Search Results (214)

The rapid growth of large-scale Internet of Things (IoT) deployments in urban environments requires accurate and energy-efficient localization methods for low-power wireless devices. In long-range wide-area networks (LoRaWAN), traditional GPS-based positioning is often impractical due to energy consumption constraints and signal propagation challenges in urban areas. This study proposes a hybrid localization system that integrates weighted centroid localization (WCL) with a machine learning (ML) regression model to improve outdoor positioning accuracy. The proposed approach first estimates approximate transmitter coordinates using a physically grounded WCL method based on received signal strength indicator (RSSI) measurements. These initial estimates are subsequently refined by ML models trained to learn nonlinear residual corrections. In addition to random partitioning, a spatial data splitting strategy is proposed and evaluated using a publicly available LoRaWAN dataset. The experimental results demonstrate that the hybrid WCL framework combined with a multilayer perceptron (MLP) significantly outperforms other ML models. The proposed method achieves a mean localization error of 160.47 m and a median error of 73.78 m. Compared to the baseline model, the integration of WCL reduces the mean localization error by approximately 29%, highlighting the effectiveness of incorporating physically interpretable priors into localization models. Full article

Rising global food demand, increasing labor costs, and farm labor shortages have created significant challenges for specialty crop production, particularly in labor-intensive tasks such as fruit harvesting. Robotic harvesting offers a promising long-term solution, yet its adoption in orchard environments remains limited due to unstructured conditions, variable lighting, and difficulties in fruit recognition and manipulation. This study presents an improved robotic fruit harvesting system, Orchard roBot (OrBot), developed by the Robotics Vision Lab at Northwest Nazarene University, with the goal of advancing autonomous apple harvesting applications. The updated OrBot platform integrates a dual-camera vision system consisting of an eye-to-hand stereo camera with a wide field of view for fruit detection and an eye-in-hand RGB-D camera for precise manipulation. The control architecture was redesigned using Robot Operating System 2 (ROS2) and Python, enabling modular subsystem development and coordination. Fruit detection was performed using a YOLOv5 deep learning model, and visual servoing was employed to guide the robotic manipulator toward the target fruit. System performance was evaluated through laboratory experiments using artificial trees and field tests conducted in a commercial apple orchard in Idaho. OrBot achieved a 100% harvesting success rate in indoor tests and a 75–80% success rate in outdoor orchard conditions. Experimental results demonstrate that the dual-camera approach significantly enhances fruit search efficiency and harvesting efficiency. Identified limitations include sensitivity to lighting conditions, end effector performance with varying fruit sizes, and depth estimation errors. Overall, the results indicate a positive potential toward effective robotic fruit harvesting and highlight key areas for future improvement in vision, manipulation, and system robustness. Full article

This paper presents an onboard, markerless perching system for a quadrotor UAV, validated in outdoor flight experiments, to reduce hovering energy during long-endurance unmanned missions. Existing autonomous landing research predominantly focuses on planar surfaces, cooperative environments with visual markers, or specialized hardware, limiting scalability to scenarios requiring detection and perching on thin rod-like targets in uncooperative outdoor settings. This study proposes a markerless perching system for autonomously perching a drone on a hurdle’s horizontal bar. The system employs a single-axis gimbal camera, altitude LiDAR, and ToF sensor, integrating perception, post-processing, and control. On the perception side, we augment a YOLOv12n-based segmentation model with a high-resolution P2 pathway for small-object detection and apply module compression for real-time inference on edge devices. Robustness is improved by jointly utilizing the full hurdle and horizontal bar while constructing negative samples to suppress false positives. On the control side, a state machine controller leverages centroid coordinates, orientation, and distance measurements to achieve a stable long-range approach and precise close-range alignment. Experiments on a Jetson Orin NX-based system demonstrate successful perching in all six outdoor flight tests. Ablation studies quantitatively analyze each component’s contribution to perching success rate and completion time. This research validates perching technology’s practical applicability through outdoor markerless perching on thin 3D structures. Full article

Primula vulgaris possesses considerable edible, medicinal, and ornamental value. It is widely applied in food and pharmaceutical development and, as an early-spring flowering plant, is used in landscaping. However, its range of applications and scope are significantly limited due to its inability to withstand high temperatures. This study aimed to investigate the heat tolerance of P. vulgaris under natural high temperatures during summer, identify the most heat-resistant varieties, and determine the optimal conditions for summer outdoor cultivation. Eight P. vulgaris varieties were selected and placed under forest shade with three different shading rates during the summer high-temperature period. Additionally, the heat damage index and the following six physiological indicators were measured: malondialdehyde (MDA) content, superoxide dismutase (SOD) activity, peroxidase (POD) activity, soluble sugar content, soluble protein content, and relative conductivity. Furthermore, a correlation analysis of the physiological indicators was conducted, and a heat tolerance evaluation was performed using the membership function method. Simultaneously, qRT-PCR was employed to analyze the expression patterns of three heat stress-related genes (PvHSP70, PvNCED6, and PvHSF24) across the different cultivars and experimental sites. Under heat stress conditions, leaf area was found to be positively and highly significantly correlated with stomatal density (p < 0.01). The heat damage index, MDA content, and relative conductivity increased significantly with prolonged stress, and they showed highly significant positive correlations. SOD activity, soluble sugar content, and soluble protein content increased to resist heat damage, while POD activity exhibited no consistent trend. Highly significant positive correlations were observed among protective enzyme activities and osmotic regulatory substances. After a comprehensive evaluation, the eight varieties were ranked according to heat tolerance as follows: “Early Punas Yellow” > “Danova Red” > “Middle Punas Rose Red” > “Middle Punas Blue” > “Middle Punas Red” > “Danova Rose White” > “Middle Punas Crimson” > “Middle Punas Scarlet”. Conclusions: “Early Punas Yellow”, “Danova Red”, and “Middle Punas Rose Red” demonstrated strong heat tolerance. In addition, the expression of PvHSP70 and PvHSF24 was significantly upregulated in heat-tolerant cultivars, while that of PvNCED6 showed a sustained increasing trend with rising temperatures. The results of a three-way ANOVA suggested that P. vulgaris exhibited different regulatory patterns among various traits under natural high-temperature stress. Morphological and integrative damage-related indicators, including leaf area, stomatal density, and the heat damage index, all presented significant “site × time” interaction effects. Meanwhile, some physiological regulatory indicators displayed more complex and inconsistent response patterns. These findings further confirm that a dense forest understory grassland is an ideal environment for the summer outdoor cultivation of P. vulgaris. Full article

Visual localization plays a critical role for mobile robots to estimate their position and orientation in GPS-denied environments. However, its efficiency, robustness, and generalization are fundamentally undermined by severe viewpoint changes and dramatic appearance variations, which present persistent challenges for image-based feature representation and pose estimation under real-world conditions. Recently, map-free visual relocalization (MFVR) has emerged as a promising paradigm for lightweight deployment and privacy isolation on edge devices, while how to learn compact and invariant image tokens without relying on structural 3D maps still remains a core problem, particularly in highly dynamic or long-term scenarios. In this paper, we propose the Debiased Multiplex Tokenizer as a novel method (termed as DMT-Loc) for efficient and versatile MFVR to address these issues. Specifically, DMT-Loc is built upon a pretrained vision Mamba encoder and integrates three key modules for relative pose regression: First, Multiplex Interactive Tokenization yields robust image tokens with non-local affinities and cross-domain descriptions. Second, Debiased Anchor Registration facilitates anchor token matching through proximity graph retrieval and autoregressive pointer attribution. Third, Geometry-Informed Pose Regression empowers multi-layer perceptrons with a symmetric swap gating mechanism operating inside each decoupled regression head to support accurate and flexible pose prediction in both pair-wise and multi-view modes. Extensive evaluations across seven public datasets demonstrate that DMT-Loc substantially outperforms existing baselines and ablation variants in diverse indoor and outdoor environments. Full article

The relationship between lifestyle, ocular biomechanical behavior, and myopia is not well established in the literature. The present study aims to describe changes in ocular biomechanics during adolescence and to explore their relationship with lifestyle factors and myopic progression. Prospective cohort study including 63 adolescents (126 eyes) with a mean age of 14.1 ± 2.6 years old examined twice over a 30 ± 0.9-month period. The data from biomechanics, biometry, corneal tomography, and lifestyle was addressed. The relationships between biomechanical changes, biometric and refractive variation, and lifestyle variables were analyzed using parametric and non-parametric statistics with a significance level of p < 0.05. A biomechanical stiffening trend was found. Axial elongation was 0.12 ± 0.17 mm, and refractive shift was −0.32 ± 0.87 D. The history of allergies was associated with greater axial growth (p = 0.032) and smaller increase in stress–strain-index (SSI) (p = 0.01). Myopization was higher in eyes with ocular surface symptoms (p = 0.049) and those with reported eye-rubbing habits (p = 0.04), with a lower gain in stiffness (p < 0.05). Outdoor activities were associated with higher gain in corneo-scleral stiffness (p < 0.05). Reduced myopization correlated directly with the increase in the SSI (p < 0.05) and inversely with the Integrated Radius (p < 0.05). Greater increases in axial length (AL), vitreous cavity length (VCL), and the ratio between VCL and AL (R_VCL/AL) correlated negatively with the increase in the SSI (p < 0.05). The increase in the R_VCL/AL correlated positively with the time spent on digital devices and negatively with the amount of outdoor activity (p < 0.05). Biomechanics may represent the physiological bridge between the environmental exposure and myopization, as lower gain in corneo-scleral stiffness was consistently associated with greater axial elongation and refractive myopization, with outdoor activity appearing to be protective. Full article

Oil leakage in oil-immersed power transformers poses a significant threat to grid reliability, potentially causing severe electrical accidents and environmental pollution if not detected in time. Detecting oil leakage outdoors, however, remains challenging due to the impact of weather conditions such as fog, humidity, and rain, which obscure the leakage signs and complicate real-time detection. To address these challenges, we propose a solution that integrates infrared thermal imaging with a CNN-SVM hybrid architecture. The core of this approach lies in shifting from traditional Softmax-cross-entropy-based empirical risk minimization (ERM) to maximum-margin-based structural risk minimization (SRM). A fully fine-tuned MobileNetV3 transforms low-contrast, boundary-softened infrared thermal images—often affected by fog and moisture—into a more discriminative high-dimensional feature space, where positive and negative samples become linearly separable. This is followed by replacing Softmax with a linear SVM and using hinge loss to enforce a margin constraint, which maximizes the classification margin and improves robustness to input perturbations. Experimental results show that our proposed method outperforms all compared models, achieving an accuracy of 0.990, significantly higher than ResNet50_BCE (0.908), EfficientNetB0 (0.925), YOLOv11n-CLS (0.930), and ViT (0.929). In terms of F1-Score (0.989) and AUC (0.995), MobileNetV3-SVM also demonstrates excellent performance, ensuring outstanding classification capability. Additionally, the model achieves an inference latency of only 6.3 ms, demonstrating excellent real-time inference performance, highlighting its potential for transformer oil monitoring applications. This research contributes to SDG 6 by preventing industrial water pollution resulting from transformer oil runoff, thereby protecting vital water sources in remote environments. Full article

The integration of outdoor camera images with three-dimensional (3D) geographic information on the observed scene is of interest for many video acquisition applications. To solve this data fusion problem, camera images have to be matched with the 3D geometry provided by a geographic information system (GIS). Considering a camera with a known geographical position, this paper proposes the use of a dense local azimuth–elevation map (LAEM) derived from a gridded digital elevation model (DEM) to represent the data and thus facilitate the matching of GIS and image data. To each regularly sampled azimuth and elevation angle pair, this map assigns the geographic point derived from the DEM viewed in this direction. The problem of computing the LAEM from the DEM is closely related to that of surface rendering, for which solutions exist in computer graphics. However, rendering software cannot be used directly in this case, since their view directions are constrained by the pinhole camera model and the apparent colour, rather than the position of the viewed point, is assigned to the viewing direction. Therefore, this paper also proposes a specific algorithm for the computation of the LAEM from the DEM. A MATLAB^® implementation of the algorithm is also provided, which is tailored to process the DEM dataset swissALTI3D from the Swiss Federal Office of Topography swisstopo. Full article

Accurate and continuous outdoor pedestrian positioning using smartphones remains challenging in complex environments like urban canyons, where Global Navigation Satellite System (GNSS) signals are frequently degraded or blocked, and Pedestrian Dead Reckoning (PDR) suffers from cumulative errors. To address this, this paper proposes a novel fusion method based on a Robust Adaptive Cubature Kalman Filter (RACKF). The core of our approach is a two-stage filtering architecture: the first stage employs a quaternion-based RACKF to optimally fuse gyroscope and magnetometer data for robust heading estimation; the second stage performs the core fusion of GNSS observations with an enhanced 3D PDR solution. Key innovations include an adaptive noise estimation strategy combining fading and limited memory weighting, a robust M-estimator-based mechanism to suppress outliers, and the integration of differential barometric height measurements. Experimental results demonstrate that the proposed method achieves a horizontal positioning accuracy of 3.28 m (RMSE), outperforming standalone GNSS and improving 3D PDR by 25.97% and 10.39%, respectively. This work provides a practical, infrastructure-free solution for robust smartphone-based outdoor navigation. Full article

Position-Based Visual Servoing (PBVS) and Image-Based Visual Servoing (IBVS) struggle to balance end effector pose accuracy and robustness in apple picking. They are also prone to target loss and control singularities. A progressive Hybrid Automatic Switching Visual Servoing (HAVS) method is proposed and applied to an apple-picking robotic system. HAVS integrates PBVS and IBVS to coordinate control of the manipulator end effector pose. A depth-based switching function is designed. When target depth is below an optimal threshold, the controller switches to PBVS for precise final positioning. This reduces target loss and control singularities. An adaptive proportional-derivative (PD) controller with fuzzy gain scheduling updates the control gains online to enhance responsiveness and stability. The hardware consists of a six-axis manipulator, a depth camera, and a mobile base. You Only Look Once version 5 (YOLOv5) performs apple detection and generates control commands. Indoors, success rate was 96%, which was 4 and 10 percentage points higher than PBVS only and IBVS only. Average picking time was 12.5 s, 0.3 s, and 1.1 s shorter. Outdoors, success rate was 87.5%, average time was 13.2 s, and damage rate was 4.2%. This method provides a reference implementation for visual servo control in agricultural picking robots. Full article

Wheelchair provision remains an essential component of rehabilitation and participation support for children with disabilities, yet there is limited evidence on how wheelchairs are incorporated into daily activities and schooling decisions in rural low-resource contexts where environmental, social, and service constraints are substantial. This study employed a strictly exploratory multiple case study design involving two children with disabilities. Two home visits were conducted for each case, and a qualitative, descriptive cross-case analysis was conducted by integrating semi-structured interview data with WeeFIM scores and ICF Environmental Factors ratings. Wheelchair provision supported short-distance mobility and engagement in household and community activities and reduced some caregiving demands. Positive experiences during outdoor mobility and community interactions contributed to enjoyment and confidence. However, inaccessible housing, limited transportation, and family concerns about safety and readiness continued to inhibit broader independence and school enrollment. Both children remained outside formal schooling, while activities offered by Special Education Centers provided meaningful but limited opportunities for social interaction and development. The findings highlight not only practical implications but also the conceptual importance of environmental constraints and the ambivalent role of family support in shaping participation in rural settings. Full article

The Global Navigation Satellite System (GNSS) is commonly used for outdoor positioning. However, its effectiveness diminishes in urban canyons and indoor environments attributed to signal blockage. This study aims to explore the potential of GNSS signals penetrating indoor spaces through windows and to enhance indoor positioning with Three-Dimensional Mapping-Aided (3DMA) GNSS, a concept generally applied outdoors. The research employs a 3D model of a corridor with manually labeled window locations to predict satellite visibility within indoor areas. The study integrates Pedestrian Dead Reckoning (PDR) with an indoor Shadow-matching (I-SM) technique, utilizing an Extended Kalman Filter (EKF) to improve positioning accuracy. One of the findings indicates that the proposed method significantly enhances positioning performance and its availability, achieving a root mean square error (RMSE) that is 2 m better than using PDR alone or single epoch I-SM. The study concludes that integrating GNSS with I-SM technique and PDR can optimize an indoor positioning solution and highlights the potential for improved navigation solutions in complex urban environments. Full article

Cultural practices such as Catalonia’s correfocs (fire parades) represent a vibrant expression of intangible heritage. Outdoor activities are conditioned by weather and threatened by climate change. This study analyses the long-term evolution of night-time thermal conditions during correfoc festivals performed in six Catalan towns located on the coast and in the pre-coastal region from 1950 to 2023, using reanalysis-based indicators of air temperature, humidity, and perceived heat as a first exploratory step prior to incorporating in situ meteorological records. Specifically, the Heat Index (HI) and the Universal Thermal Climate Index (UTCI) were computed for the typical event window (21:00–23:00 local time) to assess changes in human thermal comfort. Results reveal a clear and statistically significant warming trend in most pre-coastal locations—particularly Reus, El Vendrell, and Vilafranca—while coastal cities such as Barcelona exhibit weaker or non-significant changes, likely due to maritime moderation. The frequency and intensity of positive temperature anomalies have increased since the 1990s, with a growing proportion of events falling into “caution” or “moderate heat stress” categories under HI and UTCI classifications. These findings demonstrate that correfocs are now celebrated under markedly warmer night-time conditions than in the mid-twentieth century, implying a tangible rise in thermal discomfort and potential safety risks for participants. By integrating climatic and cultural perspectives, this research shows that rising night-time heat can constrain attendance, participation conditions, and event scheduling for correfocs, thereby directly exposing weather-sensitive form of intangible cultural heritage to climate risks. It therefore underscores the need for climate adaptation frameworks and to promote context-specific strategies to sustain these community-based traditions under ongoing Mediterranean warming. Full article

We present an inverse designed metalens for long-wave infrared (LWIR) imaging tailored to consumer and Internet of Things (IoT) platforms. Conventional LWIR optics either rely on costly specialty materials or suffer from low efficiency and narrow fields of view (FoV), limiting scalability. Our approach integrates adjoint-based inverse design with fabrication-aware constraints and a cone-shaped source model that efficiently captures oblique incidence during optimization. The resulting multi-level metalens achieves a wide FoV up to 100° while maintaining robust focusing efficiency and stable angle-to-position mapping on low-power $4\times 4$ microbolometer arrays representative of edge devices. We further demonstrate application-level imaging on $4\times 4$ microbolometer arrays, showing that the proposed metalens delivers a substantially wider FoV than a commercial narrow FoV lens while meeting low-resolution, low-cost, and low-power constraints for edge LWIR modules. By eliminating bulky multi-element stacks and reducing cost and form factor, the proposed design provides a practical pathway to compact, energy-efficient LWIR modules for consumer applications such as occupancy analytics, smart-building automation, mobile sensing, and outdoor fire surveillance. Full article

This study aimed to design a virtual reality (VR)–based mountaineering education system and examined its effects on junior high school students’ learning outcomes, motivation, and cognitive load in compass operation and map reading. The system integrated 3D terrain models and interactive mechanisms across four instructional modules: Direction Recognition, Map Symbols, Magnetic Declination Adjustment, and Resection Positioning. By incorporating immersive 3D environments and hands-on virtual exercises, the system simulates authentic mountaineering scenarios, enabling students to develop essential field orientation and navigation skills. An experimental design was implemented, with participants assigned to either an experimental group learning with the VR system or a control group receiving slide-based instruction. Data were collected using pre-tests, post-tests, and questionnaires, and analyzed using SPSS for descriptive statistics, paired-sample t-tests, independent-sample t-tests, and one-way ANCOVA at a significance level of α = 0.05. The findings indicated that the experimental group achieved significantly higher post-test learning performance than the control group (F = 6.37, p = 0.014). Moreover, significant or highly significant improvements were observed across the four dimensions of learning motivation—attention, relevance, confidence, and satisfaction. The experimental group also exhibited a significantly lower extraneous cognitive load (p = 0.024). Therefore, the VR mountaineering education system provides an immersive, safe, and effective approach to teaching mountaineering and outdoor survival skills. Full article