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A Wideband 8×8 Slot Array Antenna Using Gap Waveguide MLW Coaxial Line Technology for mmWave Applications

IEEE Antennas and Wireless Propagation Letters 2025年

作者： Farahbakhsh, Ali Zarifi, Davood Vosoogh, Abbas Bencivenni, Carlo Mrozowski, Michal Gdansk University of Technology Department of Microwave and Antenna Engineering Faculty of Electronics Telecommunications and Informatics Gdansk80-233 Poland Graduate University of Advanced Technology Department of Electrical and computer engineering Kerman Iran Gapwaves Ab Gothenburg Sweden

The paper presents a wideband 8×8-element slot antenna array operating in the E-band. The proposed design employs a low-loss coaxial waveguide transmission line filled with air implemented using multilayer waveguide (MLW) technology by vertically stacking thin, unconnected metal plates to meet the typical radiation pattern and bandwidth requirements of a slot antenna array. To validate the concept, the simulation and measurement results of an 8×8 slot antenna array are presented. The proposed antenna array exhibits |S11| © 2025 IEEE.

关键词： Millimeter waves

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Design of an Ultra-thin Metamaterial Absorber with Machine Learning-Assisted Absorption Prediction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Hossain, Md. Jakir Mubashsara, Nafisa Khan, Riasat Matin, Mohammad Abdul Sarigiannidis, Panagiotis Goudos, Sotirios K. North South University Department of Electrical and Computer Engineering Dhaka Bangladesh University of Western Macedonia Department of Informatics and Telecommunications Engineering Kozani Greece Aristotle University of Thessaloniki ELEDIA@AUTH School of Physics Thessaloniki541 24 Greece

An absorber is a block of material employed to absorb a portion of the energy of an incident particle. Research on absorbers is rapidly expanding as a result of their prospective applications in several fields, including wireless communications and military purposes. However, conventional absorbers have dense size and design complexity that make them unsuitable for a multitude of practical applications. Metamaterial-based absorbers (MMAs) have garnered significant attention with respect to their low density, narrow layers, and high absorption capacity. In this paper, we introduce a new ultra-thin trilayered Ni–MgF2–Ni metamaterial absorber structure, which is designed using machine learning. It has a balanced and symmetrical layout with a metal-dielectric-metal arrangement and a high-temperature endurance. Its resonator design is defined by mathematical deduction from the Conference matrix. The performance of MMA has been evaluated at wavelengths ranging from 250 nm to 1150 nm, as well as in various modes-TM, TE, and TEM. The results demonstrate that the proposed MMA has exceptional absorption capacities, with an overall absorption of 96.18% within the test range. It offers excellent mean absorption percentages of 99% in the optical region (350 to 750 nm), 95.23% in the ultraviolet (UV) range, and 94.38% in the near-infrared (NIR) range. Furthermore, MMA exhibits an absorption efficiency of 99.99% at a particular wavelength of 424.93 nm. In this work, various machine learning techniques have been applied to predict the absorption of the proposed MMA design. The XGBoost meta-learner-based Stacking ensemble machine learning technique achieves the highest prediction efficiency. Finally, the explainable AI technique with the LIME framework has been used to analyze the predictions of applied machine learning models. © 2020 IEEE.

关键词： Magnesium compounds

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Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions

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Discover Internet of Things 2025年第1期5卷 1-23页

作者： Mazhar, Tehseen khan, Sunawar Shahzad, Tariq khan, Muhammad Amir Saeed, Mamoon M. Awotunde, Joseph Bamidele Hamam, Habib School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science International Islamic University Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Science and Information Technology School Education Department Government of Punjab 31200 Layyah Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized. © The Author(s) 2025.

关键词： Electronic health record

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A Comprehensive Survey on GenAI-Enabled 6G: Technologies, Challenges, and Future Research Avenues

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年

作者： Sheraz, Muhammad Chuah, Teong Chee Tareen, Wajahat Ullah khan Al-Habashna, Ala'a Saeed, Sohail Imran Ahmed, Manzoor Lee, It Ee Guizani, Mohsen Multimedia University Faculty of Artificial Intelligence and Engineering Selangor Cyberjaya63100 Malaysia University of Jeddah College of Engineering Department of Electrical and Electronic Engineering Jeddah21589 Saudi Arabia Al Hussein Technical University School of Computing and Informatics Amman Jordan Bahria University Department of Computer Engineering Karachi Pakistan School of Computer Science and Information Engineering Hubei Engineering University Xiaogan432000 China Abu Dhabi United Arab Emirates

The integration of artificial intelligence (AI) in 6G demonstrates a transformative leap in redefining network efficiency, intelligence, and adaptability. However, AI largely leverages discriminative models relying on labelled and quality data, where data accessibility remains serious concern. Generative AI (GenAI) has gained traction due to its immense potential in resolving the issue of data scarcity, complexity, and incompleteness. GenAI models excel in understanding underlying data distributions, enabling them to generate synthetic data that mirrors real-world patterns. GenAI supports adaptive learning and scenario modeling, making it indispensable for addressing the unpredictability and complexity inherent in 6G networks. Since the complexity of wireless communication systems is increasing and the demand for such systems is growing, GenAI presents new ideas for enhancing network performance, increasing system efficiency, and developing intelligent decision-making capabilities. This survey paper investigates the promising role of GenAI in the evolution of 6G networks. An in-depth discussion of notable GenAI models is presented, outlining their application in enhancing key network components. Specifically, the application of GenAI in advanced technologies including reconfigurable intelligent surfaces (RIS), unmanned aerial vehicles (UAVs), digital twins (DTs), and integrated sensing and communications (ISACs) is thoroughly investigated with respect to optimize the adaptability, flexibility, and robustness of the wireless networks. Moreover, use cases of GenAI-enabled wireless networks are presented to highlight the realization of GenAI in 6G. The paper also presents the lessons learned, existing challenges, and future research directions. This paper systematically explores GenAI and its pivotal role in the development of 6G, providing a foundation for researchers to further investigate and advance GenAI-enabled 6G. © 2020 IEEE.

关键词： Deep learning

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Evolving Multi-Branch Attention Convolutional Neural Networks for Online RIS Configuration

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Stamatelis, George Stylianopoulos, Kyriakos Alexandropoulos, George C. National and Kapodistrian University of Athens Department of Informatics and Telecommunications Panepistimiopolis Ilissia Athens15784 Greece University of Illinois Chicago Department of Electrical and Computer Engineering IL60601 United States

In this paper, we consider the problem of jointly controlling the configuration of a Reconfigurable Intelligent Surface (RIS) with unit elements of discrete responses and a codebook-based transmit precoder in RIS-empowered Multiple-Input Single-Output (MISO) communication systems. The adjustable elements of the RIS and the precoding vector need to be jointly modified in real time to account for rapid changes in the wireless channels, making the application of complicated discrete optimization algorithms impractical. We present a novel Multi-Branch Attention Convolutional Neural Network (MBACNN) architecture for this design objective which is optimized using NeuroEvolution (NE), leveraging its capability to effectively tackle the non-differentiable problem arising from the discrete phase states of the RIS elements. The channel matrices of all involved links are first passed to separate self-attention layers to obtain initial embeddings, which are then concatenated and passed to a convolutional network for spatial feature extraction, before being fed to a per-element multi-layered perceptron for the final RIS phase configuration calculation. Our MBACNN architecture is then extended to multi-RIS-empowered MISO communication systems, and a novel NE-based optimization approach for the online distributed configuration of multiple RISs is presented. The superiority of the proposed single-RIS approach over both learning-based and classical discrete optimization benchmarks is showcased via extensive numerical evaluations over both stochastic and geometrical channel models. It is also demonstrated that the proposed distributed multi-RIS approach outperforms both distributed controllers with feedforward neural networks and fully centralized ones. © 2015 IEEE.

关键词： Convolutional neural networks

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Supervised methods of machine learning for email classification: a literature survey

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Systems Science and Control engineering 2025年第1期13卷

作者： AlShaikh, Muath Alrajeh, Yasser Alamri, Sultan Melhem, Suhib Abu-Khadrah, Ahmed Cybersecurity Department College of Engineering Al Ain University Abu Dhabi United Arab Emirates Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Electrical Engineering Al-Balqa Applied University Faculty of Engineering As-Salt Jordan

In today’s digital landscape, email is acknowledged as a critical conduit for global data exchanges. With a surge in data volume, malefactors exploit user identities, leading to data misuse. Cybercriminals employ electronic transgressions such as phishing and spam to orchestrate security infractions. Machine learning counters these breaches using myriad techniques, demonstrating significant efficiency in identifying phishing emails. We can divide machine learning into two types: supervised and unsupervised. Supervised learning requires pre-training the model on labelled datasets, amalgamating classification, and regression learning. Notably, supervised methodologies such as support vector machines (SVMs), naive Bayes, decision trees, neural networks, random forests, and deep learning have been exploited for spam filtering. This review delves into issues concerning spam filtering and email classification through supervised machine learning techniques, offering a comprehensive evaluation of strategies, methods, performance indicators, and the benefits and drawbacks of different research. This information allows researchers to assess the efficiency and effectiveness of supervised learning algorithms, laying the foundation for advanced email categorization techniques. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Support vector regression

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Energy Efficient Transmission Parameters Selection Method Using Reinforcement Learning in Distributed LoRa Networks

Energy Efficient Transmission Parameters Selection Method Us...

引用

IEEE Conference on Wireless Communications and Networking

作者： Ryotai Airiyoshi Mikio Hasegawa Tomoaki Ohtsuki Aohan Li Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan Department of Electrical Engineering Tokyo University of Science Tokyo Japan Department of Information and Computer Science Keio University Yokohama Japan

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the increase in demand for Internet of Things (IoT) applications, the number of IoT devices has drastically grown, making spectrum resources seriously insufficient. Transmission collisions and retransmissions increase power consumption. Therefore, even in long-range (LoRa) networks, selecting appropriate transmission parameters, such as channel and transmission power, is essential to improve energy efficiency. However, due to the limited computational ability and memory, traditional transmission parameter selection methods for LoRa networks are challenging to implement on LoRa devices. To solve this problem, a distributed reinforcement learning-based channel and transmission power selection method is proposed, which can be implemented on the LoRa devices to improve energy efficiency in this paper. Specifically, the channel and transmission power selection problem in LoRa networks is first mapped to the multi-armed-bandit (MAB) problem. Then, an MAB-based method is introduced to solve the formulated transmission parameter selection problem based on the acknowledgment (ACK) packet and the power consumption for data transmission of the LoRa device. The performance of the proposed method is evaluated by the constructed actual LoRa network. Experimental results show that the proposed method performs better than fixed assignment, adaptive data rate low-complexity (ADR-Lite), and e-greedy-based methods in terms of both transmission success rate and energy efficiency.

关键词： Performance evaluation Power demand LoRa Reinforcement learning Energy efficiency Internet of Things

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Analysis of the Performance of Transformers and Ensemble Schemes for Twitter US Airlines Sentiment Analysis 24

Analysis of the Performance of Transformers and Ensemble Sch...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Konstantinos Kyritsis Isidoros Perikos Charalampos M. Liapis Vaggelis Kapoulas Michael Paraskevas Electrical and Computer Engineering University of the Peloponnese Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece Department of Mathematics University of Patras Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece Computer Technology Institute and Press Diophantus Patras Greece Electrical and Computer Engineering University of the Peloponnese Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece

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A Convolutionaly-Informed XLM-RoBERTa for Classifying Short Text Inquires in Greek School Network 24

A Convolutionaly-Informed XLM-RoBERTa for Classifying Short ...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Charalampos M. Liapis Isidoros Perikos Konstantinos Kyritsis Vaggelis Kapoulas Michael Paraskevas Department of Mathematics University of Patras Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece Electrical and Computer Engineering University of the Peloponnese Patras Greece Computer Technology Institute and Press Diophantus Patras Greece Electrical and Computer Engineering University of the Peloponnese Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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