arXiv subjects

Minhee Kang

Publications and source records attributed to Minhee Kang.

16 recordsLinked to original sources

arXiv2026

Knapsack-based Online Sensor Selection for Vehicle State Estimation

As connected and autonomous driving technologies advance, vehicles increasingly rely on data from external sensors. Although this information can enhance state estimation, processing all available streams imposes significant communication and computational costs. To address this challenge, we introduce a Sensor Management Center (SMC) that selects a low-cost subset of external sensors in real time while satisfying chance-constrained error bounds derived from an Extended Kalman Filter (EKF) covariance. We formulate the selection problem as a multidimensional minimum knapsack problem and adopt a deficiency-weighted greedy algorithm as an approximate yet efficient solution. The proposed approach is validated through MATLAB simulations and experiments on a 1:15-scale cooperative driving testbed.

eess.SY↗

arXiv2026

High-Density Automated Valet Parking with Relocation-Free Sequential Operations

In this paper, we present DROP, high-Density Relocation-free sequential OPerations in automated valet parking. DROP addresses the challenges in high-density parking & vehicle retrieval without relocations. Each challenge is handled by jointly providing area-efficient layouts and relocation-free parking & exit sequences, considering accessibility with relocation-free sequential operations. To generate such sequences, relocation-free constraints are formulated as explicit logical conditions expressed in boolean variables. Recursive search strategies are employed to derive the logical conditions and enumerate relocation-free sequences under sequential constraints. We demonstrate the effectiveness of our framework through extensive simulations, showing its potential to significantly improve area utilization with relocation-free constraints. We also examine its viability on an application problem with prescribed operational order. The results from all experiments are available at: https://drop-park.github.io.

eess.SY↗

arXiv2026

HyDRA: Hybrid Domain-Aware Robust Architecture for Heterogeneous Collaborative Perception

In collaborative perception, an agent's performance can be degraded by heterogeneity arising from differences in model architecture or training data distributions. To address this challenge, we propose HyDRA (Hybrid Domain-Aware Robust Architecture), a unified pipeline that integrates intermediate and late fusion within a domain-aware framework. We introduce a lightweight domain classifier that dynamically identifies heterogeneous agents and assigns them to the late-fusion branch. Furthermore, we propose anchor-guided pose graph optimization to mitigate localization errors inherent in late fusion, leveraging reliable detections from intermediate fusion as fixed spatial anchors. Extensive experiments demonstrate that, despite requiring no additional training, HyDRA achieves performance comparable to state-of-the-art heterogeneity-aware CP methods. Importantly, this performance is maintained as the number of collaborating agents increases, enabling zero-cost scaling without retraining.

cs.CV↗

arXiv2026

Giant Hole-doping in 2H-WSe2 via Ta Substitution

The family of transition metal dichalcogenides (TMDCs) has been regarded as promising candidates for future electronics, valleytronics, spintronics, and optoelectronics. While most of TMDCs are intrinsic n-type semiconductors due to electron donation from chalcogen vacancies, realizing intrinsic p-type TMDCs and achieving precise control over their electronic properties remain challenging. In this work, we introduce a powerful approach to obtain intrinsic hole doping by substituting Ta atom into 2H-WSe2. A combining study of molecular beam epitaxy growth and in-situ angle-resolved photoemission spectroscopy characterization clearly reveals that Ta substitution induces a significant hole doping and provides a possible way of a semiconductor-to-metal transition in 2H-WSe2.

cond-mat.mtrl-sci↗

arXiv2025

Miniature Testbed for Validating Multi-Agent Cooperative Autonomous Driving

Cooperative autonomous driving, which extends vehicle autonomy by enabling real-time collaboration between vehicles and smart roadside infrastructure, remains a challenging yet essential problem. However, none of the existing testbeds employ smart infrastructure equipped with sensing, edge computing, and communication capabilities. To address this gap, we design and implement a 1:15-scale miniature testbed, CIVAT, for validating cooperative autonomous driving, consisting of a scaled urban map, autonomous vehicles with onboard sensors, and smart infrastructure. The proposed testbed integrates V2V and V2I communication with the publish-subscribe pattern through a shared Wi-Fi and ROS2 framework, enabling information exchange between vehicles and infrastructure to realize cooperative driving functionality. As a case study, we validate the system through infrastructure-based perception and intersection management experiments.

cs.RO↗

arXiv2025

Advances in imaging techniques for the study of individual bacteria and their pathophysiology

Bacterial heterogeneity is pivotal for adaptation to diverse environments, posing significant challenges in microbial diagnostics and therapeutic interventions. Recent advancements in high-resolution optical microscopy have revolutionized our ability to observe and characterize individual bacteria, offering unprecedented insights into their metabolic states and behaviors at the single-cell level. This review discusses the transformative impact of various high-resolution imaging techniques, including fluorescence and label-free imaging, which have enhanced our understanding of bacterial pathophysiology. These methods provide detailed visualizations that are crucial for developing targeted treatments and improving clinical diagnostics. We highlight the integration of these imaging techniques with computational tools, which has facilitated rapid, accurate pathogen identification and real-time monitoring of bacterial responses to treatments. The ongoing development of these optical imaging technologies promises to significantly advance our understanding of microbiology and to catalyze the translation of these insights into practical healthcare solutions.

physics.bio-ph↗

arXiv2024

Rethinking the Role of Infrastructure in Collaborative Perception

Collaborative Perception (CP) is a process in which an ego agent receives and fuses sensor information from surrounding vehicles and infrastructure to enhance its perception capability. To evaluate the need for infrastructure equipped with sensors, extensive and quantitative analysis of the role of infrastructure data in CP is crucial, yet remains underexplored. To address this gap, we first quantitatively assess the importance of infrastructure data in existing vehicle-centric CP, where the ego agent is a vehicle. Furthermore, we compare vehicle-centric CP with infra-centric CP, where the ego agent is now the infrastructure, to evaluate the effectiveness of each approach. Our results demonstrate that incorporating infrastructure data improves 3D detection accuracy by up to 10.30%, and infra-centric CP shows enhanced noise robustness and increases accuracy by up to 46.47% compared with vehicle-centric CP.

cs.CV↗

arXiv2024

A GPT-based Decision Transformer for Multi-Vehicle Coordination at Unsignalized Intersections

In this paper, we explore the application of the Decision Transformer, a decision-making algorithm based on the Generative Pre-trained Transformer (GPT) architecture, to multi-vehicle coordination at unsignalized intersections. We formulate the coordination problem so as to find the optimal trajectories for multiple vehicles at intersections, modeling it as a sequence prediction task to fully leverage the power of GPTs as a sequence model. Through extensive experiments, we compare our approach to a reservation-based intersection management system. Our results show that the Decision Transformer can outperform the training data in terms of total travel time and can be generalized effectively to various scenarios, including noise-induced velocity variations, continuous interaction environments, and different vehicle numbers and road configurations.

cs.RO↗

arXiv2024

Band-selective simulation of photoelectron intensity and converging Berry phase in trilayer graphene

Berry phase is one of the key elements to understand quantum-mechanical phenomena such as the Aharonov-Bohm effect and the unconventional Hall effect in graphene. The Berry phase in monolayer and bilayer graphene has been manifested by the anisotropic distribution of photoelectron intensity along a closed loop in the momentum space as well as its rotation by a characteristic angle upon rotating light polarization. Here we report the band-selective simulation of photoelectron intensity of trilayer graphene to understand its Berry phase within the tight-binding formalism. ABC- and ABA-stacked trilayer graphene show characteristic rotational angles of photoelectron intensity distribution, as predicted from their well-known Berry phases. Surprisingly, however, in ABA-stacked trilayer graphene, the rotational angle changes upon approaching toward the band touching point between the conduction and valence bands, which suggest that Berry phase changes as a function of binding energy. The binding energy-dependent Berry phase is attributed to the enhanced hybridization of the two electron bands of ABA-stacked trilayer graphene that converge at the band touching point, resulting in the converging Berry phase. These findings will provide an efficient way of tuning Berry phase and hence exotic phenomena stemming from the Berry phase.

cond-mat.mtrl-sci↗

arXiv2024

ParCon: Noise-Robust Collaborative Perception via Multi-module Parallel Connection

In this paper, we investigate improving the perception performance of autonomous vehicles through communication with other vehicles and road infrastructures. To this end, we introduce a novel collaborative perception architecture, called ParCon, which connects multiple modules in parallel, as opposed to the sequential connections used in most other collaborative perception methods. Through extensive experiments, we demonstrate that ParCon inherits the advantages of parallel connection. Specifically, ParCon is robust to noise, as the parallel architecture allows each module to manage noise independently and complement the limitations of other modules. As a result, ParCon achieves state-of-the-art accuracy, particularly in noisy environments, such as real-world datasets, increasing detection accuracy by 6.91%. Additionally, ParCon is computationally efficient, reducing floating-point operations (FLOPs) by 11.46%.

cs.CV↗

arXiv2021

Tunable Kondo resonance at a pristine two-dimensional Dirac semimetal on a Kondo insulator

Proximity of two different materials leads to an intricate coupling of quasiparticles so that an unprecedented electronic state is often realized at the interface. Here, we demonstrate a resonance-type many-body ground state in graphene, a non-magnetic two-dimensional Dirac semimetal, when grown on SmB6, a Kondo insulator, via thermal decomposition of fullerene molecules. This ground state is typically observed in three-dimensional magnetic materials with correlated electrons. Above the characteristic Kondo temperature of the substrate, the electron band structure of pristine graphene remains almost intact. As temperature decreases, however, the Dirac fermions of graphene become hybridized with the Sm 4f states. Remarkable enhancement of the hybridization and Kondo resonance is observed with further cooling and increasing charge carrier density of graphene, evidencing the Kondo screening of the Sm 4f local magnetic moment by the conduction electrons of graphene at the interface. These findings manifest the realization of the Kondo effect in graphene by the proximity of SmB6 that is tuned by temperature and charge carrier density of graphene.

cond-mat.str-el↗

arXiv2020

Concomitant enhancement of electron-phonon coupling and electron-electron interaction in graphene decorated with ytterbium

The interplay between electron-electron interaction and electron-phonon coupling has been one of the key issues in graphene as it can provide information on the origin of enhanced electron-phonon coupling in graphene by foreign atoms. In ytterbium-decorated graphene on SiC substrate, electron-phonon coupling exhibits strong enhancement compared to that of as-grown graphene. Based on angle-resolved photoemission study, the presence of ytterbium is also found to result in the decrease of Fermi velocity, revealing the enhancement of electron-electron interaction within the Fermi liquid theory. Our finding on the concomitant enhancement of electron-electron interaction and electron-phonon coupling suggests a possibility of the interplay between the two representative many-body interactions in graphene decorated with foreign atoms.

cond-mat.mtrl-sci↗

arXiv2020

A plausible method of preparing the ideal p-n junction interface of a thermoelectric material by surface doping

Recent advances in two-dimensional (2D) crystals make it possible to realize an ideal interface structure that is required for device applications. Specifically, a p-n junction made of 2D crystals is predicted to exhibit an atomically well-defined interface that will lead to high device performance. Using angle-resolved photoemission spectroscopy, a simple surface treatment was shown to allow the possible formation of such an interface. Ta adsorption on the surface of a p-doped SnSe shifts the valence band maximum towards higher binding energy due to the charge transfer from Ta to SnSe that is highly localized at the surface due to the layered structure of SnSe. As a result, the charge carriers of the surface are changed from holes of its bulk characteristics to electrons, while the bulk remains as a p-type semiconductor. This observation suggests that the well-defined interface of a p-n junction with an atomically thin {\it n}-region is formed between Ta-adsorbed surface and bulk.

cond-mat.mtrl-sci↗

arXiv2018

Gapped Nearly Free-Standing Graphene on an SiC(0001) Substrate Induced by Manganese Atoms

The electron band structure of manganese-adsorbed graphene on an SiC(0001) substrate has been studied using angle-resolved photoemission spectroscopy. Upon introducing manganese atoms, the conduction band of graphene completely disappears and the valence band maximum is observed at 0.4 eV below Fermi energy. At the same time, the slope of the valence band decreases, approaching the electron band structure calculated using the local density approximation method. While the former provides experimental evidence of the formation of nearly free-standing graphene on an SiC substrate, concomitant with a metal-to-insulator transition, the latter suggests that its electronic correlations can be modified by foreign atoms. These results pave the way for promising device applications using graphene that is semiconducting and charge neutral.

cond-mat.mtrl-sci↗

arXiv2018

Emergence of Kondo resonance in graphene intercalated with cerium

The interaction between a magnetic impurity, such as cerium (Ce) atom, and surrounding electrons has been one of the core problems in understanding many-body interaction in solid and its relation to magnetism. Kondo effect, the formation of a new resonant ground state with quenched magnetic moment, provides a general framework to describe many-body interaction in the presence of magnetic impurity. In this Letter, a combined study of angle-resolved photoemission (ARPES) and dynamic mean-field theory (DMFT) on Ce-intercalated graphene shows that Ce-induced localized states near Fermi energy, $E_{\rm F}$, hybridized with the graphene $π$ band, exhibit gradual increase in spectral weight upon decreasing temperature. The observed temperature dependence follows the expectations from the Kondo picture in the weak coupling limit. Our results provide a novel insight how Kondo physics emerges in the sea of two-dimensional Dirac electrons.

cond-mat.str-el↗

arXiv2014

Tidal stripping stellar substructures around four metal-poor globular clusters in the Galactic bulge

We investigate the spatial density configuration of stars around four metal-poor globular clusters (NGC 6266, NGC 6626, NGC 6642 and NGC 6723) in the Galactic bulge region using wide-field deep J, H, and K imaging data obtained with the WFCAM near-infrared array on United Kingdom Infrared Telescope. Statistical weighted filtering algorithm for the stars on the color-magnitude diagram is applied in order to sort cluster member candidates from the field star contamination. In two-dimensional isodensity contour maps of the clusters, we find that all of the four globular clusters exhibit strong evidence of tidally stripping stellar features beyond tidal radius, in the form of tidal tail or small density lobes or chunk. The orientations of the extended stellar substructures are likely to be associated with the effect of the dynamic interaction with the Galaxy and the cluster space motion. The observed radial density profiles of the four globular clusters also describe the extended substructures; they depart from theoretical King and Wilson models and have an overdensity feature with a break in a slope of profile at the outer region of clusters. The observed results could imply that four globular clusters in the Galactic bulge region have experienced strong environmental effect such as tidal force or bulge or disk shock of the Galaxy in the dynamical evolution of the globular clusters. These observational results provide us further constraints to understand the evolution of clusters in the Galactic bulge region as well as the formation of the Galaxy.

astro-ph.GA↗