arXiv subjects

Jun Seong Lee

Publications and source records attributed to Jun Seong Lee.

2 recordsLinked to original sources

arXiv2026

LQ-rPPG: A Label-Quantized Coarse-to-Fine Learning Framework for Remote Physiological Measurement

Remote photoplethysmography (rPPG) enables non-contact measurement of physiological signals from facial videos, offering strong potential for remote healthcare and daily health monitoring. Driven by this potential, various deep learning-based rPPG methods have been proposed to improve rPPG estimation. However, previous deep learning-based rPPG methods have paid little attention to the quality of training labels and their impact on model learning. Contact-based PPG signals used as training labels often contain noise and variability caused by motion artifacts, inconsistent sensor contact, and morphological distortions. Such label inconsistency can lead models to overfit to the label noise and variability and consequently degrade generalization performance. To address this issue, we propose LQ-rPPG, a label-quantized coarse-to-fine learning framework for robust rPPG estimation. LQ-rPPG consists of a label quantization module and a coarse-to-fine rPPG estimation model. The label quantization module transforms continuous PPG signals into multi-bit quantized pseudo labels with reduced noise and variability. The coarse-to-fine estimation model progressively refines rPPG signals under hierarchical supervision guided by the multi-bit pseudo labels. This design alleviates overfitting to label-specific variations and enables the model to learn structured and consistent representations. As a result, LQ-rPPG achieves robust and generalizable rPPG estimation even under challenging conditions. Experiments on multiple benchmark datasets demonstrate that LQ-rPPG achieves strong performance in both intra- and cross-dataset evaluations, while reducing parameters and multiply-accumulate operations by 88% and 29%, respectively, and increasing throughput by 191%. The code is available at https://github.com/Anonymous-repo-code/LQ-rPPG.

cs.CV↗

arXiv2020

First-principles-derived effective mass approximation for the improved description of quantum nanostructures

The effective mass approximation (EMA) could be an efficient method for the computational study of semiconductor nanostructures with sizes too large to be handled by first-principles calculations, but the scheme to accurately and reliably introduce EMA parameters for given nanostructures remains to be devised. Herein, we report on an EMA approach based on first-principles-derived data, which enables accurate predictions of the optoelectronic properties of quantum nanostructures. For the CdS/ZnS core/shell quantum rods, for which we recently reported its experimental synthesis, we first carry out density functional theory (DFT) calculations for an infinite nanowire to obtain the nanoscopic dielectric constant, effective mass, and Kohn-Sham potential. The DFT-derived data are then transferred to the finite nanorod cases to set up the EMA equations, from which we estimate the photoluminescence (PL) characteristics. Compared with the corresponding method based on bulk EMA parameters and abrupt potential, we confirm that our EMA approach more accurately describes the PL properties of nanorods. We find that, in agreement with the experimentally observed trends, the optical gap of nanorods is roughly determined by the nanorod diameter and the PL intensity is reduced with increasing the nanorod length. The developed methodology is additionally applied to CdSe nanoplatelets, where reliable experimental data became recently available. Here, we again obtain excellent agreements between calculated and measured optical gap values, confirming the generality of our approach. It is finally shown that the abrupt confinement potential approximation most adversely affects the accuracy of EMA simulations.

cond-mat.mes-hall↗