Search Results (1,622)

Unmanned Surface Vehicles (USVs) play a critical role in maritime monitoring, environmental protection, and emergency response, necessitating accurate scene understanding in complex aquatic environments. Conventional semantic segmentation methods often fail to capture global context and lack physical boundary consistency, limiting real-world performance. This paper proposes USV-Seg, a unified segmentation framework integrating a vision-language model, the Segment Anything Model (SAM), DINOv2-based visual features, and a physically constrained refinement module. We design a task-specific <Describe> Token to enable fine-grained semantic reasoning of navigation scenes, considering USV-to-shore distance, landform complexity, and water surface texture. A mask selection algorithm based on multi-layer Intersection-over-Prediction (IoP) heads improves segmentation precision across sky, water, and obstacle regions. A boundary-aware correction module refines outputs using estimated sky-water and land-water boundaries, enhancing robustness and realism. Unlike prior works that simply apply vision-language or geometric post-processing in isolation, USV-Seg integrates structured scene reasoning and scene-aware boundary constraints into a unified and physically consistent framework. Experiments on a real-world USV dataset demonstrate that USV-Seg outperforms state-of-the-art methods, achieving 96.30% mIoU in challenging near-shore scenarios. Full article

This study presents a comprehensive performance evaluation of hybrid global navigation satellite system (GNSS) configurations in urban canyon environments across South Korea, focusing on the integration of Global Positioning System (GPS) with the BeiDou, GLONASS, Galileo, Quasi Zenith Satellite System (QZSS), and Navigation with Indian Constellation (NavIC) constellations. Simulation scenarios representing pedestrian, vehicular, and unmanned aerial vehicle (UAV) movements are used to analyze the positioning accuracy and reliability of each hybrid system. The results indicate that GPS–BeiDou and GPS–QZSS combinations consistently provide superior accuracy and continuous satellite visibility, with GPS–BeiDou achieving centimeter-level precision in the UAV scenario. In contrast, GPS–GLONASS and GPS–NavIC systems exhibit higher error rates and less stable performance. These findings emphasize the critical role of satellite availability, receiver altitude, and signal compatibility in achieving robust positioning. Although the results are specific to South Korea, the proposed evaluation framework is broadly applicable and can help other countries assess hybrid GNSS performance to guide the design and optimization of their regional navigation satellite systems. Full article

Deep space exploration missions face technical challenges such as long-distance communication delays and high-precision autonomous positioning. Traditional ground-based telemetry and control as well as inertial navigation schemes struggle to meet mission requirements in the complex environment of deep space. As a vision-based autonomous navigation technology, image-based navigation enables spacecraft to obtain real-time images of the target celestial body surface through a variety of onboard remote sensing devices, and it achieves high-precision positioning using stable terrain features, demonstrating good autonomy and adaptability. Craters, due to their stable geometry and wide distribution, serve as one of the most important terrain features in deep space image-based navigation and have been widely adopted in practical missions. This paper systematically reviews the research progress of deep space image-based navigation technology, with a focus on the main sources of remote sensing data and a comprehensive summary of its typical applications in lunar, Martian, and asteroid exploration missions. Focusing on key technologies in image-based navigation, this paper analyzes core methods such as surface feature detection, including the accurate identification and localization of craters as critical terrain features in deep space exploration. On this basis, the paper further discusses possible future directions of image-based navigation technology in response to key challenges such as the scarcity of remote sensing data, limited computing resources, and environmental noise in deep space, including the intelligent evolution of image navigation systems, enhanced perception robustness in complex environments, hardware evolution of autonomous navigation systems, and cross-mission adaptability and multi-body generalization, providing a reference for subsequent research and engineering practice. Full article

Autonomous driving represents a transformative advancement with the potential to significantly impact daily mobility, including enabling independent vehicle operation for individuals with visual disabilities. The commercialization of autonomous driving requires guaranteed safety and accuracy, underscoring the need for robust localization and environmental perception algorithms. In cost-sensitive platforms such as delivery robots and electric vehicles, cameras are increasingly favored for their ability to provide rich visual information at low cost. Despite recent progress, existing visual–inertial odometry systems still suffer from degraded accuracy in challenging conditions, which limits their reliability in real-world autonomous navigation scenarios. Estimating 3D positional changes using only 2D image sequences remains a fundamental challenge primarily due to inherent scale ambiguity and the presence of dynamic scene elements. In this paper, we present a visual–inertial odometry framework incorporating a depth estimation model trained without ground-truth depth supervision. Our approach leverages a self-supervised learning pipeline enhanced with knowledge distillation via foundation models, including both self-distillation and geometry-aware distillation. The proposed method improves depth estimation performance and consequently enhances odometry estimation without modifying the network architecture or increasing the number of parameters. The effectiveness of the proposed method is demonstrated through comparative evaluations on both the public KITTI dataset and a custom campus driving dataset, showing performance improvements over existing approaches. Full article

This study challenges the conventional wisdom that investor reactions to Environmental, Social, and Governance (ESG) information are primarily driven by disclosure sentiment. We propose and test an alternative hypothesis: that for investors navigating information-rich environments, the frequency of ESG disclosures can serve as a more potent signal of a firm’s underlying commitment and risk profile than the sentiment of the announcements themselves. Focusing on Taiwan’s capital market—a globally pivotal technology hub—we analyze 2576 firm-initiated ESG events from 2014 to 2023 using an event study methodology. We innovate by employing a BERT-based NLP model, specifically fine-tuned for Traditional Chinese, to disentangle the effects of disclosure frequency from sentiment. Our results reveal that announcement frequency is a more robust predictor of abnormal returns than sentiment, but its effect is highly contingent on the ESG pillar. A higher frequency of negative Social (S) and Governance (G) disclosures incurs a significant market penalty, whereas frequent proactive Environmental (E) disclosures are rewarded. These findings establish a “disclosure frequency premium/penalty” and offer critical, nuanced insights for corporate strategy and sustainable investment. By demonstrating how communication patterns shape market perceptions, this research directly informs UN SDG 12 (Responsible Production) and SDG 16 (Strong Institutions). Full article

This study presents a new multi-index hierarchical model for flood risk assessment which incorporates three indicator indexes—hazard, vulnerability, and exposure—to develop a five-level risk scale. The methodology is applied to historical data on flood events in The Slovak Republic between 2001 and 2010. The input values are characterized in more detail through the use of weighted values to provide a more balanced overall risk assessment. The original formula used to calculate the risk levels was found to produce results with overly high numerical values, and therefore the multiplication step of the formula was replaced by addition to insure greater simplicity and ease of use. This refined methodology introduces a novel quantitative approach to risk assessment, offering flexibility and variability in the indicator layer. The methodology can be adapted to assess risk at either the macro or micro scale and at more specific periods of time. The resulting risk values offer a nuanced understanding of risk levels across different indexes and underscores the method’s innovation. Full article

Simultaneous Localization and Mapping (SLAM) is crucial for the safe navigation of autonomous systems. Its accuracy is not based solely on the robustness of the algorithm employed or the metrological performances of the sensor, but rather on a combination of both factors. In this work, we present a comprehensive comparison framework for evaluating LiDAR-SLAM systems, focusing on key performance indicators including absolute trajectory error, uncertainty, number of tracked features, and computational time. Our case study compares two LiDAR-inertial SLAM configurations: one based on a motorized optomechanical scanner (the Ouster OS1) with a 360° field of view and the other based on MEMS scanners (the Livox Horizon) with a limited field of view and a non-repetitive scanning pattern. The sensors were mounted on a UGV for the experiments, where data were collected by driving the UGV along a predefined path at different speeds and angles. Despite substantial differences in field of view, detection range, and noise, both systems demonstrated comparable trajectory estimation performance, with average absolute trajectory errors of 0.25 m for the Livox-based system and 0.24 m for the Ouster-based system. These findings underscore the importance of sensor–algorithm co-design and demonstrate that even cost-effective, lower-field-of-view solutions can deliver competitive SLAM performance in real-world conditions. Full article

Red blood cell (RBC), accounting for approximately 45% of total blood volume, are essential for oxygen delivery and carbon dioxide removal. Their unique biconcave morphology, high surface area-to-volume ratio, and remarkable deformability enable them to navigate microvessels narrower than their resting diameter, ensuring efficient microcirculation. RBC deformability is primarily determined by membrane viscoelasticity, cytoplasmic viscosity, and cell geometry, all of which can be altered under various physiological and pathological conditions. Reduced deformability is a hallmark of numerous diseases, including sickle cell disease, malaria, diabetes mellitus, sepsis, ischemia–reperfusion injury, and storage lesions in transfused blood. As these mechanical changes often precede overt clinical symptoms, RBC deformability is increasingly recognized as a sensitive biomarker for disease diagnosis, prognosis, and treatment monitoring. Over the past decades, diverse techniques have been developed to measure RBC deformability. These include single-cell methods such as micropipette aspiration, optical tweezers, atomic force microscopy, magnetic twisting cytometry, and quantitative phase imaging; bulk approaches like blood viscometry, ektacytometry, filtration assays, and erythrocyte sedimentation rate; and emerging microfluidic platforms capable of high-throughput, physiologically relevant measurements. Each method captures distinct aspects of RBC mechanics, offering unique advantages and limitations. This review synthesizes current knowledge on the pathophysiological significance of RBC deformability and the methods for its measurement. We discuss disease contexts in which deformability is altered, outline mechanical models describing RBC viscoelasticity, and provide a comparative analysis of measurement techniques. Our aim is to guide the selection of appropriate approaches for research and clinical applications, and to highlight opportunities for developing robust, clinically translatable diagnostic tools. Full article

Ant Colony Optimization (ACO) has been widely used in engineering implementation due to its simplicity and effectiveness. However, it often faces challenges such as slow convergence, susceptibility to local optima, and generating paths with excessive turning points. To address these limitations, this paper introduces a Novel Multi-Strategy Enhanced Ant Colony Optimization algorithm (NMS-EACO) for mobile robot path planning under nonholonomic constraints. NMS-EACO integrates five key strategies: an A*-guided heuristic function, an adaptive enhanced pheromone update rule, a state transition probability under nonholonomic constraints, a smoothing factor embedded in the state transition probability, and a global path smoothing technique. Comprehensive simulation experiments are conducted across six distinct map types, with comparisons made against six existing algorithms through extensive trials.Results demonstrate that NMS-EACO significantly improves convergence speed, enhances global search capability, and reduces path irregularities. These results validate the robustness and efficiency of the proposed multi-strategy method for nonholonomic mobile robot navigation. Full article

In Global Navigation Satellite System (GNSS)-denied environments, navigation using signals of opportunity (SOP) from Low Earth Orbit (LEO) satellites is considered a feasible alternative. Compared with single-constellation systems, multiple-constellation LEO systems offer improved satellite visibility and geometric diversity, which enhances positioning continuity and accuracy. To allocate weights among heterogeneous observations, prior studies have employed the Helmert variance component estimation (HVCE) method, which iteratively determines relative weight ratios of different observation types through posterior variance estimation. HVCE enables error modeling and weight adjustment without prior noise information but is highly sensitive to outliers, making it vulnerable to their impact. This study proposes a Robust HVCE-based dual-constellation weighted positioning method. The approach integrates prior weighting based on satellite elevation, observation screening based on characteristic slopes, HVCE, and IGG-III robust estimation to achieve dynamic weight adjustment and suppress outliers. Experimental results over a 33.9 km baseline demonstrate that the proposed method attains Two-Dimensional (2D) and Three-Dimensional (3D) positioning accuracies of 12.824 m and 23.230 m, corresponding to improvements of 29% and 16% over conventional HVCE weighting, respectively. It also outperforms single-constellation positioning and equal-weighted fusion, confirming the effectiveness of the proposed approach. Full article

Ship acoustic signal classification is essential for vessel identification, underwater navigation, and maritime security. Traditional methods struggle with the non-stationary nature and noise of ship acoustic signals, reducing classification accuracy. To address these challenges, we propose an automated pipeline that integrates Empirical Mode Decomposition (EMD), adaptive wavelet filtering, feature selection, and a Bayesian-optimized Random Forest classifier. The framework begins with EMD-based decomposition, where the most informative Intrinsic Mode Functions (IMFs) are selected using Signal-to-Noise Ratio (SNR) analysis. Wavelet filtering is applied to reduce noise, with optimal wavelet parameters determined via SNR and Stein’s Unbiased Risk Estimate (SURE) criteria. Features extracted from statistical, frequency domain (FFT), and time–frequency (wavelet) metrics are ranked, and the top 11 most important features are selected for classification. A Bayesian-optimized Random Forest classifier is trained using the extracted features, ensuring optimal hyperparameter selection and reducing computational complexity. The classification results are further enhanced using a majority voting strategy, improving the accuracy of the final object identification. The proposed approach demonstrates high accuracy, improved noise suppression, and robust classification performance. The methodology is scalable, computationally efficient, and suitable for real-time maritime applications. Full article

Navigation line extraction is vital for visual navigation with agricultural machinery. The current methods primarily utilize plant canopy detection frames to extract feature points for navigation line fitting. However, this approach is highly susceptible to environmental changes, causing position instability and reduced extraction accuracy. To address this problem, this study aims to develop a robust navigation line extraction method that overcomes canopy-based feature instability. We propose extracting feature points from root detection frames for navigation line fitting. Compared to canopy points, root feature point positions remain more stable under natural interference and less prone to fluctuations. A dataset of corn crop row images under multiple growth environments was collected. Based on YOLOv8n (You Only Look Once version 8, nano model), we proposed the RS-LineNet lightweight model and introduced a root subordination relationship filtering algorithm to further improve detection precision. Compared with the YOLOv8n model, RS-LineNet achieves 4.2% higher precision, 16.2% improved recall, and an 11.8% increase in mean average precision (mAP₅₀), while reducing the model weight and parameters to 32% and 23% of the original. Navigation lines extracted under different environments exhibit an 0.8° average angular error, which is 3.1° lower than canopy-based methods. On Jetson TX2, the frame rate exceeds 12 FPS, meeting practical application requirements. Full article

Recent advancements in the healthcare sector have reached a pivotal juncture, catalysed by the emergence of Digital Twin (DT) technologies. These innovations facilitate the development of virtual replicas that accurately simulate real-world conditions, thereby transforming traditional approaches to medical analysis, diagnostics, and treatment planning. Although widely successful in manufacturing, the adoption of Digital Twins in healthcare is relatively limited, particularly regarding their impact on clinical efficiency and patient outcomes. This study addresses three primary research questions: (1) How does Digital Twin technology improve individualised patient treatments and care quality? (2) What is the role of Digital Twin technology in accurately predicting patient responses to medical interventions? (3) What are the significant challenges of integrating Digital Twin technology into healthcare? Synthesising findings from 70 peer-reviewed articles, this review identifies critical knowledge gaps and provides practical recommendations for healthcare stakeholders to effectively navigate these challenges. This research proposes a conceptual framework illustrating the lifecycle of Digital Twin implementation in healthcare and outlines essential strategies for successful adoption. It emphasises the importance of robust infrastructure, clear regulatory guidance, and ethical practices to fully leverage the advantages of DT technologies. Nevertheless, this review acknowledges its limitations, including reliance on secondary data and the absence of empirical validation. Future research should focus on practical applications, diverse healthcare contexts, and broader stakeholder perspectives to comprehensively assess real-world impacts. Full article

The increasing demand for on-orbit servicing (OOS), such as satellite life extension and space debris removal, has highlighted the need for research into precise relative navigation between space objects. Model-based tracking (MBT) was applied using the imaging data for relative navigation, incorporating SPNv2 (Spacecraft Pose Network v2) for an initial pose estimation. Furthermore, the performance of General Loss was evaluated by applying it during the model tracking processes and comparing it with seven other robust M-estimators, including Tukey, Welsch, and Huber. The simulations were conducted in a ROS–Gazebo environment that emulated a rendezvous with the International Space Station (ISS). Six approach profiles were generated by pairing three mutually different conic-section apertures with two attitude modes—boresight locked on the ISS versus boresight fixed on the inertial origin—producing six distinct spiral trajectories that bring the chaser from 500 m to 100 m along the depth axis of the camera. General Loss achieved superior estimation accuracy in most profiles. Thus, the proposed algorithm, which integrates General Loss into the MBT-based relative navigation framework, provides robust and stable performance in the presence of diverse residual distributions and outliers. In the few instances where it did not yield the very best results, the initial error arose from matching virtual edges—generated according to the sample weight distribution—to the actual edges in the image frame; notably, by the end of the simulation, when the camera reached a depth of approximately 100 m, these errors were substantially reduced. Thus, the proposed algorithm, which integrates General Loss into the MBT-based relative navigation framework, provides robust and stable performance in the presence of diverse residual distributions and outliers. Full article

Sound source localization (SSL) adds a spatial dimension to auditory perception, allowing a system to pinpoint the origin of speech, machinery noise, warning tones, or other acoustic events, capabilities that facilitate robot navigation, human–machine dialogue, and condition monitoring. While existing surveys provide valuable historical context, they typically address general audio applications and do not fully account for robotic constraints or the latest advancements in deep learning. This review addresses these gaps by offering a robotics-focused synthesis, emphasizing recent progress in deep learning methodologies. We start by reviewing classical methods such as time difference of arrival (TDOA), beamforming, steered-response power (SRP), and subspace analysis. Subsequently, we delve into modern machine learning (ML) and deep learning (DL) approaches, discussing traditional ML and neural networks (NNs), convolutional neural networks (CNNs), convolutional recurrent neural networks (CRNNs), and emerging attention-based architectures. The data and training strategy that are the two cornerstones of DL-based SSL are explored. Studies are further categorized by robot types and application domains to facilitate researchers in identifying relevant work for their specific contexts. Finally, we highlight the current challenges in SSL works in general, regarding environmental robustness, sound source multiplicity, and specific implementation constraints in robotics, as well as data and learning strategies in DL-based SSL. Also, we sketch promising directions to offer an actionable roadmap toward robust, adaptable, efficient, and explainable DL-based SSL for next-generation robots. Full article