Dong Min Kim

Wenqian Huang, Dong Min Kim, Wenrui Ding et al.

When considering unidirectional communication for unmanned aerial vehicles (UAVs) as flying Base Stations (BSs), either uplink or downlink, the system is limited through the co-channel interference that takes place over line-of-sight (LoS) links. This paper considers two-way communication and takes advantage of the fact that the interference among the ground devices takes place through non-line-of-sight (NLoS) links. UAVs can be deployed at the high altitudes to have larger coverage, while the two-way communication allows to configure the transmission direction. Using these two levers, we show how the system throughput can be maximized for a given deployment of the ground devices.

Hyeongheon Cha, Dong Min Kim, Hye Won Chung et al.

Test-Time Adaptation (TTA) adjusts models using unlabeled test data to handle dynamic distribution shifts. However, existing methods rely on frequent adaptation and high computational cost, making them unsuitable for resource-constrained edge environments. To address this, we propose SNAP, a sparse TTA framework that reduces adaptation frequency and data usage while preserving accuracy. SNAP maintains competitive accuracy even when adapting based on only 1% of the incoming data stream, demonstrating its robustness under infrequent updates. Our method introduces two key components: (i) Class and Domain Representative Memory (CnDRM), which identifies and stores a small set of samples that are representative of both class and domain characteristics to support efficient adaptation with limited data; and (ii) Inference-only Batch-aware Memory Normalization (IoBMN), which dynamically adjusts normalization statistics at inference time by leveraging these representative samples, enabling efficient alignment to shifting target domains. Integrated with five state-of-the-art TTA algorithms, SNAP reduces latency by up to 93.12%, while keeping the accuracy drop below 3.3%, even across adaptation rates ranging from 1% to 50%. This demonstrates its strong potential for practical use on edge devices serving latency-sensitive applications. The source code is available at https://github.com/chahh9808/SNAP.