Search Results (20,792)

The sixth-generation (6G) mobile communication systems are anticipated to be operational by 2030, prompting extensive research efforts by governments and private entities. Designed to meet societal, economic, and technological demands unaddressed by fifth-generation (5G) networks, 6G integrates scalability, security, and reliability with ubiquity and resource-intensive artificial intelligence. Envisaged as multi-band, decentralized, autonomous, flexible, and user-centric, 6G networks incorporate innovative technologies, including cell-free (CF), three-dimensional heterogeneous networks (3D HetNet), reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), as well as artificial intelligence/machine learning (ML). In 6G 3D HetNets, the densification of access points (APs) continues, accommodating increased connections and traffic volumes, alongside the use of higher frequency bands. Although 6G networks are not fully standardized, they target demanding Quality of Service (QoS) standards, such as a peak data rate of 1.0 Tbps and latency of 0.1 ms. This paper conducts a comprehensive literature review on radio resource management (RRM) in 6G cell-free and 3D HetNet systems, emphasizing challenges such as interference mitigation. It presents a taxonomy of RRM approaches, systematically studying, categorizing, and qualitatively analyzing recent techniques, outlining the current state, and indicating future trends, technologies, and challenges shaping 6G systems. Full article

To address the bottlenecks of low sample efficiency and poor control accuracy in traditional single-layer reinforcement learning during autonomous driving overtaking, this paper proposes an overtaking decision and control system based on hierarchical reinforcement learning to decouple complex tasks in spatial and temporal dimensions. A heterogeneous two-layer architecture is constructed, where the upper layer adopts the Proximal Policy Optimization algorithm to generate macroscopic discrete decisions, while the lower layer employs Twin Delayed Deep Deterministic Policy Gradient combined with Long Short-Term Memory to achieve smooth continuous control of steering and acceleration by perceiving temporal features of dynamic obstacles. A composite reward mechanism, integrating hard safety constraints and soft efficiency incentives, is designed to balance safety, efficiency, and comfort. Experimental results in complex scenarios with multiple interfering vehicles and random lane-changing behaviors demonstrate that the proposed system improves the training convergence speed by approximately 30% within 500,000 steps compared to single-layer algorithms. In tests across varying traffic densities, the system achieves a 98.3% success rate in medium-density scenarios with a collision rate of only 0.6%. In high-density challenges, the success rate remains above 95%, with the collision rate reduced by about 80% compared to baseline models. Furthermore, the lateral control deviation is strictly limited to within 0.2 m, and the longitudinal safety distance remains stable above 5 m. This system provides a robust, high-efficiency paradigm for autonomous overtaking. Full article

Satellite constellations are transforming space systems from isolated spacecraft into networked, software-defined platforms capable of on-orbit perception, decision making, and adaptation. Yet many of the existing AI studies remain centered on single-satellite inference, while constellation-scale autonomy introduces fundamentally new algorithmic requirements: learning and coordination under dynamic inter-satellite connectivity, strict SWaP-C limits, radiation-induced faults, non-IID data, concept drift, and safety-critical operational constraints. This survey consolidates the emerging field of on-orbit space AI through three complementary paradigms: (i) federated learning for cross-satellite training, personalization, and secure aggregation; (ii) multi-agent algorithms for cooperative planning, resource allocation, scheduling, formation control, and collision avoidance; and (iii) collaborative sensing and distributed inference for multi-satellite fusion, tracking, split/early-exit inference, and cross-layer co-design with constellation networking. We provide a system-level view and a taxonomy that unifies collaboration architectures, temporal mechanisms, and trust models. Full article

The operation of driver assistance systems and autonomous vehicles requires a sensor system and a control algorithm. Sensors provide information to detect people, vehicles and objects in the vehicle’s environment; however, their performance can be degraded by adverse environmental conditions and contamination. This literature review identified factors that reduce sensor visibility, such as weather conditions and external contamination. In this study, the detection efficiency of state-of-the-art neural network-based object detectors was examined in a simulation environment using a synthetic dataset. A custom dataset comprising six urban and suburban traffic scenarios was created, including clean images and ten contaminated variants per scene with increasing mud coverage. The results show that contamination leads to a measurable reduction in detection performance across all models. Smaller variants are more sensitive to degradation, while medium-complexity models provide a favorable balance between robustness and computational cost. Increasing model size yields limited additional robustness, and performance differences between architectures highlight the importance of model design. Furthermore, the spatial distribution of contamination, particularly near the image center, has a significant impact on performance in addition to its overall extent. Full article

Assessing individual animal health is essential for detecting early ecological stress that may scale to population-level impacts. Yet, conventional capture-based methods are invasive and logistically challenging, particularly for large mammals. This study evaluates the accuracy of drone-based morphometric measurements as a non-invasive approach for estimating elephants’ Body Condition Index (BCI). Research was conducted in Way Kambas National Park, Sumatra, using a DJI Matrice 300 RTK equipped with a multisensor camera to acquire aerial imagery, primarily from a top-down perspective. Morphometric parameters were extracted through image preprocessing, segmentation, and edge detection using an OpenCV-based Canny algorithm, followed by coordinate and Euclidean distance analyses. Drone-derived measurements were validated against field-based morphometry in captive Sumatran elephants. Linear regression revealed strong agreement between methods, with R² values ranging from 0.91 to 0.97. Mid-body width showed the highest accuracy (R² = 0.97, MAPE = 2.66%, RMSE = 2.36), while other body dimensions also performed consistently well. BCI-related morphometric ratios exhibited minimal differences between drone and field measurements, confirming methodological reliability. As an exploratory extension, a preliminary allometric scaling framework was applied to estimate body condition proxies in free-ranging wild elephants except for mid-body width; however, these estimates are model-derived from total body length and should be interpreted as indicative rather than as direct morphometric assessments of body condition. These findings demonstrate that drone-based photogrammetry provides a validated, practical, and non-invasive method for morphometric measurement in captive elephants, with promising but as yet incompletely validated potential for application to wild populations. Full article

Heart rate variability (HRV) is a key physiological marker for autonomic nervous system function and cardiovascular health. Photoplethysmography (PPG) is commonly used to derive HRV metrics in wearable and low-cost monitoring systems. This study presents a comparative assessment of basic HRV metrics obtained from a MAX30102 optical sensor and a custom analog circuitry with an ADS1115 analog-to-digital converter (ADC). Both measurement pathways were carefully aligned using analog high-pass and low-pass filters and a consistent digital filtering pipeline, ensuring that the frequency bands relevant to HRV were preserved. PPG signals were recorded simultaneously, and inter-beat intervals were extracted to calculate the Standard Deviation of NN intervals (SDNN), Root Mean Square of Successive Differences (RMSSD), and Percentage of successive NN intervals >50 ms (pNN50) across multiple 30-s windows. Bland–Altman analysis was employed to evaluate agreement between the two methods. Results indicate that the analog circuit with an ADS1115 achieves comparable HRV basic metrics to the MAX30102 sensor, with improved Signal-to-Noise Ratio (SNR) due to high-resolution ADC and low-noise analog amplification. These findings demonstrate that a carefully designed analog acquisition system can reliably reproduce HRV basic parameters from PPG signals, providing an alternative approach for low-cost, flexible biosensing platforms. Full article

Spiking Neural Networks (SNNs) executed on neuromorphic hardware promise energy-efficient, low-latency inference well-suited to edge deployment in size, weight, and power-constrained environments such as autonomous vehicles, wearable devices, and unmanned aerial platforms. However, a coherent research pathway to deployment of neuromorphic devices remains elusive. This paper presents a structured review and position on the state of SNN-based vision across four interconnected dimensions: network architectures, training methodologies, event-based datasets and simulation techniques, and neuromorphic computing hardware. We survey the evolution from shallow convolutional SNNs to spiking Transformers and hybrid designs which leverage the advantages of SNNs and conventional artificial neural networks. We also examine surrogate gradient training and ANN-to-SNN conversion approaches, catalogue real-world and simulated event-based datasets, and assess the landscape of neuromorphic platforms ranging from rigid mixed-signal architectures to fully-configurable digital systems. Our analysis reveals that while each area has matured considerably in isolation, critical integration challenges persist. In particular, event-based datasets remain scarce and lack standardisation, training methodologies introduce systematic gaps relative to deployment hardware, and access to neuromorphic platforms is restricted by proprietary toolchains and limited development kit availability. We conclude that bridging these integration gaps, rather than advancing individual components alone, represents the most important and least addressed work required to realise the potential of SNN-based vision at the edge. Full article

Pedestrian interaction remains a central safety challenge for autonomous driving, particularly under non-compliant or adversarial pedestrian behavior. Existing research and evaluations predominantly test against rule-following pedestrians, leaving a gap in understanding how learning-based agents handle worst-case interactions. We introduce the Jaywalkers Library, a novel configurable benchmark in CARLA with three adversarial pedestrian archetypes (Intruder, Indecisive Crosser, and Protester). We evaluate a DreamerV3 agent trained with sparse rewards, where the only pedestrian-specific signal is a terminal collision penalty. Evaluation employs a frozen-policy protocol with explicit train–test separation. Safety behavior is decomposed into endpoint outcomes, evasion dynamics, and efficiency costs. Under nominal conditions, the agent achieves high route completion and generalizes to an unseen town, whereas under adversarial exposure, an archetype-sensitive evasion strategy emerges. The agent swerves at speed against dynamic pedestrians but decelerates against the slow-moving Protester. Collision rates reveal a counterintuitive difficulty ordering in which the Protester is the hardest, followed by the Intruder, with the Indecisive Crosser as the most survivable. These findings show that a sparse terminal penalty suffices for emergent pedestrian avoidance in a world-model agent, but that effectiveness is bounded by the world model’s ability to predict pedestrian persistence. Full article

Heart rate variability (HRV) has emerged as a key biomarker for assessing autonomic nervous system activity, stress, fatigue, and emotional states. With the rapid development of wearable sensor technologies, HRV analysis has expanded from clinical environments to real-world, continuous monitoring. This review summarizes current applications of HRV metrics in wearable devices, including fitness tracking, mental stress assessment, sleep quality evaluation, and early detection of physiological or psychological disorders. Recent advances in photoplethysmography (PPG)-based HRV estimation have enabled noninvasive and user-friendly measurement, though challenges remain in accuracy under motion and variable environmental conditions. We also discuss methodological considerations, such as artifact correction, data segmentation, and the integration of HRV with other biosignals for multimodal analysis. Emerging research suggests that combining HRV with metrics such as respiration rate, skin conductance, and accelerometry can enhance robustness and interpretability in dynamic settings. Finally, future directions are proposed toward personalized health analytics, emotion-aware computing, and real-time adaptive feedback systems. This review highlights the growing potential of wearable HRV analysis as a foundation for preventive healthcare and human–machine symbiosis. Full article

Trajectory planning in dynamic multi-vehicle interaction environments faces three critical challenges, including the difficulty of quantifying spatial risk distributions, the complexity of characterizing behavioral uncertainty arising from the multimodal maneuvers of surrounding vehicles, and the challenge of simultaneously optimizing multiple competing objectives such as safety, efficiency, comfort, and energy consumption. To address these challenges, this paper proposes an Improved Driving Risk Field-based Multi-objective Trajectory Optimization (IDRF-MTO) method. First, a joint spatiotemporal social attention mechanism achieves unified modeling of spatial interactions, temporal dependencies, and spatiotemporal coupling, combined with a lateral–longitudinal intent strategy for multimodal trajectory prediction. Second, an improved dynamic risk field model is constructed comprising three components: a vehicle risk field that incorporates spatial orientation and motion direction factors for anisotropic risk representation, along with a collision tendency factor that converts objective risk into effective risk; a predicted trajectory risk field that achieves anticipatory quantification of future risk from surrounding vehicles through confidence-weighted fusion; and a driving environment risk field that encapsulates road geometry, static obstacles, and environmental conditions. Finally, a multi-objective cost function embedding risk field gradients is formulated, and multi-objective coordinated optimization is realized through a three-dimensional spatiotemporal situation graph with adaptive safety sampling. Simulation results demonstrate that the proposed method enhances safety while simultaneously improving comfort and efficiency and reducing energy consumption, exhibiting excellent planning performance in complex dynamic environments. Full article

In low-latency edge-intelligence scenarios such as autonomous driving and industrial edge analytics, the processing of large-scale sensor data imposes extremely stringent requirements on communication latency. However, the high overhead of the traditional TCP protocol makes it difficult to satisfy such demands, while the semantic gap between the high-performance RoCE protocol and the standard Socket API prevents existing applications from directly exploiting its advantages. To address this problem, this paper proposes TransBridge, a lightweight user-space communication middleware that transparently bridges TCP and RoCE. Its design is realized through three key innovations: a transparent user-space compatibility architecture that enables unmodified Socket-based applications to benefit from RoCE performance; a microsecond-level low-latency transmission engine that bypasses kernel and protocol stack overhead; and a lightweight lock-free resource management mechanism based on a decentralized peer-to-peer architecture and deferred buffer updates. Experiments on a real RoCE network show that TransBridge significantly outperforms mainstream schemes: it achieves an average round-trip latency of 5.926 $\mathsf{\mu }$s for 16 B messages and a throughput of 20.254 Gbps for 16 KB messages; in the Fast DDS application-level evaluation, it achieves a throughput of 188 Mbps and an average round-trip latency of about 150 $\mathsf{\mu }$s. The results indicate that TransBridge can provide transparent and effective RoCE acceleration for existing Socket-based applications in resource-constrained edge environments. Full article

Autonomous robots are increasingly integrated into social contexts, making affective human–robot interaction (HRI) critical for their effectiveness and acceptance. However, existing research remains dispersed across domains and techniques, lacking a unified framework to characterize core robotic capabilities. To address this gap, we adopt a capability-oriented perspective and conduct a comprehensive literature review, through which we propose a structured taxonomy of capabilities for robots in affective HRI. The taxonomy comprises five core dimensions: perception (recognizing human internal states), strategy (planning responses based on human states and context), expression (conveying robot lifelikeness and social presence), sustainability (maintaining effective and reliable operation over time), and ethics (ensuring behavior within ethical constraints). By organizing diverse research efforts into a structured framework, this taxonomy provides a systematic foundation for designing socially competent robots and guiding future research. Full article

Formation reconstruction for large-scale unmanned aerial vehicle (UAV) swarms faces critical challenges in computational complexity and safe navigation within high-density environments. To address the O(N³) computational bottleneck of traditional assignment algorithms, this study proposes a Dynamic Grouping Task Assignment (DGTA) method based on a hierarchical sorting strategy. Furthermore, an Integrated Hierarchical Control (IHC) framework is developed by coupling fuzzy logic velocity regulation and linear trajectory prediction with an improved Artificial Potential Field (APF) method. Numerical simulation results demonstrate that the DGTA method significantly enhances efficiency; specifically, when the swarm is partitioned into three complete groups, the task assignment time for a 600-UAV swarm is reduced by over 90% compared to non-grouping approaches. Additionally, the IHC framework reduces hazardous incidents by more than 27% in congested scenarios. This DGTA-IHC structure successfully balances global optimality with real-time safety, providing a scalable solution for the autonomous coordination of ultra-large-scale swarms. Full article

This study falls within the field of lifelong education and investigates the role of aesthetic-sensory experiences—particularly those mediated by digital visual arts—in the development of critical thinking, a key competence identified in the European LifeComp Framework. In light of contemporary social transformations characterised by complexity, uncertainty, and change, critical thinking emerges as a transversal skill of fundamental importance for the education of conscious and autonomous citizens. The objective of this research is to analyse how digital sensory experiences, integrated with innovative teaching methodologies—such as laboratory-based teaching and collaborative learning—can foster the activation and enhancement of critical thinking in educational contexts. The work is based on a narrative analysis of the literature, conducted through the consultation of academic databases (e.g., Scopus, ERIC, and Google Scholar), and is developed within a theoretical framework encompassing digital education, laboratory didactics, and collaborative learning strategies. Studies published in recent years were selected according to their relevance to digital aesthetic experiences and critical thinking in educational contexts and were analysed through a thematic synthesis of the main conceptual contributions. The knowledge activities include the selection, categorisation, and discussion of recent studies that relate aesthetic experiences to the development of soft skills, in line with the principles of visual education understood as aesthetic and critical literacy in visual languages. The results of the review indicate that the intentional use of aesthetic digital environments, in combination with active and reflective teaching approaches, can stimulate complex cognitive processes and significantly contribute to the formation of critical thinking. The contribution implements an interdisciplinary approach among visual education, digital education, and experiential aesthetics, emphasising the need for further empirical research to consolidate the evidence and guide the implementation of innovative educational practices based on this approach. Full article

The mechanism by which fish enhance hydrodynamic performance through collective swimming is a research hotspot in the field of underwater bionic robots. This study employs the Immersed Boundary-Lattice Boltzmann Method (IB-LBM) to conduct numerical simulations on a two-dimensional, single-degree-of-freedom (1-DOF) autonomous propulsion bionic fish swarm. It systematically investigates the effects of swarm size and inter-individual spacing on swimming speed and cost of transport (CoT) under two typical configurations: series and parallel arrangements. Findings reveal that hydrodynamic benefits are highly dependent on the spatiotemporal evolution of flow field structures. In the series configuration, an optimal spacing range of 1.5 L to 2.0 L exists within the school, where the “wake capture” effect is pronounced. Trailing fish achieve a maximum speed increase of approximately 41.1% while significantly reducing energy consumption. However, as spacing increases to 2.5 L, the cooperative gain for front and middle-row individuals rapidly diminishes, and the lead fish even experiences significant performance loss. Uniquely, the trailing fish in the four-fish formation exhibits distinct flow field reorganization and performance recovery at the 4.5 L trailing position. In the parallel formation, the “channel effect” and “blocking effect” of the fluid dominate. The study identifies 0.4 L laterally as the critical instability spacing under the investigated kinematic regime, where strong destructive interference causes a sharp deterioration in individual swimming performance. Additionally, the parallel formation exhibits pronounced positional differentiation. Central individuals, constrained by dual lateral flow fields, experience restricted lateral wake expansion and accelerated energy dissipation, resulting in significantly weaker escape capabilities from low-speed conditions compared to marginal individuals. The vortex-dynamic mechanism revealed herein provides theoretical foundations for formation control in multi-fish biomimetic cooperative systems. Full article