Search Results (5,009)

UAV swarms provide a flexible sensing platform for smart-city crowd surveillance, but cooperative aerial monitoring remains challenging due to dynamic pedestrian distributions, partial observability, and the trade-off between visual coverage and recognition accuracy. In particular, flying at higher altitudes increases the field of view but reduces recognition accuracy, while low-altitude flight improves visual quality at the cost of limited coverage. To address these challenges, this paper proposes an environment-aware cooperative navigation framework that integrates spatiotemporal density prediction with multi-agent reinforcement learning. The surveillance area is modeled as a spatiotemporal graph, where sparse and partial UAV observations are used to predict future pedestrian-density maps and confidence intervals. The predicted density and uncertainty, together with empirical recognition error, UAV position, flight height, battery state, and historical observations, are incorporated into MARL-based policy learning. The learned policy enables UAVs to cooperatively adjust movement and altitude decisions under the centralized training and decentralized execution paradigm. Extensive simulations in UAV-based crowd surveillance environments demonstrate that the proposed framework achieves a more favorable coverage–error trade-off than representative heuristic, prediction-based, single-agent reinforcement learning, and multi-agent reinforcement learning baselines. The results show that prediction-aware and accuracy-aware cooperation improves pedestrian-level surveillance performance under dynamic and partially observable crowd distributions. Full article

Background and Objectives: As Romanian health systems deploy artificial intelligence (AI), uptake depends on navigating the EU AI Act, GDPR, the Medical Device Regulation (MDR), and national rules. We measured AI legislation literacy, governance readiness, and adoption intentions among Romanian healthcare professionals, identified implementation phenotypes, and tested whether confidence mediates the literacy–adoption link. Materials and Methods: In a multicenter cross-sectional survey (N = 109), participants completed a 20-item AI Legislation Literacy Index (0–20) plus scales rated form one to five measuring legislative confidence, adoption intention, readiness, trust, and perceived compliance burden. We used PCA and k-means clustering, multivariable logistic regression for high adoption intention (≥4), and covariate-adjusted mediation (5000 bootstrap resamples). Results: Mean age was 38.7 ± 9.8 years, and 60.6% of participants were female. Literacy was moderate (11.2 ± 4.1/20) and familiarity favored GDPR (69.7%) over the EU AI Act (25.7%). Literacy correlated with confidence (=0.52), whereas confidence correlated with adoption intention (=0.41); trust correlated positively (=0.44) and burden correlated negatively (=−0.29) with adoption. High adoption intention was noted in 50.5% of participants and was independently associated with higher literacy (aOR 1.85 per +1 SD; 95% CI 1.20–2.85), higher trust (aOR 1.72; 1.13–2.63), lower burden (aOR 0.64; 0.43–0.95), and prior AI training (aOR 2.10; 1.03–4.29). Three phenotypes emerged (Confident Adopters n = 44; Cautious Compliers n = 36; Skeptical Low Literacy n = 29), with adoption scores of 4.2 ± 0.5 vs. 3.1 ± 0.7 in the highest and lowest groups. Mediation showed a partial indirect effect via confidence (0.13; 95% CI 0.05–0.24). Conclusions: AI legislation literacy, confidence, trust, and perceived burden are key, modifiable determinants of AI adoption intentions; phenotype-guided strategies can target training, governance support, and post-deployment monitoring readiness. The revised framing explicitly situates these determinants within recent AI-specific regulatory and technical developments, including high-risk AI obligations, AI-enabled medical device change control, generative/large multimodal model risks, and lifecycle monitoring. Full article

Real-time kinematic (RTK) navigation, which enables centimeter-level positioning accuracy through carrier-phase differential correction, provides high-accuracy positioning for orchard robots, but short-term outages caused by canopy occlusion and signal interference may interrupt path guidance and increase lateral drift. To address this issue, this study proposes a vision-based takeover navigation method for orchard robots under short-term RTK failure conditions. First, an improved YOLOv11-based road segmentation and completion model, termed YOLOv11-VF, was developed. By introducing a Squeeze-and-Excitation (SE) channel attention mechanism, the model jointly perceives visible road regions and occluded road completion regions, thereby producing continuous and complete road semantic representations. Second, a structured geometric road representation was constructed from the segmentation results to extract the navigation reference line, and a joint direction-position constraint mechanism was established by integrating the reference line with the robot reference view axis. A hierarchical constraint strategy based on a travel corridor and a deadband region was further designed to jointly determine heading deviation and lateral drift. Finally, road segmentation, navigation-line extraction, parameter analysis, and vision-based takeover experiments were conducted in a standardized orchard environment. The results showed that YOLOv11-VF achieved Precision, Recall, AP₅₀, mAP_@0.5:0.95, and F1 values of 92.31%, 88.56%, 94.40%, 67.41%, and 90.40, respectively, showing the best overall segmentation performance among all compared models while maintaining good real-time performance. The proposed method also demonstrated high consistency in navigation-line extraction and maintained mean absolute deviations of 0.0176 ± 0.0041 m to 0.0718 ± 0.0138 m during RTK outage intervals over 10 repeated trials, indicating good path-following capability and operational stability. Full article

Terrain-aided navigation (TAN) enables autonomous positioning through fusing prior terrain databases with real-time sensor measurements in GNSS-denied environments. Typical factors, including wide beam width and terrain elevation variations, introduce inaccuracies in elevation measurements, degrading the performance of classical elevation-based TAN methods. The SAR altimeter operates in nadir-looking mode to acquire range–Doppler projection images with inherent cross-track ambiguity for positioning based on image information, yet its accuracy is limited by single-feature and fixed-grid approaches. In this paper, we introduce an adaptive positioning framework for the SAR altimeter that combines XGBoost-based multi-feature fusion with Bayesian particle filtering. First, a fast DDM template generation algorithm is employed to improve computational efficiency. Then, an ensemble learning framework integrating complementary similarity features is introduced to achieve robust single-frame matching. Additionally, a Bayesian filtering-based dynamic grid construction method is developed to concentrate particles in high-probability regions, eliminating boundary truncation errors inherent to fixed approaches. The proposed method’s primary advantage is the reliable three-dimensional localization under extreme radar configurations, such as wide beam width and high-altitude maneuvering platforms. Experimental results based on both simulated and real data validate the method, demonstrating superior positioning performance under wide-beam conditions. Full article

This paper explores the advocacy of Daisaku Ikeda (1928–2023) for intercultural dialogue as a means of fostering peace. It does so by bringing his thought into dialogue with Martin Wight (1913–1972), an English international relations theorist whose three traditions model explored two forms of dehumanization: a radical solidarist position that dehumanizes by treating all people the same, and an extreme form of pluralism that leads us to a realist position that there is no morality except for a group’s own truth. Wight’s model is especially helpful for drawing out the tensions in Ikeda’s writings between peacebuilding processes centered on solidarism and those centered on pluralism. But, at the same time, this model benefits from a sustained conversation with Ikeda because Wight’s conceptualization of a middle path was highly Eurocentric and too state-centric. Ultimately, Wight’s model gives us a new vocabulary and the context to understand Ikeda’s advocacy, but Ikeda’s approach to intercultural dialogue, deeply rooted in Buddhist humanism and prioritizing citizen diplomacy and education, went farther than Wight in theorizing and practicing how to create a healthy middle ground between the two forms of dehumanization. When viewed together, both scholars also address the broader ambivalence in the literature about why, how and when religion(s) contribute to violence and peace by emphasizing the courage and faith needed to navigate a middle path between extremes. Full article

We propose a navigation signal authentication scheme for civilian GNSS receivers that exploits the intermodulation components introduced by constant envelope modulation without relying on modernized authenticatable signals or direct access to authorized spreading codes. Since the product of multiple authorized spreading codes is unpredictable and does not reveal the original codes, the spreading code of the intermodulation component can serve as an authentication feature similar to that of authorized signals. The receiver obtains the intermodulation spreading code via a communication link, then authenticates the GNSS signal by detecting the presence of this code through a correlation-based hypothesis test. We analyze the scheme using the operational GPS L1 signal as an example and compare its performance with Chimera (proposed for GPS), OSNMA (proposed for Galileo), and authorized spreading code authentication in PROSPA. Results show that the proposed scheme achieves robustness and security comparable to Chimera, while avoiding the authorization restrictions associated with authorized codes. Under a 63.82 kbps communication rate and a civilian signal $C/N_0$ of 30 dB-Hz, its authentication efficiency exceeds Chimera’s fast channel, with substantially lower data storage requirements. Full article

Long and narrow shielded confined spaces, represented by traffic tunnels and underground utility tunnels, constitute critical application scenarios for indoor and underground positioning services. Despite their relatively simple geometric configurations, such environments suffer from severe spatial distortion of geometric dilution of precision (GDOP). Coupled with pervasive low-elevation signal propagation and intensive multipath reflection effects, conventional BeiDou Navigation Satellite System (BDS) positioning services are unable to provide continuous and reliable coverage in these scenarios. To date, existing research on high-precision pseudolite positioning for narrow confined spaces remains largely confined to theoretical analysis and laboratory experimental verification, while systematic studies on application-oriented signal atlas feature network design are significantly insufficient, forming a prominent gap that restricts the practical engineering deployment of relevant technologies. To address the aforementioned technical bottlenecks, this paper proposes a novel BDS pseudolite signal atlas network design method to improve the continuity, stability and comprehensive positioning performance in spatially distorted narrow shielded environments. Field vehicular tests were carried out in actual engineering tunnels and underground utility tunnels to systematically analyze the variation characteristics of raw BDS pseudolite observation data, including pseudorange, carrier phase, carrier-to-noise ratio (C/N₀) and Doppler shift. The test results verified that kinematic Doppler parameters exhibited outstanding stability in complex shielded environments with strong multipath interference. On this basis, a spatial feature model based on kinematic Doppler measurements was constructed, and wavelet denoising technology was adopted to extract effective typical spatial feature parameters. Combined with the deterministic one-to-one mapping relationship between Doppler peak characteristics and spatial positions, a multi-peak kinematic Doppler atlas was established, which eliminates the dependence on pre-deployment data collection, dedicated database construction and offline model training. Furthermore, comprehensively considering multi-dimensional constraints such as spatial environment scale, carrier dynamic characteristics and terminal output rate, the atlas network scheme was optimized to achieve a balanced trade-off among positioning detection accuracy, absolute positioning precision and suppression of the pseudolite near-far effect. Comparative experimental results demonstrate that the proposed BDS pseudolite atlas network effectively resolves the inherent GNSS positioning difficulty in long and narrow shielded spaces. Benefiting from the rational spectral peak configuration strategy, the system can satisfy the continuous and stable positioning requirements of multiple carrier types including motor vehicles and railway locomotives under variable motion speeds and terminal output rates. This study provides a robust and feasible technical solution for high-precision BDS positioning services in long and narrow shielded confined spaces, and holds favorable engineering application prospects for underground navigation scenarios. Full article

University orientation programmes are a primary mechanism through which new students become familiar with campus facilities, academic spaces, and institutional procedures. However, many orientation activities are delivered as single in-person sessions, limiting opportunities for students to revisit spatial and procedural information after the event. To help address this constraint, a digital twin-inspired metaverse orientation application, the Digital Twin Metaverse Orientation (DTMO), was designed in Unity and hosted on Spatial.io as a spatially faithful virtual replica of a faculty environment. An exploratory pilot evaluation was conducted with 30 university students from multiple faculties after a facilitator-guided orientation session. The System Usability Scale (SUS), Net Promoter Score (NPS), and two open-ended questions were used to examine perceived usability, recommendation intention, and the reasons underpinning recommendation decisions. The application obtained a mean SUS score of 86.83, corresponding to an excellent perceived-usability rating, and an NPS of 53.33, indicating positive immediate recommendation intention. Qualitative responses suggested that participants valued the DTMO for engagement, accessibility, ease of navigation, and support for spatial familiarisation, while some participants emphasised that it should complement rather than replace physical orientation. These pilot findings indicate promising user reception in a small, guided-session sample, but they do not establish orientation effectiveness, learning transfer, wayfinding performance, retention, belonging, institutional integration, or sustained use. Further research with broader samples and outcome-based measures is therefore needed. Full article

Aiming at the problem that complex underwater environments induce outliers in Doppler Velocity Log (DVL) measurements, which degrade the navigation accuracy of the Strapdown Inertial Navigation System (SINS)/DVL integrated system, this paper proposes a soft-gating Gaussian–Student’s t mixture robust Kalman filter (MRKF). Firstly, the measurement noise is modeled as a mixture of Gaussian and Student’s t distributions to adapt to normal stationary noise and abrupt outliers, respectively. Secondly, a logistic soft-gating weight is constructed based on the innovation Mahalanobis distance to adaptively balance the output contributions of the standard Kalman Filter (KF) and the variational Bayesian Student’s t filter. Finally, moment matching is adopted to realize the weighted fusion of two-branch posterior distributions, and an equivalent Gaussian posterior estimation is obtained. Simulation results under the considered SINS/DVL integrated navigation scenarios show that the proposed MRKF maintains estimation accuracy close to the standard KF under nominal Gaussian measurement noise. In the designed DVL outlier-injection scenario, the proposed MRKF achieves a position RMSE of $53.39\mathrm{m}$, compared with $878.75\mathrm{m}$, $58.84\mathrm{m}$, and $56.49\mathrm{m}$ for the nominal KF, Huber KF (HKF), and Student’s-t variational Bayesian KF (STVBKF), respectively. These results indicate that the proposed MRKF can improve robustness against DVL outliers while maintaining competitive estimation accuracy under the simulated conditions. Full article

In satellite-denied environments such as urban canyons, tunnels, and underground parking facilities, achieving high-precision autonomous positioning for vehicles remains a critical challenge. Although high-precision inertial measurement units (IMUs) can provide accurate dead reckoning, their deployment is limited by cost, size, and power consumption, making low-cost, microelectromechanical systems IMUs (MIMUs) an attractive alternative solution. However, the single MIMU suffers from substantial measurement noise and bias instability, leading to rapid error divergence that cannot sustain long-term autonomous navigation. To address the above issues, this paper proposes an autonomous positioning system based on a wheel-mounted MIMU array (Wheel-AINS). The system adopts a differential layout in which multiple low-cost MIMU chips are installed at the center of each of the left and right rear wheels, forming redundant sensor arrays. By differentially fusing symmetrically mounted chips, common-mode noise and zero bias are effectively canceled while the wheel rotation provides natural rotational modulation. The fused gyroscope outputs and known wheel radius are then used to estimate the vehicle forward speed, replacing traditional odometers. The estimated wheel speed and vehicle kinematic constraints are then integrated within a Kalman filter framework to suppress the error divergence of the inertial navigation system. A dedicated embedded hardware prototype with multi-chip synchronous acquisition and wireless transmission was developed. Three groups of urban road tests with total distances of 0.85 km, 2.14 km, and 2.49 km were conducted. The results indicate that the average position drift rate of the Wheel-AINS is 0.50%, and the average heading RMSE is 12.2°. The closure error of the 2.49 km trajectory is 10.43 m, reduced by approximately 80% compared with a single MIMU. The ablation experiment reveals that the MIMU array fusion module is the primary source of accuracy improvement, reducing the position RMSE from 155.0 m to 10.1 m, while the dual-wheel distance constraint further optimizes the position RMSE to 8.2 m, but increases the heading RMSE from 13.3° to 13.6°. This demonstrates that the proposed method can substantially improve autonomous positioning accuracy while maintaining a notably low system cost, providing a viable technical pathway for long-endurance vehicle navigation in satellite-denied environments. Full article

In this paper, we propose a 1-bit algorithm using spatial information to improve the accuracy of Kalman filter (KF)-based location estimation. The proposed algorithm aims to improve position estimation accuracy by re-estimating values outside a feasible region as being at the boundary of that region, based on the information that the user is present within that feasible region. This approach enhances position estimation accuracy without significantly increasing complexity. This paper discusses two methods for applying the 1-bit algorithm and verifies their performance by comparing Time of Arrival (ToA), the ToA-based KF, and the ToA-based KF with the 1-bit algorithm through simulations under three scenarios. Performance analysis was conducted from two perspectives: cumulative distribution function (CDF) and average position error (APE). The ToA-based KF with a 1-bit algorithm demonstrated the best performance. The proposed approach improved performance without high computational complexity and is suitable for real-time applications, making it applicable to indoor positioning, robot navigation, and wireless sensor networks that require high positioning accuracy. Full article

Reliable outdoor localization is essential for autonomous mobile robots, where the Global Navigation Satellite System (GNSS) is widely used to provide global positioning information. However, GNSS signals are often degraded in real-world environments due to occlusions, multipath effects, and environmental interference, leading to unstable localization and degraded navigation performance. This paper proposes an adaptive multi-sensor fusion framework for robust outdoor localization and path tracking under weak GNSS conditions. The proposed system integrates GNSS, LiDAR, wheel odometry, and inertial measurement unit (IMU) measurements within an Extended Kalman Filter (EKF) framework. To address the limitations of GNSS, an adaptive weighting mechanism is introduced to dynamically adjust the influence of GNSS observations based on signal quality indicators. Furthermore, a GNSS quality-aware mode-switching strategy is developed, enabling seamless transition between GNSS-dominant localization and multi-sensor fusion-based localization. In the fusion mode, LiDAR, odometry, and IMU jointly provide robust pose estimation, while GNSS acts as a weak global constraint. The IMU further enhances heading estimation, improving orientation stability and path tracking performance. The estimated pose is then used for trajectory tracking using a path-following controller. Experimental results conducted in outdoor environments demonstrate that the proposed framework significantly improves localization robustness and path tracking performance under degraded GNSS conditions. Compared with raw GNSS localization, the proposed method reduces the mean localization error by 47.2% and decreases the root mean square localization error by 55.5%, while maintaining smoother and more continuous trajectory estimation in weak GNSS environments. Full article

The emerging potential of low-Earth-orbit (LEO) satellite-based Positioning, Navigation, and Timing services has increased the need for real-time, stable, and accurate orbit determination techniques. Here, we propose a method for estimating sub-meter-level LEO satellite orbits using Global Navigation Satellite System (GNSS) code pseudorange observations. To mitigate ionospheric delay, a dual-frequency ionosphere-free combination was applied, while code-carrier smoothing was employed to reduce code observation noise. A satellite weighting model based on Signal-in-Space Range Error was developed to reflect the orbit and clock error characteristics of different GNSS, and a robust weighting scheme was applied to alleviate the impact of observation outliers. Further, Galileo High Accuracy Service corrections compensated for orbit, clock and code bias errors. The algorithm was validated using the GNSS observation data collected from the Sentinel-6A satellite on 10 August 2023. Each successively applied technique gradually improved orbit determination accuracy, achieving up to a 51% reduction in 3D root mean square error (RMSE). The final RMSE values in the radial, along-track, cross-track, and 3D components were 39.4, 18.8, 23.5, and 49.6 cm, respectively. Temporal analysis showed no distinct periodicity in orbit errors and no significant correlation with satellite visibility or ground track. Full article

Background/Objectives: Robotic-assisted bronchoscopy has emerged as an advanced technique for the evaluation of peripheral pulmonary lesions, offering improved navigation and targeting accuracy. While several studies investigating other diagnostic modalities have identified factors associated with higher diagnostic yield, such determinants remain poorly defined for shape-sensing robotic-assisted bronchoscopy (ssRAB). This study therefore aimed to identify predictors of diagnostic yield in robotic bronchoscopy. Methods: This retrospective single-center study included all consecutive patients who underwent ssRAB (ION^TM system, Intuitive Surgical, Sunnyvale, CA, USA) between August 2024 and March 2026. Lung nodules undergoing marker placement only or procedures performed without cone-beam CT (CBCT) guidance were excluded. Collected variables included demographic characteristics, lesion size, lesion density (solid, part-solid, ground-glass), biopsy modality, and number of biopsy samples obtained. Diagnostic yield was defined as a definitive pathological diagnosis of the target lesion. Predictors of diagnostic success were assessed using univariable logistic regression. Results: In total, 111 pulmonary nodules were included in the analysis. The overall diagnostic yield was 88.3% (98/111). The mean patient age was 64.94 ± 7.9 years, with a predominance of female patients (58.4%). No significant associations were observed between diagnostic yield and lesion size (odds ratio [OR] 1.014 per mm; p = 0.764), lesion density (p = 0.892), or biopsy instrument (p = 0.835). However, an increased number of biopsy samples showed a positive association with diagnostic yield, showing a statistical trend (OR 1.22 per additional sample; p = 0.084). Conclusions: Robotic-assisted bronchoscopy provides a high diagnostic yield for peripheral pulmonary lesions. The number of biopsy samples appears to be the most relevant modifiable factor influencing diagnostic success, underscoring the importance of adequate tissue acquisition. In contrast, lesion characteristics and biopsy modality did not significantly affect outcomes in this cohort. Full article

To achieve precise trajectory tracking for surface vessels subject to unknown dynamics, strict physical limitations, and external disturbances, this paper proposes an Adaptive Fixed-Time Control Framework that ensures a deterministic response under full constraints. First, navigation safety is guaranteed by employing a Barrier Lyapunov Function (BLF) to strictly confine vessel position states, enabling constrained position tracking without requiring prior knowledge of the desired trajectory. Second, addressing the input constraint aspect of the “full constraints” problem, a fixed-time auxiliary system is introduced to compensate for nonlinearities induced by actuator saturation, thereby maintaining control feasibility. Central to this framework is the realization of a deterministic response; by incorporating fixed-time convergence theory, the controller guarantees that velocity tracking errors converge within a predefined time bound independent of initial conditions. Furthermore, an RBF neural network combined with adaptive techniques is utilized to estimate unknown dynamics and external disturbance bounds online, enhancing robustness and safety in realistic marine environments. Full article