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ECG-Image-Database: A Dataset of ECG Images with Real-World Imaging and Scanning Artifacts;A Foundation for computerized ECG Image Digitization and Analysis

arXiv

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arXiv 2024年

作者： Reyna, Matthew A. Deepanshi Weigle, James Koscova, Zuzana Campbell, Kiersten Shivashankara, Kshama Kodthalu Saghafi, Soheil Nikookar, Sepideh Motie-Shirazi, Mohsen Kiarashi, Yashar Seyedi, Salman Clifford, Gari D. Sameni, Reza Department of Biomedical Informatics Emory University AtlantaGA30322 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA30332 United States

Objective: We introduce ECG-Image-Database, a large and diverse collection of electrocardiogram (ECG) images generated from ECG time-series data that exhibit real-world scanning, imaging, and physical artifacts. This dataset addresses the need for digitizing paper-based and non-digital ECGs for computerized analysis, providing a foundation for developing robust machine and deep learning models capable of ECG image digitization. Approach: We used ECG-Image-Kit, an open-source Python toolkit, to generate realistic images of 12-lead ECG printouts from raw ECG time-series data. We include images with realistic distortions, including noise, wrinkles, stains, and perspective shifts generated both digitally and physically. The toolkit was applied to 977 12-lead ECG records from the PTB-XL database and 1,000 from Emory Healthcare to create high-fidelity synthetic ECG images. These 1,977 unique images were subjected to both programmatic distortions using ECG-Image-Kit and to physical effects, such as soaking, staining, and mold growth, followed by scanning and photography under various lighting conditions to create real-world artifacts, producing a total of 35,595 images. Main results: The resulting dataset includes 35,595 software-labeled ECG images (generated from 1,977 unique records) from different sources (in Germany and the USA) with a wide range of imaging artifacts and distortions. The dataset provides ground truth time-series data alongside the corresponding images, offering a reference for training and evaluating machine and deep learning models for ECG digitization and classification. The images vary in quality, ranging from clear scans of clean papers to noisy photographs of degraded papers, enabling the development of more generalizable and robust digitization algorithms. Significance: ECG-Image-Database addresses a critical gap in cardiovascular research by supporting the development of machine learning models capable of classifying or converting ECG images int

关键词： Photography

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Integrating Blockchain Technology with PKI for Secure and Interoperable Communication in 5G and Beyond Vehicular Networks

Integrating Blockchain Technology with PKI for Secure and In...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Fetulhak Abdurahman Shewajo Abdelwahab Boualouache Sidi Mohammed Senouci Ines El-Korbi Bouziane Brik Kinde Anlay Fante DRIVE Lab University of Burgundy Nevers France Faculty of Electrical and Computer Engineering Jimma Institute of Technology Jimma University Jimma FSTM University of Luxembourg Luxembourg Computer Science Department College of Computing and Informatics Sharjah University Sharjah UAE

Security and privacy are crucial in V2X networks due to sensitive user information. Public Key Infrastructure (PKI) is widely used in C-ITS to ensure security and privacy. However, the practical implementation of PKI faces challenges in achieving seamless communication across diverse ITS projects worldwide. The absence of interoperability between PKI systems and unre-solved issues in existing PKI standards hinder global adoption. Despite available security standardizations using centralized PKI technology for V2X, a universally adopted PKI-based security architecture is necessary. Furthermore, the progress made in 5G V2X technology has demonstrated significant potential for revolutionizing V2X communication in the future. Enhancing the level of trust through integration of the 5G Core Network (5GC) into the PKI security mechanism can lead to more secure and efficient V2X communication. To address these challenges, we propose a blockchain-based architecture that integrates the 5GC network with the PKI infrastructure, aiming to enhance privacy and security in 5G V2X communication. Our solution is designed to be distributed and interoperable, aligned with existing ETSI ITS PKI standard. By utilizing Hyperledger Fabric (HLF) platform, a permissioned blockchain framework, we present the architecture and conduct a comprehensive security analysis to ensure compliance with security and privacy requirements of V2X communications.

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Cooperative Plug-and-Play-KalmanNet for 4D situational awareness in autonomous driving

Cooperative Plug-and-Play-KalmanNet for 4D situational aware...

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Industrial Cyber-Physical Systems (ICPS)

作者： Nikos Piperigkos Alexandros Gkillas Christos Anagnostopoulos Aris S. Lalos Computer Engineering & Informatics Department University of Patras Greece AviSense.AI Patras Science Park Greece Athena Research Center Industrial Systems Institute Patras Science Park Greece

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

Fully operational autonomous driving requires accurate 4D situational awareness from vehicles, i.e., 3D location information over time. For that purpose, each vehicle establishes connectivity links with its direct neighboring vehicles, collects, exchanges and fuses multi-modal measurements in order to increase the accuracy of localization information. In this work, we focus on efficiently tracking the unknown system dynamics, and as a matter of fact boosting the performance of 4D situational awareness, without knowing beforehand the statistics of sensor measurement noise. More specifically, we exploit the so-called KalmanNet, a recurrent neural network, which has been explicitly designed to estimate the covariance and Kalman gain matrices of standard Extended Kalman Filter through a typical deep learning approach. Pre-trained KalmanNet is then used as the core module of a cooperative localization approach which effectively realizes dynamic 4D situational awareness and improves sensor fusion performance. An extensive evaluation study in CARLA autonomous driving simulator demonstrates that our cooperative plug-and-play approach significantly improves localization accuracy, as well as reduces drift error which is a critical error metric in the automotive industry.

关键词： Location awareness Solid modeling Accuracy Uncertainty Three-dimensional displays System dynamics Kalman filters

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Directional Metasurface Using Transistor-Loaded Circularly Polarized Antenna Elements

Directional Metasurface Using Transistor-Loaded Circularly P...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023

作者： Chatzichristodoulou, David Vryonides, Photos Psychogiou, Dimitra Nikolaou, Symeon Frederick Research Center Nicosia Cyprus Rf and Microwave Solutions Ltd Cyprus School of Engineering University College Cork CorkT12 K8AF Ireland Tyndall National Institute CorkT12 R5CP Ireland Frederick University Dept. Electrical Computer Engineering and Informatics Nicosia Cyprus

ISBN: (纸本)9781665442282

This paper discusses the RF design of a compact metasurface that enables directional propagation characteristics. It allows for circularly-polarized waves to penetrate through its volume in one direction of propagation whereas it absorbs them in the reverse one. Directionality is achieved by combining two transistors with two circularly-polarized patch antennas in each unit cell of the metasurface. A compact multi-layer integration scheme is proposed and is demonstrated through electromagnetic (EM) simulations at S-band. The proposed metasurface configuration exhibits directional characteristics with the following performance metrics. BW in the forward direction: 3.4-3.7 GHz, maximum Gain in the forward direction: 29 dB, isolation in the reversed direction> 50 dB. © 2023 IEEE.

关键词： Microstrip antennas

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A Machine Learning Approach for the Underwater Investigation of Malicious Activity in a Wireless Sensor Network

A Machine Learning Approach for the Underwater Investigation...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Murali Dhar M S Mahaveerakannan R F. Ravindaran Kommisetti MurthyRaju Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu Department of Spatial Informatics The Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering (Cyber Security) Karpagam College of Engineering Coimbatore Tamil Nadu Department of Electronics and Communication Engineering Shri Vishnu Engineering College For Women BHIMAVARAM Andha Pradesh

Underwater Wireless Sensor Networks (UWSN) have drawn increased interest from researchers in recent years due to advancements in underwater tracking, the implementation of multiple uses, and marine monitoring. Both researchers and business are drawn to the UWSN field. It is also susceptible to hostile attacks and threats because of the harsh undersea surroundings, its own capabilities, and the open acoustic channel. These features make it simple for attackers to intercept information across the source and destination. Many studies have been done to secure UWSN from malicious attacks, but the majority of them rely on a threshold that is determined before deployment or a training set of data. Because there is no proven security foundation, it is difficult and challenging for UWSN to identify harmful attacks. To identify harmful attacks in a UWSN, we support vector machines in this research. SVM produces good results and requires significantly less time to train than neural networks. This study will help the researchers design the routing protocols to cover the known security threats and help industries manufacture the devices to observe these threats and security issues.

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A Comparative Study of Text Classification Using Artificial Intelligence Models

A Comparative Study of Text Classification Using Artificial ...

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Current Research in engineering and Science Applications (ICCRESA), International Conference on

作者： Maysaa H. Abdulameer Mahmood Z. Abdullah Informatics Institute for Post-Graduation Studies Iraqi Commission for Computer & informatics Baghdad Iraq College of Engineering Al-Mustansiriyah University Baghdad Iraq

The presented study provides experiments for carrying out empirical studies related to deep learning, especially convolution networks, concerning novel dataset classification (video and audio). The study will utilize the architecture of the text classification problem in the document domain with the datasets related to documents collected from many projects on real document cases. The process of building a new dataset and the existence of such data followed the urgent need that is specialized in educational lectures. Test and evaluate the two new datasets (Audio and Video) with the published dataset Book dataset by using deep learning methods in classification and machine learning algorithms, then compare the Accuracy results for each dataset with both classification methods, which present that new datasets bring better results than Book dataset which are (95% and 90 % ) for new datasets and 83% for Book dataset by using Deep learning, in machine learning algorithms the results are (91%, 77%, and 46 % ). Finally, the two classification techniques are used to maximize the benefits of the classification process. In the selection process, the system will choose deep learning for classifying the datasets than using machine learning algorithms.

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AI-based Drone Assisted Human Rescue in Disaster Environments: Challenges and Opportunities

arXiv

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arXiv 2024年

作者： Papyan, Narek Kulhandjian, Michel Kulhandjian, Hovannes Aslanyan, Levon Department of Telecommunication Engineering Russian-Armenian University Yerevan Armenia Department of Electrical and Computer Engineering Rice University HoustonTX United States Department of Electrical and Computer Engineering California State University Fresno FresnoCA United States Institute for Informatics and Automation Problems National Academy of Sciences Yerevan Armenia

In this survey we are focusing on utilizing drone-based systems for the detection of individuals, particularly by identifying human screams and other distress signals. This study has significant relevance in post-disaster scenarios, including events such as earthquakes, hurricanes, military conflicts, wildfires, and more. These drones are capable of hovering over disaster-stricken areas that may be challenging for rescue teams to access directly, enabling them to pinpoint potential locations where people might be trapped. Drones can cover larger areas in shorter timeframes compared to ground-based rescue efforts or even specially trained search dogs. Unmanned aerial vehicles (UAVs), commonly referred to as drones, are frequently deployed for search-and-rescue missions during disaster situations. Typically, drones capture aerial images to assess structural damage and identify the extent of the disaster. They also employ thermal imaging technology to detect body heat signatures, which can help locate individuals. In some cases, larger drones are used to deliver essential supplies to people stranded in isolated disaster-stricken areas. In our discussions, we delve into the unique challenges associated with locating humans through aerial acoustics. The auditory system must distinguish between human cries and sounds that occur naturally, such as animal calls and wind. Additionally, it should be capable of recognizing distinct patterns related to signals like shouting, clapping, or other ways in which people attempt to signal rescue teams. To tackle this challenge, one solution involves harnessing artificial intelligence (AI) to analyze sound frequencies and identify common audio "signatures". Deep learning-based networks, such as convolutional neural networks (CNNs), can be trained using these signatures to filter out noise generated by drone motors and other environmental factors. Furthermore, employing signal processing techniques like the direction of arrival (DOA) ba

关键词： Drones

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An Exploratory Case Study on Effort Estimation in Microservices

An Exploratory Case Study on Effort Estimation in Microservi...

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Euromicro Conference on Software engineering and Advanced Applications (SEAA)

作者： Hüseyin Ünlü Tuna Hacaloğlu Neslihan Küçükateş Ömüral Neslihan Çalişkanel Onur Leblebici Onur Demirörs Computer Engineering Izmir Institute of Technology Izmir Turkey Information Systems Engineering Atilim University Ankara Turkey Informatics Institute Middle East Technical University Ankara Turkey Univera Inc. Izmir Turkey

Software project management plays an important role in producing high-quality software, and effort estimation can be considered as a backbone for successful project management. Size is a very significant attribute of software by being the only input to perform early effort estimation. Even though functional size measurement methods showed successful results in effort estimation of traditional data-centric architectures such as monoliths, they were not designed for today’s architectures which are more service-based and decentralized such as microservices. In these new systems, the event concept is highly used specifically for communication among different services. By being motivated by this fact, in this study, we looked for more microservice-compatible ways of sizing microservices using events and developed a method accordingly. Then, we conducted an exploratory case study in an organization using agile methods and measured the size of 17 Product Backlog Items (PBIs) to assess how this proposed method can be useful in effort estimation in microservices. The implication from the case study is that despite performing a more accurate effort estimation using the proposed size measurement than COSMIC, we were unable to significantly outperform using the total number of events. However, our suggested approach demonstrated to us a different way to use software size in terms of events, namely, to determine the coupling complexity of the project. This finding can be beneficial specifically when evaluating the change requests.

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Online Resource Allocation for User Experience Improvement in Heterogeneous MEC Systems

Online Resource Allocation for User Experience Improvement i...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Weiya Ni Jingwen Tong Liqun Fu Institute of Artificial Intelligence Xiamen University Xiamen China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong School of Informatics Xiamen University Xiamen China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Mobile edge cloud (MEC) has emerged as a critical technology for enabling low-latency and real-time mobile device applications. However, an efficient resource allocation framework for improving the user experience in MEC with heterogeneous users is still missing, especially considering the recent sparks of AI-generated content applications. This paper proposes a double-closed-loop online resource allocation (DORA) framework for user experience improvement. This framework employs inner and outer loops to construct the optimal online allocation strategy and recommend a suitable strategy for different types of users, respectively. Based on the DORA framework, we put forth OR2A-HetU, an Online Resource Recommendation and Allocation algorithm for Heterogeneous Users, to solve this resource allocation problem. The OR2A-HetU algorithm proceeds sequentially and can converge to the optimal solution when the time horizon is sufficiently large. The numerical results show that the proposed algorithm outperforms the baseline algorithms, and the user complaint rate decreases from 48.8% to 27.5% when the available resources increase.

关键词： Multi-access edge computing User experience Stability analysis Real-time systems Mobile handsets Resource management Sparks Low latency communication Optimization Numerical stability

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Fuzzy Attack Tree: Assessing Cyberattack Risk Using Attack Tree and Fuzzy Logic 3

Fuzzy Attack Tree: Assessing Cyberattack Risk Using Attack T...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Naik, Nitin Jenkins, Paul Grace, Paul Naik, Dishita Song, Jingping Prajapat, Shaligram Mishra, Durgesh Yang, Longzhi Boongoen, Tossapon Iam-On, Natthakan School of Informatics and Digital Engineering Aston University United Kingdom Cardiff School of Technologies Cardiff Metropolitan University United Kingdom Birmingham City University United Kingdom Software College Northeastern University China International Institute of Professional Studies Devi Ahilya University India School of Computer Science & Information Technology Symbiosis University of Applied Sciences India Northumbria University Department of Computer and Information Sciences United Kingdom Aberystwyth University Department of Computer Science United Kingdom

ISBN: (纸本)9798350343274

Organisations and users have been experiencing significant rises in cyberattacks and their severity, which means that they require a greater awareness and understanding of the anatomy of cyberattacks, to prevent and mitigate their effects. In analysing cyberattacks, there are a number of different approaches that may be used to assess their potential risks and effects. However, these are utilised in specific types of cyberattacks and their analysis, which means they cannot be applied in every situation or cyberattack. Moreover, several other factors may influence the decision to use these approaches, such as cost, complexity, skills and adaptability. As a result, continuous research to design and enhance these approaches is undertaken to produce a generic, cost-effective, easy and adaptable approach. This paper presents one such approach to assess the risk of cyberattacks utilising an attack tree and fuzzy logic. An attack tree is a systematic and illustrative method for describing an attack on a system and analysing its taxonomy and other aspects. Subsequently, the probability and risk of each leaf node in the attack tree are calculated using the proposed formulas. Finally, fuzzy logic enables decision making based on imprecise data and heuristics to obtain the overall risk of attack. This proposed approach comprises systematic steps to accomplish an assessment of any cyberattack and its associated risks in an uncomplicated and effective manner, enabling its prevention and mitigation to be determined. The paper illustrates an application of the proposed approach to assess the risk of an information theft attack on an organisation, which can then be utilised to assess the risk of other cyberattacks. © 2023 IEEE.

关键词： Decision making

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