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International Conference on computer Vision Workshops (ICCV Workshops)

作者： Amirali Molaei Amirhossein Aminimehr Armin Tavakoli Amirhossein Kazerouni Bobby Azad Reza Azad Dorit Merhof School of Computer Engineering Iran University of Science and Technology Iran School of Electrical Engineering Iran University of Science and Technology Iran Department of Electrical Engineering and Computer Science South Dakota State University USA Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Faculty of Informatics and Data Science University of Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics, demonstrating remarkable results. By utilizing neural networks to parameterize data through implicit continuous functions, INRs offer several benefits. Recognizing the potential of INRs beyond these domains, this survey aims to provide a comprehensive overview of INR models in the field of medical imaging. In medical settings, numerous challenging and ill-posed problems exist, making INRs an attractive solution. The survey explores the application of INRs in various medical imaging tasks, such as image reconstruction, segmentation, registration, novel view synthesis, and compression. It discusses the advantages and limitations of INRs, highlighting their resolution-agnostic nature, memory efficiency, ability to avoid locality biases, and differentiability, enabling adaptation to different tasks. Furthermore, the survey addresses the challenges and considerations specific to medical imaging data, such as data availability, computational complexity, and dynamic clinical scene analysis. It also identifies future research directions and opportunities, including integration with multi-modal imaging, real-time and interactive systems, and domain adaptation for clinical decision support. To facilitate further exploration and implementation of INRs in medical image analysis, we have provided a compilation of cited studies along with their available open-source implementations on ${\color{Magenta}GitHub}$. Finally, we aim to consistently incorporate the most recent and relevant papers regularly.

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Design of an ice detector for electric tramways 15

Design of an ice detector for electric tramways

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15th IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics, ECMSM 2021

作者： Bilek, Petr Horak, Marcel Jandura, Pavel Mach, Ondrej Richter, Ales Institute of Mechatronics and Computer Engineering Technical University of Liberec Faculty of MechatronicsInformatics and Interdisciplinary Studies Liberec Czech Republic Technical University of Liberec Faculty of Engineering Department of Glass Producing Machines and Robotics Liberec Czech Republic

ISBN: (纸本)9781538617571

The article describes design and development of an ice detection device that uses a real 1 m part of an electric copper trolley wire for its operation. The wire part is oriented in the same direction as the trolley wire in the given place and thus a high compliance of the ice sensor with real conditions (spatial dimensions, orientation and heat capacity) is achieved. An ice meter equipped with this sensor can determine the robustness and duration of ice phenomena on trolley wires much more accurately and uses this information to deduce the risk of endangering public transport. The ice detector works on the principle of strain gauge load cell weighing. For its own diagnostics, the device can also measure temperature and relative humidity and is equipped with heating wires so that the movable parts of the device do not freeze. © 2021 IEEE.

关键词： Wire

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Virtual Reality as Learning Media for Lampung Historical Heritage 1

Virtual Reality as Learning Media for Lampung Historical Her...

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1st International Conference on Converging Technology in Electrical and Information engineering, ICCTEIE 2021

作者： Sulistiono, Wahyu Eko Muhammad, Meizano Ardhi Andrian, Rico Martinus Nama, Gigih Forda Ghuffrony Rezaldhy, S. Annisa, Resty Mulyani, Yessi Djausal, Anisa Nuraisa University of Lampung Informatics Engineering Bandar Lampung Indonesia University of Lampung Computer Science Bandar Lampung Indonesia University of Lampung Mechanical Engineering Bandar Lampung Indonesia University of Lampung Medical Science Bandar Lampung Indonesia

ISBN: (纸本)9781665400817

Lampung Province has a lot of historical heritage which is a world heritage for future human generations. The Lampung Museum, which was founded in 1975, plays a role in ensuring the continuity of history in the people of Lampung. Dissemination of Lampung history can be improved through education 4.0. Virtual Reality is a technology that allows a user to interact with the environment that exists in the virtual world and can provide information. Virtual Reality can help disseminate the history of Lampung with interactive and humanistic interaction models for historical objects that are presented without being bound by space and time. In addition, the ongoing COVID-19 pandemic has had a major impact on the performance of the Lampung museum in disseminating the history of Lampung. The method used to develop Virtual Reality of Lampung Historical Heritage consists of seven stages, namely Problem Formulation, Determination of Motivation, Idea Development, Design, Prototype Making, Testing, Advanced Development, and Implementation/Release. Eight historical objects were modeled and enriched with information regarding their relationship to Lampung history, namely the Lampung Museum, Thay Hin Bio Vihara, Al-Anwar Mosque, Lampung Siger Tower, Krakatau Monument, Kerti Bhuana Temple, Nuwo Sesat Traditional House, and the Japanese Cave. It is hoped that with the availability of Virtual Reality of Lampung Historical Heritage, the dissemination of Lampung history can be carried out more progressively through education 4.0. © 2021 IEEE.

关键词： Virtual reality

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An Advanced Data Fabric Architecture Leveraging Homomorphic Encryption and Federated Learning

arXiv

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arXiv 2024年

作者： Rieyan, Sakib Anwar News, Raisul Kabir Muntasir Rahman, A.B.M. Khan, Sadia Afrin Jawad Zaarif, Sultan Tasneem Alam, Golam Rabiul Hassan, Mohammad Mehedi Ianni, Michele Fortino, Giancarlo Department of Computer Science and Engineering School of Data and Sciences BRAC University 66 Mohakhali Dhaka1212 Bangladesh Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Department of Informatics Modeling Electronics and Systems University of Calabria CS Rende87036 Italy

Data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning architecture, the global model is trained based on the learned parameters of several local models that eliminate the necessity of moving data to a centralized repository for machine learning. This paper introduces a secure approach for medical image analysis using federated learning and partially homomorphic encryption within a distributed data fabric architecture. With this method, multiple parties can collaborate in training a machine-learning model without exchanging raw data but using the learned or fused features. The approach complies with laws and regulations such as HIPAA and GDPR, ensuring the privacy and security of the data. The study demonstrates the method’s effectiveness through a case study on pituitary tumor classification, achieving a significant level of accuracy. However, the primary focus of the study is on the development and evaluation of federated learning and partially homomorphic encryption as tools for secure medical image analysis. The results highlight the potential of these techniques to be applied to other privacy-sensitive domains and contribute to the growing body of research on secure and privacy-preserving machine learning. © 2024, CC BY.

关键词： Medical imaging

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Attention-Enhanced Co-Interactive Fusion Network (AECIF-Net) for Automated Structural Condition Assessment in Visual Inspection

arXiv

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arXiv 2023年

作者： Zhang, Chenyu Yin, Zhaozheng Qin, Ruwen Department of Civil Engineering Stony Brook University 2427 Computer Science Stony BrookNY11794 United States Department of Computer Science Department of Biomedical Informatics AI Institute Stony Brook University 2313B Computer Science Building Stony BrookNY11794 United States

Efficiently monitoring the condition of civil infrastructure requires automating the structural condition assessment in visual inspection. This paper proposes an Attention-Enhanced Co-Interactive Fusion Network (AECIF-Net) for automatic structural condition assessment in visual bridge inspection. AECIF-Net can simultaneously parse structural elements and segment surface defects on the elements in inspection images. It integrates two task-specific relearning subnets to extract task-specific features from an overall feature embedding. A co-interactive feature fusion module further captures the spatial correlation and facilitates information sharing between tasks. Experimental results demonstrate that the proposed AECIF-Net outperforms the current state-of-the-art approaches, achieving promising performance with 92.11% mIoU for element segmentation and 87.16% mIoU for corrosion segmentation on the test set of the new benchmark dataset Steel Bridge Condition Inspection Visual (SBCIV). An ablation study verifies the merits of the designs for AECIF-Net, and a case study demonstrates its capability to automate structural condition assessment. Copyright © 2023, The Authors. All rights reserved.

关键词： Inspection

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Computation Pre-Offloading for MEC-Enabled Vehicular Networks via Trajectory Prediction

arXiv

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arXiv 2024年

作者： Zhang, Ting Yang, Bo Yu, Zhiwen Cao, Xuelin Alexandropoulos, George C. Zhang, Yan Yuen, Chau School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710129 China School of Cyber Engineering Xidian University Shaanxi Xi’an710071 China Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece Department of Informatics University of Oslo Oslo0316 Norway School of Electrical and Electronics Engineering Nanyang Technological University Singapore

Task offloading is of paramount importance to efficiently orchestrate vehicular wireless networks, necessitating the availability of information regarding the current network status and computational resources. However, due to the mobility of the vehicles and the limited computational resources for performing task offloading in near-real-time, such schemes may require high latency, thus, become even infeasible. To address this issue, in this paper, we present a Trajectory Prediction-based Preoffloading Decision (TPPD) algorithm for analyzing the historical trajectories of vehicles to predict their future coordinates, thereby allowing for computational resource allocation in advance. We first utilize the Long Short-Term Memory (LSTM) network model to predict each vehicle's movement trajectory. Then, based on the task requirements and the predicted trajectories, we devise a dynamic resource allocation algorithm using a Double Deep Q-Network (DDQN) that enables the edge server to minimize task processing delay, while ensuring effective utilization of the available computational resources. Our simulation results verify the effectiveness of the proposed approach, showcasing that, as compared with traditional real-time task offloading strategies, the proposed TPPD algorithm significantly reduces task processing delay while improving resource utilization. Copyright © 2024, The Authors. All rights reserved.

关键词： Vehicular ad hoc networks

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Enabling light coupling between nanowires and low refractive index contrast optical waveguides towards scalable quantum circuits 28

Enabling light coupling between nanowires and low refractive...

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Integrated Optics: Devices, Materials, and Technologies XXVIII 2024

作者： Tsintzos, Symeon I. Tsimvrakidis, Konstantinos Sinani, Alexandra Gates, James C. Elshaari, Ali W. Smith, Peter G. R. Zwiller, Val Riziotis, Christos Theoretical and Physical Chemistry Institute National Hellenic Research Foundation Athens11635 Greece Department of Informatics and Computer Engineering University of West Attica Egaleo12243 Greece Optoelectronics Research Centre University of Southampton SouthamptonSO17 1BJ United Kingdom Stockholm106 91 Sweden Institute for Advanced Modelling and Simulation University of Nicosia Nicosia2417 Cyprus Qubitech Quantum Technologies Chalandri Athens15231 Greece

ISBN: (纸本)9781510670389

A primary constraint in the major photonic integration platform of Silica-on-Silicon, especially when combined with fabrication approaches like Direct Laser Writing is the optical waveguides' low refractive index contrast, leading thus to limitations for efficient coupling with currently available state-of-the-art single photon emitters such as semiconductor nanowires with quantum dots (NWQD). We propose and demonstrate a novel approach to drastically enhance the light coupling between silica based Laser-written channel waveguides and NWQDs, by incorporating an optical microsphere in their intermediate space. It is demonstrated that the induced photonic nanojet action of a suitably designed microsphere illuminated by the NWQD, excites efficiently the channel waveguide's modes and can enable light coupling to a degree even above 50%. The proposed method is reasonably robust to imperfections and misalignments and could be implemented by current state-of-the-art micro/nano patterning techniques. It is anticipated that the practical implementation of the method will allow the integration of multiple quantum emitters in silica based hybrid integrated circuits thus enabling their scalability towards for quantum computing and sensing applications. © 2024 SPIE.

关键词： Silica

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A Comparison Study on Generative Adversarial Networks (GANs) for Satellite Image Upscaling

A Comparison Study on Generative Adversarial Networks (GANs)...

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IEEE Conference on Systems, Process & Control (ICSPC)

作者： Brandon Kah Wei Li Muhammed Basheer Jasser Bayan Issa Samuel-Soma M. Ajibade Hui Na Chua Richard T.K. Wong Muzaffar Hamzah Department of Computing and Information Systems School of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Malaysia Research Centre For Human-Machine Collaboration (HUMAC)School of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Malaysia Faculty of Informatics Engineering University of Aleppo Syria Department of Computer Engineering Istanbul Ticaret University Istanbul Turkiye Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia

ISBN: (数字)9798350391398

ISBN: (纸本)9798350391404

Satellite images that are publicly available on the internet such as Google Maps are mostly low-quality and blurry which is not very useful for research or decision-making due to the lack of details. Recently image upscaling has been increasing in popularity to address several real-world problems where one approach is using Convolutional Neural Networks (CNNs). However, CNNs have difficulty in restoring fine details and this has led to the use of Generative adversarial networks (GANs) for image scaling. In this paper, three GAN models are trained which are Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN), Image Restoration Using Swin Transformer (SwinIR-Medium), and Dual Aggregation Transformer (DAT-2). The models are evaluated with well-known image metrics to determine each model’s effectiveness in upscaling low-resolution satellite images. The results show that all models can reconstruct the image to some degree where the best out of the three is DAT-2. Further fine-tuning of the best model by changing the loss functions has shown improvements in structural integrity for the upscaled image.

关键词： Training Photography Measurement Web services Generative adversarial networks Transformers Satellite images Image restoration Particle swarm optimization Image reconstruction

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Sound of Reason: Using AI to build a Digital Twin after the Kanon of Pythagoras in school settings

Sound of Reason: Using AI to build a Digital Twin after the ...

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Smart World Congress (SWC), IEEE

作者： Zach Anthis Alexandros Basiakoulis Kyriakos Efstathiou Marianna Efstathiou Department of Communication Culture and Media UCL Centre for Digital Innovation (powered by Amazon Web Services) University College London London United Kingdom Department of Mechanical Engineering Laboratory for Machine Tools and Manufacturing Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Electrical Engineering Computer Engineering and Informatics Digital Heritage Research Lab Cyprus University of Technology Limassol Cyprus

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

This paper presents ongoing work on kanON, an AI-driven EdTech tool designed to foster interactive learning and support Digital Cultural Heritage (DCH) preservation. kanON facilitates the digital reconstruction of the infamous Pythagorean monochord, by allowing users to seamlessly manipulate parameters such as origin (location and epoch), materials, and shape/dimensions. Through the integration of Machine Learning (ML) algorithms, the tool predicts missing archaeological data and generates real-time 3D renderings based on user input, accompanied by corresponding confidence metrics. A mixed-methods (pilot) study was conducted in classroom environments to evaluate its educational impact via quantitative Likert scale assessments and qualitative content analysis of educator feedback. Preliminary results demonstrate high levels of student engagement and reflect positive educator responses, highlighting its potential to enhance the in-class accessibility and understanding of complex historical concepts, as well as to enable the preservation and exploration of cultural artifacts through digital means. Future work will focus on refining kanON and expanding its applicability within both educational and DCH contexts, by introducing the capability of outputting a fully operational digital twin, that integrates mixed reality functionalities. The idea is to enable users to later engage with their (personalized) model dynamically (e.g., readjusting string length and tension), thus allowing them to produce different soundscapes in real-time. This advancement aims to deepen the pedagogical impact by merging tactile interaction with visual and auditory feedback, further enhancing the educational experience via immersive, experiential learning.

关键词： Visualization Uncertainty Three-dimensional displays Machine learning Educational technology Real-time systems User experience Digital twins Cultural differences Usability

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Extraction of Gene Regulatory Relation Using BioBERT

Extraction of Gene Regulatory Relation Using BioBERT

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Essien, Clement He, Fei Hannink, Mark Popescu, Mihail Xu, Dong University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States Northeast Normal University School of Information Science and Technology Changchun China University of Missouri Department of Biochemistry Bond Life Sciences Center ColumbiaMO United States University of Missouri Department of Health Management and Informatics ColumbiaMO United States University of Missouri Department of Electrical Engineering and Computer Science Bond Life Sciences Center ColumbiaMO United States

ISBN: (纸本)9781665468190

Relation Extraction (RE) is a critical task typically carried out after Named Entity recognition for identifying gene-gene association from scientific publication. Current state-of the-art tools have limited capacity as most of them only extract entity relations from abstract texts. The retrieved gene-gene relations typically do not cover gene regulatory relations. There still exists a lot of room for improvement. In this work, we propose GeREx, a transformer based RE tool for identifying gene regulatory relations from full texts by incorporating labeling from pathway figures. GeREx achieved an F1-Score of 83.45% when evaluated on an independent test dataset. © 2022 IEEE.

关键词： Genes

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