https://scholar.dgist.ac.kr/handle/20.500.11750/16002 Sat, 04 Apr 2026 13:36:10 GMT 2026-04-04T13:36:10Z https://scholar.dgist.ac.kr/handle/20.500.11750/60015 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. Sun, 31 Aug 2025 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60015 2025-08-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60014 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. Sun, 30 Nov 2025 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60014 2025-11-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/58296 Title: Deep Minimax Classifiers for Imbalanced Datasets With a Small Number of Minority Samples Author(s): Choi, Hansung; Seo, Daewon Abstract: The concept of a minimax classifier is well-established in statistical decision theory, but its implementation via neural networks remains challenging, particularly in scenarios with imbalanced training data having a limited number of samples for minority classes. To address this issue, we propose a novel minimax learning algorithm designed to minimize the risk of worst-performing classes. Our algorithm iterates through two steps: a minimization step that trains the model based on a selected target prior, and a maximization step that updates the target prior towards the adversarial prior for the trained model. In the minimization, we introduce a targeted logit-adjustment loss function that efficiently identifies optimal decision boundaries under the target prior. Moreover, based on a new prior-dependent generalization bound that we obtained, we theoretically prove that our loss function has a better generalization capability than existing loss functions. During the maximization, we refine the target prior by shifting it towards the adversarial prior, depending on the worst-performing classes rather than on per-class risk estimates. Our maximization method is particularly robust in the regime of a small number of samples. Additionally, to adapt to overparameterized neural networks, we partition the entire training dataset into two subsets: one for model training during the minimization step and the other for updating the target prior during the maximization step. Our proposed algorithm has a provable convergence property, and empirical results indicate that our algorithm performs better than or is comparable to existing methods. All codes are publicly available at https://github.com/hansung-choi/TLA-linear-ascent. © IEEE. Mon, 31 Mar 2025 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/58296 2025-03-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/58151 Title: Hypergraph-Based Source Codes for Function Computation Under Maximal Distortion Author(s): Basu, Sourya; Seo, Daewon; Varshney, Lav R. Abstract: This work investigates functional source coding problems with maximal distortion, motivated by approximate function computation in many modern applications. The maximal distortion treats imprecise reconstruction of a function value as good as perfect computation if it deviates less than a tolerance level, while treating reconstruction that differs by more than that level as a failure. Using a geometric understanding of the maximal distortion, we propose a hypergraph-based source coding scheme for function computation that is constructive in the sense that it gives an explicit procedure for finding optimal or good auxiliary random variables. Moreover, we find that the hypergraph-based coding scheme achieves the optimal rate-distortion function in the setting of coding for computing with side information and achieves the Berger-Tung sum-rate inner bound in the setting of distributed source coding for computing. It also achieves the El Gamal-Cover inner bound for multiple description coding for computing and is optimal for successive refinement and cascade multiple description problems for computing. Lastly, the benefit of complexity reduction of finding a forward test channel is shown for a class of Markov sources. Wed, 30 Nov 2022 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/58151 2022-11-30T15:00:00Z