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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Ferreira, João P. K. Pinto, João P. Moura, Júlia Li, Yi Castro, Cristiano L. Angelov, Plamen Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Brazil School of Computing and Communications Lancaster University United Kingdom

ISBN: (纸本)9798331510831

Fixed-wing aerial vehicles are equipped with functional-ities such as ILS (instrument landing system), PAR (precision approach radar) and, DGPS (differential global positioning system), enabling fully automated landings. How-ever, these systems impose significant costs on airport op-erations due to high installation and maintenance requirements. Moreover, since these navigation parameters come from ground or satellite signals, they are vulnerable to in-terference. A more cost-effective and independent alternative for guiding landing is a vision-based system that de-tects the runway and aligns the aircraft, reducing the pi-lot's cognitive load. This paper proposes a novel frame-work that addresses three key challenges in developing au-tonomous vision-based landing systems. Firstly, to over-come the lack of aerial front-view video data, we created high-quality videos simulating landing approaches through the generator code available in the LARD (landing approach runway detection dataset) repository. Secondly, in contrast to former studies focusing on object detection for finding the runway, we chose the state-of-the-art model Lo-RAT to track runways within bounding boxes in each video frame. Thirdly, to align the aircraft with the designated landing runway, we extract runway keypoints from the re-sulting LoRAT frames and estimate the camera relative pose via the Perspective-n-Point algorithm. Our experimental re-sults over a dataset of generated videos and original images from the LARD dataset consistently demonstrate the proposed framework's highly accurate tracking and alignment capabilities. Our approach source code and the Lo-RAT model pre-trained with LARD videos are available at https://***/jpklock2/vision-based-landing-guidance © 2025 IEEE.

关键词： Global positioning system

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Vision-Based Landing Guidance Through Tracking and Orientation Estimation

Vision-Based Landing Guidance Through Tracking and Orientati...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： João P. K. Ferreira João P. Pinto Júlia Moura Yi Li Cristiano L. Castro Plamen Angelov Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais School of Computing and Communications Lancaster University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

关键词： Visualization Codes Accuracy Atmospheric modeling Pose estimation Video sequences Radar tracking Generators Aircraft navigation Global Positioning System

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Drought propagation in an Andean basin in South America: a spatio-temporal analysis approach with monitoring indices

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Arabian Journal of Geosciences 2025年第3期18卷 1-21页

作者： Ocampo-Marulanda, Camilo Mera-Franco, Julián David Lizcano, Víctor Augusto Carvajal-Escobar, Yesid Canchala, Teresita Zorrilla-Millan, Daniel Felipe Caicedo-Bravo, Eduardo Water Resources Engineering and Soil Research Group (IREHISA) School of Natural Resources and Environmental Engineering Universidad del Valle Cali Colombia Graduate Program in Biometrics and Applied Statistics Department of Statistics and Informatics Federal Rural University of Pernambuco Recife Brazil School of Exact Sciences and Engineering Universidad Sergio Arboleda Bogotá Colombia Programa de Ingeniería Ambiental Facultad de Ingeniería Universidad Mariana Pasto Colombia School of Electrical and Electronic Engineering Universidad del Valle Cali Colombia

Drought is a climatic phenomenon that has increased in severity over time. This study analyzed drought spread in the Alto Patía basin (13,047 km2) in South America, applying the Standardized Precipitation Index (SPI), Vegetation Health Index (VHI), and Streamflow Drought Index (SDI) to assess meteorological, agricultural, and hydrological drought, respectively. Synchronous and asynchronous correlations were estimated, and Wavelet analysis was conducted to verify relationships among drought types. Results show that the lag between drought types varies based on the intensity, temporal continuity, and spatial extent of meteorological drought. A lag of zero months (1–2 months) was observed between meteorological and hydrological drought when moderate (severe) meteorological drought partially (fully) covers the basin. Agricultural drought, however, does not always correspond to meteorological drought during isolated rainfall events. As the first study in Colombia to systematically connect these drought types, this research addresses a gap in understanding how drought impacts progress across systems in regions with complex rainfall patterns. These findings offer critical insights into the interdependencies among drought types, supporting enhanced drought monitoring and early warning systems in similar climatic regions. By identifying specific lags between meteorological and hydrological droughts, this study provides practical guidance for land management, water conservation, and crop planning, offering a foundation for future research on drought resilience strategies.

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HAAQI-Net: A Non-Intrusive Neural Music Audio Quality Assessment Model for Hearing Aids

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1877-1892页

作者： Dyah A. M. G. Wisnu Stefano Rini Ryandhimas E. Zezario Hsin-Min Wang Yu Tsao Taiwan International Graduate Program—Social Network and Human-Centered Computing Institute of Information Science Academia Sinica Taipei Taiwan College of Informatics National Chengchi University Taipei Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Electrical Engineering Chung Yuan Christian University Taiwan

This paper introduces HAAQI-Net, a non-intrusive music audio quality assessment model for hearing aid users. Unlike traditional methods such as Hearing Aid Audio Quality Index (HAAQI), which requires intrusive reference signal comparisons, HAAQI-Net offers a more accessible and computationally efficient alternative. Leveraging a bidirectional long short-term memory architecture with attention mechanisms and features extracted from a pre-trained BEATs model, it can predict HAAQI scores directly from music audio clips and hearing loss patterns. The experimental results demonstrate that, compared to the traditional HAAQI as the reference, HAAQI-Net achieves a linear correlation coefficient (LCC) of 0.9368, a Spearman's rank correlation coefficient (SRCC) of 0.9486, and a mean squared error (MSE) of 0.0064, while significantly reducing the inference time from 62.52 seconds to 2.54 seconds. Furthermore, a knowledge distillation strategy was applied, reducing the parameters by 75.85% and inference time by 96.46%, while maintaining strong performance (LCC: 0.9071, SRCC: 0.9307, MSE: 0.0091). To expand its capabilities, HAAQI-Net was adapted to predict subjective human scores, mean opinion score (MOS), by fine-tuning. This adaptation significantly improved the prediction accuracy. Furthermore, the robustness of HAAQI-Net was evaluated under varying sound pressure level (SPL) conditions, revealing optimal performance at a reference SPL of 65 dB, with the accuracy gradually decreasing as SPL deviated from this point. The advancements in subjective score prediction, SPL robustness, and computational efficiency position HAAQI-Net as a reliable solution for music audio quality assessment, significantly contributing to the development of efficient and accurate models in audio signal processing and hearing aid technology.

关键词： Hearing aids Quality assessment Training Feature extraction Speech processing Auditory system Computational modeling Adaptation models Transformers Multiple signal classification

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ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicular CAN Bus Networks

ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicula...

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Jonathan Rendel William Balte Harrison Kurunathan Hazem Ismail Ali Alexandre Dos Santos Roque Wagner Ourique De Morais Mahdi Fazeli Department for Computing and Electronics for Real-time and Embedded Systems (CERES) School of Information Technology (ITE) Halmstad University Halmstad Sweden CISTER/ISEP Polytechnic Institute of Porto Portugal Graduate Program of Electrical Engineering Federal University of Rio Grande do Sul - UFRGS Porto Alegre Brazil

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Zero-day attacks present a significant security threat to vehicular networks, exploiting vulnerabilities at both software and hardware levels within such systems that remain undiscovered. Mitigating these threats is essential to ensuring the safety and security of vehicular systems. Support Vector Machine (SVM) is a good candidate for anomaly detection of zero-day attacks within vehicular networks because it can handle highdimensional data and effectively distinguish between normal and abnormal patterns in complex and dynamic environments. A trained SVM on the normal operation data of in-vehicular network can identify flag deviations, thus making it effective in the detection of any previously unknown attack patterns, which is a common behaviour of zero-day attacks. In this paper, we introduce an anomaly detection method called “ZeroCAN” which models the behaviour of every single electronic control unit on the network with a separate SVM and a set of high-level features that capture the timing and data payload aspects of CANbus traffic. This approach achieves an anomaly detection rate of over $\mathbf{9 9 \%}$ and a false positive rate below $\mathbf{0. 0 1 \%}$ during normal operation in most cases.

关键词： Support vector machines Telecommunication traffic Feature extraction Data models Timing Security Vehicle dynamics Anomaly detection Standards Payloads

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Adaptive Contextual Caching for Mobile Edge Large Language Model Service

arXiv

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arXiv 2025年

作者： Liu, Guangyuan Liu, Yinqiu Wang, Jiacheng Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

Mobile edge Large Language Model (LLM) deployments face inherent constraints, such as limited computational resources and network bandwidth. Although Retrieval-Augmented Generation (RAG) mitigates some challenges by integrating external knowledge bases, inefficient cache management can still result in high retrieval latency and frequent cache updates. To address these issues, we propose an Adaptive Contextual Caching (ACC) framework that anticipates user needs by proactively caching semantically relevant data for mobile-edge LLMs. ACC utilizes a deep reinforcement learning (DRL) module to refine cache replacement policies, balancing user context, document similarity, and the overhead associated with cache misses. Experimental results demonstrate that ACC increases cache hit rates to over 80% after only 11 training episodes, outperforming FIFO, LRU, and semantic-only caching while reducing retrieval latency by up to 40%. In particular, ACC also reduces local caching overhead (i.e., the cost of updating the cache when a miss occurs) by as much as 55%, enabling scalable, low-latency LLM services in resource-constrained edge environments. © 2025, CC BY.

关键词： Deep reinforcement learning

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Methodology for optimizing electrical grounding grids in stratified soils using advanced calculation techniques and evolutionary algorithms

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Swarm and Evolutionary Computation 2025年 95卷

作者： Silva, Carlos Leandro Borges da Pires, Thyago Gumeratto Silva Filho, Antonio Marcelino Bulhões, Junio Santos Belo, Orlando M. Oliveira Rodrigues, Clóves Gonçalves Coimbra, Antonio Paulo Calixto, Wesley Pacheco Electrical Mechanical & Computer Engineering School Federal University of Goias Goiania Goias PC 74.605-220 Brazil Institute of Systems and Robotics Coimbra University CoimbraPC 3030-290 Portugal Department of Informatics School of Engineering University of Minho BragaPC 4710-057 Portugal Electrical Power Systems Study Group Federal Institute of Tocantins Palmas TocantinsPC 77.021-090 Brazil Graduate Program in Production and Systems Engineering Pontifical Catholic University of Goias Goiania Goias PC 74605-010 Brazil Mato Grosso Brazil Technology Research and Development Center Federal Institute of Goias Goiania Goias PC 74.055-110 Brazil

This paper presents a practical methodology for optimizing the geometry of electrical grounding grids at industrial frequencies of 50Hz and 60Hz, integrating advanced calculation techniques and evolutionary algorithms to improve the safety and operational performance of electrical grounding systems. The proposed approach is particularly beneficial for industrial automation and control systems, where effective grounding is necessary to maintain system reliability and prevent downtime. This methodology employs mathematical modeling and computational tools to optimize grid parameters, ensuring compliance with safety standards while reducing operational costs, thus contributing to the overall efficiency of automated systems in industrial environments. The study reports a reduction of up to 66% in the number of vertical rods and 40% in horizontal conductors compared to traditional methods. These results indicate that the proposed methodology can significantly reduce material usage and costs while maintaining electrical safety in accordance with regulatory standards, making it applicable to a wide range of industrial settings, including substations and automated facilities. © 2025 The Authors

关键词： Electric grounding

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Applying an Agile Science Roadmap to Integrate and Evaluate Ethical Frameworks Throughout the Lifecycle and Use of Artificial Intelligence Tools in the Intensive Care Unit

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Critical Care Nursing Clinics of North America 2025年第2期37卷 347-363页

作者： Lindroth, Heidi Sahajwani, Juhi Hudson, Mark Heier, Laura Gonzalez, Andrew A. Bhattacharyya, Anirban Zheng, Zhi Boustani, Malaz Herasevich, Vitaly McGowan, Michelle Barry, Barbara Division of Nursing Research Department of Nursing Mayo Clinic Rochester MN United States Center for Aging Research Regenstrief Institute School of Medicine Indiana University Indianapolis IN United States Center for Health Innovation and Implementation Science School of Medicine Indiana University Indianapolis IN United States Department of Critical Care Medicine Mayo Clinic Jacksonville FL United States School of Psychology and Counselling The Open University Milton Keynes United Kingdom Department of Graduate Nursing Viterbo University La Crosse WI United States Department of Neurosurgery Mayo Clinic Rochester MN United States Center for Health Services Research Regenstrief Institute School of Medicine Indiana University Indianapolis IN United States Surgical Outcomes and Quality Improvement Center Department of Surgery School of Medicine Indiana University Indianapolis IN United States Department of Electrical Engineering University of Notre Dame Notre Dame IN United States Department of Anesthesiology and Perioperative Medicine Mayo Clinic Rochester MN United States Biomedical Ethics Departments of Quantitative Health Sciences and Artificial Intelligence and Informatics Biomedical Ethics Research Program Mayo Clinic Rochester MN United States Kern Center Mayo Clinic Rochester MN United States

This article summarizes existing ethical frameworks for healthcare artificial intelligence (AI) and ambient sensing technology, such as computer vision, and examines their application to improve patient outcomes in the intensive care unit (ICU). Integrating ethical considerations such as privacy, fairness, and autonomy into the lifecycle of an AI tool is necessary to fully harvest AI's potential to deliver safe, high-quality, personalized, and low-cost healthcare services that provide positive experiences for patients, families, and clinicians while improving health outcomes. Drawing from agile science, the article proposes a practical roadmap for clinician and researcher use to identify, integrate, and monitor ethical considerations throughout the lifecycle of an AI tool with the intention to improve patient care. A use case illustrates the application of the agile science-informed roadmap that details the development of a passive digital marker for delirium severity.

关键词： Agile science AI tools Artificial intelligence Computer vision Delirium severity Ethical frameworks for health care AI ICU Passive digital marker

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Comparison of Fetal Heart Rate Detection Using Envelope Extraction and Shannon Energy

Comparison of Fetal Heart Rate Detection Using Envelope Extr...

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2019 International Seminar on Application for Technology of Information and Communication, iSemantic 2019

作者： Setiawan, Agung W. Sujatmiko, Wahyu Biomedical Engineering Research Group School of Electrical Engineering and Informatics ITB Bandung Indonesia Graduate Program in Electrical Engineering School of Electrical Engineering and Informatics ITB Bandung Indonesia

ISBN: (纸本)9781728138329

Heart rate monitoring is widely used as a general procedure to examine fetal health condition. Fetal heart rate can be used to identified possibilities of fetal anomaly and warn the obstetrician to provide immediate intervention before any further abnormalities. The purpose of this research is to compare fetal heart rate detection result using envelope extraction and Shannon energy envelope method. There are 115 fetal phonocardiography and maternal phonocardiography that is used in this research. The dataset was obtained from Hafez Hospital, Shiraz University of Medical Science. Envelope extraction method can be used to detect fetal heart rate signal with 89.2% accuracy for all fetal phonocardiography signal while Shannon energy envelope method can be used to detect fetal heart rate signal with 88.3% accuracy for all fetal phonocardiography signal. © 2019 IEEE.

关键词： Phonocardiography

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Enhancing dynamic ensemble selection: combining self-generating prototypes and meta-classifier for data classification

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Neural computing and Applications 2024年第32期36卷 20295-20320页

作者： Manastarla, Alberto Silva, Leandro A. Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University R. da Consolação 930 - Consolação SP Sao Paulo01302-907 Brazil Computing and Informatics Faculty and Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University R. da Consolação 930 - Consolação SP Sao Paulo01302-907 Brazil

In dynamic ensemble selection (DES) techniques, the competence level of each classifier is estimated from a pool of classifiers, and only the most competent ones are selected to classify a specific test sample and predict its class labels. A significant challenge in DES is efficiently estimating classifier competence for accurate prediction, especially when these techniques employ the K-Nearest Neighbors (KNN) algorithm to define the competence region of a test sample based on a validation set (known as the dynamic selection dataset or DSEL). This challenge is exacerbated when the DSEL does not accurately reflect the original data distribution or contains noisy data. Such conditions can reduce the precision of the system, induce unexpected behaviors, and compromise stability. To address these issues, this paper introduces the self-generating prototype ensemble selection (***) framework, which combines meta-learning with prototype selection. The proposed meta-classifier of *** supports multiple classification algorithms and utilizes meta-features from prototypes derived from the original training set, enhancing the selection of the best classifiers for a test sample. The method improves the efficiency of KNN in defining competence regions by generating a reduced and noise-free DSEL set that preserves the original data distribution. Furthermore, the *** framework facilitates tailored optimization for specific classification challenges through the use of hyperparameters that control prototype selection and the meta-classifier operation mode to select the most appropriate classification algorithm for dynamic selection. Empirical evaluations of twenty-four classification problems have demonstrated that *** outperforms state-of-the-art DES methods as well as traditional single-model and ensemble methods in terms of accuracy, confirming its effectiveness across a wide range of classification contexts. © The Author(s), under exclusive licence to Springer-Ver

关键词： Nearest neighbor search

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