arXiv subjects

Yeongjun Kim

Publications and source records attributed to Yeongjun Kim.

10 recordsLinked to original sources

arXiv2026

Nematic Phase Transitions in 1D Flat Band Condensates

We investigate the ground-state properties of one-dimensional Gross-Pitaevskii flat-band lattices which are parametrized through their compact localized state (CLS) amplitudes. We uncover a CLS geometry-driven phase transition into a macroscopically degenerate nematic state with broken time-reversal symmetry. The transition is marked by the appearance of constant-density flat-band states and a vanishing sound velocity. We demonstrate that even infinitesimal onsite interactions can destabilize a $k=0$ plane wave condensate, driving the system into a nematic manifold, which persists for any interaction strength. For the particular choice of constant density CLSs which can tile the lattice, we identify additional families of continuously degenerate ground states characterized by vanishing phase stiffness. Utilizing Bogoliubov-de Gennes excitations and parallel tempering, we show that these tiling phases are thermally selected at low temperatures via an order-by-disorder mechanism. We exemplify our findings for different classes of flat bands. Our findings also reveal that the sound velocity in flat-band condensates is a sensitive probe of the underlying nematic phase transitions.

cond-mat.stat-mech↗

arXiv2026

User-Centric Stream Sensing for Grant-Free Access: Deep Learning with Covariance Differencing

Grant-free (GF) access is essential for massive connectivity but faces collision risks due to uncoordinated transmissions. While user-side sensing can mitigate these collisions by enabling autonomous transmission decisions, conventional methods become ineffective in overloaded scenarios where active streams exceed receive antennas. To address this problem, we propose a differential stream sensing framework that reframes the problem from estimating the total stream count to isolating newly activated streams via covariance differencing. We analyze the covariance deviation induced by channel variations to establish a theoretical bound based on channel correlation for determining the sensing window size. To mitigate residual interference from finite sampling, a deep learning (DL) classifier is integrated. Simulations across both independent and identically distributed flat Rayleigh fading and standardized channel environments demonstrate that the proposed method consistently outperforms non-DL baselines and remains robust in overloaded scenarios.

eess.SP↗

arXiv2025

Real space decay of flat band projectors from compact localized states

Flatbands (FB) with compact localized eigenstates (CLS) fall into three main categories, controlled by the algebraic properties of the CLS set: orthogonal, linearly independent, linearly dependent (singular). A CLS parametrization allows us to continuously tune a linearly independent FB into a limiting orthogonal or a linearly dependent (singular) one. We derive the asymptotic real space decay of the flat band projectors for each category. The linearly independent FB is characterized by an exponentially decaying projector and a corresponding localization length $\xi$, all dressed by an algebraic prefactor. In the orthogonal limit, the localization length is $\xi=0$, and the projector is compact. The singular FB limit corresponds to $\xi \rightarrow \infty$ with an emerging power law decay of the projector. We obtain analytical estimates for the localization length and the algebraic power law exponents depending on the dimension of the lattice and the number of bands involved. Numerical results are in excellent agreement with the analytics. Our results are of relevance for the understanding of the details of the FB quantum metric discussed in the context of FB superconductivity, the impact of disorder, and the response to local driving.

cond-mat.mes-hall↗

arXiv2025

Realization and characterization of an all-bands-flat electrical lattice

We construct an electrical all-bands-flat (ABF) lattice and experimentally generate compact localized states (CLSs) therein. The lattice is a diamond (rhombic) chain and implemented as a network of capacitors and inductors, as well as voltage inverters (using operational amplifiers) in order to introduce a $\pi$-phase flux within each diamond. The network's normal modes split into three flat bands, and the corresponding CLSs can be excited in isolation via a two-node driving at the flat band frequencies. We also examine the role of the lattice edges and their interaction with the CLSs. Finally, we compare the experimental results to tight-binding predictions and obtain very good agreement. This analysis paves the way for further experimental implementations of ABF systems in electric networks, especially with an eye towards exploring their interplay with nonlinearity.

cond-mat.mes-hall↗

arXiv2025

Joint Spectrum Sensing and Resource Allocation for OFDMA-based Underwater Acoustic Communications

Underwater acoustic (UWA) communications generally rely on cognitive radio (CR)-based ad-hoc networks due to challenges such as long propagation delay, limited channel resources, and high attenuation. To address the constraints of limited frequency resources, UWA communications have recently incorporated orthogonal frequency division multiple access (OFDMA), significantly enhancing spectral efficiency (SE) through multiplexing gains. Still, {the} low propagation speed of UWA signals, combined with {the} dynamic underwater environment, creates asynchrony in multiple access scenarios. This causes inaccurate spectrum sensing as inter-carrier interference (ICI) increases, which leads to difficulties in resource allocation. As efficient resource allocation is essential for achieving high-quality communication in OFDMA-based CR networks, these challenges degrade communication reliability in UWA systems. To resolve the issue, we propose an end-to-end sensing and resource optimization method using deep reinforcement learning (DRL) in an OFDMA-based UWA-CR network. Through extensive simulations, we confirm that the proposed method is superior to baseline schemes, outperforming other methods by 42.9 % in SE and 4.4 % in communication success rate.

eess.SP↗

arXiv2025

Mobility edges and fractal states in quasiperiodic Gross-Pitaevskii chains

We explore properties of a Gross-Pitaevskii chain subject to an incommensurate periodic potential, i.e., a nonlinear Aubry-Andre model. We show that the condensate crucially impacts the properties of the elementary excitations. In contrast to the conventional linear Aubry-Andre model, the boundary between localized and extended states (mobility edge) exhibits nontrivial branching. For instance, in the high-density regime, tongues of extended phases at intermediate energies penetrate the domain of localized states. In the low-density case, the situation is opposite, and tongues of localized phases emerge. Moreover, intermediate critical (fractal) states are observed. The low-energy phonon part of the spectrum is robust against the incommensurate potential. Our study shows that accounting for interactions, already at the classical level, lead to highly nontrivial behavior of the elementary excitation spectrum.

cond-mat.dis-nn↗

arXiv2023

Compact Localized States in Electric Circuit Flatband Lattices

We generate compact localized states in an electrical diamond lattice, comprised of only capacitors and inductors, via local driving near its flatband frequency. We compare experimental results to numerical simulations and find very good agreement. We also examine the stub lattice, which features a flatband of a different class where neighboring compact localized states share lattice sites. We find that local driving, while exciting the lattice at that flatband frequency, is unable to isolate a single compact localized state due to their non-orthogonality. Finally, we introduce lattice nonlinearity and showcase the realization of nonlinear compact localized states in the diamond lattice. Our findings pave the way of applying flatband physics to complex electric circuit dynamics.

cond-mat.mes-hall↗

arXiv2022

Flat Band Induced Metal-Insulator Transitions for Weak Magnetic Flux and Spin-Orbit Disorder

We consider manifolds of tunable all-band flat (ABF) lattices in dimensions d = 1, 2, parametrized by a manifold angle parameter {\theta}. We study localization properties of eigenstates in the presence of weak magnetic flux disorder and weak spin-orbit disorder. We demonstrate that weakly disordered ABF lattices are described by effective scale-free models where the disorder strength is scaled out. For weak magnetic flux disorder we observe sub-exponential localization at flatband energies in d = 1, which differs from the usual Anderson localization. We also find diverging localisation length at flatband energies for weak flux values in d = 2, however the character of the eigenstates at these energies is less clear. For weak spin-orbit coupling disorder in d = 2 we identify a tunable metal-insulator transition with mobility edges. We also consider the case of mixed spin-orbit and diagonal disorder and obtain the metal-insulator transition driven by the manifold parameter {\theta}.

cond-mat.mes-hall↗

arXiv2021

Metal-insulator transition in infinitesimally weakly disordered flatbands

We study the effect of infinitesimal onsite disorder on d-dimensional all bands flat lattices. The lattices are generated from diagonal Hamiltonians by a sequence of (d + 1) local unitary transformations parametrized by angles $θ_i$. Without loss of generality, we consider the case of two flat bands separated by a finite gap $Δ$. The perturbed states originating from the flat bands are described by an effective tight binding network with finite on- and off-diagonal disorder strength which depends on the manifold angles $θ_i$. The original infinitesimal onsite disorder strength W is only affecting the overall scale of the effective Hamiltonian. Upon variation of the manifold angles for d = 1 and d = 2 we find that localization persists for any choice of local unitaries, and the localization length can be maximized for specific values of $θ_i$. Instead, in d = 3 we identify a non-perturbative metal-insulator transition upon varying the all bands flat manifold angles.

cond-mat.dis-nn↗

arXiv2020

Privacy-Protection Drone Patrol System based on Face Anonymization

The robot market has been growing significantly and is expected to become 1.5 times larger in 2024 than what it was in 2019. Robots have attracted attention of security companies thanks to their mobility. These days, for security robots, unmanned aerial vehicles (UAVs) have quickly emerged by highlighting their advantage: they can even go to any hazardous place that humans cannot access. For UAVs, Drone has been a representative model and has several merits to consist of various sensors such as high-resolution cameras. Therefore, Drone is the most suitable as a mobile surveillance robot. These attractive advantages such as high-resolution cameras and mobility can be a double-edged sword, i.e., privacy infringement. Surveillance drones take videos with high-resolution to fulfill their role, however, those contain a lot of privacy sensitive information. The indiscriminate shooting is a critical issue for those who are very reluctant to be exposed. To tackle the privacy infringement, this work proposes face-anonymizing drone patrol system. In this system, one person's face in a video is transformed into a different face with facial components maintained. To construct our privacy-preserving system, we have adopted the latest generative adversarial networks frameworks and have some modifications on losses of those frameworks. Our face-anonymzing approach is evaluated with various public face-image and video dataset. Moreover, our system is evaluated with a customized drone consisting of a high-resolution camera, a companion computer, and a drone control computer. Finally, we confirm that our system can protect privacy sensitive information with our face-anonymzing algorithm while preserving the performance of robot perception, i.e., simultaneous localization and mapping.

cs.CV↗