Search Results (418)

Background/Objectives: Artificial intelligence (AI) is increasingly proposed to improve surgical planning, guidance, and postoperative surveillance. Yet many promising applications remain disconnected from the full surgical pathway and the feasible limitations of clinical deployment. In contrast to prior reviews that primarily catalog AI use cases, this review combines the literature to define the translational pathway—from label design through staged validation to workflow integration—required for clinically deployable computed tomography (CT)-based surgical AI. CT and particularly computed tomography angiography (CTA) are especially usable sources for surgical AI because they provide a standardized three-dimensional anatomic model that is already embedded in many clinical workflows. In autologous breast reconstruction, deep inferior epigastric perforator (DIEP) flap CTA offers an unusually strong model system: the anatomy is discrete, surgeon decisions are actionable, and downstream operative and postoperative outcomes are measurable. These characteristics make DIEP reconstruction suitable not only for technical model development, but also for exacting testing of how CT-based AI should be annotated, validated, displayed, and governed. Methods: This focused narrative review combines evidence across the surgical workflow, spanning preoperative planning and risk stratification, intraoperative support, and postoperative monitoring. Reporting standards, implementation frameworks, governance, and regulatory sources were also considered when directly relevant to clinical deployment. Results: Across the available literature on breast reconstruction with the DIEP flap, preoperative CTA has been associated with reductions in operative time of approximately 54–76 min in individual studies. Semi-automated perforator mapping can reduce review time from 2 to 3 h to approximately 30 min. Intraoperative extended-reality tools and surgeon-facing navigation systems illustrate the importance of the ‘last mile’ of translation, while postoperative monitoring models show how imaging-linked data can support a closed-loop learning system. Across these stages, recurring limits include target mismatch, weak external validation, protocol variability, inconsistent reporting, limited subgroup analysis, and inadequate integration of economic and governance considerations. Conclusions: We argue that the next important step is not a generic autonomous model, but a clinically deployable DIEP-CTA-AI program. The practical blueprint proposed here is staged: structured anatomical labels, separate imaging, surgeons’ decisions, and outcome reference standards, dense intermediate endpoints, retrospective and external validation, reader studies, prospective silent deployment, and workflow-impact assessment. If implemented in this way, DIEP flap CTA can serve as a practical blueprint for CT-based AI translation in surgery more broadly. Full article

Artificial intelligence (AI) is increasingly transforming gastrointestinal endoscopy by supporting lesion detection, lesion characterization, quality assessment, and clinical risk prediction. Hemostatic decision-making represents a particularly complex field for AI integration, as therapeutic decisions are often made rapidly in the presence of active bleeding, impaired visualization, unstable patients, and variable lesion accessibility. This review critically examines the current evidence for AI-assisted decision-making in gastrointestinal endoscopy and endoscopic hemostasis, with emphasis on gastrointestinal bleeding, prediction of hemostatic therapy requirements, bleeding-risk stratification, rebleeding prediction, transfusion support, and post-procedural monitoring. Available studies suggest that machine learning and deep learning models may outperform conventional scoring systems in selected retrospective or validation cohorts, improve recognition of high-risk lesions, support less experienced endoscopists, and contribute to more individualized management of non-variceal bleeding, variceal bleeding, and capsule endoscopy findings. However, prospective interventional evidence remains sparse, and most available models are limited by retrospective design, single-center datasets, incomplete external validation, black-box decision-making, heterogeneous reporting, workflow barriers, and uncertain cost-effectiveness. AI should therefore be regarded as an adjunctive decision-support tool rather than an autonomous replacement for clinical judgment. Its future value will depend on prospective multicenter validation, explainability, real-time usability, regulatory clarity, post-deployment surveillance, and evidence of improved patient-centered outcomes before widespread implementation in emergency endoscopy practice. Full article

Reliable perception of small floating objects is critical for the management of aquatic environments and supports key applications, including the autonomous navigation of Unmanned Surface Vehicles (USVs), waterborne debris monitoring, and Search and Rescue (SAR) operations. However, accurate detection in dynamic water-surface environments remains a significant challenge, as targets are frequently obscured by high-frequency wave clutter, and feature distributions are destabilized by covariate shifts caused by illumination. To address these limitations, this study proposes YOLO11-MCN, a real-time detection framework that integrates two architectural components specifically designed for water-surface monitoring. The Multi-Scale Contextual Attention (MSCA) module distinguishes target signatures from background noise by aggregating contextual information across heterogeneous receptive fields, thereby suppressing false positives generated by waves. The Channel Normalization Attention Mechanism (CNAM) addresses illumination instability through feature statistic calibration based on Group Normalization, effectively mitigating covariate shifts induced by extreme lighting variations. Furthermore, these components are complemented by a high-resolution P2 detection head, which recovers the geometric details of small-scale targets typically lost during downsampling. Extensive experiments conducted on a dataset of 5812 images demonstrate that YOLO11-MCN achieves an mAP@0.5 of 92.7%, outperforming the YOLO11n baseline by 5.9 percentage points. Robustness evaluations confirm that MSCA and CNAM significantly reduce missed detections under severe wave clutter and backlighting conditions. With a recall of 90.5%, an inference speed of 94 FPS on desktop hardware, and a compact footprint of 3.89M parameters and 14.8 GFLOPs, the proposed framework offers a robust and efficient solution for intelligent water-surface surveillance systems within the single-class detection paradigm evaluated in this study, with strong potential for edge-device deployment following platform-specific optimization. Full article

Object detection technology has been widely applied in fields such as autonomous driving and security surveillance, where it serves as a vital component of intelligent systems. However, under adverse hazy weather conditions, objects obscured by haze and their edges may lose detailed visual information. Existing object detection methods lack targeted mechanisms to address these challenges, resulting in a marked decline in detection accuracy in complex environments. To this end, this paper proposes an object detection method based on an end-to-end robust detection framework, termed the Physics-informed Frequency-Enhanced Network (PFE-Net). We propose a physics-guided visibility enhancement module (PG-VEM) that leverages the atmospheric scattering model and dark channel prior to adaptively compensate for degraded image features under adverse weather conditions, thereby restoring image details and contrast. Meanwhile, the frequency-domain edge-awareness module (FD-EPM) explicitly enhances the geometric contours of blur-obscured objects through Fourier transform and high-pass filtering, thereby improving the discriminability of edge features. Comprehensive experiments were conducted on both the real-world RTTS dataset and the synthetic VOChaze dataset to validate the effectiveness of the proposed approach. The results indicate that the proposed method achieves significant improvements in accuracy, particularly under complex weather conditions, demonstrating excellent all-weather environmental perception capabilities. This method has important practical engineering value and can substantially enhance the safety of autonomous driving and security surveillance systems under adverse weather conditions. Full article

Recent systems for position and movement monitoring are increasingly enhanced by the integration of advanced AI techniques. Besides traditional analytical and algorithmic approaches, which are still applied in areas such as signal processing, sensor fusion, and kinematic modeling, there is a growing body of research that leverages AI-based methods to improve accuracy, robustness, and real-time decision-making capabilities. Artificial neural networks and deep learning methods are more and more often used for tasks such as predicting movement trajectories, detecting position anomalies, and approximating complex motion patterns. The main aim of this work is to provide the main contributions of the recent publications to the current state of the field. Key trends, challenges, and prospects for their future development are also highlighted. Initial statistical analysis was conducted based on responses to queries formulated for searching engines of leading online databases since 2006. Next, the retrieved articles from the last 6 years were subjected to a more detailed analysis. They were divided into thematic areas, including models for human pose estimation; systems for motion detection and tracking, with special attention to human movement; and, eventually, more specialized applications such as action recognition, autonomous driving, motion analysis, and surveillance. The architectures of the created models, the methods for parameter tuning or training, the input datasets used, and the result evaluation metrics were classified. Finally, some more general conclusions were drawn. Full article

Wide-area maritime surveillance is an increasingly important focus for countries with large Exclusive Economic Zones (EEZ), such as Portugal, which are responsible for monitoring and protecting these zones and their resources. Passive acoustic autonomous buoy networks equipped with hydrophones are a promising approach for wide-area maritime surveillance. However, achieving a discrete, low-cost system introduces many technical challenges. This work describes a practical, low-power, two-state architecture that separates continuous ship detection from detailed vessel class classification. First, an always-on microcontroller performs continuous binary ship presence detection and triggers the higher-power classifier only when a vessel is detected. The high-accuracy acoustic classifier was tested across embedded controllers to identify the minimum platform capable of sustaining its intended 1 Hz classification rate. A Raspberry Pi 5 achieved the 1 s target with a measured continuous consumption of 4 W; however, adding sensing, storage, and communications is expected to raise the always-on consumption to around 5 W. If this node was used by itself, a week-long autonomy requirement, therefore, would imply 840 Wh of usable energy storage, and recovering this deficit rapidly under limited insolation would require several hundred watts of photovoltaic capacity, driving both buoy volume and cost up. To address this, an always-on edge node based on an ESP32-S3 microcontroller was implemented, running a lightweight binary detection of a vessel presence model trained in Edge Impulse using a subset of Ocean Networks Canada recordings. The edge node consumes $0.69$ W continuously and is intended to trigger a wake-up line to power the higher-performance node only when a ship is detected, reducing average energy demand while maintaining the ability to run a richer classifier on demand. The presented architecture, profiling workflow, and energy calculations provide a path to power-aware passive acoustic monitoring systems suitable for extended maritime deployments. Full article

This paper investigates the trajectory planning problem for multiple unmanned aerial vehicles (UAVs) tasked with monitoring ground targets in complex, uneven terrains. The key challenge lies in maintaining continuous Line-of-Sight (LoS) while satisfying non-holonomic motion constraints and handling terrain-induced occlusions. To address this problem, we propose a Beam-search Model Predictive Control (BMPC) framework. The method integrates a first-order kinematic predictor for target motion estimation and a proactive safety altitude margin to guide UAVs toward favorable viewpoints before occlusions occur. The proposed approach is validated through extensive simulations based on high-resolution Digital Elevation Models (DEMs). Monte Carlo results demonstrate a significant reduction in LoS occlusion, decreasing the average occlusion rate from $38.75\pm 26.12\%$ to near zero in the noise-free case, compared with conventional reactive MPC methods. Under perception noise with a standard deviation of $1.5$ m, the LoS retention rate remains above $99\%$, indicating strong robustness to sensing uncertainty. In addition, the algorithm maintains stable computational performance, with an average execution time of approximately $1.68$ s per step in a non-optimized simulation environment. The proposed framework provides an effective solution for autonomous aerial surveillance in environments with substantial elevation variations, such as mountainous regions and urban canyons, by achieving a balance between tracking continuity and computational tractability. Full article

Urban radio spectrum monitoring is becoming increasingly complex due to the rapid growth of wireless devices, unauthorized emissions, and dynamic electromagnetic environments in smart cities. Traditional spectrum analysis approaches, based on manual operation or static detection techniques, are no longer sufficient to ensure scalable, autonomous, and secure monitoring. The convergence of two emergent technologies—Large Language Models (LLMs) and the Model Context Protocol (MCP)—facilitates a fundamental shift in radio monitoring. We define this as the AICEBERG paradigm: a novel, stratified architecture where a high-level, intelligent agentic interface (the peak) abstracts the underlying complexity of SCPI-driven hardware integration and radio governance protocols (the foundational base). This autonomous framework provides the necessary objective rigor to audit the stochastic ‘ocean of electromagnetic waves’ characteristic of modern smart cities, ensuring a stable platform for regulatory enforcement amidst high-density signal interference. The proposed system implements a three-layer processing flow, enabling high-level natural language commands to be translated into validated and secure hardware actions on RF spectrum analyzers. A dual-server design separates operational execution from safety validation, ensuring controlled SCPI command handling, parameter verification, and instrument health monitoring. Experimental validation demonstrates the feasibility of autonomous measurement execution. The results show that the proposed architecture reduces human dependency, enhances reproducibility and lowers the expertise barrier required for RF spectrum surveillance. To the best of our knowledge, AICEBERG represents one of the first integrated frameworks to bridge LLMs with SCPI-compliant hardware through the MCP for autonomous radio governance. Full article

In sandstorm environments, a large number of suspended particles in the air absorb and scatter light, causing strong color bias, low contrast, and blurred details in images. These degradations reduce the reliability of computer vision applications in surveillance systems, intelligent transportation systems, unmanned aerial vehicle monitoring, and outdoor autonomous driving systems. A complete sandstorm image enhancement method was developed in this study by combining sky detection, color correction, contrast enhancement, and adaptive dark channel prior (ADCP) dehazing. The Lab color space was used to correct the color bias. The L channel was enhanced using normalized gamma correction and contrast-limited adaptive histogram equalization to improve brightness and contrast. Then, the sky region is detected to avoid over-processing, preserving the natural appearance of the sky region. Finally, ADCP is applied to non-sky regions for further dehazing. Experiments show that the proposed method provides better subjective and objective performance compared to other algorithms. Full article

Multi-object tracking (MOT) is a key technology for enabling autonomous navigation of unmanned surface vehicle (USV) as it provides continuous perception of surrounding maritime targets and supports navigation decision-making. However, videos acquired on maritime platforms typically suffer from challenges such as platform-induced jitter and nonlinear object motion, which significantly degrade tracking performance. To address these challenges, this paper builds upon ByteTrack by incorporating an adaptive Kalman filtering scheme and proposing a density-aware association strategy, resulting in a novel tracker termed the Marine-Aware Adaptive Tracker (MAAT). Specifically, an adaptive Kalman filter is introduced to increase the contribution of high-confidence detections during the state update process, thereby enhancing the stability and robustness of state estimation. Furthermore, to better mitigate the frequent identity switches caused by severe platform jitter from the USV observation platform, a density-aware association strategy is proposed. This strategy dynamically adjusts the composition of the cost matrix according to the density of high-confidence targets, enabling more reliable data association under varying scene conditions. Finally, the proposed tracking algorithm is evaluated against several state-of-the-art methods on the Singapore Maritime Dataset. It achieves competitive performance, attaining 44.37 MOTA and 43.857 IDF1. Moreover, MAAT operates in real time, running at 41.4 FPS. The experimental results demonstrate that MAAT is capable of performing accurate and real-time multi-object tracking in dynamic maritime environments with surface fluctuations, thereby providing effective technical support for intelligent maritime surveillance applications. Full article

Agentic artificial intelligence—systems that reason, plan, and act autonomously within governed workflows—is converging with autonomous electric mobility and urban robotics to reshape how cities govern, move, and manage physical space. We argue that the simultaneous arrival of these three vectors is triggering a transformation comparable in scope to the Industrial Revolution. Cities that deploy across all three domains are becoming the new hubs of innovation: they concentrate talent, accelerate knowledge circulation, enable cross-fertilisation, and generate hybrid proposals that no single vector could produce alone. Just as Manchester, Birmingham, and the Ruhr became the defining centres of industrialisation because steam, textiles, iron, and coal recombined through the proximity of the engineers and entrepreneurs who moved between them, a small number of cities today are pulling ahead because they host the shared talent pool around which agentic governance, autonomous mobility, and urban robotics co-evolve. Conceptually, we extend the mirroring hypothesis in two directions: dynamically, arguing that organisations and urban ecosystems converge toward the configurations new technologies make possible; and ontologically, arguing that agentic AI introduces non-human agents into organisational architectures, requiring hybrid human–AI coordination. We formalise this dynamic as five propositions (P1–P5) of cumulative recursive hybridisation (CRH), operating through four reinforcing feedback loops—data, regulation, infrastructure, and talent. Together, these loops explain why the emerging urban order is path-dependent: early movers accumulate compounding advantages, while latecomers face exponentially rising costs of entry. We demarcate CRH from adjacent frameworks—general-purpose technologies, organisational complementarities, and complex adaptive systems—and test it against counterfactual evidence from failed, stalled, and Global South trajectories (Sidewalk Toronto, the Cruise rollback, Songdo, Bengaluru). We also examine its political-economy, equity, and surveillance limits. Drawing on comparative evidence from public-sector chatbot deployments, autonomous mobility ecosystems in the United States and China, and emerging urban robotics cases, we conclude that what is at stake is not incremental modernisation but the construction of a new urban order. The cities that act as innovation hubs for the agentic AI era will shape global standards, attract global talent, and define the institutional templates that others eventually adopt—much as the industrial cities of the eighteenth and nineteenth centuries did. Full article

Unmanned Aerial Vehicles (UAVs) have become widely used tools for different applications including surveillance, search and rescue, and package delivery. However, autonomous path planning in dynamic environments with moving obstacles, wind disturbances, and energy constraints remains a significant challenge. This paper proposes a novel Multi-Head Attention Deep Q-Network with Prioritized Experience Replay (MA-DQN + PER) that integrates bio-inspired attention mechanisms with deep reinforcement learning for efficient UAV path planning. Our approach features a 46-dimensional state space that captures all environmental information, including static obstacles, wind conditions, and energy status. The proposed Attention-QNetwork architecture uses four specialized attention heads to selectively focus on different aspects of the environment, including obstacle avoidance, target tracking and energy management, and wind compensation. To improve sample efficiency and convergence speed, we incorporate Prioritized Experience Replay (PER) as well as Prioritized Experience Replay (PER) with a sum-tree data structure to improve sample efficiency and convergence speed. A curriculum learning strategy that includes 10 difficulty levels is designed to progressively enhance the agent’s capabilities. Extensive simulations demonstrate that our MA-DQN + PER approach reaches a 96% task success rate (defined as the percentage of episodes where the UAV successfully reaches the target without collision or battery depletion), while the convergence speed was 68% quicker than that of the baseline DQN. Our method demonstrates superior performance in path efficiency (+17%), energy consumption reduction (−26%), and collision avoidance compared to state-of-the-art algorithms. Full article

The integration of automated learning and video analysis enables the development of intelligent systems that can operate effectively in uncertain scenarios. These systems can autonomously identify dominant motion dynamics, depending on the theoretical framework used for representation and the learning process used for pattern identification. Current literature offers a state-based approach to describe the key temporal and spatial relationships required to understand motion dynamics. An important aspect of this approach is determining when the number of positively learned rules from a given information source is sufficient to detect dominant motion in automatic surveillance scenarios. This is crucial, as it affects both the variability of movements that monitored subjects can exhibit within the camera’s field of view and the resources needed for effective implementation. This study addresses these gaps through a grammar-based sufficiency criterion, which posits that learning is complete when production rule growth stabilizes, under the assumption of system stationarity. The stability criterion evaluates whether the most probable rules are learned over time, and whenever a high-growth rule is added, it is used to update the criterion. We outline several benefits of having a formal criterion for determining when a symbolic surveillance system has a robust model that explains the observed motion dynamics. Our hypothesis is that a correct model can consistently account for the majority of motion dynamics over time in an automated learning process. The proposed approach is evaluated by modeling motion dynamics in several scenarios using the SEQUITUR algorithm as input and computing the probability of stability along the learning curve, which indicates when the model reaches a steady state of consistent learning. Experimental validation was conducted in real-world scenarios under varying acquisition conditions. The results show that the proposed method achieves robust modeling performance, with accuracy values ranging from 83.56% to 95.92% in dynamic environments. Full article

Effective port and coastal surveillance require persistent monitoring, flexible deployment, and reliable target detection in dynamic maritime environments. This paper presents a system- and deployment-oriented autonomous tethered drone architecture, integrated with AI-based perception, for persistent maritime surveillance in ports and coastal areas. Mounted on a moving maritime platform and powered through a tether, the drone provides a persistent elevated viewpoint without the endurance limitations of conventional battery-powered Unmanned Aerial Vehicles (UAVs). The system combines maritime platform integration, tethered flight operation, fail-safe and safety mechanisms, and a distributed Artificial Intelligence (AI) pipeline for real-time object detection and tracking. The perception module is based on YOLOv8m for vessel detection and BoT-SORT for multi-object tracking, enabling continuous monitoring of maritime targets in realistic operational scenarios. Field trials conducted from moving vessels in maritime environments demonstrate autonomous take-off and landing, stable surveillance operation under realistic wind and wave conditions, and effective vessel detection and tracking on real image sequences. The results show the potential of AI-enabled tethered drone surveillance as a persistent and operationally relevant tool for maritime monitoring and security. Full article

This paper details the development of an autonomous robotic solution for the long-term surveillance of low-level radiation in nuclear facilities. Implementing such a system mitigates personnel health risks by minimizing radiation exposure and automating a mundane, repetitive task. To address the inherent challenges of deploying robots in highly unstructured environments, the core contribution of this work is a novel, error-tolerant behavioral architecture. Specifically, a custom behavior tree is designed to absorb execution imperfections and tolerate environmental uncertainties. This allows the robot to adapt and continue its mission rather than experiencing a hard failure. Bayesian optimization is utilized to perform adaptive mapping via a manipulator-equipped Spot quadruped robot, which features a Kromek Sigma50 gamma spectrometer attached to its end effector. Experiments were conducted in an obstacle-rich testbed using a Cesium-137 source. The results demonstrate the feasibility of the proposed system and its behavioral design approach, as the robot successfully performed adaptive mapping and correctly identified the location and approximate intensity of the radiation source. Full article