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2024 28th International Conference on Information Technology, IT 2024

作者： Ognjanovic, Ivana Lakovic, Luka Sendelj, Ramo Bokor, Laszlo Kara, Peter A. Reich, Christoph Madan, Manav Roganovic, Milovan Zouilas, Emmanouil Mantas, John Erakovic, Jevto Radusinovic, Tanja Rakocevic, Nada University of Donja Gorica Faculty for Information Systems and Technologies Podgorica81000 Montenegro University of Donja Gorica Humanistic Studies Podgorica81000 Montenegro Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary Furtwangen University Institute of Data Science Cloud Computing and It Security Furtwangen78120 Germany Clinic for Neurology Clinical Centre of Montenegro Podgorica81000 Montenegro National and Kapodistrian University of Athens Faculty of Nursing School of Health Sciences Goudi11527 Greece Chamber of Economy of Montenegro Podgorica81000 Montenegro

ISBN: (纸本)9798350369618

In order for economies to expand and develop globally, innovation is essential. A popular statistic for evaluating innovation between countries is the Global Innovation Index (GII), which provides a thorough analysis of the state of innovation worldwide. In this context, this paper aims to contribute to the literature by examining the multi-factoral effects of the innovation ecosystem on innovation development (internal causal effects), and the impact of global trends and crises on the innovation ecosystem (external causal effects) in Montenegro. The resources provided in this paper offer valuable insights into the innovation landscape in Montenegro and the Western Balkans, as well as the impact of globalization, global trends, and crises on innovation in the region. The paper also discusses the function of innovation hubs such as the DigNest project, in promoting cooperation between academic institutions, government agencies and corporations, and supporting the exchange of knowledge and fresh concepts. © 2024 IEEE.

关键词： Ecosystems

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Evaluating Pretrained Deep Learning Models for COVID-19 Detection in Medical Imaging

Evaluating Pretrained Deep Learning Models for COVID-19 Dete...

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Renewable Energy Technologies and Modern Communications systems: Future and Challenges, Conference on

作者： A. AL-Shehri Bushra Sahar F. Sabbeh Mohamed M. Dessouky Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Department of Data Science College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt

ISBN: (数字)9798331519445

ISBN: (纸本)9798331519452

COVID-19 is a respiratory disease for which reverse transcription-polymerase chain reaction (RT-PCR) is the standard detection method. This study introduces a hybrid deep learning approach to support the diagnosis of respiratory abnormalities, using COVID-19 as a case study. Multiple datasets were utilized, including the COVID-19 Radiography dataset (Kaggle), the SARS-CoV-2 CTScan dataset, and proprietary data from the King Faisal Medical Complex. Seven pre-trained deep learning models—VGG16, VGG19, MobileNet, DenseNet201, ResNet50, InceptionResNetV2, and InceptionV3—were evaluated for their performance. InceptionResNetV2 and InceptionV3 achieved perfect accuracy (100%) on the COVID-19 Radiography dataset and 99% accuracy on the SARS-CoV-2 CT-Scan dataset. Additionally, augmentation techniques were explored to improve model robustness. This research contributes significantly to medical imaging, offering practical insights for advancing diagnostic and monitoring practices for COVID-19 and other respiratory conditions.

关键词： COVID-19 Deep learning Solid modeling Accuracy data augmentation Robustness Diagnostic radiography Medical diagnostic imaging X-ray imaging Standards

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Electromagnetic Scattering by Elliptical Dielectric Cylinder Using the WCIP Method

Electromagnetic Scattering by Elliptical Dielectric Cylinder...

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Microwave and Antenna Symposium (IMAS), International

作者： Ahmed Boutta Noemen Ammar Asmaa Zugari Naji Yebari Department of Mathematics Faculty of Science University Abdelmalek Essaadi Tetouan Morocco NOCCS Laboratory Engineering School of Sousse (ENISO) Sousse university Sousse Tunisia Intelligent Systems Design (ISD) Laboratory Faculty of Science University Abdelmalek Essaadi Tetouan Morocco

ISBN: (数字)9798350356359

ISBN: (纸本)9798350356366

This paper aims to investigate the distribution of the electromagnetic field near an infinite dielectric elliptical cylinder. The Waves Concept Iterative Process method is formulated in elliptical approach to address electromagnetic problems involving a dielectric elliptical cylinder. The proposed method is based on the introduction of incident and reflected waves tangential to the elliptical surface. These waves are defined by a linear relationship between the electric field and the current density. A spectral reflexion operators relating the incidents waves to reflected ones are developed. The new approach consists in describing the wave relations in both spectral and spatial domains. The toggling between the spatial and spectral domain is ensured by the Discrete Angular Mathieu Transformation and its converse. The wave equations are solved using an iterative process in the presence of the excitation source. As a numerical example, we consider an infinite dielectric cylinder illuminated by a TM plane wave at an oblique incidence. The diffracted electric field is computed on both sides of the cylindrical surface as well as in the surrounding region for various incidence angles. The results obtained confirm the continuity conditions.

关键词： Microwave antennas Surface waves Diffraction Electromagnetic scattering Dielectrics Iterative methods Electric fields Spatial resolution Surface treatment Spectral analysis

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Investigation of the significance of the plaque morphology evolution in plaque rupture events using computational biomechanics

Investigation of the significance of the plaque morphology e...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Dimitrios S. Pleouras Panagiotis K. Siogkas Vassiliki T. Potsika Vasilis D. Tsakanikas Sokratis S. Dimos Michalis D. Mantzaris Igor Koncar Dimitrios I. Fotiadis Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Department of Vascular and Endovascular Surgery Faculty of Medicine University of Belgrade Belgrade Serbia Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems Biomedical Research Institute FORTH University of Ioannina Ioannina Greece

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Forwarded by the technological urge of this era, several computational methods are implemented to give further insights into possible outcomes of diseases. In this context, atherosclerosis, which is one of the most fatal diseases nowadays, is treated alike, where several computational models are proposed annually allowing for the evaluation of several outcomes for patient specific cases. Among them, one of the most significant models is able to predict the atherosclerotic evolution over time. In this proof-of-concept study, we aim to investigate the effect of plaque morphology on plaque rupture in a two-case scenario - a longitudinal and a bulk plaque evolution in 3D-reconstructed patient-specific carotids arteries. Our approach is based on a three-step process: i) the implementation of a state-of-the-art plaque growth model that predicts evolving and new plaques in real patient specific carotid arteries, ii) the selection of 2 patient cases, one with longitudinal plaque evolution and one with bulk plaque evolution and, finally, iii) the evaluation the maximum principal stress over the plaques and the endothelium layers to assess the plaque rupture risk. The results indicate that the evolving plaques towards the lumen, not only cause stenoses but also are more prone to rupture. Clinical relevance- This proof-of-concept work establishes that the plaques that grow towards the luminal border present with a higher risk of potential rupture compared to plaques that grow longitudinally, thus giving valuable insights to clinicians for important decision making regarding potential endarterectomy procedures.

关键词： Evolution (biology) Shape Computational modeling Morphology Atherosclerosis Predictive models Lumen Carotid arteries Stress Diseases

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Future Resource Bank for ISAC: Achieving Fast and Stable Win-Win Matching for Both Individuals and Coalitions

arXiv

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arXiv 2025年

作者： Qi, Houyi Liwang, Minghui Hosseinalipour, Seyyedali Fu, Liqun Zou, Sai Ni, Wei School of Informatics Xiamen University Fujian China Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China Department of Electrical Engineering University at Buffalo SUNY NY United States College of Big Data and Information Engineering Guizhou University China Data61 CSIRO Australia

Future wireless networks must support emerging applications where environmental awareness is as critical as data transmission. Integrated Sensing and Communication (ISAC) enables this vision by allowing base stations (BSs) to allocate bandwidth and power to mobile users (MUs) for communications and cooperative sensing. However, this resource allocation is highly challenging due to: (i) dynamic resource demands from MUs and resource supply from BSs, and (ii) the selfishness of MUs and BSs. To address these challenges, existing solutions rely on either real-time (online) resource trading, which incurs high overhead and failures, or static long-term (offline) resource contracts, which lack flexibility. To overcome these limitations, we propose the Future Resource Bank for ISAC, a hybrid trading framework that integrates offline and online resource allocation through a level-wise client model, where MUs and their coalitions negotiate with BSs. We introduce two mechanisms: (i) Offline Role-Friendly Win-Win Matching (offRFW2M), leveraging overbooking to establish risk-aware, stable contracts, and (ii) Online Effective Backup Win-Win Matching (onEBW2M), which dynamically reallocates unmet demand and surplus supply. We theoretically prove stability, individual rationality, and weak Pareto optimality of these mechanisms. Through simulations, we show that our framework improves social welfare, latency, and energy efficiency compared to existing methods. Copyright © 2025, The Authors. All rights reserved.

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Machine learning approaches for predicting Ankle Dorsi Plantar Moments in Cerebral Palsy gait analysis: A comparative study

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Gait & Posture 2024年 113卷 198-199页

作者： Mustafa Erkam Özates Firooz Salami Sebastian I. Wolf Yunus Ziya Arslan Turkish German University Department of Electrical and Electronics Engineering- Faculty of Engineering Istanbul Turkey Heidelberg University Hospital Clinic for Orthopedics and Trauma Surgery Heidelberg Germany Turkish German University Department of Robotics and Intelligent Systems- Institute of Graduate Studies in Science and Engineering- Istanbul Turkey

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An Interactive Annotation Method Based on Incremental Learning 2

An Interactive Annotation Method Based on Incremental Learni...

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2nd International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Duan, Xiusheng Sun, Guohua Cao, Jingya Zhang, Yuyang Yang, Jingchao Han, Duyu Hebei Polytechnic Institute Department of Artificial Intelligence and Big Data Shijiazhuang China Hebei Provincial University Road Traffic Perception Intelligent Application Technology Research and Development Center Shijiazhuang China Shijiazhuang Tiedao University School of Mechanical Engineering Shijiazhuang China Northeastern University Faculty of Robot Science and Engineering Shenyang China Hebei Jiaotong Vocational and Technical College Shijiazhuang China

ISBN: (纸本)9798350331417

In recent years, military science and technology has been developed rapidly, and new equipments has been equipped in the army. Augmented Reality (AR) technology provides the possibility to solve the problem of equipments operation training. But in the training process, One of the essential technologies is how to location and identify the operation keys. Obviously, the sample set of equipments operation keys is the basis of key recognition, and a certain scale of sample set is an indispensable element to ensure the performance of the recognition model. On the basis of manually establishing a small sample set, this paper puts forward a mechanism based on interactive automatic sample labeling and sample set expanding, the target recognition model was updated by incremental learning method at the same time. © 2023 IEEE.

关键词： data annotation incremental learning interaction sample augmentation

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Localization of a Crack in Moving Cylindrical Ferromagnetic Rods by Measuring the Fourier Coefficients of the Leakage Magnetic Flux

Localization of a Crack in Moving Cylindrical Ferromagnetic ...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Kanta Shiku Masafumi Kuromizu Yuji Gotoh Takaaki Nara Graduate School of Information Science and Technology The University of Tokyo bunkyo-ku Japan Division of Materials Science and Production Engineering Graduate School of Engineering Oita University Oita Japan Intelligent Mechanical Systems Program Faculty of Science and Technology Oita University Oita Japan

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

Electromagnetic nondestructive inspection for high-speed moving ferromagnetic materials is necessary for manufacturing efficiency. In this paper, we present a novel leakage magnetic flux (LMF) method to estimate the crack position in a moving cylindrical ferromagnetic rod. The method directly measures the first-order Fourier coefficients of the LMF moving with the crack by using only two coils with DC excitation. The location of a crack is estimated by the proposed method, as demonstrated for an S45C cylindrical rod.

关键词： Location awareness Coils Magnetic flux Inspection Ferromagnetic materials Manufacturing Electromagnetics

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LabSI-SAT: Low-cost CubeSat Nanosatellite Prototype Assembly Demonstration

LabSI-SAT: Low-cost CubeSat Nanosatellite Prototype Assembly...

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International engineering, sciences and Technology Conference (IESTEC)

作者： Juan Rodríguez-Chong Danilo Cáceres-Hernandez Intelligent Systems Laboratory Faculty of Electrical Engineering Tecnology University of Panama Panama Panama National Research System (SNI) National Secretariat of Science Technology and Innovation (SENACYT) Panama Panama

ISBN: (数字)9798350377156

ISBN: (纸本)9798350377163

This document is a report on the lessons learned during the construction of a low-cost nanosatellite prototype using commercially available components. It examines the system control models and sensors that can be utilized, as well as ways to enhance the reliability and availability of both hardware and software. The project was conducted using the space race achievements of other countries as a reference, with the goal of enabling other nations seeking to develop in this field to embark on their own space endeavors.

关键词： Prototypes Aerospace electronics Control systems Software Sensor systems Hardware Software reliability Sensors CubeSat Assembly

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AGHINT: Attribute-Guided Representation Learning on Heterogeneous Information Networks with Transformer

arXiv

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arXiv 2024年

作者： Yuan, Jinhui Lu, Shan Duan, Peibo He, Jieyue School of Computer Science and Engineering Southeast University Nanjing China Nanjing FiberHome Tiandi Co. Ltd. Nanjing China Department of Data Science and Artificial Intelligent Faculty of Information Technology Monash University Suzhou China

Recently, heterogeneous graph neural networks (HGNNs) have achieved impressive success in representation learning by capturing long-range dependencies and heterogeneity at the node level. However, few existing studies have delved into the utilization of node attributes in heterogeneous information networks (HINs). In this paper, we investigate the impact of inter-node attribute disparities on HGNNs performance within the benchmark task, i.e., node classification, and empirically find that typical models exhibit significant performance decline when classifying nodes whose attributes markedly differ from their neighbors. To alleviate this issue, we propose a novel Attribute-Guided heterogeneous Information Networks representation learning model with Transformer (AGHINT), which allows a more effective aggregation of neighbor node information under the guidance of attributes. Specifically, AGHINT transcends the constraints of the original graph structure by directly integrating higher-order similar neighbor features into the learning process and modifies the message-passing mechanism between nodes based on their attribute disparities. Extensive experimental results on three real-world heterogeneous graph benchmarks with target node attributes demonstrate that AGHINT outperforms the state-of-the-art. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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