Search Results (284)

At present, pedestrian navigation systems using smartphones have become common in daily activities. For their ubiquitous, accurate, and reliable services, map information collection is essential for constructing comprehensive spatial databases. Previously, we have developed a map information collection tool to extract building information using Google Maps, optical character recognition (OCR), geolocation, and web scraping with smartphones. However, indoor navigation often suffers from inaccurate localization due to degraded GPS signals inside buildings and Simultaneous Localization and Mapping (SLAM) estimation errors, causing position errors and confusing augmented reality (AR) guidance. In this paper, we present an improved map information collection tool to address this problem. It captures 360° panoramic images to build 3D models, apply photogrammetry-based mesh reconstruction to correct geometry, and georeference point clouds to refine latitude–longitude coordinates. For evaluations, experiments in various indoor scenarios were conducted. The results demonstrate that the proposed method effectively mitigates positional errors with an average drift correction of 3.15 m, calculated via the Haversine formula. Geometric validation using point cloud analysis showed high registration accuracy, which translated to a 100% task completion rate and an average navigation time of 124.5 s among participants. Furthermore, usability testing using the System Usability Scale (SUS) yielded an average score of 96.5, categorizing the user interface as ’Best Imaginable’. These quantitative findings substantiate that the integration of 360° imaging and photogrammetric correction significantly enhances navigation reliability and user satisfaction compared with previous sensor fusion approaches. Full article

Emotions are classified into the valence dimension (positive and negative) and the arousal dimension (low and high). Using electrocardiogram (ECG) phase space diagrams and a deep learning approach, emotional states were identified in this study. The DREAMER database was utilized for training and testing the classification model developed. We examined different ECG phase space parameters and compared different deep learning models, including the Visual Geometry Group and Residual networks, and a simple convolutional neural network (CNN) with attention modules. Among the models, a simple four-layer CNN integrated with a convolutional block attention module showed the best performance. Experimental results indicate that the model achieved an accuracy of 87.89% for the valence dimension and 91.79% for the arousal dimension. Compared with existing models, the developed model demonstrates superior performance in emotion recognition. Emotional changes produce noticeable variations in the trajectory patterns of ECG phase space diagrams, which enhance the model’s ability to recognize emotions, even when using relatively simple networks. Full article

The quality of rebar construction is a critical factor that significantly affects the structural stability of reinforced concrete structures. Various automated inspection technologies have been developed to overcome the limitations of conventional labor-intensive inspection methods. However, owing to the complex geometry of rebar arrangements and challenging site conditions, existing approaches still face difficulties in achieving the high accuracy and real-time performance required for practical applications. To address these limitations, this study proposes an automated rebar-spacing inspection technique based on color recognition of painted tie wires with the aim of improving the efficiency and accuracy of data recognition and processing. Field experimental results demonstrated that the use of fluorescent-green tie wires in the HSV color space minimized false detections and achieved a high average recognition rate of 92.6% with the identification of optimal threshold ranges. Furthermore, by utilizing tie-wire intersection coordinates, the stable identification of rebar arrangement conditions and reliable estimation of rebar spacing were achieved, even under conditions with missing data. The proposed automated inspection method can enable more efficient data acquisition and processing under complex construction site conditions while providing accurate and reliable inspection results. Full article

Effective detection of wood defects is essential for maximizing wood use in a sustainable industry. However, traditional methods often struggle with complex textures and irregular shapes. This work introduces MSFE-YOLOv11-OBB, an advanced framework for oriented object detection. To tackle localization and scale challenges, we propose several key innovations: (1) a Recalibration Feature Pyramid Network (FPN) with attention modules to enhance contour accuracy, (2) a CSP-PTB module that integrates CNN-based local features with transformer-based global reasoning to create a more robust pattern representation, and (3) an LSRFAConv module designed to capture subtle structural cues, improving the detection of tiny cracks. Experimental results on an industrial dataset show that our model achieves an mAP@50 of 76.2%, improving over the baseline by 4.7% while maintaining a real-time speed of 86.99 FPS. Comparative analyses confirm superior boundary fitting and multiscale recognition capabilities. By effectively characterizing defect orientation and geometry, this framework offers an intelligent, high-precision solution for automated wood detection, significantly enhancing industrial processing efficiency and resource sustainability. Full article

Large sports stadiums require robust real-time monitoring due to high crowd density, complex spatial configurations, and limited network infrastructure. This research evaluates a hybrid edge–cloud architecture implemented in a national stadium in Thailand. The proposed framework integrates diverse surveillance subsystems, including automatic number plate recognition, face recognition, and panoramic cameras, with edge-based processing to enable real-time situational awareness during high-attendance events. A simulation based on the stadium’s physical layout and operational characteristics is used to analyze coverage patterns, processing locations, and network performance under realistic event scenarios. The results show that geometry-informed sensor deployment ensures continuous visual coverage and minimizes blind zones without increasing camera density. Furthermore, relocating selected video processing tasks from the cloud to the edge reduces uplink bandwidth requirements by approximately 50–75%, depending on the processing configuration, and stabilizes data transmission during peak network loads. These findings suggest that processing location should be considered a primary architectural design factor in smart stadium systems. The combination of edge-based processing with centralized cloud coordination offers a practical model for scalable, safety-oriented monitoring solutions in high-density public venues. Full article

Copper(II) coordination complexes with coumarin-derived heterocyclic ligands are promising in inorganic therapeutics for anticancer and antimicrobial applications. To establish quantitative structure–activity relationships for lead design, we studied six copper(II) complexes (Cu1–Cu6)with four- and five-coordinate geometries using Density Functional Theory, Conceptual Density Functional Theory, and visualization analyses. Geometry optimization at M06/6-31G(d)+DZVP revealed distorted coordination environments from $d^9$ Jahn–Teller effects. Tridentate $N_{2}O$-chelatedcomplexes (Cu4–Cu6) showed greater aqueous stability ($\Delta G_{solv}=-43$ to $-50$ kcal·mol⁻¹) than four-coordinate analogs ($-29$ to $-31$ kcal·mol⁻¹). CDFT global descriptors contrasted reactivity: four-coordinate Cu1–Cu2 had higher electron affinity (>4.2 eV) and electrophilicity (>5.7 eV), suggesting propensity for redox cycling and for undergoing nucleophilic attack by DNA bases, whereas Cu4–Cu6 displayed increased chemical hardness (3.43–3.54 eV) and lower electrophilicity (≈3.8 eV), implying enhanced kinetic stability and bioavailability. Frontier orbital analysis indicated ligand-to-metal charge transfer via a LUMO delocalized over the $\pi$-conjugated coumarin, facilitating intercalation by $\pi$-$\pi$ stacking. The visualization showed strong covalent bonds (blue isosurfaces) stabilizing the metal and dispersive $\pi$ interactions (green surfaces) on the ligand, enabling solvent interactions and biomolecular recognition. Tridentate $N_{2}O$ coordination thus balances electronic stability and biological reactivity, making Cu4–Cu6 promising for further study. Full article

Seismic monitoring is a crucial step in ensuring the safety and resilience of building structures. The implementation of effective monitoring systems, particularly across large-scale, complex building clusters, is currently hindered by the limitations of traditional sensor placement methods, which suffer from low efficiency, high subjectivity, and difficulties in replication. This paper proposes an innovative AI-based Automated Layout Method for seismic monitoring devices, leveraging building geometric recognition to provide a scalable, quantifiable, and reproducible engineering solution. The core methodology achieves full automation and quantification by innovatively employing a dual-channel approach (images and vectors) to parse architectural floor plans. It first converts complex geometric features—including corner coordinates, effective angles, and concavity/convexity attributes—into quantifiable deployment scoring and density functions. The method implements a multi-objective balanced control system by introducing advanced engineering metrics such as key floor assurance, central area weighting, spatial dispersion, vertical continuity, and torsional restraint. This approach ensures the final sensor configuration is scientifically rigorous and highly representative of the structure’s critical dynamic responses. Validation on both simple and complex Reinforced Concrete (RC) frame structures consistently demonstrates that the system successfully achieves a rational sensor allocation under budget constraints. The placement strategy is physically informed, concentrating sensors at critical floors (base, top, and mid-level) and strategically utilizing external corner points to maximize the capture of torsional and shear responses. Compared with traditional methods, the proposed approach has distinct advantages in automation, quantification, and adaptability to complex geometries. It generates a reproducible installation manifest (including coordinates, sensor types, and angle classification) that directly meets engineering implementation needs. This work provides a new, efficient technical pathway for establishing a systematic and sustainable seismic risk monitoring platform. Full article

The development of Geospatial Artificial Intelligence (GeoAI), combining deep learning and remote sensing imagery, is of great interest for automated spatial inference and decision-making support. In this paper, a GeoAI-based efficient object detection framework named RS-YOLO is introduced by adopting the YOLOv11 architecture. The model integrates Dynamic Convolution for adaptive receptive field adjustment, Selective Kernel Attention for multi-path feature aggregation, and the MPDIoU loss function for geometry-aware localization. The proposed approach outperforms in experimental results on the TGRS-HRRSD dataset of 13 scenes from different geospatial scenarios, giving an 89.0% mAP and an 87 F1-score. Beyond algorithmic advancement, RS-YOLO provides a GeoAI-based analytical tool for applications such as urban infrastructure monitoring, land use management, and transportation facility recognition, enabling spatially informed and sustainable decision-making in complex remote sensing environments. Full article

Water supply and drainage networks are essential components of urban infrastructure, directly influencing both residents’ quality of life and the efficiency of city operations through their safety and stability. Over time, these networks often develop unstructured turbid water conditions (referring to scenarios with irregular pipe geometries due to defects and low visibility caused by suspended matter), which present challenges for traditional maintenance methods. Leveraging the advantages of spatial visualization, three-dimensional environmental reconstruction technology has emerged as a promising solution to address these issues, while also advancing the use of intelligent maintenance technologies within water supply and drainage systems. This paper focuses on the causes of unstructured turbid water in these networks, and evaluates the optimization, effectiveness, and limitations of turbid water imaging, image feature recognition, and 3D environmental reconstruction technologies. Additionally, it reviews the current technical challenges and outlines potential future research directions, aiming to support the development and application of 3D reconstruction technologies for pipeline networks under unstructured turbid water conditions. Full article

We introduce Recognition Geometry (RG), an axiomatic framework in which geometric structure is not assumed a priori but derived. The starting point of the theory is a configuration space together with recognizers that map configurations to observable events. Observational indistinguishability induces an equivalence relation, and the observable space is obtained as a recognition quotient. Locality is introduced through a neighborhood system, without assuming any metric or topological structure. A finite local resolution axiom formalizes the fact that any observer can distinguish only finitely many outcomes within a local region. We prove that the induced observable map $\overline{R}:{\mathcal{C}}_R\to \mathcal{E}$ is injective, establishing that observable states are uniquely determined by measurement outcomes with no hidden structure. The framework connects deeply with existing approaches: C^*-algebraic quantum theory, information geometry, categorical physics, causal set theory, noncommutative geometry, and topos-theoretic foundations all share the measurement-first philosophy, yet RG provides a unified axiomatic foundation synthesizing these perspectives. Comparative recognizers allow us to define order-type relations based on operational comparison. Under additional assumptions, quantitative notions of distinguishability can be introduced in the form of recognition distances, defined as pseudometrics. Several examples are provided, including threshold recognizers on ${\mathbb{R}}^n$, discrete lattice models, quantum spin measurements, and an example motivated by Recognition Science. In the last part, we develop the composition of recognizers, proving that composite recognizers refine quotient structures and increase distinguishing power. We introduce symmetries and gauge equivalence, showing that gauge-equivalent configurations are necessarily observationally indistinguishable, though the converse does not hold in general. A significant part of the axiomatic framework and the main constructions are formalized in the Lean 4 proof assistant, providing an independent verification of logical consistency. Full article

In the last decade, the interdisciplinary field of neuroarchitecture has grown significantly, revealing measurable links between architectural features and human neural processing. This review synthesizes current research at the nexus of neuroscience and architecture, with a focus on how emerging virtual reality (VR) and artificial intelligence (AI) technologies are being utilized to understand and enhance human spatial experience. We systematically reviewed literature from 2015 to 2025, identifying key empirical studies and categorizing advances into three themes: core components of neuroarchitectural research; the use of physiological sensors (e.g., EEG, heart rate variability) and virtual reality to gather data on occupant responses; and the integration of neuroscience with AI-driven analysis. Findings indicate that built environment elements (e.g., geometry, curvature, lighting) influence brain activity in regions governing emotion, stress, and cognition. VR-based experiments combined with neuroimaging and physiological measures enable ecologically valid, fine-grained analysis of these effects, while AI techniques facilitate real-time emotion recognition and large-scale pattern discovery, bridging design features with occupant emotional responses. However, the current evidence base remains nascent, limited by small, homogeneous samples and fragmented data. We propose a four-domain framework (somatic, psychological, emotional, cognitive-“SPEC”) to guide future research. By consolidating methodological advances in VR experimentation, physiological sensing, and AI-based analytics, this review provides an integrative roadmap for replicable and scalable neuroarchitectural studies. Intensified interdisciplinary efforts leveraging AI and VR are needed to build robust, diverse datasets and develop neuro-informed design tools. Such progress will pave the way for evidence-based design practices that promote human well-being and cognitive health in built environments. Full article

Reverse engineering (RE) has been increasingly adopted in metal manufacturing to digitize legacy parts, connect “as-is” geometry to mechanical performance, and enable agile repair and remanufacturing. This review consolidates scan-to-simulation workflows that transform 3D measurement data (optical/laser scanning and X-ray computed tomography) into simulation-ready models for structural assessment and manufacturing decisions, with an explicit focus on sustainability. Key steps are reviewed, from acquisition planning and metrological error sources to point-cloud/mesh processing, CAD/feature reconstruction, and geometry preparation for finite-element analysis (watertightness, defeaturing, meshing strategies, and boundary condition transfer). Special attention is given to uncertainty quantification and the propagation of geometric deviations into stress, stiffness, and fatigue predictions, enabling robust accept/reject and repair/replace choices. Sustainability is addressed through a lightweight reporting framework covering material losses, energy use, rework, and lead time across the scan–model–simulate–manufacture chain, clarifying when digitalization reduces scrap and over-processing. Industrial use cases are discussed for high-value metal components (e.g., molds, turbine blades, and marine/energy parts) where scan-informed simulation supports faster and more reliable decision making. Open challenges are summarized, including benchmark datasets, standardized reporting, automation of feature recognition, and integration with repair process simulation (DED/WAAM) and life-cycle metrics. A checklist is proposed to improve reproducibility and comparability across RE studies. Full article

Similarity search on image embeddings is a common practice for image retrieval in machine learning and pattern recognition systems. Approximate nearest neighbor (ANN) methods enable scalable similarity search on large datasets, often approaching sub-linear complexity. Yet, little empirical work has examined how ANN neighborhood geometry differs from that of exact k-nearest neighbors (k-NN) search as the neighborhood size increases under constrained search effort. This study quantifies how approximate neighborhood structure changes relative to exact k-NN search as k increases across three experimental conditions. Using multiple random subsets of 10,000 images drawn from the STL-10 dataset, we compute ResNet-50 image embeddings, perform an exact k-NN search, and compare it to a Hierarchical Navigable Small World (HNSW)-based ANN search under controlled hyperparameter regimes. We evaluated the fidelity of neighborhood structure using neighborhood overlap, average neighbor distance, normalized barycenter shift, and local intrinsic dimensionality (LID). Results show that exact k-NN and ANN search behave nearly identically when $efSearch>k$. However, as the neighborhood size grows and $efSearch$ remains fixed, ANN search fails abruptly, exhibiting extreme divergence in neighbor distances at approximately $k\approx 2$–$3.5\times efSearch$. Increasing index construction quality delays this failure, and scaling search effort proportionally with neighborhood size ($efSearch=\alpha \times k$ with $\alpha \ge 1$) preserves neighborhood geometry across all evaluated metrics, including LID. The findings indicate that ANN search preserves neighborhood geometry within its operational capacity but abruptly fails when this capacity is exceeded. Documenting this behavior is relevant for scientific applications that approximate embedding spaces and provides practical guidance on when ANN search is interchangeable with exact k-NN and when geometric differences become nontrivial. Full article

Fyn is a Src-family tyrosine kinase implicated in synaptic dysfunction and neuroinflammation across multiple neurodegenerative disorders, including Alzheimer’s disease (AD) and Parkinson’s disease (PD). Saracatinib (AZD0530) is a potent Src-family inhibitor that has been explored as a repurposed therapeutic; however, its clinical utility is limited by poor kinase selectivity caused by high sequence conservation within Src-family ATP-binding sites. Here, we combine surface plasmon resonance (SPR) and X-ray crystallography to define saracatinib recognition by the Fyn kinase domain (KD). SPR single-cycle kinetics shows that saracatinib binds the isolated Fyn KD and full-length Fyn with low-nanomolar affinity, whereas dasatinib binds with subnanomolar affinity and markedly slower dissociation. We determined the crystal structure of the Fyn KD-saracatinib complex at 2.22 Å resolution. The kinase adopts an active-like conformation with the DFG motif and αC-helix in the ‘in’ state and a conserved β3 αC Lys-Glu salt bridge. Saracatinib occupies the adenine and ribose pockets, and engages the hinge through direct and water-mediated hydrogen bonding while complementing a hydrophobic back pocket by van der Waals contacts. Comparison with reported saracatinib-bound structures of other kinases suggests that the active-state geometry observed for Fyn creates a pocket not observed in inactive-like complexes, providing a structural handle for designing Fyn-selective inhibitors. Comparison with all saracatinib-bound kinase co-structures currently available in the PDB (ALK2 and PKMYT1) indicates a conserved monodentate hinge binding mode but kinase-dependent αC-helix conformations, providing a structural rationale for designing Fyn-selective analogues. Full article

Manual pill identification is often inefficient and error-prone due to the large variety of medications and frequent visual similarity among pills, leading to misuse or dispensing errors. These challenges are exacerbated when pill imprints are engraved, curved, or irregularly arranged, conditions under which conventional optical character recognition (OCR)-based methods degrade significantly. To address this problem, we propose GO-PILL, a geometry-aware OCR pipeline for robust pill imprint recognition. The framework extracts text centerlines and imprint regions using the TextSnake algorithm. During imprint refinement, background noise is suppressed and contrast is enhanced to improve the visibility of embossed and debossed imprints. The imprint localization and alignment stage then rectifies curved or obliquely oriented text into a linear representation, producing geometrically normalized inputs suitable for OCR decoding. The refined imprints are processed by a multimodal OCR module that integrates a non-autoregressive language–vision fusion architecture for accurate character-level recognition. Experiments on a pill image dataset from the U.S. National Library of Medicine show that GO-PILL achieves an F1-score of 81.83% under set-based evaluation and a Top-10 pill identification accuracy of 76.52% in a simulated clinical scenario. GO-PILL consistently outperforms existing methods under challenging imprint conditions, demonstrating strong robustness and practical feasibility. Full article