arXiv subjects

Gaeun Kim

Publications and source records attributed to Gaeun Kim.

10 recordsLinked to original sources

arXiv2026

Clock-Shift-Aided Blind Doppler Matching in Passive SDR Monitoring Data for LEO Doppler Positioning

Low Earth orbit (LEO) satellite signals have recently attracted increasing attention as opportunistic signals for positioning, navigation, and timing. Most Doppler-based LEO positioning studies rely on tracked satellite signals or raw in-phase/quadrature (IQ) samples that preserve signal-level structures for acquisition, Doppler tracking, or satellite association. In contrast, passive software-defined radio (SDR) monitoring systems often retain only time-frequency power scans, in which LEO Doppler traces appear as unlabeled ridges without raw IQ samples or identifiable signal structures. This paper proposes a clock-shift-aided segment-based blind Doppler matching method that associates unlabeled Doppler traces with candidate satellites predicted using two-line element (TLE) data. The proposed framework consists of slope-consistent Doppler segment generation, clock-shift-aided temporal alignment, and multi-metric satellite matching based on Doppler-rate and trajectory-shape consistency. The proposed method was evaluated using a three-hour subset of open Starlink/OneWeb monitoring data collected in the United Kingdom. The clock-offset search selected an effective offset of +19 s, for which 56 of 69 segments had at least one slope-compatible candidate, compared with 19 segments at zero shift. Using the resulting satellite associations, the batch positioning error over the full three-hour period was 104.2 m, whereas the RMSE-based and slope-based simple-segment baselines failed to converge near the reference receiver location.

eess.SP↗

arXiv2026

Explainable AI-Driven Neural Activity Analysis in Parkinsonian Rats under Electrical Stimulation

Parkinson's disease (PD) is characterized by motor dysfunction and abnormal neural oscillations. This study combined graphene-based cortical stimulation with an explainable artificial intelligence (XAI) framework to investigate spatial and frequency-related features contributing to pre- and post-stimulation classification in Parkinsonian rats. Electrocorticogram (ECoG) signals were recorded before and after stimulation and classified using EEGNet and DeepConvNet. Layer-wise relevance propagation (LRP) and DeepLIFT were applied to identify frequency bands and recording channels contributing to the classification. The anatomical locations of these channels were mapped onto a cortical map. XAI analyses were performed separately for each animal to examine individual attribution patterns. Our XAI analysis revealed frequency-band- and region-specific contributions to pre- and post-stimulation classification in Parkinsonian rats. Our approach provided complementary insights into the features contributing to pre- and post-stimulation ECoG classification alongside PSD-based analyses. These findings highlight the potential of combining graphene-based cortical stimulation with explainable deep learning to identify classification-relevant features across frequency bands and cortical areas in pre- and post-stimulation recordings.

q-bio.NC↗

arXiv2026

On-Chain Behavioral Signatures for Classifying Centralized Exchange Addresses on Ethereum Using Proof-of-Reserves

Centralized exchanges (CEXs) are key gateways between traditional finance and digital asset markets, yet identifying their blockchain addresses remains challenging. Existing approaches often rely on opaque attribution labels or treat exchanges as secondary classification targets, leaving generalization to unseen exchanges insufficiently examined. This study develops a classification framework using Proof-of-Reserves (PoR) disclosures, with 3,033,857 Ethereum addresses from 15 CEXs and five Non-CEX categories. We derive 58 on-chain behavioral features and evaluate LightGBM against logistic regression (LR) under a leave-one-exchange-out protocol. Across five lookback windows (30-730 days), LightGBM consistently outperformed LR, with macro PR-AUC increasing from 0.6611 to 0.9594 using the selected 17 features, compared with 0.7697 for LR at 730 days. Robustness checks showed comparable performance when feature selection and hyperparameter optimization were repeated across splits and held-out exchanges, while exchange-balanced reweighting improved macro PR-AUC. SHAP analysis and feature-group ablation identified gas behavior, ERC-20 token activity, and internal transactions as key predictive signals in the constructed dataset. These findings support interpretable CEX address screening for blockchain monitoring, while the released feature dataset enables reproducible follow-up research.

cs.CY↗

arXiv2026

LEO Doppler Matching from Power Spectrum Data with Continuity-Based Segmentation and Multi-Position Clock Offset Estimation

Low Earth orbit (LEO) satellite Doppler measurements extracted from passive software-defined radio (SDR) spectrum data require temporal alignment with predicted satellite trajectories for reliable satellite association. In our previous framework, Doppler slope information was used during both segment generation and clock offset estimation, while the clock offset was estimated at a single coarse receiver position. This study proposes a satellite matching framework based on continuity-based Doppler segmentation and multi-position clock offset estimation to reduce these dependencies. Doppler segments are first generated using only temporal and frequency continuity, thereby separating segment generation from the slope criterion used for clock offset estimation. The clock offset is then estimated using five coarse receiver positions, consisting of one city-level nominal position and four surrounding positions, through a two-stage procedure based on slope compatibility and a combined Doppler cost. The aligned segments are subsequently associated with candidate satellites using a matching score based on the same Doppler slope and bias-removed RMSE metrics, and the resulting associations are evaluated through receiver localization. Experiments using six hours of passive Starlink/OneWeb monitoring data show that continuity-based segmentation alone did not improve receiver localization compared with slope-based segmentation under single-position clock offset estimation. However, applying the proposed multi-position clock offset estimation to the same continuity-based Doppler segments reduced the localization error from 32.99 km to 1.84 km, achieving the lowest error among the evaluated methods. The results for the evaluated dataset show improved spatial consistency of satellite associations when multi-position clock offset estimation is applied to continuity-based Doppler segments.

eess.SP↗

arXiv2026

Cylindrical Mechanical Projector for Omnidirectional Fringe Projection Profilometry

The demand for 360-degree 3D reconstruction has significantly increased in recent years across various domains such as the metaverse and 3D telecommunication. Accordingly, the importance of precise and wide-area 3D sensing technology has become emphasized. While the digital fringe projection method has been widely used due to its high accuracy and implementation flexibility, it suffers from fundamental limitations such as unidirectional projection and a restricted available light spectrum. To address these issues, this paper proposes a novel 3D reconstruction method based on a cylindrical mechanical projector. The proposed method consists of a rotational stage and a cylindrical pattern generator with ON/OFF slots at two distinct intervals, enabling omnidirectional projection of multi-frequency phase-shifted fringe patterns. By applying a multi-wavelength unwrapping algorithm and a quasi-calibration technique, the system achieves high-accuracy 3D reconstruction using only a single camera. Experimental results, supported by repeatability and reproducibility analyses together with a measurement uncertainty evaluation, confirm reliable measurement performance and practical feasibility for omnidirectional 3D reconstruction. The expanded uncertainty of the reconstructed depth was evaluated as 0.215 mm.

cs.CV↗

arXiv2025

Determination of Bandwidth of Q-filter in Disturbance Observers to Guarantee Transient and Steady State Performance under Measurement Noise

Q-filter-based disturbance observer (DOB) is one of the most widely used robust controller due to its design simplicity. Such simplicity arises from that reducing the time constant of low pass filters, not only ensures robust stability but also enhances nominal performance recovery -- ability to recover the trajectory of nominal closed-loop system. However, in contrast to noise-free environment, excessively small time constant can rather damage the nominal performance recovery under measurement noise. That is, minimizing time constant is no longer immediately guaranteeing nominal performance recovery. Motivated by this observation, this paper concentrates on determination of time constant to ensure transient and steady state performance. This analysis uses Lyapunov method based on the coordinate change inspired by the singular perturbation theory. As a result, we present an affordable noise level and open interval for the time constant that guarantees both the required performances. The analysis can also lead to theoretical demonstration on that excessively reducing time constant is assured to achieve target performance only for noise-free case.

eess.SY↗

arXiv2024

StablePrompt: Automatic Prompt Tuning using Reinforcement Learning for Large Language Models

Finding appropriate prompts for the specific task has become an important issue as the usage of Large Language Models (LLM) has expanded. Reinforcement Learning (RL) is widely used for prompt tuning, but its inherent instability and environmental dependency make it difficult to use in practice. In this paper, we propose StablePrompt, which strikes a balance between training stability and search space, mitigating the instability of RL and producing high-performance prompts. We formulate prompt tuning as an online RL problem between the agent and target LLM and introduce Adaptive Proximal Policy Optimization (APPO). APPO introduces an LLM anchor model to adaptively adjust the rate of policy updates. This allows for flexible prompt search while preserving the linguistic ability of the pre-trained LLM. StablePrompt outperforms previous methods on various tasks including text classification, question answering, and text generation. Our code can be found in github.

cs.CL↗

arXiv2024

Team HYU ASML ROBOVOX SP Cup 2024 System Description

This report describes the submission of HYU ASML team to the IEEE Signal Processing Cup 2024 (SP Cup 2024). This challenge, titled "ROBOVOX: Far-Field Speaker Recognition by a Mobile Robot," focuses on speaker recognition using a mobile robot in noisy and reverberant conditions. Our solution combines the result of deep residual neural networks and time-delay neural network-based speaker embedding models. These models were trained on a diverse dataset that includes French speech. To account for the challenging evaluation environment characterized by high noise, reverberation, and short speech conditions, we focused on data augmentation and training speech duration for the speaker embedding model. Our submission achieved second place on the SP Cup 2024 public leaderboard, with a detection cost function of 0.5245 and an equal error rate of 6.46%.

eess.AS↗

arXiv2020

Motion Correction of 3D Dynamic Contrast-Enhanced Ultrasound Imaging without Anatomical Bmode Images

In conventional 2D DCE-US, motion correction algorithms take advantage of accompanying side-by-side anatomical Bmode images that contain time-stable features. However, current commercial models of 3D DCE-US do not provide side-by-side Bmode images, which makes motion correction challenging. This work introduces a novel motion correction (MC) algorithm for 3D DCE-US and assesses its efficacy when handling clinical data sets. In brief, the algorithm uses a pyramidal approach whereby short temporal windows consisting of 3-6 consecutive frames are created to perform local registrations, which are then registered to a master reference derived from a weighted average of all frames. We evaluated the algorithm in 8 patients with metastatic lesions in the liver using the Philips X6-1 matrix transducer at a frame rate of 1-3 Hz. We assessed improvements in original vs. motion corrected 3D DCE-US cine using: i) frame-to-frame volumetric overlap of segmented lesions, ii) normalized correlation coefficient (NCC) between frames (similarity analysis), and iii) sum of squared errors (SSE), root-mean-squared error (RMSE), and r-squared (R2) quality-of-fit from fitted time-intensity curves (TIC) extracted from a segmented lesion. Overall, results demonstrate significant decreases in 3D DCE-US motion after applying the proposed algorithm. We noted significant improvements in frame-to-frame lesion overlap across all patients, from 68% without correction to 83% with motion correction (p = 0.023). Frame-to-frame similarity as assessed by NCC also significantly improved on two different sets of time points from 0.694 (original cine) to 0.862 (corresponding MC cine) and 0.723 to 0.886. TIC analysis displayed a significant decrease in RMSE (p = 0.018) and a significant increase in R2 goodness-of-fit (p = 0.029) for the patient cohort.

eess.IV↗

arXiv2019

Human Gist Processing Augments Deep Learning Breast Cancer Risk Assessment

Radiologists can classify a mammogram as normal or abnormal at better than chance levels after less than a second's exposure to the images. In this work, we combine these radiologists' gist inputs into pre-trained machine learning models to validate that integrating gist with a CNN model can achieve an AUC (area under the curve) statistically significantly higher than either the gist perception of radiologists or the model without gist input.

physics.med-ph↗