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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Voutsinas, Stylianos Mandourarakis, Ioannis Koutroulis, Eftichios Karolidis, Dimitrios Voyiatzis, Ioannis Samarakou, Maria University of West Attica Department of Informatics and Computer Engineering Greece Technical University of Crete School of Electrical and Computer Engineering Greece

ISBN: (纸本)9781450398541

Decarbonization of energy generation is required to address the urgent need to combat climate change and meet the Paris Agreement targets. Photovoltaic Systems (PVS) are a major contributor to this solution, and they are expected to grow significantly faster than any other renewable-energy technology in the coming years due to their appealing features. This paper discusses the development of a two-way communication protocol between two transceivers and a custom-designed communication board installed on each PV array. With this configuration, it is possible to transmit the measurements of each PV cell in the array to a data recording and monitoring service. Additionally, the application can customize the operation of the strings that make up the array, by broadcasting special commands to them. A prototype experimental setup was constructed in order to test the communication between the PV array communication board and the uplink/downlink transceivers. The experimental results verify the feasibility and effectiveness of the proposed communication protocol between the Electronics Interface System (EIS) and the Smart PV array since the communication protocol meets the specification for transmitting the information from 1000 smart PV cells within one minute, as well as the strict timing requirements by the protocol for transmitting the parameters to the PV strings. © 2022 ACM.

关键词： Smart power grids

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A Hydroelectric Power Plant Brief: Classification and Application of Artificial Intelligence 17

A Hydroelectric Power Plant Brief: Classification and Applic...

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17th IEEE International Symposium on Applied Computational Intelligence and informatics, SACI 2023

作者： Shahgholian, Ghazanfar Moazzami, Majid Zanjani, Sayed Mohammadali Mosavi, Amir Fathollahi, Arman Islamic Azad University Najafabad Branch Department of Electrical Engineering Najafabad Iran Obuda University John von Neumann Faculty of Informatics Budapest Hungary Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark

ISBN: (纸本)9798350321104

Hydropower is a reliable, clean, and efficient alternative to conventional fossil fuels and other renewable energy sources. The hydro turbine is the core of a hydropower plant, and the proper maintenance and operation of all other components are essential for maximizing energy production. Besides electricity generation, hydropower plants play a critical role in storing irrigation and drinking water and controlling floods. This study presents a concise overview of hydroelectric power plant classification based on the output power generated by peak water drop and storage. Pumped storage water plants are the most applicable classification based on water conditions. The study reviews the application of Artificial Intelligence (AI) in various aspects of hydropower plants, highlighting the potential benefits and challenges of integrating AI technologies in the energy production process. This paper emphasizes the importance of proper maintenance and operation of hydropower plants and provides insights into AI's potential role in optimizing energy production and improving plant efficiency. © 2023 IEEE.

关键词： Hydroelectric power plants

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XMeta-OS: A Conceptual Framework for Unified Resource Management in Distributed Edge-Cloud Environments 3rd

XMeta-OS: A Conceptual Framework for Unified Resource Manag...

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3rd International Conference on Advances in Computing Research, ACR 2025

作者： Zacharioudakis, Eleftherios Kakoulli, Elena Evripidou, Salomi Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Pafos Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9783031876462

This paper introduces XMeta-OS, a meta-operating system specially designed with Linux as its foundation to unify and optimize resource management for the distributed edge-cloud when dynamic use of GPU resources is possible. This approach offer a new GPU abstraction layer and dynamic edge-cloud allocation model that offer enhanced flexibility and resource utilization over existing systems like Kubernetes and OpenStack. By using likely sensitive data, the model applied cloud-pushed deployment model. XMeta-OS manages computer resources in real-time. Therefore, fulfilling high performance various needs. It allocates CPU and GPU in real-time to workloads requiring massive parallel processing, such as machine learning, scientific simulations, real-time data analytics, and transportation tasks. Fabrication in the laboratory of an edge-cloud environment can help to assess the scalability, fault tolerance and efficiency of the system. It showcases the overall architectural design of XMeta-OS. Changes to the kernel and security solutions for safe and effective distribution are suggested. This article presents the design of the XMeta-OS. The future of XMeta-OS entails usage and testing with which we see latency, energy, computation and other such measurements in labs. XMeta-OS is a solution to cost performance, emerging application, large-scale edge-cloud deployments, and distributed GPU-enabled infrastructures. Meta-Operating System in Edge-Cloud Continuum for dynamic resource management of GPU. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster computing

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Uplink OFDMA-Based Random Access Mechanism with Bursty Arrivals for IEEE 802.11ax Systems

IEEE Networking Letters

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IEEE Networking Letters 2022年第1期4卷 34-38页

作者： Cheng, Ray-Guang Yang, Chi-Ming Firmansyah, Bariq S. Harwahyu, Ruki Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei106 Taiwan School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung40132 Indonesia Department of Electrical Engineering University of Indonesia Depok16424 Indonesia

IEEE 802.11ax defines a new channel access scheme of OFDMA-based random access (UORA) for varying number of associated stations (STAs) to send their requests or data to the access point (AP) in a contention manner. This letter presents an analytical model to investigate the performance of UORA in transient condition under bursty arrivals with arbitrary distribution. The key performance metrics of access success probability;average access delay;cumulative distribution function (CDF) for the number of transmissions;and utilization of random access resource units (RA-RUs) were derived. Simulation results verified the accuracy of the proposed analytical model. © 2019 IEEE.

关键词： Analytical models

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Benchmarking changepoint detection algorithms on cardiac time series

arXiv

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arXiv 2024年

作者： Cakmak, Ayse Selen Reinertsen, Erik Nemati, Shamim Clifford, Gari D. Department of Biomedical Informatics Emory University United States School of Electrical and Computer Engineering Georgia Institute of Technology United States Department of Biomedical Informatics University of California at San Diego United States

The pattern of state changes in a biomedical time series can be related to health or disease. This work presents a principled approach for selecting a changepoint detec- tion algorithm for a specific task, such as disease classification. Eight key algorithms were compared, and the performance of each algorithm was evaluated as a function of temporal tolerance, noise, and abnormal conduction (ectopy) on realistic artificial cardiovascular time series data. All algorithms were applied to real data (cardiac time series of 22 patients with REM-behavior disorder (RBD) and 15 healthy controls) using the parameters selected on artificial data. Finally, features were derived from the de- tected changepoints to classify RBD patients from healthy controls using a K-Nearest Neighbors approach. On artificial data, Modified Bayesian Changepoint Detection al- gorithm provided superior positive predictive value for state change identification while Recursive Mean Difference Maximization (RMDM) achieved the highest true positive rate. For the classification task, features derived from the RMDM algorithm provided the highest leave one out cross validated accuracy of 0.89 and true positive rate of 0.87. Automatically detected changepoints provide useful information about subject's phys- iological state which cannot be directly observed. However, the choice of change point detection algorithm depends on the nature of the underlying data and the downstream application, such as a classification task. This work represents the first time change point detection algorithms have been compared in a meaningful way and utilized in a classification task, which demonstrates the effect of changepoint algorithm choice on application *** Codes 92C30, 92C32, 03H10, 62H30, 68Q07, 8T07, 78-10, 92-10, 62R07, 68T09, 68T10 © 2024, CC BY-NC-SA.

关键词： Time series

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ICP Algorithm Based Liver Rigid Registration Method Using Liver and Liver Vessel Surface Mesh 23

ICP Algorithm Based Liver Rigid Registration Method Using Li...

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2023 Research in Adaptive and Convergent Systems, RACS 2023

作者： Kim, Soohyun Koo, Kyoyeong Park, Taeyong Lee, Jeongjin Department of Computer Science and Engineering Soongsil University Seoul Korea Republic of Department of Biomedical Informatics Hallym University Medical Center Gyeonggi-do Korea Republic of School of Computer Science and Engineering Soongsil University Seoul Korea Republic of

ISBN: (纸本)9798400702280

To improve1 the survival rate of hepatocellular carcinoma (HCC), early diagnosis and treatment are essential. Early diagnosis HCC often involves comparing and analyzing hundreds of computed tomography (CT) images, which is a kind of subjective judgment and is also a time-consuming process. In this paper, we propose a liver rigid registration method using liver and liver vessel surface mesh to enable fast and objective diagnosis of HCC. The proposed method involves segmenting the liver and liver vessel regions from abdominal CT images, generating surface meshes, and performing liver rigid registration based on the Iterative Closest Point (ICP) algorithm using the generated meshes. We evaluate the accuracy of the proposed method through experiments, demonstrating its potential for rapid and objective diagnosis of HCC. The performance evaluations show that the proposed method aids in the early diagnosis and treatment of HCC fast and objectively. © 2023 ACM.

关键词： Medical imaging

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Analysis of data from UAVs for surveillance and threat identification in maritime areas 8

Analysis of data from UAVs for surveillance and threat ident...

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8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Lampropoulos, Fotis Daramouskas, Ioannis Perikos, Isidoros Paraskevas, Michael University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece Hellenic Coast Guard Patras Greece University of Patras Computer Technology Institute and Press "diophantus" Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press "diophantus" Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350373615

In this paper, we present a methodology for drones for recognizing different types of objects in maritime areas. The concept and the aim is to assist the national maritime surveillance authorities in the identification of treats and the recognition of the exact type of the treat. The methodology relies on the use of deep learning networks and YOLO framework as well as regression models. The YOLO model detects where each object is and which label should be applied. In this way, object detection is subject to the analysis of Machine Learning-based approaches and Deep Learning-based approaches providing more information about the video or an image than traditional approaches to recognition. These approaches are used to identify groups of pixels that may individually belong to an object. This then feeds a regression model with the help of a convolutional neural network and more specifically, R-CNN, Fast R-CNN, Faster R-CNN and after that object detection algorithm approximates the location of the object and gives its label at the same time. The purpose of the application is to detect objects with an emphasis on identifying one or more targets of interest from data of a video or an image capture. An experimental study was conducted in real-world conditions and revealed quite interesting findings. © 2023 IEEE.

关键词： computer vision

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The Deep Learning-based Intelligent System for Extracting Information from e-KTP 2

The Deep Learning-based Intelligent System for Extracting In...

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2nd International Conference on Software engineering and Information Technology, ICoSEIT 2024

作者： Fuadi, Zaki Muchtar, Kahlil Subianto, Muhammad Riza, Hammam Oktiana, Maulisa Nizamuddin Universitas Syiah Kuala Department of Informatics Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Jakarta10340 Indonesia

ISBN: (纸本)9798350317503

This research implements an Intelligent Information System for Text Recognition on Indonesian Identity Cards (eKTP) based on Deep Learning architecture. The aim of this research is to develop a system capable of accurately and efficiently detecting and recognizing text on e-KTP images. The first phase of this research involves selecting ideal images from the e-KTP dataset. Subsequently, pre-processing is conducted to crop the image sides and merge them with background images to create a more diverse dataset and obtain the e-KTP image masks as training data. After selecting the ideal images, the total training data amounts to 144 images. The U-Net architecture is chosen as the deep learning method for image segmentation in this research, while for text detection and recognition, two neural networks, CRAFT and TRBA (TPS-ResNet-BiLSTM-Attention), are employed. The testing, based on accuracy values, Dice coefficients, and IoU for the segmentation process, results in a U-Net model accuracy of 99.52%. Text detection and recognition follow confidence values. © 2024 IEEE.

关键词： Intelligent systems

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An Investigation of Triple-Tuned Traps for MRI Receiver Coil Design 46

An Investigation of Triple-Tuned Traps for MRI Receiver Coil...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Busher, Joseph Hou, Jue Wright, Steven M. McDougall, Mary P. Texas A&m University Department of Electrical and Computer Engineering College StationTX77843 United States Texas A&m University Department of Biomedical Engineering College StationTX77843 United States

ISBN: (纸本)9798350371499

Multinuclear MRI/S offers a powerful tool for noninvasive diagnostics and disease monitoring but remains largely underutilized in part due to the intrinsically low sensitivity of X-nuclei scans. This has led to continued research interest into the optimization of multi-nuclear coil designs for double-and triple-tuned coils that can facilitate scaling into higher channel count arrays to increase sensitivity. Here we present a new design for a parallel trap network that can create a simultaneous triple-tuned coil for 1H, 31P and 23Na at 4.7T. Coils were characterized both on the bench and in the scanner for the relative SNR of the different multi-tuning networks. The parallel network was shown to have a relative SNR gain of over 20% compared to a series network for 1H imaging. X-nuclei had a decrease in SNR of less than 10% for both 31P and 23Na. Future work involves further optimization of inductor design and placement to improve the X-nuclei sensitivity. © 2024 IEEE.

关键词： Tuning

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Ultra Wideband Highly Efficient Cross Polarization Conversion Metasurface for Multi-Band Applications

Ultra Wideband Highly Efficient Cross Polarization Conversio...

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2023 Advances in Science and engineering Technology International Conferences, ASET 2023

作者： Osman, Malaz Yousaf, Jawad Almajali, Eqab Hussein, Mousa I. Alja'afreh, Saqer S. Altaf, Amir Elahi, Manzoor Abu Dhabi University Electrical Computer and Biomedical Engineering Department Abu Dhabi United Arab Emirates University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates United Arab Emirates University Department of Electrical Engineering Al Ain United Arab Emirates Mutah University Electrical Engineering Department Alkarak Jordan Sungkyunkwan University Electrical and Computer Engineering Department Suwon Korea Republic of School of Electrical and Electronic Engineering Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9781665454742

This work proposes a square and circular split ring resonators-based metausrfae structure for stable cross-polarization-conversion (CPC) applications in C, X, Ku, partial K, sub-5G and 6 G bands. The proposed unit cell structure is a promising candidate for applications in advanced communications systems and polarizers due to its wide frequency coverage, angular stability, and ultra-wideband operations. The polarization conversion unit is shown to be converting incident x- or y-polarized EM waves to their cross-polarized components in two wide frequency bands of 5.2-15 GHz and 17.4-19.3 GHz, respectively. The polarization conversion ratio (PCR) reached 100% at resonance frequencies (5.5 GHz, 8.2 GHz, 13.2 GHz, and 18.6 GHz) of realized metasurface. The reported structure maintains angular stability against oblique incident angles (up to 40°) for the 5-15 GHz frequency range. © 2023 IEEE.

关键词： Kurchatovium

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