Search Results (99)

Visually impaired users face significant challenges in navigating complex indoor environments due to limited spatial awareness and lack of real-time semantic guidance. This paper proposes a multimodal navigation system integrating environmental perception with vision-language models (VLMs). It provides context-aware and explainable guidance without requiring additional infrastructure. The proposed system combines RTAB-Map for localization, YOLO-World for open-vocabulary object detection, and a lightweight language model for semantic reasoning and natural language interaction. To evaluate our system, experiments are conducted using the RePOPE benchmark to assess hallucination in vision-language understanding. Real-world indoor navigation experiments are also performed. The results show that integrating perception with language-based reasoning improves precision by up to 2.29% and consistently enhances F1-score compared to baseline VLM approaches. Real-world experiments further demonstrate reliable navigation performance, including multi-floor path planning and obstacle-aware guidance. Hence, the proposed system effectively enhances spatial understanding and reduces hallucination, providing a practical and scalable solution for assistive navigation. Full article

The reinforcement and renovation of existing buildings constitute an important component of the future development of the civil engineering industry. Such projects typically require the original construction drawings of the building. However, for older structures, the original paper-based drawings may be damaged or lost. Moreover, traditional manual surveying and mapping methods are time-consuming, labor-intensive, and limited in accuracy. To address these issues, this paper proposes a floor plan generation method for existing buildings that integrates deep learning and stereo vision based on a fusion of synthetic and real data. First, collaborative modeling and automated rendering between a large language model and Blender are implemented based on the Model Context Protocol (MCP), enabling indoor scene modeling and image acquisition to construct a synthetic dataset containing structural components such as doors, windows, and walls. Meanwhile, manually annotated real indoor images are incorporated. Synthetic and real data are mixed in different proportions to form multiple dataset configurations for model training and validation. Subsequently, the SegFormer model is employed to perform semantic segmentation of indoor components. Combined with stereo camera calibration results, disparity computation is conducted to extract the three-dimensional spatial coordinates of component corner points. On this basis, the architectural floor plan is generated according to the spatial geometric relationships among structural components. Experimental results demonstrate that the proposed method effectively reduces the need for manual annotation and on-site measurement, providing an efficient technical solution for indoor floor plan generation of existing buildings. Full article

Climate change is a critical global challenge, and the building sector accounts for nearly 30% of global greenhouse gas (GHG) emissions, remaining a key target for mitigation. Indoor environments contribute significantly to GHG emissions, primarily through heating, cooling, lighting, and occupant-driven energy use. Indoor mapping, serving as the foundation for Digital Twins (DTs), provides a spatiotemporal framework that integrates sensor data with Building Information Modelling (BIM), Geographic Information Systems (GIS), and Internet of Things (IoT) to support energy-efficient, low-carbon building operations. This review examined the role of indoor mapping in understanding, modelling, and reducing GHG emissions in buildings. It synthesized current advancements in indoor spatial data acquisition, ranging from Light Detection And Ranging (LiDAR) and Simultaneous Localization and Mapping (SLAM) to deep learning-based floor plan extraction, and evaluated their contribution to improved indoor environmental analysis. The review highlighted emerging techniques, challenges, and gaps, particularly the limited integration of physical indoor spaces with virtual layers representing assets, occupants, and equipment. Addressing this gap requires embedding spatial modelling as an intermediate analytical layer that structures and contextualizes sensor data to support spatiotemporal decision-making. Overall, this review demonstrated that indoor mapping plays a critical role in transforming spatial information into actionable insights, enabling more accurate energy modelling, enhanced real-time building management, and stronger data-driven strategies for GHG mitigation in the built environment. Full article

Inertial localization is particularly valuable in GPS-denied environments such as indoors. However, localization using only Inertial Measurement Units (IMUs) suffers from drift caused by motion-process noise and sensor biases. This paper introduces Uncertainty-aware Map-constrained Inertial Localization (UMLoc), an end-to-end framework that jointly models IMU uncertainty and map constraints to achieve drift-resilient positioning. UMLoc integrates two coupled modules: (1) a Long Short-Term Memory (LSTM) quantile regressor, which estimates the specific quantiles needed to define $68\%$, $90\%$ and $95\%$ prediction intervals serving as a measure of localization uncertainty and (2) a Conditioned Generative Adversarial Network (CGAN) with cross-attention that fuses IMU dynamic data with distance-based floor-plan maps to generate geometrically feasible trajectories. The modules are trained jointly, allowing uncertainty estimates to propagate through the CGAN during trajectory generation. UMLoc was evaluated on three datasets, including a newly collected 2-h indoor benchmark with time-aligned IMU data, ground-truth poses and floor-plan maps. Results show that the method achieves a mean drift ratio of $5.9\%$ over a $70\mathrm{m}$ travel distance and an average Absolute Trajectory Error (ATE) of $1.36\mathrm{m}$, while maintaining calibrated prediction bounds. Full article

The acoustic design of outpatient halls is often ignored, yet the acoustic environment significantly impacts patients’ physical and mental well-being as well as their visit experience. This paper takes the outpatient hall of a hospital in South China as a case, employs in-situ acoustic measurement, and conducts a quantitative analysis of its indoor acoustic environment through acoustic simulation. The in-situ measurements show that the noise level, speech intelligibility and reverberation time in the hall all fail to meet standard requirements. The poor acoustic quality is mainly due to the lack of acoustic design. Consequently, this study proposes measures to improve the outpatient hall’s acoustic environment from two aspects, namely sound absorption and building form design. These measures include sound absorption treatment, adjustment of the hall’s floor height and optimization of planar length-to-width ratio. The redesigned outpatient hall plan demonstrates an evident enhancement in acoustic quality and validates the effectiveness of the proposed redesign strategy. This study can provide practical guidance for the design and acoustic renovation of healthcare buildings and can also offer new insights for the redesign of hospital outpatient halls from the perspective of improving the acoustic environments. Full article

The deployment of robust Internet of Things (IoT) networks within smart buildings requires a thorough understanding of radio propagation in complex indoor environments. Long Range (LoRa) technology is a promising solution for such applications due to its long range and low power consumption. However, its performance in multi-floor structures is heavily influenced by site-specific propagation conditions. This paper presents an empirical characterization of LoRa signal propagation at 433 MHz within a four-story university building. Extensive measurements of Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR) were conducted to model both large-scale and small-scale fading effects. A log-distance path loss model with a Floor Attenuation Factor (FAF) was derived, yielding a path loss exponent of $n=2.53$, an FAF of $5.52$ dB per floor, and a log-normal shadowing standard deviation of $\sigma =6.93$ dB. Time-varying small-scale fading was successfully characterized by a Markov-modulated process (Markov Small-Scale Fading). Furthermore, a non-linear relationship between RSSI and SNR was identified and modeled using a four-parameter logistic function, revealing a dynamic range of approximately 30 dB for the transceivers and a minimum measurable RSSI of −125 dBm. The results validate the proposed models and demonstrate that LoRa can provide reliable, building-wide wireless sensor coverage, offering essential guidelines for the planning and deployment of indoor IoT infrastructure in multi-floor environments. Full article

Residential buildings on the Qinghai–Tibet Plateau face persistent thermal discomfort due to high-altitude climatic extremes. This study investigates how building morphology—including aspect ratio (AR), orientation, and area scaling—affects indoor thermal comfort. Field surveys in Xinghai County informed representative dwelling reconstructions, which were simulated using Ladybug 1.8.0 and Honeybee 1.8.0. Thermal performance was evaluated using PMV, SET, Winter solstice apparent form factor (WSAFF), and surface-to-volume ratio (S/V). Results indicate that compact, near-square forms enhance seasonal thermal stability, with higher WSAFF improving winter solar gains but raising summer overheating risk. South-facing orientations (0° to −30°) optimize summer comfort, while geometric scaling (0.4–2.0) stabilizes indoor temperatures and improves summer PMV and SET, though winter benefits are limited. Comparison of prototype layouts shows that elongated footprints increase vertical variation in comfort, highlighting upper-floor sensitivity to geometry. The study provides a climate-specific framework linking building form with indoor thermal performance. These insights offer practical guidance for sustainable settlement planning and adaptive building design in cold, high-altitude regions. Full article

As people spend more time indoors, the impact of the built environment on psychological health has attracted growing attention. Yet existing studies often have difficulty capturing decision-makers’ reference dependence and loss aversion under uncertainty. To bridge this gap, we propose an evaluation framework comprising three first-level criteria—Outdoor Environment, Physical Comfort (including thermal, lighting, and color environments), and Acoustic Comfort—and determine combined weights by integrating subjective analytic hierarchy process (AHP) judgments with objective entropy weighting based on triangular fuzzy numbers (TFNs). We further incorporate prospect–regret theory to represent loss aversion, expectation-based reference points, and counterfactual regret/rejoicing, and couple it with the VIKOR compromise ranking method, forming an integrated “TFN + Prospect–Regret + VIKOR” approach. The proposed method is applied to four retrofit alternatives for an open-plan office floor (approximately 1200 m²), each emphasizing outdoor environment, physical comfort, acoustic comfort, or no single priority. Experts assessed the schemes using fuzzy linguistic variables. The results show that lighting conditions, thermal comfort, color scheme, and internal noise control receive the highest comprehensive weights. Extensive sensitivity analyses across value/weighting functions and regret-aversion parameters indicate that the ranking of alternatives remains stable while exhibiting clearer separation. Comparative analyses further suggest that, although the overall ordering is consistent with baseline methods, the proposed model increases score dispersion and improves discriminative power. Overall, by explicitly accounting for decision-makers’ psychological behavior and information uncertainty, the framework enables robust and interpretable selection of retrofit schemes for existing office spaces. Full article

Population aging is driving the need for unobtrusive, continuous monitoring solutions in residential care environments. Radio-frequency (RF)-based technologies such as Ultra-Wideband (UWB) and millimeter-wave (mmWave) radar are particularly attractive for providing detailed information on presence and movement while preserving privacy. Building on a UWB–mmWave localization system deployed in a senior living residence, this paper focuses on the data-processing methodology for extracting quantitative mobility indicators from long-term indoor monitoring data. The system combines a device-free mmWave radar setup in bedrooms and bathrooms with a tag-based UWB positioning system in common areas. For mmWave data, an adaptive short-term average/long-term average (STA/LTA) detector operating on an aggregated, normalized radar energy signal is used to classify micro- and macromovements into bedroom occupancy and non-sedentary activity episodes. For UWB data, a partially constrained Kalman filter with a nearly constant velocity dynamics model and floor-plan information yields smoothed trajectories, from which daily gait- and mobility-related metrics are derived. The approach is illustrated using one-day samples from three users as a proof of concept. The proposed methodology provides individualized indicators of bedroom occupancy, sedentary behavior, and mobility in shared spaces, supporting the feasibility of combined UWB and mmWave radar sensing for longitudinal routine analysis in real-world elderly care environments. Full article

This study investigates the acoustic conditions of a design studio (Studio 130) in the Department of Interior Architecture and Environmental Design at Afyon Kocatepe University by integrating 14 weeks of continuous noise measurements with perception data collected from 192 students. Noise measurements were conducted in accordance with ISO 3382-3:2022 guidelines at three locations—window front, door side, and studio midpoint—during morning, noon, and evening periods, with 10 min recordings at each session. The results indicate that when students were present, the equivalent continuous noise level (Leq) reached an average of 65.5 dB(A), with peak levels rising to 72.3 dB(A) during jury sessions. These values substantially exceed the recommended 35 dB(A) classroom threshold by the World Health Organization and the 35–45 dB(A) limits specified in national regulations for indoor educational spaces. Survey findings reveal that 88% of students experienced loss of concentration, 72% reported decreased productivity, 60% had difficulty communicating, and 52% reported fatigue due to noise exposure. Pearson correlation analysis demonstrated a strong relationship between measured noise levels and reported negative effects (r = 0.966). Moreover, independent samples t-test results confirmed that student presence significantly increased studio noise levels (t = 4.98, p < 0.001). The novelty of this research lies in its combined use of longitudinal objective measurements and subjective perception data, addressing the unique open-plan, collaborative, and critique-based pedagogical structure of design studios. The findings highlight that acoustic comfort is a critical component of learning quality in studio-based education. Based on the results, the study proposes several design and material interventions—including spatial dividers, acoustic ceiling panels, fabric-wrapped absorbers, and impact-reducing flooring—to enhance auditory comfort. Overall, the study emphasizes the necessity of integrating acoustic design strategies into studio pedagogy to support concentration, communication, and learning performance. Full article

Accurate, reliable and infrastructure-free indoor positioning using a smartphone is considered an essential topic for applications such as indoor emergency response and indoor path planning. While the inertial measurement units (IMU) offer continuous and high-frequency motion data, pedestrian dead reckoning (PDR) based on IMU data suffers from significant and accumulative errors. Map-aided particle filters (PFs) are important pose estimation frameworks that have exhibited capabilities to eliminate drifts by incorporating additional constraints from a pre-built floor map, without relying on other wireless or perception-based infrastructures. However, despite the recent approaches, a key challenging issue remains: existing map-aided PF-PDR solutions are computationally demanding, as they typically rely on a large number of particles and require map boundaries to eliminate non-matching particles. This process introduces substantial computational overhead, limiting efficiency and real-time performance on resource-constrained platforms such as smartphones. To address this key issue, this work proposes a novel map-aided PF-PDR framework that leverages a smartphone’s IMU data and a pre-built vectorized floor plan map. The proposed method introduces an adaptive PF-PDR solution that detects particle convergence using a cross-entropy distance of the particles and a Gaussian distribution. The number of particles is reduced significantly after a convergence is detected. Further, in order to reduce the computational cost, only the heading is included in particle attitude sampling. The heading is estimated accurately by levelling gyroscope measurements to a virtual plane, parallel to the ground. Experiments are performed using a dataset collected on a smartphone and the results demonstrate improved performance, especially in drift reduction, achieving an mean position error of 0.9 m and a processing rate of 37.0 Hz. Full article

The automatic reconstruction of existing buildings has gained momentum through the integration of Building Information Modeling (BIM) into architecture, engineering, and construction (AEC) workflows. This study presents a hybrid methodology that combines deep learning with surface-based techniques to automate the generation of 3D models and 2D floor plans from unstructured indoor point clouds. The approach begins with point cloud preprocessing using voxel-based downsampling and robust statistical outlier removal. Room partitions are extracted via DBSCAN applied in the 2D space, followed by structural segmentation using the RandLA-Net deep learning model to classify key building components such as walls, floors, ceilings, columns, doors, and windows. To enhance segmentation fidelity, a density-based filtering technique is employed, and RANSAC is utilized to detect and fit planar primitives representing major surfaces. Wall-surface openings such as doors and windows are identified through local histogram analysis and interpolation in wall-aligned coordinate systems. The method supports complex indoor environments including Manhattan and non-Manhattan layouts, variable ceiling heights, and cluttered scenes with occlusions. The approach was validated using six datasets with varying architectural characteristics, and evaluated using completeness, correctness, and accuracy metrics. Results show a minimum completeness of 86.6%, correctness of 84.8%, and a maximum geometric error of 9.6 cm, demonstrating the robustness and generalizability of the proposed pipeline for automated as-built BIM reconstruction. Full article

The present study was motivated by the need to enhance indoor air quality and reduce airborne disease transmission in dense urban environments where high-rise residential buildings face challenges in achieving effective natural ventilation. The problem lies in the lack of scalable and convenient tools to optimize natural ventilation rate, particularly in urban settings with varying building heights. To address this, the scientific technique developed with an innovative metric, the urbanized natural ventilation effectiveness factor (uNVeF), integrates regression analysis of wind direction, velocity, air change rate per hour (ACH), window configurations, and building height to quantify ventilation efficiency. By employing a field measurement methodology, the measurements were conducted across 25 window-opening scenarios in a 13.9 m² residential unit on the 35/F of a Hong Kong public housing building, supplemented by the Hellman Exponential Law with a site-specific friction coefficient (0.2907, R² = 0.9232) to estimate the lower floor natural ventilation rate. The results confirm compliance with Hong Kong’s statutory 1.5 ACH requirement (Practice Note for Authorized Persons, Registered Structural Engineers, and Registered Geotechnical Engineers) and achieving a peak ACH at a uNVeF of 0.953 with 75% window opening. The results also revealed that lower floors can maintain 1.5 ACH with adjusted window configurations. Using the Wells–Riley model, the estimation results indicated significant airborne disease infection risk reductions of 96.1% at 35/F and 93.4% at 1/F compared to the 1.5 ACH baseline which demonstrates a strong correlation between ACH, uNVeF and infection risks. The uNVeF framework offers a practical approach to optimize natural ventilation and provides actionable guidelines, together with future research on the scope of validity to refine this technique for residents and developers. The implications in the building industry include setting up sustainable design standards, enhancing public health resilience, supporting policy frameworks for energy-efficient urban planning, and potentially driving innovation in high-rise residential construction and retrofitting globally. Full article

This paper presents a novel, four-port, rectangular microstrip, inset-feed multiple-input and multiple-output (MIMO) antenna array, enhanced with metamaterials for improved gain and isolation, specifically designed for multi-operator 5G open radio access network (ORAN)-based indoor software-defined radio (SDR) applications. ORAN is an open-source interoperable framework for radio access networks (RANs), while SDR refers to a radio communication system where functions are implemented via software on a programmable platform. A 3 × 3 metamaterial (MTM) superstrate is placed above the MIMO antenna array to improve gain and reduce the mutual coupling of MIMO. The proposed MIMO antenna operates over a 300 MHz bandwidth (3.5–3.8 GHz), enabling shared infrastructure for multiple operators. The antenna’s dimensions are 75 × 75 × 18.2 mm³. The antenna possesses a reduced mutual coupling less than −30 dB and a 3.5 dB enhancement in gain with the help of a novel 3 × 3 MTM superstrate 15 mm above the radiating MIMO elements. A performance evaluation based on simulated results and lab measurements demonstrates the promising value of key MIMO metrics such as a low envelope correlation coefficient (ECC) < 0.002, diversity gain (DG) ~10 dB, total active reflection coefficient (TARC) < −10 dB, and channel capacity loss (CCL) < 0.2 bits/sec/Hz. Real-world testing of the proposed antenna for ORAN-based sub-6 GHz indoor wireless systems demonstrates a downlink throughput of approximately 200 Mbps, uplink throughput of 80 Mbps, and transmission delays below 80 ms. Additionally, a walk test in an indoor environment with a corresponding floor plan and reference signal received power (RSRP) measurements indicates that most of the coverage area achieves RSRP values exceeding −75 dBm, confirming its suitability for indoor applications. Full article

Lecture halls are characterized by large spatial dimensions, deep floor plans, and high occupant densities. Lectures are typically conducted using multimedia and blackboard-based teaching, placing higher demands on the indoor light and thermal environment compared to standard classrooms. This study aims to simulate the interrelationships between multiple building envelope parameters and building performance, in order to improve visual and thermal comfort while reducing energy consumption in cold-region lecture halls. Based on seven key envelope parameters—including openable window area ratio, west-facing window-to-wall ratio, exterior insulation thickness, shading element spacing, angle and width, and window glass type—a multi-objective optimization framework was established. The optimization process targeted three key performance indicators—useful daylight illuminance (UDI), energy use intensity (EUI), and thermal comfort percentage (TCP)—in the context of a stepped classroom. The results show that increasing the thickness of exterior insulation and reducing the width of shading components contribute positively to photothermal comfort without compromising thermal and visual performance. Compared with the baseline design, optimized schemes that incorporate appropriate west-facing window-to-wall ratios, openable window areas, insulation thicknesses, and external shading designs can reduce annual energy consumption by up to 10.82%, and increase UDI and TCP by 12.79% and 36.41%, respectively. These improvements are also found to be economically viable. Full article