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arXiv 2025年

作者： Martini, Luca Zolezzi, Daniele Iacono, Saverio Vercelli, Gianni Viardo Department of Languages and Modern Culture University of Genova Genova16124 Italy Department of Computer Science and Technology Bioengineering Robotics and Systems Engineering University of Genova Genova16145 Italy

The reconstruction of low-resolution football broadcast images presents a significant challenge in sports broadcasting, where detailed visuals are essential for analysis and audience engagement. This study introduces a multi-stage generative upscaling framework leveraging Diffusion Models to enhance degraded images, transforming inputs as small as 64 × 64 pixels into high-fidelity 1024 × 1024 outputs. By integrating an image-to-image pipeline, ControlNet conditioning, and LoRA fine-tuning, our approach surpasses traditional upscaling methods in restoring intricate textures, sharp edges, and domain-specific elements such as player details and jersey logos. The custom LoRA is trained on a custom football dataset, ensuring adaptability to sports broadcast needs. Experimental results demonstrate substantial improvements over conventional models, with ControlNet refining fine details and LoRA enhancing task-specific elements. These findings highlight the potential of diffusion-based image reconstruction in sports media, paving the way for future applications in automated video enhancement and real-time sports analytics. © 2025, CC BY-NC-ND.

关键词： Image reconstruction

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IBURD: Image Blending for Underwater Robotic Detection

arXiv

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arXiv 2025年

作者： Hong, Jungseok Singh, Sakshi Sattar, Junaed Department of Computer Science and Engineering Minnesota Robotics Institute University of Minnesota–Twin Cities 100 Union St SE MinneapolisMN55455 United States

We present an image blending pipeline, IBURD, that creates realistic synthetic images to assist in the training of deep detectors for use on underwater autonomous vehicles (AUVs) for marine debris detection tasks. Specifically, IBURD generates both images of underwater debris and their pixel-level annotations, using source images of debris objects, their annotations, and target background images of marine environments. With Poisson editing and style transfer techniques, IBURD is even able to robustly blend transparent objects into arbitrary backgrounds and automatically adjust the style of blended images using the blurriness metric of target background images. These generated images of marine debris in actual underwater backgrounds address the data scarcity and data variety problems faced by deep-learned vision algorithms in challenging underwater conditions, and can enable the use of AUVs for environmental cleanup missions. Both quantitative and robotic evaluations of IBURD demonstrate the efficacy of the proposed approach for robotic detection of marine debris. Copyright © 2025, The Authors. All rights reserved.

关键词： Image annotation

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Towards Designing a Medication Assistive Intelligent System with Automated Insulin-dose Prediction for Elderlies

Towards Designing a Medication Assistive Intelligent System ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Abrar Ahmed Minhazul Islam Mahi M. Akhtaruzzaman Jamal Uddin Tanvin Fahim Shahryer Sachitra Halder Muhammad Towfiqur Rahman Md Shofiqul Islam Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh Department of Computer Science & Engineering University Asia Pacific (UAP) Dhaka Bangladesh Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Geelong Victoria

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Medication management poses significant challenges for many patients, particularly the elderlies, who often struggle with keeping track of their medication schedules and taking the correct dosages. To address this issue, this study aims to design an AI-integrated Medication Management and Assistive Unit (AMMAU) for elderlies with some important features like, schedule reminder, medicine recognition and count, early indication of shortage, sorted slots, and so on. Automated insulin-dose prediction and alert system makes the system unique and more demanding at these current scenarios. For this study, a suitable machine learning model is designed, analyzed, verified, and embedded in the proposed system so that the elderly diabetic patients can get alert, further compare with the current insulin-doses. Though, the proposed system is currently focusing on only the Basal insulin doses prediction, the system will definitely reduce the risks associated with wrong management of medication by degrading the chances of missed doses or taking wrong pills for elderlies at home.

关键词： Training Schedules Analytical models Prototypes Machine learning Predictive models Internet of Things Older adults Insulin Physical design

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Towards backward compatibility of ADRC: revisiting classical state-feedback control with integral compensator

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Bulletin of the Polish Academy of sciences: Technical sciences 2025年第2期73卷

作者： Mrotek, Mikolaj Michalski, Jacek Madonski, Rafal Pazderski, Dariusz Retinger, Marek Faculty of Control Robotics and Electrical Engineering Institute of Robotics and Machine Intelligence Poznan University of Technology Piotrowo 3a Poznan60-965 Poland Faculty of Automatic Control Electronics and Computer Science Department of Automatic Control and Robotics Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Faculty of Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Poznan University of Technology Piotrowo 3a Poznan60-965 Poland

In this paper, the problem of backward compatibility of active disturbance rejection control (ADRC) is investigated. The goal is to contextualize ADRC to deliver its interpretations from the established field of linear control systems. For this study, a control algorithm, denoted here as integral disturbance rejection control (IDRC), is considered that combines classical state-feedback control with an integral compensator. At first, an interpretation of ADRC is involved in terms of existing state-space control approaches. Next, a transition to the frequency domain is performed, which is justified as a significant part of practical control engineering is conducted in that domain. For assumed specific plant structures, both ADRC and IDRC are then holistically compared in terms of transfer function representation and frequency characteristics, as well as steady-state convergence conditions. Such a juxtaposition helps to highlight the similarities and differences of both approaches, whereas the utilized bandwidth parameterization is shown to bring the control system to the same form, thus indicating some interesting practical aspects. Finally, the theoretical results concerning both considered control structures are validated in a set of numerical simulations and experiments conducted on a laboratory hardware testbed. © 2025 The Author(s).

关键词： Disturbance rejection

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CLEAR-Net: A Cascaded Local and External Attention Network for Enhanced Dehazing of Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Ali, Anas M. El-Rabaie, El-Sayed M. Ramadan, Khalil F. El-Samie, Fathi E. Abd El-Shafai, Walid Prince Sultan University Robotics and Internet-of-Things Laboratory Riyadh12435 Saudi Arabia Faculty of Electronic Engineering Menoufia University Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt Computer Science Department Riyadh11586 Saudi Arabia

Atmospheric haze degrades remote sensing images by obscuring critical details, diminishing data interpretation accuracy. This paper presents CLEAR-Net: Cascaded Local and External Attention Resource Network, a novel dehazing framework designed to enhance clarity and detail in remote sensing images. CLEAR-Net employs a two-block cascaded architecture to address both global and local haze effects. The first block utilizes an Outer Global Attention (OGA) mechanism, processing global features via a U-Net Inner Attention (UIA) module to mitigate widespread haze. The second block focuses on Outer Local Attention (OLA), refining local image features through reintegration with the UIA module. An ensemble method selects the most effective model weights based on perceptual quality metrics, and a composite loss function emphasizes the preservation of high-frequency details to maintain textural and edge fidelity. CLEAR-Net's efficacy is evaluated on the SateHaze1k and RICE datasets, outperforming existing state-of-theart dehazing techniques. On SateHaze1k, CLEAR-Net achieves an average PSNR of 27.876 dB, surpassing the next best model by approximately 4.81%;under moderate fog, it attains a PSNR of 30.824 dB, about 9.8% higher than existing methods. On the RICE dataset, which includes challenging cloud cover, CLEARNet achieves PSNRs of 36.873 dB on RICE1 and 34.624 dB on RICE2, improving over next-best models by approximately 18.2% and 12%, respectively, and attains the lowest LPIPS values, indicating superior perceptual quality. © 2008-2012 IEEE.

关键词： Demulsification

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Innovative Swing Mechanism for Sustainable Energy Generation: Design, Performance, and IoT Integration 6

Innovative Swing Mechanism for Sustainable Energy Generation...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Okwu, M.O. Abubakar, N. Edward, B.A. Oreko, B.U. Otanocha, O.B. Orikpete, O.F. Maware, C. Oyekale, J.O. Ori, C. Tartibu, L. Khalid, S. Department of Mechanical Engineering Federal University of Petroleum Resources Effurun Nigeria Computer Science Department Brunel University London UxbridgeUB8 3PH United Kingdom University of Port Harcourt Choba Nigeria Fujio Cho Engineering Technology University of Kentucky 210E Center for Robotics and Manufacturing Building United States 814-131 Wentworth Drive HalifaxNS Canada Department of Mechanical and Industrial Engineering Technology University of Johannesburg South Africa AJK Mirpur Pakistan

In this study, the choice of mild steel for the swing mechanism was essential due to its mechanical properties, strength, and environmental resistance. The swing system, incorporating a stand, swing, connecting rod, spur gear, bearings, freewheel, battery, dynamo, and sprocket, was designed for sustainable energy generation without compromising user safety. The design balanced ergonomic needs with performance characteristics, ensuring the structure could endure applied stresses. IoT integration allowed for advanced monitoring, enabling real-time data analysis, performance optimization, and issue detection. The study examined how swinging weight and oscillation frequency impacted the output voltage and current, findings showed that output voltage varied with the swinger's weight. The swing mechanism, capable of generating 6-8 volts, successfully converted kinetic energy into electrical energy. This study serves as a renewable energy education model with IoT integration, demonstrating innovative energy solutions for parks, schools, playgrounds, and communities. © 2024 The Authors. Published by Elsevier B.V.

关键词： energy generation IoT monitoring Mechanical design Renewable Energy Swing mechanism

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Tuning Language Models for Medical Interviews with Pediatric and Juvenile Patients

Tuning Language Models for Medical Interviews with Pediatric...

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International Conference on Advancements in Computational sciences (ICACS)

作者： Jesús Bouza Rodrigo Alarcón Fernando Fernández Department of IT SingularBeacon SL Madrid Spain Scientific research Inrobics Social Robotics Spain Computer Science and Engineering Department Universidad Carlos III de Madrid Leganés Spain

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

This paper presents the fine-tuning of a pre-trained language model for use in medical interviews with child and adolescent patients. Using DialoGPT-small, synthetic data have been generated that allow the model to be trained to generate contextually relevant questions tailored to patient responses. The main objective is to improve the interaction between the physician and the patient, promoting a comfortable and safe environment for children. The results obtained show that the model can generate coherent questions with metrics that demonstrate adequate generalization. Areas for improvement have been identified, such as the management of question repetition, proposing future implementations with conversational memory and multi-modal analysis.

关键词： Measurement Adaptation models Analytical models Pediatrics Computational modeling Data models Interviews Tuning Synthetic data Context modeling

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Distributed Computation of Source, Target, and Mixed Strongly Connected Components in Multi-agent Systems 16th

Distributed Computation of Source, Target, and Mixed Stron...

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16th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2024

作者： Ramos, Guilherme Pequito, Sérgio Department of Computer Science and Engineering Instituto Superior Técnico University of Lisbon Lisbon1049-001 Portugal Instituto de Telecomunicações Lisbon1049-001 Portugal Department of Electrical and Computer Engineering Instituto Superior Técnico University of Lisbon Lisbon1049-001 Portugal Institute for Systems and Robotics Instituto Superior Técnico University of Lisbon Lisbon1049-001 Portugal

ISBN: (纸本)9783031817236

Strongly Connected Components (SCCs) play a crucial role in understanding the structural properties of directed graphs, with a plethora of applications across various domains including engineering, computer science and social sciences. In this paper, we propose a method for computing both the source SCCs and target SCCs in a distributed fashion. Our method leverages the decentralized nature of distributed systems to efficiently identify SCCs within large-scale graphs. We illustrate the proposed methods with examples. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distributed computer systems

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Verifying Nonlinear Neural Feedback Systems using Polyhedral Enclosures

arXiv

引用

arXiv 2025年

作者： Akinwande, Samuel I. Sidrane, Chelsea Kochenderfer, Mykel J. Barrett, Clark Department of Aeronautical and Astronautical Engineering Stanford University StanfordCA94305 United States Department of Computer Science Stanford University StanfordCA94305 United States Division of Robotics Perception and Learning Intelligent Systems Department School of Electrical Engineering & Computer Science KTH Royal Institute of Technology Stockholm Sweden

As dynamical systems equipped with neural network controllers (neural feedback systems) become increasingly prevalent, it is critical to develop methods to ensure their safe operation. Verifying safety requires extending control theoretic analysis methods to these systems. Although existing techniques can efficiently handle linear neural feedback systems, relatively few scalable methods address the nonlinear case. We propose a novel algorithm for forward reachability analysis of nonlinear neural feedback systems. The approach leverages the structure of the nonlinear transition functions of the systems to compute tight polyhedral enclosures (i.e., abstractions). These enclosures, combined with the neural controller, are then encoded as a mixed-integer linear program (MILP). Optimizing this MILP yields a sound over-approximation of the forward-reachable set. We evaluate our algorithm on representative benchmarks and demonstrate an order of magnitude improvement over the current state of the art. Copyright © 2025, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

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ACO-GA-Based Optimization to Enhance Global Path Planning for Autonomous Navigation in Grid Environments

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IEEE Transactions on Evolutionary Computation 2025年

作者： Heng, Han Rahiman, Wan Universiti Sains Malaysia Engineering Campus School of Electrical and Electronic Engineering Penang14300 Malaysia Universiti Sains Malaysia Engineering Campus Nibong Tebal Penang14300 Malaysia Daffodil Robotics Laboratory Department of Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh

Exploring path planning techniques for autonomous vehicles to find the safest and quickest routes is fascinating. Among these techniques, generating grid-based maps is notable for simplifying the path planning process, especially in complex environments. This study introduces a novel optimization approach, the modified ant colony optimization and genetic algorithm (MACOGA), which is tailored for path planning in grid environments. MACOGA combines ant colony optimization (ACO) with genetic algorithm (GA) to efficiently navigate grid spaces. First, ACO identifies potential routes within a grid map, and GA optimizes them to find the optimal route. A key feature of MACOGA is its probabilistic prediction mechanism, which improves node selection by integrating the heuristic factors and probabilistic calculations of feasible potential paths, which significantly increases the likelihood of generating feasible initial paths and attains higher success rates in various environmental tests. The enhanced GA in the MACOGA incorporates ACO's pheromones and heuristic factors, and introduces adaptive probability crossover and mutation operations. Additionally, a path reconnection operation ensures favorable length and smoothness. To evaluate MACOGA's effectiveness, six planar models of varying sizes and complexities were studied. Results show MACOGA's superiority in path length and execution time, achieving the shortest path and planning time in several environments. The shortest path can be optimized to yield an improvement of up to 6%. Regarding runtime, even in the complex scenarios, it executes in just 0.3 seconds, outperforming other comparative algorithms. Furthermore, the success rate can reach 100% in relatively complex environments. © 2025 IEEE.

关键词： Ant colony optimization

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