Search Results (233)

Indoor location-based services and applications are heavily dependent on the currentness of indoor data. Therefore, it is crucial to update indoor spatial information promptly and efficiently to ensure its relevance and reliability. Maintaining the topological consistency of geometric objects presents a significant challenge in updating indoor data. Consequently, this paper introduces an incremental updating method for 3D indoor data that considers topological linkages. The first step involves categorizing different types of building component changes and their corresponding indoor space alterations, followed by a detailed analysis of the topological linkage types for indoor features. On the basis of these identified changes, a set of updating operators is developed to handle various types of indoor space alterations. The experimental results demonstrate that the proposed updating operations effectively maintain the topological relationships of solids and the topological adjacency relationships of adjacent solids. This method facilitates efficient querying of indoor spatial information and topological adjacencies, thereby providing a robust data foundation for indoor location-based services and applications. Full article

With the rapid advancement of urbanization in China, the demand for community-based elderly care facilities (CECFs) has been increasing. One pressing challenge is the question of how to provide CECFs that not only meet the health needs of the elderly but also make efficient use of limited urban land resources. This study addresses this issue by adopting an integrated multi-method research framework that combines multi-objective optimization (MOO) algorithms, Spearman rank correlation analysis, ensemble learning methods (Random Forest combined with SHapley Additive exPlanations (SHAP), where SHAP enhances the interpretability of ensemble models), and Self-Organizing Map (SOM) neural networks. This framework is employed to identify optimal building configurations and to examine how different architectural parameters influence key daylight performance indicators—Useful Daylight Illuminance (UDI) and Daylight Factor (DF). Results indicate that when UDI and DF meet the comfort thresholds for elderly users, the minimum building area can be controlled to as little as 351 m² and can achieve a balance between natural lighting and spatial efficiency. This ensures sufficient indoor daylight while mitigating excessive glare that could impair elderly vision. Significant correlations are observed between spatial form and daylight performance, with factors such as window-to-wall ratio (WWR) and wall thickness (WT) playing crucial roles. Specifically, wall thickness affects indoor daylight distribution by altering window depth and shading. Moreover, the ensemble learning models combined with SHAP analysis uncover nonlinear relationships between various architectural parameters and daylight performance. In addition, a decision support method based on SOM is proposed to replace the subjective decision-making process commonly found in traditional optimization frameworks. This method enables the visualization of a large Pareto solution set in a two-dimensional space, facilitating more informed and rational design decisions. Finally, the findings are translated into a set of practical design strategies for application in real-world projects. Full article

Robust indoor positioning, crucial to modern location-based services, increasingly leverages Channel State Information (CSI) for its superior multipath resolution over the traditional RSSI. However, current CSI-based methods are hampered by three key limitations: susceptibility to skewed, non-Gaussian noise; informational redundancy from multi-AP configurations; and spatial discontinuities arising from Euclidean-based modeling. To address these challenges, we propose a unified framework that synergistically combines three innovations: (1) an adaptive filtering pipeline that uses wavelet decomposition and dynamic Kalman updates to suppress skewed noise; (2) a graph attention network that optimizes AP selection by modeling spatiotemporal correlations; and (3) a hyperbolic covariance model that captures the intrinsic non-Euclidean geometry of signal propagation. Evaluations on experimental data demonstrate that our framework achieves superior positioning accuracy and environmental robustness over state-of-the-art methods. Crucially, the hyperbolic representation enhances resilience to obstructions by preserving the signal manifold’s true structure, thereby advancing the practical deployment of fingerprinting systems. Full article

In addition to the use of reference instruments, low-cost sensors (LCSs) are becoming increasingly popular for air quality monitoring both indoors and outdoors. These sensors provide real-time measurements of pollutants and facilitate better spatial and temporal coverage. However, these simpler devices are typically characterised by lower accuracy and precision and can be more sensitive to the environmental conditions than the reference instruments. It is therefore crucial to characterise the applicability and limitations of these instruments, for which a possible solution is their comparison with reference measurements in real-world conditions. To this end, a measurement campaign has been carried out to evaluate the PM_2.5 readings of several low- and medium-cost air quality instruments of different types and categories (IQAir AirVisual Pro, TSI DustTrak™ II Aerosol Monitor 8532, Xiaomi Mijia Air Detector, and Xiaomi Smartmi PM_2.5 Air Detector). A GRIMM EDM180 instrument was used as the reference. This campaign took place in Budapest, Hungary, from 12 November to 15 December 2020, during typically humid and foggy weather conditions, when the air pollution level was high due to the increased anthropogenic emissions, including wood burning for heating purposes. The results indicate that the individual sensors tracked the dynamics of PM_2.5 concentration changes well (in a linear fashion), but the readings deviated from the reference measurements to varying degrees. Even though the AirVisual sensors performed generally well (0.85 < R² < 0.93), the accuracy of the units showed inconsistency (13–93%) with typical overestimation, and their readings were significantly affected by elevated relative humidity levels and by temperature. Despite the overall overestimation of PM_2.5 by the Xiaomi sensors, they also exhibited strong correlation coefficients with the reference, with R² values of 0.88 and 0.94. TSI sensors exhibited slight underestimations with high explained variance (R² = 0.93–0.94) and good accuracy. The results indicated that despite the inherent bias, the low-cost sensors are capable of capturing the temporal variability of PM_2.5, thus providing relevant information. After simple and multiple linear regression-based correction, the low-cost sensors provided acceptable results. The results indicate that sensor data correction is a necessary prerequisite for the usability of the instruments. The ensemble method is a reasonable alternative for more accurate estimations of PM_2.5. Full article

In contemporary wireless communication systems, achieving precise localization of communicating devices and accurate channel estimation is crucial for enhancing operational efficiency and reliability. This study introduces a novel approach that integrates the localization task and channel estimation into a single framework. We present a multi-task neural network architecture capable of simultaneously estimating channels from multiple base stations in a blind manner while estimating user terminal coordinates in given indoor environments. This approach exploits the relationship between channel characteristics and spatial information, using the same channel state information (CSI) data to perform both tasks with a single model. We evaluate the proposed solution, assessing its effectiveness across differing antenna spacing configurations and indoor test environments using both WiFi and 5G orthogonal frequency-division multiplexing (OFDM) systems. The results show performance benefits, achieving comparable channel estimation results to other studies while simultaneously providing a localization estimate, resulting in reduced model overhead while leveraging spatial context. The presented system demonstrates potential to improve the efficiency of communication systems in real-world applications, aligning with the goals of emerging integrated sensing and communication (ISAC) systems. Results based on experimental data using the proposed solution show a 50th percentile localization error of 1.62 m for 3-tap channels and 0.89 m for 10-tap channels. Full article

Sustainable informal learning environments are increasingly recognized as critical components of educational architecture, yet their environmental and behavioral dynamics remain underexplored. Informal learning spaces (ILS) support flexible, student-driven learning beyond formal classrooms. While prior research often isolates individual environmental factors, integrated multi-domain interactions and reciprocal occupant–space dynamics receive less attention. This study adopts a dual-perspective analytical framework, combining spatial analysis and student surveys (n = 1048) across 130 ILS in five academic buildings in China. The findings highlight several environmental dimensions influencing student experience. One extracted factor combines acoustic and thermal comfort with learning atmosphere—domains seldom grouped together—indicating their collective relevance to student experience. Additionally, spatial openness and natural connectivity further enhance student experience. Importantly, the results show that frequently used spaces receive lower physical quality ratings, group collaboration areas outperform individual study zones, and spontaneously formed spaces—informally appropriated, unplanned areas such as corridors or leftover corners—score lowest. These patterns may reflect mismatches between spatial supply and use intensity, institutional investment priorities, and differing levels of student autonomy and environmental control. This research extends conventional post-occupancy evaluations by introducing a comprehensive dual-perspective framework that links spatial characteristics with user-driven dynamics, and by identifying the combined effects of multi-domain physical environmental and supportive elements on student experience. The insights offer empirical grounding and actionable strategies for campus planners and architects, including prioritizing sensory comfort, enhancing spatial diversity, and supporting student-led adaptations to promote sustainable learning environments. Full article

To address the challenges of dynamic object interference and redundant information representation in map construction for indoor dynamic environments, this paper proposes an indoor dynamic environment mapping method based on semantic fusion and hierarchical filtering. First, prior dynamic object masks are obtained using the YOLOv8 model, and geometric constraints between prior static objects and dynamic regions are introduced to identify non-prior dynamic objects, thereby eliminating all dynamic features (both prior and non-prior). Second, an initial semantic point cloud map is constructed by integrating prior static features from a semantic segmentation network with pose estimates from an RGB-D camera. Dynamic noise is then removed using statistical outlier removal (SOR) filtering, while voxel filtering optimizes point cloud density, generating a compact yet texture-rich semantic dense point cloud map with minimal dynamic artifacts. Subsequently, a multi-resolution semantic octree map is built using a recursive spatial partitioning algorithm. Finally, point cloud poses are corrected via Transform Frame (TF) transformation, and a 2D traversability grid map is generated using passthrough filtering and grid projection. Experimental results demonstrate that the proposed method constructs multi-level semantic maps with rich information, clear structure, and high reliability in indoor dynamic scenarios. Additionally, the map file size is compressed by 50–80%, significantly enhancing the reliability of mobile robot navigation and the efficiency of path planning. Full article

Soldering is a common engineering practice that releases airborne particulate matter (PM), contributing to significant long-term respiratory risk. The health impact of this exposure is significant, with up to 22% of soldering workers worldwide being diagnosed with conditions such as occupational asthma, restrictive lung disease, and bronchial obstruction. Studies have reported that soldering can produce PM_2.5 concentrations up to 10 times higher than the U.S. Environmental Protection Agency’s (EPA) 24 h exposure limit of 35.0 μg/m³—posing significant respiratory and cognitive health risks under chronic exposure. These hazards remain underappreciated by novice engineers in academic and entry-level industrial environments, where safety practices are often informal or inconsistently applied. Air purification systems offer a mitigation approach; however, performance varies significantly with model and placement, and independent validation is limited. This study uses an indoor air quality monitoring system consisting of six AeroSpec sensors to measure PM_2.5–10 concentrations during soldering sessions conducted with and without commercial air purifiers. Tests were conducted with and without a selection of commercial air purifiers, and measurements were recorded under consistent spatial and temporal conditions. Datasets were analyzed to evaluate purifier effectiveness and the influence of placement on pollutant distribution. The findings provide independent validation of air purifier capabilities and offer evidence-based suggestions for minimizing particulate exposure and improving safety in laboratory soldering environments. Full article

The integration of computer vision and deep learning into Building Information Modeling (BIM) workflows has created a growing need for structured datasets that enable the semantic segmentation of indoor building elements. This paper presents StructScan3D v1, the first version of an RGB-D dataset specifically designed to facilitate the automated segmentation and modeling of architectural and structural components. Captured using the Kinect Azure sensor, StructScan3D v1 comprises 2594 annotated frames from diverse indoor environments, including residential and office spaces. The dataset focuses on six key building elements: walls, floors, ceilings, windows, doors, and miscellaneous objects. To establish a benchmark for indoor RGB-D semantic segmentation, we evaluate D-Former, a transformer-based model that leverages self-attention mechanisms for enhanced spatial understanding. Additionally, we compare its performance against state-of-the-art models such as Gemini and TokenFusion, providing a comprehensive analysis of segmentation accuracy. Experimental results show that D-Former achieves a mean Intersection over Union (mIoU) of 67.5%, demonstrating strong segmentation capabilities despite challenges like occlusions and depth variations. As an evolving dataset, StructScan3D v1 lays the foundation for future expansions, including increased scene diversity and refined annotations. By bridging the gap between deep learning-driven segmentation and real-world BIM applications, this dataset provides researchers and practitioners with a valuable resource for advancing indoor scene reconstruction, robotics, and augmented reality. Full article

A sufficient number of sensors installed in all structural components is a prerequisite for obtaining the full-field temporal–spatial displacement and is essential for large-scale structure health monitoring. In this paper, a novel lightweight vision-based temporal–spatial deflection measurement method is proposed to measure the full-field temporal–spatial displacement of slender structures. First, the geometric and mechanical properties of slender members are introduced as the priori information to vision-based measurement. Then, a slice template matching model is proposed by deploying a one-dimensional template matching model in every pixel column of each image frame, based on traditional digital image correlation (DIC) method. An indoor experiment was carried out to verify the proposed method, and experiment results show that measurement precision of STMM agrees well with the theory and the laser ranger, with a maximum measurement error of 0.03 pixels and the root-mean-square error (RMSE) of 0.052 mm, for transient beam deflection curve; with the correlation coefficient and coefficient of determination of 0.9994 and 0.9986, for dynamic deflection–time history curves at the middle-span point. Finally, further investigation reveals that brightness inconstancy is the source of STMM measurement error. Full article

Indoor image semantic segmentation technology is applied to fields such as smart homes and indoor security. The challenges faced by semantic segmentation techniques using RGB images and depth maps as data sources include the semantic gap between RGB images and depth maps and the loss of detailed information. To address these issues, a multi-head self-attention mechanism is adopted to adaptively align features of the two modalities and perform feature fusion in both spatial and channel dimensions. Appropriate feature extraction methods are designed according to the different characteristics of RGB images and depth maps. For RGB images, asymmetric convolution is introduced to capture features in the horizontal and vertical directions, enhance short-range information dependence, mitigate the gridding effect of dilated convolution, and introduce criss-cross attention to obtain contextual information from global dependency relationships. On the depth map, a strategy of extracting significant unimodal features from the channel and spatial dimensions is used. A lightweight skip connection module is designed to fuse low-level and high-level features. In addition, since the first layer contains the richest detailed information and the last layer contains rich semantic information, a feature refinement head is designed to fuse the two. The method achieves an mIoU of 53.86% and 51.85% on the NYUDv2 and SUN-RGBD datasets, which is superior to mainstream methods. Full article

Visual SLAM systems frequently encounter challenges in accurately reconstructing three-dimensional scenes from monocular imagery in semantically deficient environments, which significantly compromises robotic operational efficiency. While conventional manual annotation approaches can provide supplemental semantic information, they are inherently inefficient, procedurally complex, and labor-intensive. This paper presents an optimized DeepLabV3+-based framework for visual SLAM that integrates image semantic segmentation with automated point cloud semantic annotation. The proposed method utilizes MobileNetV3 as the backbone network for DeepLabV3+ to maintain segmentation accuracy while reducing computational demands. In this paper, we introduce a parameter-adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm incorporating K-nearest neighbors and accelerated by KD-tree structures, effectively addressing the limitations of manual parameter tuning and erroneous annotations in conventional methods. Furthermore, a novel point cloud processing strategy featuring dynamic radius thresholding is developed to enhance annotation completeness and boundary precision. Experimental results demonstrate that our approach achieves significant improvements in annotation efficiency while preserving high accuracy, thereby providing reliable technical support for enhanced environmental understanding and navigation capabilities in indoor robotic applications. Full article

This study aims to develop a strategic framework for biophilic residential design (BRD) tailored to the diverse health profiles of seniors. To achieve this, a nationwide survey of 424 seniors in South Korea was conducted to assess their health-related quality of life (HRQoL) and preferences for BRD elements. Through principal component and cluster analyses, three HRQoL dimensions—social-economic, mental-sensory, and physical QoL—were extracted, and four distinct senior clusters were identified: Optimal Health, Physically Declining, Overall Low Health, and Socially Vulnerable. Statistically significant differences in BRD preferences were found across clusters for 11 out of 28 BRD elements (p < 0.05), particularly in categories related to sensory-based physiological stability, cognitive stimulation, and external-social connectivity. Notably, the Physically Declining Group expressed a strong preference for restorative and stable features (e.g., natural colors and ventilation systems), while the Socially Vulnerable Group prioritized elements promoting external interaction and social engagement (e.g., balconies, indoor gardens, and walkways). Based on these results, BRD elements were reclassified by function and mapped to the spatial needs of each cluster, leading to a strategic design matrix that supports adaptive and user-centered residential planning. This HRQoL-driven framework contributes a novel link between multidimensional health diagnostics and biophilic design application, moving beyond generalized aging-in-place models. The findings offer practical insights by linking BRD strategies to distinct health profiles. For practitioners, the matrix can inform spatial layouts and design priorities. For policymakers, it provides a basis for developing differentiated housing standards aligned with seniors’ health conditions. Full article

A methodology utilizing low-resolution camera data is proposed to mitigate clutter effects on radar sensors in smart indoor environments. The proposed technique suppresses clutter in distance–velocity (range–Doppler) images obtained from millimeter-wave radar by estimating clutter locations using approximate spatial information derived from low-resolution camera images. Notably, the inherent blur present in low-resolution images closely corresponds to the distortion patterns induced by clutter in radar signals, making such data particularly suitable for effective sensor fusion. Experimental validation was conducted in indoor path-tracking scenarios involving a moving subject within a 10 m range. Performance was quantitatively evaluated against baseline range–Doppler maps obtained using radar data alone, without clutter mitigation. The results show that our approach improves the signal-to-noise ratio by 2 dB and increases the target detection rate by 8.6% within the critical 4–6 m range, with additional gains observed under constrained velocity conditions. Full article

As the world moves toward a low-carbon future, a key challenge is improving buildings’ energy performance while maintaining occupant thermal comfort. Emerging digital tools such as the Internet of Things (IoT) and Building Information Modeling (BIM) offer significant potential, enabling precise monitoring and control of building systems. However, integrating these technologies into a unified Digital Twin (DT) framework remains underexplored, particularly in relation to thermal comfort. Additionally, real-world case studies are limited. This paper presents a DT-based system that combines BIM and IoT sensors to monitor and control indoor comfort in real time through an easy-to-use web platform. By using BIM spatial and geometric data along with real-time data from sensors, the system visualizes thermal comfort using a simplified Predicted Mean Vote (sPMV) index. Furthermore, it also uses a hybrid machine learning model that combines Facebook Prophet and Long Short-Term Memory (LSTM) to predict the future indoor environmental parameters. The framework enables Model Predictive Control (MPC) while providing building managers with a scalable tool to collect, analyze, visualize, and optimize thermal comfort data in real time. Full article