Search Results (29,412)

Atmospheric turbulence introduces severe spatial and geometric distortions, challenging traditional image restoration methods. We propose the Probabilistic Prior Turbulence Removal Network (PPTRN), which combines probabilistic diffusion-based prior modeling with Transformer-driven feature extraction to address this issue. PPTRN employs a two-stage approach: first, a latent encoder and Transformer are jointly trained on clear images to establish robust feature representations. Then, a Denoising Diffusion Probabilistic Model (DDPM) models prior distributions over latent vectors, guiding the Transformer in capturing diverse feature variations essential for restoration. A key innovation in PPTRN is the Probabilistic Prior-Driven Cross-Attention mechanism, which integrates the DDPM-generated prior with feature embeddings to reduce artifacts and enhance spatial coherence. Extensive experiments validate that PPTRN significantly improves restoration quality on turbulence-degraded images, setting a new benchmark in clarity and structural fidelity. Full article

High-resolution remote sensing visual question answering (RS-VQA) requires models to identify question-relevant regions and reason over fine-grained visual evidence. However, existing vision–language models usually rely on fixed global image inputs, which may lose critical local details in ultra-high-resolution imagery and struggle with sparse informative regions, large object-scale variations, and complex spatial layouts. To address these challenges, this paper proposes a crop-tool-augmented active perception framework with reinforcement learning. The framework introduces structured tokens to explicitly organize the reasoning process into question understanding, cropping decision-making, local evidence acquisition and final answer generation. Based on this design, the model can actively determine whether a cropping operation is needed and select task-relevant regions for further inspection. To enable stable tool-use and multi-turn reasoning in a compact vision–language model, we construct teacher-guided cropping reasoning trajectories from high-resolution images, question–answer pairs, and annotated regions in the LRS-GRO dataset, and use them for cold-start supervised fine-tuning of Qwen2.5-VL-3B. Furthermore, we introduce Group Relative Policy Optimization to refine the model’s active perception policy. A region-aware reward function is designed by integrating output-format constraints, reference-region coverage, answer semantic consistency, and cropping penalties, which encourages compact and informative region selection while reducing redundant tool invocations. Experiments on VRSBench, MME-RealWorld-RS, XLRS-Bench, and LRS-VQA demonstrate that the proposed method achieves competitive overall performance compared with closed-source, open-source, and remote-sensing-specific vision–language models, and obtains the best or comparable results on most benchmarks. Ablation studies further verify the effectiveness of structured supervised fine-tuning, reinforcement learning optimization, and the proposed reward design. Full article

Despite increasing use of continuous glucose monitoring (CGM) and automated insulin delivery, most children and adolescents living with type 1 diabetes (T1D) in the US do not achieve recommended glycemic targets. Structured, proactive support improves glycemic outcomes, but diabetes teams have historically lacked reimbursement pathways and scalable workflows to support between-visit care. The introduction of remote patient monitoring (RPM) billing codes has created new opportunities to align clinical need with care delivery models, although adoption remains limited. We describe implementation experiences from three pediatric diabetes centers that developed RPM programs across distinct clinical populations and institutional environments. Each site developed workflows for data review, patient outreach, documentation, and billing, supported by digital platforms designed to facilitate population-level management. Common themes emerged around needs for structured patient onboarding, multidisciplinary team alignment, standardized documentation, efficient data aggregation and population health management tools, adaptable communication pathways, and financial alignment. This practice-informed commentary synthesizes common themes across these programs and outlines key considerations for RPM implementation, including workflow design, digital infrastructure, communication strategies, and reimbursement structures. We also highlight practical challenges related to equity, patient engagement, and operational feasibility. This report aims to contextualize early clinical experience with RPM and identify factors that may influence its integration into pediatric diabetes care. Full article

Federated learning (FL) has emerged as a promising paradigm for enabling distributed intelligence in Internet of Things (IoT) environments while preserving data privacy and reducing the need for centralized data collection. However, the practical deployment of FL in IoT scenarios remains challenging due to heterogeneous data distributions, unreliable communication conditions, and the presence of faulty or malicious edge devices that can disrupt collaborative training. These limitations can significantly degrade convergence stability and predictive performance, particularly in resource-constrained and intermittently connected networks. This paper proposes a simulation-driven trust-aware federated learning framework for robust intelligent IoT networks. The proposed approach incorporates a dynamic trust-based aggregation mechanism that adaptively weights client contributions based on the consistency of their local model updates with the global model state. In addition, a controlled IoT-oriented federated simulation environment is developed to emulate heterogeneous edge conditions, including non-independent and identically distributed (non-IID) data partitioning, adversarial model manipulation, and intermittent client connectivity caused by communication dropouts. Extensive multi-seed experiments were conducted on the UCI Human Activity Recognition (UCI HAR) dataset and complemented with an auxiliary CIFAR-10 convolutional neural network (CNN) validation scenario. The evaluation considered multiple adversarial settings, including sign-flip, Gaussian-noise, scaling, and label-flip attacks, as well as communication-dropout probabilities up to 50%. In contrast with the initial FedAvg-only evaluation, the revised experimental analysis includes comparisons with representative robust aggregation baselines, namely Median, Trimmed Mean, Krum, Multi-Krum, and an auxiliary Bulyan configuration. The experimental results demonstrate that the proposed Trust-FedAvg framework substantially improves robustness over conventional FedAvg and remains competitive with established robust aggregation strategies, particularly under directional model-manipulation attacks and intermittent-connectivity conditions. Under a 20% sign-flip attack on UCI HAR, the proposed method achieved a final test accuracy of 86.2%, whereas conventional FedAvg degraded to approximately 44.7%. Furthermore, under combined adversarial and intermittent-connectivity conditions with 50% communication dropout, Trust-FedAvg maintained a final accuracy of 57.0%, compared with 21.3% for FedAvg, 24.8% for Median, and 14.6% for Trimmed Mean. The additional experiments also show that Trust-FedAvg is not universally superior across all perturbation types: under severe Gaussian-noise attacks, coordinate-wise Median and Multi-Krum provided stronger robustness in some settings. Overall, the results suggest that trust-aware aggregation can improve robustness against unreliable or malicious simulated clients while preserving a relatively simple aggregation procedure. Runtime measurements further indicate that the proposed method introduces only limited round-level overhead compared with FedAvg, while remaining simpler than more complex Byzantine-resilient alternatives. Further validation with real IoT deployments, additional sensor datasets, asynchronous communication models, energy profiling, and communication-overhead measurements is required to fully assess deployment feasibility in real IoT environments. The proposed framework provides a practical, extensible basis for the design and evaluation of resilient AI-enabled IoT networks operating under controlled but practically relevant edge-learning constraints. Full article

This paper presents a dual-layered computational framework for the robust trajectory planning and active stabilization of a robotic manipulator transporting a non-fixed payload. The primary challenge addresses the transport of a tray containing multiple objects prone to sliding, exacerbated by significant uncertainties in the system’s dynamic parameters, such as objects’ mass and inertia. The first contribution is an optimal closed-loop sensitivity-based trajectory planning algorithm that generates energy-efficient paths while minimizing the possibility of object sliding. The second contribution is an active sliding control strategy based on Nonlinear Model Predictive Control (NMPC). This algorithm dynamically adjusts the orientation of the tray, mounted to the robot’s end effector, usefully exploiting a dynamic model including inertial forces and gravity to move the object to given positions. Simulation and experimental results demonstrate that the integrated approach allows the robot to set the objects in desired positions with an average steady-state error of $6.3\times {10}^{-3}$ m across a set of ten experiments, while limiting the sliding to less than 3% of the tray dimensions during successive transportation in face of a 10% uncertainty about objects’ masses. The synergy between the uncertainty-aware planner and the NMPC controller supports robust tray-transport tasks in unstructured environments where precise dynamic modeling is unavailable. Full article

Artificial intelligence (AI) is reshaping higher education worldwide, raising tensions between efficiency, equity, and autonomy. This paper examines these dynamics in South Africa and Kenya, two countries that illustrate distinct governance frameworks and infrastructural challenges within African higher education. Using qualitative document analysis of policy frameworks, scholarly literature, and institutional reports, the study investigates how AI integration offers opportunities for personalized learning, streamlined administration, and enhanced educational quality, while simultaneously exposing risks related to algorithmic bias, digital divides, and the erosion of student agency. The findings show that AI can improve efficiency and enrich student experiences, but without ethical safeguards it may reinforce existing inequalities and diminish learner autonomy. Through situating the analysis in South Africa and Kenya, the paper contributes to debates on AI in education by demonstrating that efficiency gains must be balanced with equity and autonomy considerations. The study concludes with recommendations for educators and policymakers on responsible AI adoption, emphasizing ethical literacy, inclusive infrastructure, and participatory approaches to ensure that technological innovation enhances rather than undermines social justice in higher education. Full article

Electromagnetic wave absorbing materials are important for electromagnetic protection, radar stealth, wireless communication, and advanced electronic systems. However, traditional design methods mainly rely on repeated experiments and full-wave simulations, which are time-consuming and inefficient when dealing with complex compositions, microstructures, and multilayer structures. Machine learning provides a new route to accelerate the design of high-performance absorbers by learning the relationship among material composition, structure, electromagnetic parameters, and absorption performance. This review summarizes recent progress in machine-learning-empowered electromagnetic wave absorbing materials. First, the basic physical principles of electromagnetic wave absorption are introduced, including reflection loss, impedance matching, attenuation, and physical limits such as the Rozanov and Snoek limits. Then, typical machine learning models are discussed, including classical machine learning, deep learning, generative models, physics-informed models, large language models, and artificial-intelligence (AI) Agents. Their applications are further summarized from forward property prediction, high-throughput screening, inverse design, electromagnetic parameter decoupling, physics-informed modeling, explainability, multi-objective optimization, and data augmentation. Finally, the main challenges and future directions are discussed, including data standardization, physics-guided learning, foundation models, autonomous laboratories, and engineering-scale validation. This review shows that machine learning is changing absorber research from experience-driven trial-and-error to data-driven and knowledge-driven design, and provides a useful reference for developing next-generation electromagnetic wave absorbing materials. Full article

Background: Selecting informative training samples is a fundamental yet challenging problem in predictive modeling, particularly in heterogeneous clinical data. Although supervised learning is typically formulated as an optimization problem over model parameters, the composition of the training set substantially influences generalization performance. In this study, we propose a multi-criteria score-based sample selection framework for a machine learning setting. Method: Four sample-level scores were defined to quantify predictive uncertainty, representativeness, non-redundancy, and reliability. These scores were normalized and combined using three integration schemes: additive weighting, reliability-gated weighting, and rank-based aggregation. For each chief complaint category, a baseline model was trained either on the full training set, on score-selected subsets and on random size-matched subsets. Performance was assessed using the area under the receiver operating characteristic curve (AUROC), the area under the precision–recall curve, sensitivity, and specificity, with classification thresholds determined by the Youden index. Results: Across experiments, integrated score-based subset selection outperformed both full-data training and random subsampling in terms of mean AUROC, while often showing lower variability across chief complaints. Conclusions: The results suggest that sample utility in clinical tabular data is intrinsically multi-dimensional and that explicitly modeling this structure can improve predictive discrimination. Full article

In robotics control experiments, the balance between exploration and exploitation, as well as the accuracy of the advantage function estimation, are crucial factors that affect the effectiveness of policy optimization methods. To overcome these challenges, this paper proposes an adaptive adjustment of advantage estimation based on the policy loss and policy entropy algorithm (A3E-PLE), which can improve the exploratory capabilities of the proximal policy optimization (PPO) algorithm. Specifically, on the one hand, the policy loss is adjusted using a Gaussian distribution policy entropy to mitigate randomness and separate policy improvement from random noise, thereby improving exploration efficiency. On the other hand, to adapt flexibly to various training scenarios and further enhance the accuracy of advantage function estimation, the policy loss is incorporated into the advantage function estimation. This enables the algorithm to adaptively adjust according to changes in the strategy. Finally, the proposed reinforcement learning (RL) framework was validated using robot control simulations and complex decision-making environments. It is shown that A3E-PLE achieves higher learning efficiency and greater rewards compared to traditional generalized advantage estimation. Full article

Bilevel optimization is a fundamental framework for hierarchical decision-making, but its solution is challenging due to the implicit and typically set-valued nature of the lower-level optimality condition. In this paper, we study bilevel optimization problems through an exact-penalty reformulation based on the ${\ell }_1$-norm of the lower-level gradient. Under suitable regularity assumptions, we show that this penalty defines a distance-bound function and yields an exact penalty property for sufficiently large penalty parameters. To solve each fixed-penalty problem, we apply a prox-linear procedure that keeps the nonsmooth ${\ell }_1$ penalty in its original form and linearizes the smooth mappings. We prove a stationarity-oriented convergence guarantee for the fixed-penalty prox-linear loop. For the unconstrained simple bilevel setting, the prox-linear subproblem admits explicit dual reformulation as a box-constrained quadratic program. This dual structure enables the use of a nonmonotone spectral projected gradient method together with a closed-form primal recovery formula. Numerical experiments on the Minimum Norm Solution Problem show that the proposed method consistently achieves lower-level feasibility and upper-level accuracy, and attains a higher success rate than several existing methods on the tested instances. A Lipschitz least-squares variant is further included to provide a numerical illustration of the global exactness theorem. Full article

Liquefied natural gas (LNG) submersible centrifugal pumps are critical components in marine energy transportation systems, and fault-induced degradation may threaten operational safety, transfer reliability, and maintenance efficiency. However, condition monitoring and fault diagnosis often rely on heterogeneous multi-source data, where redundant information, unequal channel sensitivity, and inter-signal coupling may obscure discriminative fault features. To address this challenge, this paper proposes a hierarchical multi-source condition monitoring and fault diagnosis framework for LNG submersible centrifugal pumps by integrating an Entropy-Weighted Sensor Selection Method (EWSSM) with a hybrid convolutional neural network (CNN)–Transformer model. Functional information is used for front-end abnormality screening, while selected response signals are used for fault category recognition. EWSSM evaluates channel contribution and suppresses redundant inputs to construct a compact fault-sensitive input space. The CNN–Transformer model combines local feature extraction with global dependency modeling to identify complex fault patterns. A laboratory-scale fault simulation platform was established, and vibration, acoustic, internal pressure-pulsation-related response information, and operating parameter data were collected under ten operating states. Experimental results show that the proposed framework achieves effective abnormality screening and accurate fault diagnosis, with an average classification accuracy of 98.73% over repeated experiments. Covariance-difference analysis further provides interpretable evidence for condition assessment by revealing fault-related multi-source response redistribution. The proposed framework provides an effective, intelligent monitoring and diagnosis solution for LNG submersible centrifugal pumps and supports reliability-oriented operation and maintenance of marine energy transportation equipment. Full article

The automation of fruit harvesting has become an important research topic in precision agriculture due to increasing labor shortages, rising production costs, and the need for improved harvesting efficiency. Among horticultural crops, strawberries present particular challenges for robotic harvesting because of their variability in size, shape, ripeness, and frequent occlusions caused by leaves and surrounding fruit. The objective of this work is to demonstrate the feasibility of a reproducible perception-to-manipulation framework for robotic strawberry harvesting based on commercially available hardware and established computer vision techniques, rather than to propose a novel object detection algorithm. The proposed system integrates a YOLOv3-based (You Only Look Once) object detector, monocular vision for fruit localization, and a Universal Robots UR5e collaborative manipulator. Strawberry coordinates estimated from monocular images are transformed into the robot reference frame and transmitted through the XML-RPC (Extensible Markup Language-Remote Procedure Call) protocol, enabling robot positioning. The system was experimentally validated in a controlled indoor environment under different artificial illumination conditions. The YOLOv3 detector achieved a ${\mathrm{mAP}}_{0.5:0.95}$ of 37.4%, a precision of 84.2%, a recall of 76.1%, and a latency of 6.5 ms per image (153.8 FPS). The experiments also demonstrated reliable communication between the perception and robotic manipulation modules, enabling the robotic arm to reach the estimated strawberry positions. The proposed framework provides a practical and low-cost solution for integrating deep-learning-based perception with robotic manipulation and establishes a solid basis for future work on localization accuracy, automated grasping, harvesting efficiency, and deployment in real agricultural environments. Full article

Holographic synthetic aperture radar (HoloSAR) enables 360° three-dimensional reconstruction by incoherently stacking tomographic subaperture images. However, after conventional subaperture-wise TomoSAR reconstruction and non-coherent integration, the resulting 3D imagery suffers from severe dynamic range imbalance due to angle-dependent scattering responses: wide-angle strong scatterers are repeatedly amplified, whereas narrow-angle weak structures are buried below the noise floor. To address this post-processing challenge, we propose a joint statistical filtering framework operating on the reconstructed subaperture-domain 3D images that fuses the coefficient of variation, inter-subaperture correlation, and spectral entropy with adaptive discriminative-power weighting; target screening is then performed via a Gaussian mixture model-based Bayesian optimal threshold. For pixels classified as weak targets, a percentile-matching energy-balancing transformation is applied to adaptively rescale their energy to the main-target reference level while preserving relative amplitude relationships. Experiments on real-world Ku-band UAV circular SAR data demonstrate that the proposed method effectively compresses the dynamic range, suppresses background noise, and recovers weak narrow-angle structures that are lost in traditional non-coherent superposition, yielding more complete and interpretable HoloSAR 3D reconstructions. Quantitative evaluation on Ku-band UAV circular SAR data demonstrates that the proposed method improves the Target-to-Background Ratio by 0.7 dB (to 11.2 dB), achieves a Background Suppression Ratio of −5.2 dB, increases the Structural Completeness Index by 156% (to 1428.1), and compresses the original dynamic range imbalance, which exceeds 50 dB, while preserving scene physical realism (ENL ≈ 7.4 × 10⁻³). Full article

As autonomous-driving scenarios become increasingly complex, object detection in road environments remains challenging, especially for small-scale, visually ambiguous, and partially occluded targets. These difficulties are closely related to the loss of fine-grained spatial information caused by repeated downsampling and the limited consistency of multi-scale feature fusion. To address these issues, this paper proposes MFP-YOLOv11 (Multi-dimensional Focused P2 YOLOv11), a YOLOv11-based detector with enhanced multi-scale feature fusion for complex road-scene object detection. The proposed method improves the YOLOv11 architecture from the perspectives of high-resolution feature preservation, deep contextual representation, and multi-scale feature fusion consistency. Specifically, a Multi-Scale Dynamic Alignment Feature Fusion module (MDAF) is designed as the main fusion component to enhance multi-scale feature representation by modelling channel-, spatial-, and scale-level relationships among features at different resolutions. In addition, C3Ghost is selectively employed in shallow high-resolution stages to partially offset the additional computational cost introduced by the enhanced architecture, AIFI-RepBN is introduced to strengthen deep contextual representation, and Detect-P2 is added to provide high-resolution prediction compensation for small-scale object detection. Experimental results on the SODA10M dataset show that MFP-YOLOv11 achieves an mAP@0.5 of 0.697 and an mAP@0.5:0.95 of 0.483, corresponding to absolute gains of 7.0 and 5.7 percentage points over the YOLOv11 baseline, respectively. Comparative experiments, ablation studies, component-wise analysis, and qualitative visualizations show the contribution of the proposed modifications to detection performance in representative complex road scenes. Cross-dataset testing on the KITTI dataset further evaluates the performance of the proposed method under heterogeneous road-scene distributions. Overall, MFP-YOLOv11 improves Recall and mAP in complex road-scene object detection, while introducing higher computational complexity than the original baseline model. Full article

The massive amount of publicly available data has necessitated an increase in public and organizational awareness of the potential risks of leaking private data, whether intentionally or unintentionally. The damage caused by leaking these data depends on their degree of sensitivity. Disclosing a person’s or an organization’s private data via different social media platforms might threaten people’s lives or the organization’s reputation or finances. Handling big data, especially unstructured data, is challenging. Consequentially, many solutions have been proposed to detect sensitive data in structured containers. However, detecting sensitive data in unstructured containers is still challenging, especially with context-dependent and high-performance measurement results. In this study, experiments on certain machine learning models and two transformers—DistilRoberta and ALBERT—were conducted to detect unstructured, textual, context-dependent sensitive data. The results show that DistilRoberta demonstrated higher accuracy and recall, and was faster and lighter than ALBERT. Full article