Search Results (2,094)

Traditional pedestrian inertial navigation (PDR) algorithms usually assume that the carrying mode of a smartphone is fixed and remains horizontal, while ignoring the significant impact of dynamic changes in the carrying mode on heading estimation, which is the core element of PDR algorithms. In practical application scenarios, pedestrians often change their way of carrying smart terminals (e.g., calling) according to their needs, corresponding to the difference in the heading estimation method; especially when the mode is switched, it will cause a sudden change in heading, which will lead to a significant increase in the localization error if it cannot be corrected in time. Existing smart terminal carrying mode recognition methods that rely on traditional machine learning or set thresholds have poor robustness; lack of universality, especially weak diagnostic ability for mutation; and can not effectively reduce the heading error. Based on these practical problems, this paper innovatively proposes a PDR framework that tries to overcome these limitations. Based on this research purpose, firstly, this paper classifies four types of common carrying modes based on practical applications and designs a CNN-LSTM hybrid model, which can classify the four common carrying modes in near real-time, with a recognition accuracy as high as 99.68%. Secondly, based on the mode recognition results, a multi-layer heading correction strategy is introduced: (1) introducing a quaternion-based universal filter (VQF) algorithm to realize the accurate estimation of initial heading; (2) designing an algorithm to accurately detect the mode switching point and developing an adaptive offset correction algorithm to realize the dynamic compensation of heading in the process of mode switching to reduce the impact of sudden changes; and (3) considering the motion characteristics of pedestrians walking in a straight line segment where lateral displacement tends to be close to zero. This study designs a heading optimization method with lateral displacement constraints to further inhibit the drifting of the heading caused by the slight swaying of the smart terminal. In this study, two validation experiments are carried out in two different environment—an indoor corridor and a tree shelter—and the results show that based on the proposed multi-layer heading optimization strategy, the average heading error of the system is lower than 1.5°, the cumulative positioning error is lower than 1% of the walking distance, and the root mean square error of the checkpoints is lower than 2 m, which significantly reduces the positioning error and shows the effectiveness of the framework in complex environments. Full article

Fine particulate matter (PM)-induced pollution is one of the major causes of indoor air quality deterioration. Passive air purification technologies offer advantages of structural simplicity and low energy consumption, yet their spatiotemporal mass transfer characteristics remain poorly understood. This study presents a theoretical and experimental investigation of PM spatiotemporal mass transfer under the sink effect induced by an electro-convective passive air purifier. The apparent mass transfer coefficient (D_app) and PM concentration prediction models based on Fick’s second law were established, and then the space-and-time-dependent mass transfer coefficient (D_st) was determined by using the Boltzmann–Matano method. The results revealed that the absolute values of D_st quantified local migration intensity, while its sign provided directional information unattainable from conventional averaged parameters. The logarithmic values of D_app showed a consistent logarithmic relationship with distance at fixed time windows, and the validated prediction model maintained errors within ±15%, enabling accurate reconstruction of full-field concentration distributions from limited measurement points. The complementary nature of these two coefficients offers a comprehensive evaluation framework. This work advances both the theoretical understanding and practical application of passive air purification technology, offering new tools for indoor PM exposure control and purifier performance optimization. Full article

To address the critical challenges of geological hazards, such as water and mud inrush, encountered during the construction of deep-buried tunnels in China, this study investigates the hydraulic properties of remolded mud-infill materials. A multi-scale approach, integrating indoor variable-head permeability tests with scanning electron microscopy (SEM), was employed to characterize the evolutionary patterns of the permeability coefficient (k). Specifically, the research evaluates the independent influences of moisture content, dry density, and confining pressure, alongside the synergistic coupling between dry density and hydration state. The results demonstrate the following: Under independent variable conditions, k exhibits a monotonic decline with increasing dry density and confining pressure while showing a positive correlation with moisture content, with the sensitivity varying significantly across different parameter regimes; under coupled effects, the permeability in both low- and high-moisture ranges manifests a distinct “increase–decrease–increase” fluctuation as dry density rises, reaching a local peak at 2.20 g/cm³. Notably, a relative minimum k (6.12 × 10⁻⁷ cm/s) is achieved at the optimum moisture content (5.8%); micro-mechanistic analysis reveals that low-moisture samples are characterized by randomized angular particles and well-developed interconnected macropore networks, facilitating higher k values. Conversely, high-moisture samples exhibit preferential plate-like stacking dominated by occluded micropores, resulting in a substantial reduction in hydraulic conductivity. This study elucidates the multi-factor coupling mechanism governing the seepage behavior of remolded mud, providing essential theoretical benchmarks for the prediction and mitigation of water–mud outburst disasters in deep underground engineering, thereby ensuring the structural stability and operational safety of tunnel projects. Full article

During the interpretation of Borehole Radar (BHR) B-scan profiles, the accurate determination of the azimuth of geological targets in three-dimensional space is a critical issue for achieving precise anomaly localization and spatial structure inversion. However, existing directional BHR anomaly localization methods exhibit limited intelligence, insufficient adaptability to multi-site data, and weak generalization capability, rendering them inadequate for engineering applications under complex geological conditions. To address these challenges, a robust deep learning model, termed BSS-Pose-BHR, is developed based on YOLOv11n-pose for keypoint detection in directional BHR profiles. The model incorporates three key optimizations: Bi-Level Routing Attention (BRA) replaces Multi-Head Self-Attention (MHSA) in the backbone to improve computational efficiency; Conv_SAMWS enhances keypoint-related feature weighting in the backbone and neck; and Spatial and Channel Reconstruction Convolution (SCConv) is integrated into the detection head to reduce redundancy and strengthen local feature extraction, thereby improving suitability for keypoint detection tasks. In addition, a three-dimensional electromagnetic model of limestone containing a certain density of clay particles is established to construct a simulation dataset. On the simulated test set, compared with current mainstream deep learning approaches and conventional directional borehole radar anomaly localization algorithms, BSS-Pose-BHR achieves superior performance, with an mAP50(B) of 0.9686, an mAP50–95(B) of 0.7712, an mAP50(P) of 0.9951, and an mAP50–95(P) of 0.9952. Ablation experiments demonstrate that each proposed module contributes significantly to performance improvement. Compared with the baseline, BSS-Pose-BHR improves mAP50(B) by 5.39% and mAP50(P) by 0.86%, while increasing model weight by only 1.05 MB, thereby achieving a reasonable trade-off between detection accuracy and complexity. Furthermore, indoor physical model experiments validate the effectiveness of the method on measured data. Robustness experiments under different Peak Signal-to-Noise Ratio (PSNR) conditions and varying missing-trace rates indicate that BSS-Pose-BHR maintains high detection accuracy under moderate noise and data loss, demonstrating strong engineering applicability and practical value. Full article

The paper introduces a computationally efficient semantic-aware route planning framework for indoor mobile robots, designed for real-time execution on resource-constrained edge hardware (Raspberry Pi 5, CPU-only). The proposed architecture fuses monocular object detection with 2D LiDAR-based range estimation and integrates the resulting semantic annotations into the Nav2 Route Server for penalty-weighted route selection. Object localization in the map frame is achieved through the Angular Sector Fusion (ASF) pipeline, a deterministic geometric method requiring no parameter tuning. The ASF projects YOLO bounding boxes onto LiDAR angular sectors and estimates the object range using a 25th-percentile distance statistic, providing robustness to sparse returns and partial occlusions. All intrinsic and extrinsic sensor parameters are resolved at runtime via ROS 2 topic introspection and the URDF transform tree, enabling platform-agnostic deployment. Detected entities are classified according to mobility semantics (dynamic, static, and minor) and persistently encoded in a GeoJSON-based semantic map, with these annotations subsequently propagated to navigation graph edges as additive penalties and velocity constraints. Route computation is performed by the Nav2 Route Server through the minimization of a composite cost functional combining geometric path length with semantic penalties. A reactive replanning module monitors semantic cost updates during execution and triggers route invalidation and re-computation when threshold violations occur. Experimental evaluation over 115 navigation segments (legs) on three heterogeneous robotic platforms (two single-board RPi5 configurations and one dual-board setup with inference offloading) yielded an overall success rate of 97% (baseline: 100%, adaptive: 94%), with 42 replanning events observed in 57% of adaptive trials. Navigation time distributions exhibited statistically significant departures from normality (Shapiro–Wilk, p < 0.005). While central tendency differences between the baseline and adaptive modes were not significant (Mann–Whitney U, p = 0.157), the adaptive planner reduced temporal variance substantially (σ = 11.0 s vs. 31.1 s; Levene’s test W = 3.14, p = 0.082), primarily by mitigating AMCL recovery-induced outliers. On-device YOLO26n inference, executed via the NCNN backend, achieved 5.5 ± 0.7 FPS (167 ± 21 ms latency), and distributed inference reduced the average system CPU load from 85% to 48%. The study further reports deployment-level observations relevant to the Nav2 ecosystem, including GeoJSON metadata persistence constraints, graph discontinuity (“path-gap”) artifacts, and practical Route Server configuration patterns for semantic cost integration. Full article

Seamless localization across indoor and outdoor environments remains a fundamental challenge for wearable navigation systems, particularly those intended to assist visually impaired individuals. This challenge arises from the unreliability of GNSS signals in indoor and transitional spaces and the cumulative drift inherent to IMU–based dead reckoning. To address these limitations, this paper proposes a physics-informed GNSS–IMU sensor fusion framework that enables robust, real-time wearable navigation across heterogeneous environments. The proposed system dynamically adapts to environmental context, employing GNSS dominant localization in outdoor settings and PINN enhanced IMU-based dead reckoning during GNSS denied indoor operation. At the core of the framework is a tightly coupled Physics-Informed Neural Network (PINN) and Extended Kalman Filter (EKF), where the PINN embeds kinematic motion constraints to correct inertial drift and suppress sensor noise, while the EKF performs probabilistic state estimation and sensor fusion. The framework is implemented on a compact, energy-efficient wearable platform and evaluated using real-world indoor–outdoor pedestrian trajectories. Experimental results demonstrate improved localization accuracy, significantly reduced drift during indoor navigation, and stable indoor–outdoor transitions compared to conventional GNSS–IMU fusion methods. The proposed approach offers a practical and reliable solution for wearable assistive navigation and has broader applicability in smart mobility and autonomous wearable systems. Full article

Thermal comfort is among the primary determinants of habitability in the built environment. In tropical developing countries, however, its treatment in public housing policy has often been limited, fragmented, and, in many cases, subordinated to energy-saving criteria that do not adequately reflect occupant needs or local climatic diversity. This study analyses the integration of thermal comfort within housing policy using a mixed-methods approach combining regulatory analysis with post-occupancy environmental monitoring. Empirical monitoring shows average indoor temperatures between 16.3 °C and 18.5 °C, with more than 80% of recorded hours falling below adaptive comfort thresholds and a predicted dissatisfaction rate (PPD) of approximately 47%. These findings demonstrate that compliance with efficiency-centred sustainability regulation does not necessarily ensure thermally adequate indoor conditions in occupied social housing, highlighting a structural gap in current regulatory frameworks between efficiency-based compliance and thermally adequate indoor conditions in occupied social housing. The analytical framework integrates three dimensions: policy analysis, environmental performance verification, and interpretation of occupant adaptive behaviour. Rather than claiming that Bogotá is statistically representative of all tropical conditions, the paper treats it as an analytically revealing case in which tensions among efficiency-centred regulation, imported comfort standards, and constrained occupant adaptation become visible. The paper also demonstrates that the current Colombian sustainability regulation (Resolution 0194 of 2025) operationalises sustainability primarily through energy and water saving targets and climatic zoning, while lacking explicit, verifiable indicators for thermal comfort, occupant well-being, or health outcomes. Finally, the paper discusses the relevance of locally calibrated standards, standardised field methodologies, and passive design strategies within a broader agenda of energy governance, environmental equity, and housing adequacy. Full article

Mountainous areas are prone to extreme climatic conditions, and the lack of modern infrastructure makes it difficult to achieve sustainable construction. To overcome the challenges of thermal comfort, robustness, and post-occupancy performance in hazard zones like the Neelum Valley in Pakistan, this research proposes a Digital–Vernacular Integration Model (DVIM), which integrates traditional architectural expertise with modern digital technology. The research design was based on mixed-methods research with the integration of qualitative information obtained through interviews and household surveys (n = 120), and quantitative measures of indoor thermal environments and hazards-based spatial analysis. Vernacular buildings made of wood, stone, and mud were digitally reconstructed using geometric modeling with SketchUp and Autodesk Revit with building information (BIM)-based modeling for assigning materials’ properties. Simulations were carried out using DesignBuilder software with EnergyPlus engines for assessing thermal environment, snow resistance, and seismic resistance to local hazards. The incorporation of the double-layered wall resulted in the improvement of heat retention by 12 to 15%. Moreover, the optimized roof and walls of the hybrid model resulted in the reduction of the sensible heating demand by 42% when compared to the conventional log houses and nearly 80% when compared to the conventional concrete block houses of the modern era. The proposed hybrid model resulted in R-values ranging from 33 to 40 m²·K/W, which are significantly higher when compared to the R-values for conventional timber walls (R = 15 m²·K/W) and concrete block walls (R = 1.0 to 1.3 m²·K/W). These results show the effectiveness of the digitally optimized hybrid model in improving the thermal performance in severe climatic conditions. The results clearly show that the integration of traditional architecture with digital simulation can ensure that modern comfort and safety standards are met without affecting the cultural identity of the region. The proposed framework will be implemented in pilot projects to ensure that the hybrid architectural models are incorporated into regional building regulations. Full article

The integration of emerging technologies such as robotics and artificial intelligence (AI) has the potential to transform agricultural harvesting by improving efficiency, reducing waste, lowering labor dependency, and enhancing produce quality. This paper presents the development of an intelligent robotic berry harvesting system that combines deep learning–based perception with autonomous robotic manipulation for real-time strawberry harvesting. A computer vision pipeline based on the YOLOv11 segmentation model was developed and integrated into a Smart Mobile Manipulator (SMM) equipped with autonomous navigation, a 6-degree-of-freedom (6-DoF) xArm 6 robotic arm, and ROS middleware to enable real-time operation. Using a publicly available strawberry dataset comprising 2,800 images collected under ridge-planted cultivation conditions, the proposed YOLOv11-small segmentation model achieved 84.41% mAP@0.5, outperforming YOLOv11 object detection, Faster R-CNN, and RT-DETR in segmentation quality while maintaining real-time performance at 10 FPS on an NVIDIA Jetson Orin Nano edge GPU. A PCA-based fruit orientation and geometric analysis method achieved 86.5% localization accuracy on 200 test images. Controlled indoor harvesting experiments using synthetic strawberries demonstrated an overall harvesting success rate of 72% across 50 trials. The proposed system provides a general-purpose platform for berry harvesting in controlled environments, offering a scalable and efficient solution for autonomous harvesting. Full article

This paper presents an indoor self-localization framework for mobile robots, an essential component for automation in Industry 4.0 and smart environments. We evaluate a Received Signal Strength Indicator (RSSI) fingerprinting technique utilizing Long-Range (LoRa) technology to overcome the challenges of congested indoor settings. To optimize communication parameters, the Structural Similarity Index Measure (SSIM) was employed to select the most effective spreading factor, while the entropy of the RSSI database was calculated to verify fingerprint stability. For positional prediction, a Multi-layer Perceptron (MLP) neural network was developed to classify the location of the target within a grid-based experimental setup, featuring cells spaced 60 cm apart. The MLP achieved a validation accuracy of 91.8 percent during training and demonstrated high precision in classifying grid regions within a signal-dense environment. For scenarios where slow-moving robots (5 cm/s) are required, like radiation mapping, this method provide highly accurate high-level localization data.These results suggest that the proposed LoRa-MLP integration provides a robust, low-power solution for high-accuracy indoor positioning systems (IPSs) in modern industrial infrastructure. Full article

The current development of ultra-wide band localization systems focuses on reducing the number of infrastructure nodes (anchors). In certain areas and applications the full three-dimensional position is not necessary; therefore, constraining the solution brings an opportunity to use fewer anchors. In this work, soft constraining of lateral and vertical position components for Time Difference of Arrival positioning in a corridor-like scenario is presented. Implementation in extended and unscented Kalman filter solvers is described. Tests in a real environment suggests that the constraints enable reliable along-track position estimation even with two or three anchors in sight, and the accuracy is better than 30 $\mathrm{c}$$\mathrm{m}$ (RMS). Moreover, the soft nature of constraints allows for uncertainty in the constraint definition. Full article

This paper presents the H-CoRE (Heterogeneous Cooperative Multi-Robot Execution) framework designed to enable autonomous multi-robot operations in GNSS-denied environments. Built on an ROS 2-based architecture, H-CoRE enables collaborative, structured task execution through standardized software stacks. Each robot’s stack combines a high-level executive system with an agent-specific motion layer and leverages multi-sensor fusion for localization and mapping. The framework is inherently reconfigurable, allowing individual agents to operate autonomously or as part of a multi-robot team for collaborative missions. In the considered scenario, the system integrates aerial and ground vehicles, a fixed pan–tilt–zoom camera, and a human supervisory interface within a unified, modular infrastructure. The proposed system has been deployed in indoor, GNSS-denied environments, demonstrating autonomous navigation, cooperative area coverage, and real-time information sharing across multiple agents. Experimental results confirm the effectiveness of H-CoRE in maintaining general awareness and mission continuity, paving the way for future applications in search-and-rescue, inspection, and exploration tasks. Full article

Older adults often need guidance when visiting new buildings for the first time. However, indoor navigation remains challenging due to the lack of Global Positioning System (GPS) availability, visually repetitive corridors, and frequent location failures. This article presents a multimodal indoor navigation assistant that combines graph-based route planning with visual landmark verification to provide step-by-step guidance. The environment is modelled as a directed graph whose nodes are annotated with semantic landmarks, and the graph is constructed primarily from a video of the building, reducing the need for 3D scanners, beacons, or other specialised instruments. Routes are calculated using Dijkstra’s shortest-path algorithm over the semantic graph. During navigation, camera frames are analysed using a restricted vision-language recognition strategy that only considers candidate landmarks from the current and next nodes, reducing false detections and improving interpretability. To increase robustness, a temporary voting mechanism was introduced to confirm node transitions, as well as a hierarchical redirection strategy with local and global recovery. The system is implemented in two modes: handheld mode with visual cues using augmented reality arrows, mini map and voice instructions, and hands-free mode with front camera using voice instructions and keywords. Evaluation involved preliminary technical testing in the United Kingdom followed by formal user validation in Spain. During these trials, participants reported high usability, strong confidence and safety, and increased perceived independence. Full article

Indoor CO₂ forecasting supports proactive ventilation control that balances air quality with energy efficiency. While Machine Learning (ML) models have shown strong performance in controlled settings such as schools, their generalization across indoor spaces with diverse occupancy dynamics remains poorly characterized. We present a systematic benchmark of 11 forecasting models spanning simple baselines, statistical methods, classical ML, deep learning, ensembles, and foundation models using 18 weeks of IoT sensor data spanning six real-world use cases: conference rooms, dining halls, hospitals, food markets, offices and student residences. Performance depends strongly on the prediction horizon and on the regularity of occupancy-driven CO₂ patterns. Simple baselines tend to perform best at short horizons (10 min ahead), while ensembles and fine-tuned foundation models provide more robust accuracy at longer horizons (4 h ahead). Remarkably, zero-shot foundation models demonstrate the ability to outperform trained classical models in data-scarce scenarios, challenging the traditional paradigm of localized training. These findings indicate that optimal forecasting strategies are context-dependent and challenge the assumption of universal model superiority. Full article

Assistive mobility devices for small animals require reliable monitoring to ensure safe and comfortable operation without increasing system complexity or invasiveness. This study presents a low-cost monitoring platform that integrates a laser-induced graphene (LIG) contact-temperature sensor into a passive mobility device for small dogs, supported by a lightweight Internet of Things (IoT) architecture. The system combines contact temperature, ambient temperature, speed, and obstacle distance using an energy-aware acquisition strategy and prioritized wireless transmission for near-real-time monitoring. An unsupervised anomaly detection framework based on Isolation Forest identifies potentially unsafe operating conditions without labeled pathological data by leveraging absolute temperature and the differential feature $\Delta T$ between contact and ambient measurements. Experimental validation was conducted under controlled indoor conditions across six independent sessions with a small-breed dog, including static and dynamic phases to ensure repeatability. The system achieved packet delivery ratios of approximately 95%, with typical end-to-end latencies below 500 ms and worst-case delays below 850 ms. The proposed approach detected localized thermal deviations associated with friction or prolonged contact while remaining robust to normal activity- and environment-driven variations. These results demonstrate the feasibility of integrating LIG-based sensing and unsupervised analytics into assistive animal mobility platforms to enhance safety through continuous, non-invasive monitoring. Full article