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2025 International Conference on electronics, Information, and Communication, ICEIC 2025

作者： Ngoc Han, Nguyen Bui Lee, Ju-Hwan Kim, Jin-Young Kim, Hyoung-Gook Kwangwoon University Department of Electronic Convergence Engineering Seoul Korea Republic of Chonnam National University Department of Intelligent Electronics and Computer Engineering Gwangju Korea Republic of

ISBN: (纸本)9798331510756

From orchard to consumer, apples experience multiple static and dynamic stresses, leading to defects such as pest damage and surface scratches. Detecting and sorting defective apples post-harvest is crucial for enhancing market value and minimizing contamination risks. While machine vision-based sorting and grading methods have advanced, they often prioritize easily detectable defects, neglecting undetectable ones. In this work, we apply TricubeNet, a one-stage, anchor-free detector, to detect pests and scratches on the surface of apples. This empirical study addresses a gap in the literature, as TricubeN et has not been previously evaluated on fruit datasets. By carefully tuning the size hyperparameter of the Tricube kernel, we achieve the highest F1 scores of 65.34 %, demonstrating the model's potential for subtle defect detection. © 2025 IEEE.

关键词： Fruits

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Multi-Agent Deep Reinforcement Learning Based Handover Strategy for LEO Satellite Networks

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IEEE Communications Letters 2025年第5期29卷 1117-1121页

作者： Lee, Chungnyeong Bang, Inkyu Kim, Taehoon Lee, Howon Jung, Bang Chul Chae, Seong Ho Tech University of Korea Department of IT Semiconductor Convergence Engineering Siheung15073 Korea Republic of Hanbat National University Daejeon34158 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of Tech University of Korea Department of Electronics Engineering Siheung15073 Korea Republic of

The high rotation speeds and mega-constellations of low earth orbit satellites (LEO SATs) cause the inter-satellite frequent handovers (HOs) problem which can lead to substantial performance degradation. This paper proposes a novel distributed multi-agent deep Q-network based SAT HO strategy for the LEO SAT networks to simultaneously minimize the number of HOs and maximize the throughputs and the visible times of UEs while satisfying the quality-of-service (QoS) constraints of all UEs. The proposed HO scheme allows UEs to independently and simultaneously perform the HO decision makings based on their own local information, which enables to immediately adapt to the dynamic changes of the LEO SAT network environments. The numerical results demonstrated that our proposed HO strategy achieves the lowest average HO rate and the highest achievable throughputs compared to other conventional HO strategies, while ensuring a higher QoS guarantee time ratio. © 1997-2012 IEEE.

关键词： Quality of service

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Integrated Non-Terrestrial and Terrestrial Quantum Anonymous Networks

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IEEE Network 2025年第3期39卷 196-206页

作者： Khan, Awais Bui, Tinh Thanh Rehman, Junaid Ur Chatzinotas, Symeon Cotton, Simon L. Dobre, Octavia A. Duong, Trung Q. Shin, Hyundong Kyung Hee University Department of Electronics and Information Convergence Engineering Gyeonggi-do Yongin-si17104 Korea Republic of King Fahd University of Petroleum and Minerals Department of Electrical Engineering Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia University of Luxembourg Luxembourg City1855 Luxembourg Memorial University Department of Electrical and Computer Engineering St. John'sNLA1B 3X5 Canada Queen's University Belfast Department of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

Due to optical fiber limitations for quantum communication, global-scale quantum networks are possible only by integrating non-terrestrial components in the overall network architecture. Quantum networks are expected to support distributed tasks for quantum information processing, such as quantum sensing, control, communication, and computing. These networks enable emerging applications that are uniquely quantum or augmented by quantum mechanics. Leveraging quantum resources, anonymous networking can be enhanced to such an extent that even an adversary with control over all network resources cannot trace the message source, achieving perfect untraceability. To provide this untraceable global connectivity, we demonstrate the integration of non-terrestrial networks (NTNs) and terrestrial networks (TNs) for quantum anonymous communication (QAC), highlighting possible architectures and key challenges in these integrated NTN-TN quantum anonymous networks (QANs). To illustrate and benchmark the design of QAC protocols within integrated networks, we present essential quantum protocols such as anonymous conference key agreement (CKA) and anonymous broadcast. In the first case study, we propose a satellite-to-ground quantum anonymous CKA (QA-CKA) protocol and assess the anonymous key exchange rate. This QA-CKA protocol utilizes low Earth orbit satellites to generate anonymous keys among two distinct TN nodes. In the second case study, we develop an air-to-ground quantum anonymous broadcast (QAB) protocol and examine the anonymous broadcast announcement rate. This QAB protocol exploits unmanned aerial vehicles to enable a broadcasting party to transmit classical information anonymously across two distinct TNs. These QAC protocols are simulated with realistic integrated network parameters to provide practical estimates of achievable performance. Furthermore, we discuss future research directions for enabling integrated NTN-TN QANs. © 1986-2012 IEEE.

关键词： Broadcasting antennas

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A review of hybrid EEG-based multimodal human–computer interfaces using deep learning: applications, advances, and challenges

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Biomedical engineering Letters 2025年 1-32页

作者： Lee, Hyung-Tak Shim, Miseon Liu, Xianghong Cheon, Hye-Ran Kim, Sang-Gyu Han, Chang-Hee Hwang, Han-Jeong Department of Electronics and Information Engineering Korea University 2511 Sejong-ro Jochiwon-eup Sejong-si30019 Korea Republic of Department of Artificial Intelligence Tech University of Korea Siheung Korea Republic of Department of Computer Information Korea University 2511 Sejong-ro Jochiwon-eup Sejong-si30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Korea Republic of

Human–computer interaction (HCI) focuses on designing efficient and intuitive interactions between humans and computer systems. Recent advancements have utilized multimodal approaches, such as electroencephalography (EEG)-based systems combined with other biosignals, along with deep learning to enhance performance and reliability. However, no systematic review has consolidated findings on EEG-based multimodal HCI systems. This review examined 124 studies published from 2016 to 2024, retrieved from the Web of Science database, focusing on hybrid EEG-based multimodal HCI systems employing deep learning. The keywords used for evaluation were as follows: ‘Deep Learning’ AND ‘EEG’ AND (‘fNIRS’ OR ‘NIRS’ OR ‘MEG’ OR ‘fMRI’ OR ‘EOG’ OR ‘EMG’ OR ‘ECG’ OR ‘PPG’ OR ‘GSR’). Main topics explored are: (1) types of biosignals used with EEG, (2) neural network architectures, (3) fusion strategies, (4) system performance, and (5) target applications. Frequently paired signals, such as EOG, EMG, and fNIRS, effectively complement EEG by addressing its limitations. Convolutional neural networks are extensively employed for spatio-temporal-spectral feature extraction, with early and intermediate fusion strategies being the most commonly used. Applications, such as sleep stage classification, emotion recognition, and mental state decoding, have shown notable performance improvements. Despite these advancements, challenges remain, including the lack of real-time online systems, difficulties in signal synchronization, limited data availability, and insufficient explainable AI (XAI) methods to interpret signal interactions. Emerging solutions, such as portable systems, lightweight deep learning models, and data augmentation techniques, offer promising pathways to address these issues. This review highlights the potential of EEG-based multimodal HCI systems and emphasizes the need for advancements in real-time interaction, fusion algorithms, and XAI to enhance their adaptability, interpreta

关键词： Convolutional neural networks

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Pest and Scratch Detection on Apple Surface Using TricubeNet

Pest and Scratch Detection on Apple Surface Using TricubeNet

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International Conference on electronics, Information and Communications (ICEIC)

作者： Nguyen Bui Ngoc Han Ju-Hwan Lee Jin-Young Kim Hyoung-Gook Kim Department of Electronic Convergence Engineering Kwangwoon University Seoul South Korea Department of Intelligent Electronics and Computer Engineering Chonnam National University Gwangju South Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

关键词： Electric potential Surface contamination Detectors Kernel Tuning Stress Consumer electronics Sorting Defect detection

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On Spectral Intelligence in 6G URLLC Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Khan, Abd Ullah Tanveer, Muhammad Ullah, Sami Shin, Hyundong Li, Xingwang Kyung Hee University Department of Electronics and Information Convergence Engineering Yongin02447 Korea Republic of Lahore54770 Pakistan Shaheed Benazir Bhutto University Department of Computer Science Sheringal18050 Pakistan Henan Polytechnic University School of Physics and Electronics Information Engineering Jiaozuo454000 China

Cognitive radio (CR) empowered 6 G networks are deemed the key candidate technology to achieve ultra-reliable low latency communication (URLLC) with enhanced spectrum utilization efficiency. However, there are several challenges to address in achieving this objective. In particular, the sequential and random sensing performed by secondary users (SUs) to find idle channels within a given band in a CR network (CRN) leads to time and energy consumption, and processing overheads, which consequently cause early depletion of the device's energy, underutilization of the available spectrum, and prolonged delays in communication. To circumvent this problem, in this paper, a spectrum efficient scheme is proposed based on idle spectrum inference and ranking, which takes into account the devices' heterogeneity as well as their priorities in resource allocation. Based on the probabilistic approach, the scheme uses multiple parameters in a channel's evaluation and suitability assessment before selection for transmission. Markov chain modeling is leveraged to deal with the users' arrival and departure uncertainties and to derive expressions for core performance metrics, including service capacity and retainability, spectrum utilization efficiency, reliability, network unserviceable and handoff probabilities, channel availability, and communication latency. The scheme is analyzed under various patterns of users' arrivals. The acquired analytical and simulation results confirm the effectiveness of the proposed scheme compared to the state-of-the-art to realize URLLC applications. © 1967-2012 IEEE.

关键词： Resource allocation

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Joint Movement and User Association of UAV-BS for Indoor User Service: A Multi-Agent Deep Reinforcement Learning Approach

Joint Movement and User Association of UAV-BS for Indoor Use...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Tae-Yoon Kim Yujeong Ahn Jaeyeol Lee Jae-Hyun Kim Department of Artificial Intelligence Convergence Network Ajou University Suwon South Korea Department of Space Electronics and Information Engineering Ajou University Suwon South Korea Department of Electrical and Computer Engineering Ajou University Suwon South Korea

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

This paper investigates the joint optimization of movement and user association for multiple UAV base stations (UAV-BSs) to provide emergency services to indoor users. In this paper, we propose a multi-agent deep Q-network (MADQN) reinforcement learning algorithm to address this problem. Moreover, we apply a self-attention mechanism to achieve the objective more effectively. Simulation results demonstrate that the proposed algorithm outperforms conventional algorithms, such as random action and MADQN without a self-attention mechanism.

关键词： Base stations Simulation Interference Autonomous aerial vehicles Emergency services Deep reinforcement learning Trajectory Optimization Signal to noise ratio

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Optimization Satellite Power Control in Integrated Non-Terrestrial Networks Using Multi-Agent Deep Reinforcement Learning

Optimization Satellite Power Control in Integrated Non-Terre...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Jaeyeol Lee Won-Jae Lee Tae-Yoon Kim Taehan Moon Jae-Hyun Kim Department of Space Electronics and Information Engineering Ajou University Suwon South Korea Department of Artificial Intelligence Convergence Network Ajou University Suwon South Korea Department of Electrical and Computer Engineering Ajou University Suwon South Korea

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

High altitude platform station (HAPS) and low Earth orbit (LEO) satellites that provide services to the ground in the line of sight (LoS) environment are gaining attention as next-generation communication networks. Additionally, integrating HAPS and satellite networks is being researched to provide high-quality communication services to terrestrial environments. However, a critical issue in integrated networks is the increased interference, which leads to degraded signal quality for users and a higher outage probability when the same frequency is used. In this paper, we propose a method to reduce outage probability for users being served by the HAPS by adjusting the transmission power of the satellite using deep reinforcement learning (DRL). This paper uses a multi-agent deep Q-network (MADQN) and an attention-based A-MADQN, which combines MADQN with an attention mechanism. By employing reinforcement learning, the optimal transmission power can be determined. The simulations illustrate confirmed a reduction in user outage probability compared to the conventional satellite transmission power for each cell.

关键词： Satellites Attention mechanisms Power control Low earth orbit satellites Interference Probability Deep reinforcement learning Power system reliability Optimization Next generation networking

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Exploring Image-Based Approaches for Effective Synthetic Time-Series Data Generation

Exploring Image-Based Approaches for Effective Synthetic Tim...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Samgwon Oh Seungmin Oh Junghoon Lee Doyeon Kim Minsoo Hahn Jinsul Kim Department of Intelligence Electronics Computer Engineering Chonnam National University Gwangju Korea Department of Intelligence Information Convergence Chonnam National University Gwangju Korea Department of Computational and Data Science Astana IT University Astana Kazakhastan

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

The effective generation of synthetic time-series data is crucial for addressing data scarcity in various domains such as network monitoring, financial analysis, and healthcare. This study explores image-based approaches for transforming time-series data into visual representations, enabling the application of advanced image synthesis techniques. We systematically evaluate multiple transformation methods, including recurrence plots, Gramian Angular Fields (GAF), and Markov transition fields, to identify the most suitable techniques for preserving temporal and structural characteristics. The generated synthetic data are validated using statistical similarity metrics and tested on machine learning tasks, such as anomaly detection and forecasting, to assess their practical utility. Experimental results demonstrate that image-based synthetic data generation not only retains essential time-series features but also enhances model robustness under limited data conditions. This research contributes to the growing field of synthetic data generation by providing insights into the integration of time-series and image processing techniques. Future work will focus on refining these methods and exploring their scalability across diverse applications.

关键词： Measurement Visualization Scalability Data collection Generative adversarial networks Data models Forecasting Monitoring Anomaly detection Synthetic data

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Generative Diffusion Model-based Variational Inference for MIMO Channel Estimation

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IEEE Transactions on Communications 2025年

作者： Chen, Zhixiong Shin, Hyundong Nallanathan, Arumugam Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom Kyung Hee University Department of Electronics and Information Convergence Engineering Gyeonggido Yongin-si17104 Korea Republic of Kyung Hee University Department of Electronic Engineering Gyeonggido Yongin-si17104 Korea Republic of

Efficient and accurate channel estimation with low pilot overhead is essential for massive multiple-input multiple-output (MIMO) wireless communication systems to achieve high spectral and energy efficiency. This work proposes a novel variational inference method for channel estimation by utilizing the generative diffusion model as a prior. Specifically, we first train a generative diffusion model to learn the score, i.e., the gradient of the log-prior distribution, of MIMO channels to serve as a prior in the channel estimation process. The training process is unsupervised and does not rely on specific pilot structures and signal-to-noise ratios (SNRs). Thus, the learned prior is generalizable and can be directly used for channel estimation under different pilot signals and SNRs without requiring retraining. Then, we propose a variational inference method to infer the posterior distribution of the MIMO channel under given pilots and received measurements by incorporating the learned prior. Finally, we estimate the MIMO channels by sampling from the derived posterior distribution. Our simulations under various wireless propagation environments and antenna architectures demonstrate that the proposed approach achieves over 5 dB reduction in normalized mean square error and faster channel recovery compared to state-of-the-art channel estimators. Additionally, the proposed approach exhibits robust estimation performance when the test channel distribution shifts from the training distribution, even outperforming the benchmarks without distribution shifts. © 1972-2012 IEEE.

关键词： Mean square error

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