were 0.24 cm and 0.10 cm. Additionally, cloud-to-cloud distance analysis revealed that two-point clouds overlapped quite well geometrically. Point clouds were also compared in terms of homogeneity using outlier detection analysis. This analysis showed that noise in the photogrammetric point cloud had a wider distribution over the artifact. In terms of data acquisition and processing time, SLS was found to be better, while the cost was comparable. After evaluating the techniques from various perspectives, photogrammetry was found to be preferable for modeling in a museum environment due to the priority need for high texture quality from the end-user’s perspective. In this respect, SLS is highly dependent on hardware capability for both data acquisition and processing. Full article

This practice-led research examines how virtual production represents a circular return to scenographic practice, reactivating integrated modes of spatial authorship that have long underpinned screen storytelling but were obscured by industrial fragmentation. Drawing on a single-day intensive workshop at the Australian Film, Television and Radio School (AFTRS), the study analyses how spatial authorship emerged through embodied, collaborative engagement with an LED volume environment. Grounded in scenographic theory and concepts of distributed cognition and situated authorship, the article reframes virtual production as a condition that renders pre-digital, collaborative modes of making visible within contemporary screen production. The LED volume functions simultaneously as scenic environment, lighting instrument, and compositional partner, requiring participants to negotiate space, light, movement, and camera as a unified spatial event. Analysis identifies how scenographic understanding emerged through virtual scouting, world-responsive storytelling, physical-digital integration, and embodied realisation. The findings extend production design theory by challenging ocular-centric models of mise-en-scène and positioning scenographic integration as screen practice—an epistemic mode of enacting through collective, materially grounded spatial experimentation. While situated within an educational context, the study points to broader implications for how spatial authorship and collective practice are understood in contemporary screen production. Full article

As a next-generation immersive service, holographic video enables users to move freely within a virtual world. This imposes stringent requirements on wireless networks. Given the massive bandwidth capacity inherent to visible light, visible light communication (VLC) can effectively meet the transmission requirements of holographic video and is an ideal wireless technology for next-generation indoor immersive services. However, VLC channels are highly dependent on Line-of-Sight (LoS) links. Due to user mobility, traditional VLC systems relying on fixed-orientation Photodetectors (PDs) often suffer from severe channel fading, which significantly degrades the transmission performance. In this paper, we propose an indoor VLC holographic video transmission architecture supporting rotatable PDs, utilizing rotatable PDs mounted on Head-Mounted Displays (HMDs) to assist in holographic video transmission. To minimize the total transmission delay of all users, we address the holographic video transmission problem by jointly optimizing the transmit power allocation of VLC Access Points (APs) and the pitch and roll angles of the users’ PDs. By formulating the problem as a Markov Decision Process (MDP), we address it using a novel Deep Reinforcement Learning (DRL) strategy leveraging the Soft Actor–Critic (SAC) architecture. Simulation results demonstrate that the proposed scheme reduces the overall latency by up to 29.6% compared to the benchmark schemes. Furthermore, the convergence speed of the algorithm is improved by 35% compared to traditional deep reinforcement learning algorithms such as Deep Deterministic Policy Gradient (DDPG). Full article

It is proposed that the process of learning may be represented as a trajectory within the phase space of long-term memory. The research uses an artificial neural network design to explore, in theory, if starting from different points within the phase space affects how quickly learning occurs. Using a Monte Carlo method, 1000 virtual agents were trained using the Levenberg–Marquardt algorithm to recognise a large set of Arabic digits at ten different skill levels. The simulations replicated the typical learning curves observed in human learning and were successful in distinguishing ten levels of skill. First, and in line with previous research, the results provide convincing evidence that learning consolidates a selected set of pathways within the network. Second, and critical to the hypothesis, the distance in the phase space, calculated as the difference in average connectivity between skill levels, is highly predictive of both learning time and performance. The findings strongly support the hypothesis that learning represents progression along a trajectory connecting two points within the phase state landscape. As these properties may be more pronounced in biological systems because of their greater complexity, these results shed new light on individual variance in learning. Full article

Background: Orbital dermoid cysts are common benign lesions; however, deep-seated or recurrent lesions near the orbital apex pose major surgical challenges due to their proximity to critical neurovascular structures. Lateral orbitotomy remains the reference approach, but accurate osteotomies and stable reconstruction can be difficult to achieve using conventional techniques. This study reports our initial experience using a fully digital, hospital-based point-of-care (POC) workflow to enhance precision and safety in complex orbital dermoid cyst surgery. Methods: We present a case series of three patients with orbital dermoid cysts treated at a tertiary center (2024–2025) using a comprehensive digital workflow. Preoperative assessment included CT and/or MRI followed by virtual surgical planning (VSP) with orbit–tumor segmentation and 3D modeling. Cutting guides and patient-specific implants (PSIs) were manufactured in-house under a certified hospital-based POC protocol. Surgical strategies were tailored to each lesion and included piezoelectric osteotomy, intraoperative navigation, intraoperative CT, and structured-light scanning when indicated. Results: Complete en bloc resection was achieved in all cases without capsular rupture or optic nerve injury. Intraoperative CT confirmed complete lesion removal and accurate PSI positioning and fitting. Structured-light scanning enabled radiation-free postoperative monitoring when used. All patients preserved full ocular motility, visual acuity, and facial symmetry, with no complications or recurrences during follow-up. Conclusions: The integration of VSP, in-house POC manufacturing, and image-guided surgery within a lateral orbitotomy approach provides a reproducible and fully integrated workflow. This strategy appears to improve surgical precision and safety while supporting optimal long-term functional and aesthetic outcomes in challenging orbital dermoid cyst cases. Full article

Microalgae are photosynthetic microorganisms capable of fixing CO₂ to produce O₂ and a wide variety of metabolites of interest. Attempts have been made to describe their growth dynamics using mathematical models; however, these models fail to fully represent the dynamics of this bioprocess. Therefore, achieving maximum biomass production in the shortest possible time represents a control challenge due to the nonlinear and time-varying dynamics. Some classic control strategies implemented for this bioprocess are totally or partially dependent on a mathematical model, resulting in controllers with low performance, implementation complexity, and limited robustness. This is where the Virtual Reference Feedback Tuning (VRFT) approach becomes relevant, as it is a model-free control strategy. VRFT is based on the iterative generation of a virtual reference with the aim of minimizing steady-state error, without requiring an explicit model of the bioprocess. Its implementation involves the collection of experimental data in open loop, the minimization of a cost function in closed loop, and the linearization of the system around a stable equilibrium point. This work presents the design and implementation of a VRFT-based control strategy applied to the closed cultivation of the microalga Scenedesmus obliquus UTEX 393 in three flat photobioreactors at laboratory scale. The variables controlled using this strategy were temperature, photosynthetically active light intensity, and level. The experimental results showed that the pre-established references were met. A steady-state temperature of 25 ± 0.625 °C, a PAR (Photosynthetically Active Radiation) light intensity of 100 ± 5 µmol·m⁻²·s⁻¹, and level control that ensured a constant volume of the culture medium were achieved. This suggests that VRFT is a viable control alternative for this type of bioprocess under nominal conditions. Full article

Precise positioning is critical for autonomous uncrewed aerial vehicle (UAV) navigation, especially in GNSS-denied environments where radio-based signals are unreliable. This study presents an optical camera communication (OCC)-based positioning method that enables real-time 3D coordinate estimation using aviation obstruction light-emitting diodes (LEDs) as optical transmitters and a UAV-mounted camera as the receiver. In the proposed system, absolute positional identifiers are encoded into color-shift-keying-modulated optical signals emitted by fixed LEDs and captured by the UAV camera. The UAV’s 3D position is estimated by integrating the decoded LED information with geometric constraints through the Perspective-n-Point algorithm, eliminating the need for satellite or RF-based localization infrastructure. A virtual offshore wind farm, developed in Unreal Engine, was used to experimentally evaluate the feasibility and accuracy of the method. Results demonstrate submeter localization precision over a 50,000 cm flight path, confirming the system’s capability for reliable, real-time positioning. These findings indicate that OCC-based positioning provides a cost-effective and robust alternative for UAV navigation in complex or communication-restricted environments. The offshore wind farm inspection scenario further highlights the method’s potential for industrial operation and maintenance tasks and underscores the promise of integrating optical wireless communication into autonomous UAV systems. Full article

With industrial transformation and the rise in the 15 min community life circle, optimizing walkability and preserving industrial heritage are key to revitalizing former industrial areas. This study, focusing on Shijingshan District in Beijing, proposes a walkability evaluation framework integrating multi-source big data and street-level perception. Using Points of Interest (POI) classification, which refers to the categorization of key urban amenities, pedestrian network modeling, and street view image data, a Walkability Friendliness Index is developed across four dimensions: accessibility, convenience, diversity, and safety. POI data provide insights into the spatial distribution of essential services, while pedestrian network data, derived from OpenStreetMap, model the walkable road network. Street view image data, processed through semantic segmentation, are used to assess the quality and safety of pedestrian pathways. Results indicate that core communities exhibit higher Walkability Friendliness Index scores due to better connectivity and land use diversity, while older and newly developed areas face challenges such as street discontinuity and service gaps. Accordingly, targeted optimization strategies are proposed: enhancing accessibility by repairing fragmented alleys and improving network connectivity; promoting functional diversity through infill commercial and service facilities; upgrading lighting, greenery, and barrier-free infrastructure to ensure safety; and delineating priority zones and balanced enhancement zones for differentiated improvement. This study presents a replicable technical framework encompassing data acquisition, model evaluation, and strategy development for enhancing walkability, providing valuable insights for the revitalization of industrial districts worldwide. Future research will incorporate virtual reality and subjective user feedback to further enhance the adaptability of the model to dynamic spatiotemporal changes. Full article

This paper is a result of the search for design assumptions for a sensor to detect oil dispersed in the sea waters (oil-in-water emulsions). Our approach is based on analyzing changes in the underwater solar radiance (L) field caused by the presence of oil droplets in the water column. This method would enable the sensor to respond to the presence of oil contaminants dispersed in the surrounding environment, even if they are not located directly at the measurement point. This study draws on both literature sources and the results of current numerical modeling of the spread of solar light in the water column to account for both downward and upward irradiance (Es). The core principle of the analysis involves simulating the paths of a large number of virtual solar photons in a seawater model defined by spatially distributed Inherent Optical Properties (IOPs). The IOPs data were taken from the literature and pertain to the waters of the southern Baltic Sea. The optical properties of the oil used in the model correspond to crude oil extracted from the Baltic shelf. The obtained results were compared with previously published spectral analyses of an analogous polluted sea model, considering vertical downward radiance, vertical upward radiance, and downward and upward irradiance. It was found that the optimal wavelength ratio of 555/412, identified for these quantities, is also applicable to scalar irradiance. The findings indicate that the most effective way to determine this index is by measuring it using a sensor with its window oriented in the direction of upward-traveling light. Full article

Background: Virtual reality (VR) technology has emerged as a promising tool for promoting physical activity through immersive gaming experiences. This study aimed to compare the physiological responses and perceived exertion during VR gaming using two different locomotion interfaces: omnidirectional treadmill and traditional controllers. Methods: Twenty-one university students (7 women, 14 men; age 23.5 ± 1.4 years) participated in a crossover study comparing physical activity intensity during VR gaming using traditional controllers versus an omnidirectional treadmill (Virtuix Omni). Participants played VRZ Torment for 15 min in each condition, separated by 30 min washout periods. Physiological responses were measured using indirect calorimetry (Cortex METAMAX® 3B), heart rate monitoring (Polar V800), and subjective ratings of perceived exertion (RPE). Exercise intensity was classified according to established guidelines, and user satisfaction was assessed using a 10-point scale. Results: Omnidirectional treadmill locomotion resulted in significantly higher physiological responses and perceived exertion across all measured variables compared to controller-based movement: heart rate (76.7 ± 11.7% vs. 51.7 ± 9.5% HRmax, p < 0.001), metabolic equivalents (7.3 ± 1.7 vs. 2.1 ± 0.3 METs, p < 0.001), and RPE (14.4 ± 2.9 vs. 9.3 ± 1.5, p < 0.001). Treadmill gaming achieved vigorous-intensity exercise, while controller gaming remained at light intensity. User satisfaction was significantly higher with treadmill locomotion (8.5 ± 1.3 vs. 5.0 ± 2.3, p < 0.001). Strong correlations were observed between physiological measures only during high-intensity treadmill exercise. Conclusions: Omnidirectional treadmill VR gaming achieves vigorous-intensity physical activity sufficient to meet health recommendations, while traditional controller gaming provides only light-intensity exercise. These findings support the potential of locomotion-enhanced VR systems for health promotion. Full article

Accurate and robust calibration of multifocal plenoptic cameras is essential for high-precision 3D light field reconstruction. In this work, we propose a blur feature-guided cascaded calibration for the plenoptic camera. First, white images at different aperture values are used to estimate the high-confidence center point and radius of micro-images, and the defocus theory is used to estimate the initial values of the intrinsic parameters. Second, the gradient value is introduced to quantify the degree of blurring of the corner points, which are then divided into three types: clear, semi-clear, and blurred. Furthermore, a joint geometric constraint model of epipolar lines and virtual depth is constructed, and the coordinates of the semi-clear and blurred corner points are optimized in a step-by-step manner by using the clear corner point coordinates. The micro-image center ray projection equation is then devised to assist in the optimization of the microlens array core parameters and establish blur-adaptive credibility weights, thereby constructing a global nonlinear optimization. Finally, the proposed method is tested on both simulated and captured datasets, and the results exhibit superior performance when compared with the established methods described by Labussière, Nousias, and Liu. The proposed method excels in corner feature extraction, calibration accuracy of both internal and external parameters, and calibration sensitivity when applied to multifocal-length light field cameras, highlighting its advantages and robustness. Full article

With the growing use of head-mounted displays (HMDs) such as smart glasses, text input remains a challenge, especially in mobile environments. Conventional methods like physical keyboards, voice recognition, and virtual keyboards each have limitations—physical keyboards lack portability, voice input has privacy concerns, and virtual keyboards struggle with accuracy due to a lack of tactile feedback. FlickPose is a novel text input system designed for smart glasses and mobile HMD users, integrating flick-based input and hand pose recognition. It features two key selection methods: the touch-panel method, where users tap a floating UI panel to select characters, and the raycast method, where users point a virtual ray from their wrist and confirm input via a pinch motion. FlickPose uses five left-hand poses to select characters. A machine learning model trained for hand pose recognition outperforms Random Forest and LightGBM models in accuracy and consistency. FlickPose was tested against the standard virtual keyboard of Meta Quest 3 in three tasks (hiragana, alphanumeric, and kanji input). Results showed that raycast had the lowest error rate, reducing unintended key presses; touch-panel had more deletions, likely due to misjudgments in key selection; and frequent HMD users preferred raycast, as it maintained input accuracy while allowing users to monitor their text. A key feature of FlickPose is adaptive tracking, which ensures the keyboard follows user movement. While further refinements in hand pose recognition are needed, the system provides an efficient, mobile-friendly alternative for HMD text input. Future research will explore real-world application compatibility and improve usability in dynamic environments. Full article

As vital material carriers of human civilization, earthen sites are experiencing continuous surface deterioration under the combined effects of weathering and anthropogenic damage. Traditional surface conservation techniques, due to their poor compatibility and limited reversibility, struggle to address the compound challenges of micro-scale degradation and macro-scale deformation. With the deep integration of digital twin technology, spatial information technologies, intelligent systems, and sustainable concepts, earthen site surface conservation technologies are transitioning from single-point applications to multidimensional integration. However, challenges remain in terms of the insufficient systematization of technology integration and the absence of a comprehensive interdisciplinary theoretical framework. Based on the dual-core databases of Web of Science and Scopus, this study systematically reviews the technological evolution of surface conservation for earthen sites between 2000 and 2025. CiteSpace 6.2 R4 and VOSviewer 1.6 were used for bibliometric visualization analysis, which was innovatively combined with manual close reading of the key literature and GPT-assisted semantic mining (error rate < 5%) to efficiently identify core research themes and infer deeper trends. The results reveal the following: (1) technological evolution follows a three-stage trajectory—from early point-based monitoring technologies, such as remote sensing (RS) and the Global Positioning System (GPS), to spatial modeling technologies, such as light detection and ranging (LiDAR) and geographic information systems (GIS), and, finally, to today’s integrated intelligent monitoring systems based on multi-source fusion; (2) the key surface technology system comprises GIS-based spatial data management, high-precision modeling via LiDAR, 3D reconstruction using oblique photogrammetry, and building information modeling (BIM) for structural protection, while cutting-edge areas focus on digital twin (DT) and the Internet of Things (IoT) for intelligent monitoring, augmented reality (AR) for immersive visualization, and blockchain technologies for digital authentication; (3) future research is expected to integrate big data and cloud computing to enable multidimensional prediction of surface deterioration, while virtual reality (VR) will overcome spatial–temporal limitations and push conservation paradigms toward automation, intelligence, and sustainability. This study, grounded in the technological evolution of surface protection for earthen sites, constructs a triadic framework of “intelligent monitoring–technological integration–collaborative application,” revealing the integration needs between DT and VR for surface technologies. It provides methodological support for addressing current technical bottlenecks and lays the foundation for dynamic surface protection, solution optimization, and interdisciplinary collaboration. Full article

A novel, environmentally friendly cloud point extraction (CPE) method based on 4-nitrocatechol (H₂L) was developed in this study to spectrophotometrically determine trace vanadium. This method utilizes a mixed micelle-mediated system comprising a cationic surfactant (cetylpyridinium chloride, CPC) and a nonionic surfactant (Triton X-114). In contrast to conventional CPE, the present approach does not employ centrifugation to separate the two phases. The distinguishing characteristic of the extracted species, (CP⁺)[V^VOL₂], is its ability to absorb light across the entire visible spectrum. The measurement at 670 nm, where the complex displays a local maximum, is advantageous for two primary reasons. Firstly, the blank exhibits virtually no absorption, a property that engenders stable and reproducible results. Secondly, selectivity is high because almost all other metal complexes have absorption bands at shorter wavelengths. The proposed method has the following characteristics: a linear range of 2–305 ng mL⁻¹, a limit of detection of 0.6 ng mL⁻¹, a molar absorptivity coefficient of 1.22 × 10⁵ M⁻¹ cm⁻¹, a Sandell sensitivity of 0.42 ng cm⁻², and a blue applicability grade index (BAGI) of 67.5. Its efficacy was demonstrated in the analysis of mineral water, a spent vanadium-containing catalyst, and a dietary supplement. Full article

Unbounded dynamic scene reconstruction is crucial for applications such as autonomous driving, robotics, and virtual reality. However, existing methods struggle to reconstruct dynamic scenes in unbounded outdoor environments due to challenges such as lighting variation, object motion, and sensor limitations, leading to inaccurate geometry and low rendering fidelity. In this paper, we proposed Gaussian-UDSR, a novel 3D Gaussian-based representation that efficiently reconstructs and renders high-quality, unbounded dynamic scenes in real time. Our approach fused LiDAR point clouds and Structure-from-Motion (SfM) point clouds obtained from an RGB camera, significantly improving depth estimation and geometric accuracy. To address dynamic appearance variations, we introduced a Gaussian color feature prediction network, which adaptively captures global and local feature information, enabling robust rendering under changing lighting conditions. Additionally, a pose-tracking mechanism ensured precise motion estimation for dynamic objects, enhancing realism and consistency. We evaluated Gaussian-UDSR on the Waymo and KITTI datasets, demonstrating state-of-the-art rendering quality with an 8.8% improvement in PSNR, a 75% reduction in LPIPS, and a fourfold speed improvement over existing methods. Our approach enables efficient, high-fidelity 3D reconstruction and fast real-time rendering of large-scale dynamic environments, while significantly reducing model storage overhead. Full article