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    <title>Repository Community: null</title>
    <link>https://scholar.dgist.ac.kr/handle/20.500.11750/16001</link>
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        <rdf:li rdf:resource="https://scholar.dgist.ac.kr/handle/20.500.11750/60200" />
        <rdf:li rdf:resource="https://scholar.dgist.ac.kr/handle/20.500.11750/60015" />
        <rdf:li rdf:resource="https://scholar.dgist.ac.kr/handle/20.500.11750/60014" />
        <rdf:li rdf:resource="https://scholar.dgist.ac.kr/handle/20.500.11750/59243" />
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    <dc:date>2026-05-01T07:09:10Z</dc:date>
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  <item rdf:about="https://scholar.dgist.ac.kr/handle/20.500.11750/60200">
    <title>Activity recognition and user identification using mmWave radar with a shared-backbone graph network and task-specific heads</title>
    <link>https://scholar.dgist.ac.kr/handle/20.500.11750/60200</link>
    <description>Title: Activity recognition and user identification using mmWave radar with a shared-backbone graph network and task-specific heads
Author(s): Eom, Jun Yong; Seo, Daewon
Abstract: Identity-aware activity recognition is a key enabler for customized services. However, joint modeling of activity recognition and user identification from wireless signals remains underexplored. This work presents a dual-task graph model for millimeter-wave (mmWave) frequency-modulated continuous-wave (FMCW) radar point-cloud sequences. We construct directed graphs that capture a user’s spatial structure and motion over time. A shared graph neural backbone processes these graphs and produces node embeddings that encode local spatial features and short-term dynamics. Each task-specific head first aggregates node embeddings into a graph-level representation and then performs activity or identity classification. Experiments on two public datasets demonstrate that the proposed scheme achieves classification performance comparable to single-task baselines for both activity recognition and user identification while maintaining low-latency inference. Codes are available at https://github.com/junyongeom/mmActId/ . © 2026 The Authors.</description>
    <dc:date>2026-03-31T15:00:00Z</dc:date>
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  <item rdf:about="https://scholar.dgist.ac.kr/handle/20.500.11750/60015">
    <title>On the Fundamental Tradeoff of Joint Communication and QCD: The Monostatic Case</title>
    <link>https://scholar.dgist.ac.kr/handle/20.500.11750/60015</link>
    <description>Title: On the Fundamental Tradeoff of Joint Communication and QCD: The Monostatic Case
Author(s): Lim, Sung Hoon; Seo, Daewon
Abstract: This paper investigates the fundamental tradeoff between communication and quickest change detection (QCD) in integrated sensing and communication (ISAC) systems under a monostatic setup. We introduce a novel Joint Communication and quickest Change subblock coding Strategy (JCCS) that leverages feedback to adapt coding dynamically based on real-time state estimation. The achievable rate-delay region is characterized using state-dependent mutual information and KL divergence, providing a comprehensive framework for analyzing the interplay between communication performance and detection delay. Moreover, we provide a partial converse demonstrating the asymptotic optimality of the proposed detection algorithm within the JCCS framework. To illustrate the practical implications, we analyze binary and MIMO Gaussian channels, revealing insights into achieving optimal tradeoffs in ISAC system design. © 2025 Elsevier B.V., All rights reserved.</description>
    <dc:date>2025-08-31T15:00:00Z</dc:date>
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  <item rdf:about="https://scholar.dgist.ac.kr/handle/20.500.11750/60014">
    <title>Region-of-Interest-Guided Deep Joint Source-Channel Coding for Image Transmission</title>
    <link>https://scholar.dgist.ac.kr/handle/20.500.11750/60014</link>
    <description>Title: Region-of-Interest-Guided Deep Joint Source-Channel Coding for Image Transmission
Author(s): Choi, Hansung; Seo, Daewon
Abstract: Deep joint source-channel coding (deepJSCC) methods have shown promising improvements in communication performance over wireless networks. However, existing approaches primarily focus on enhancing overall image reconstruction quality, which may not fully align with user experiences, often driven by the quality of regions of interest (ROI). Motivated by this, we propose ROI-guided joint source-channel coding (ROI-JSCC), a novel deepJSCC framework that prioritizes high-quality transmission of ROI. The ROI-JSCC consists of four key components: (1) Image ROI embedding, (2) ROI-guided split processing, (3) ROI-based loss function design, and (4) ROI-adaptive bandwidth allocation. Together, these components enable ROI-JSCC to selectively enhance the ROI reconstruction quality at varying ROI positions while maintaining overall image quality with minimal computational overhead. Experimental results under diverse communication environments demonstrate that ROI-JSCC significantly improves ROI reconstruction quality while maintaining competitive average image quality compared to recent state-of-the-art methods.</description>
    <dc:date>2025-11-30T15:00:00Z</dc:date>
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  <item rdf:about="https://scholar.dgist.ac.kr/handle/20.500.11750/59243">
    <title>특징 중요도 인식 기반 이미지 전송을 위한 심층-통합 소스 채널 코딩 시스템 및 방법</title>
    <link>https://scholar.dgist.ac.kr/handle/20.500.11750/59243</link>
    <description>Title: 특징 중요도 인식 기반 이미지 전송을 위한 심층-통합 소스 채널 코딩 시스템 및 방법
Author(s): 서대원; 최한성; 손민서</description>
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