Yu-Chi Huang | Searcharxiv

arXiv2026

Quantifying chirality of phonons

Recent years have witnessed growing interest in chiral phonons, lattice vibrations carrying angular momentum and exhibiting handedness, as revealed by helicity-dependent optical phenomena. Despite this progress, a quantitative characterization of phonon chirality as a dynamical property has remained elusive. In this work, we propose a theoretical framework to quantify the dynamical chirality of lattice vibrations. We introduce two quantitative measures: momentum-resolved dynamical chirality, which provides a mode- and wave-vector-resolved picture of phonon chirality, and the bulk dynamical chirality, which characterizes the collective behavior of thermally populated chiral phonons. Using first-principles calculations for both chiral and achiral materials, we demonstrate how these quantities capture the handedness and population imbalance of phonon modes and serve as a means to distinguish the enantiomers of chiral crystals.

cond-mat.mtrl-sci↗

arXiv2023

Worker Activity Recognition in Manufacturing Line Using Near-body Electric Field

Manufacturing industries strive to improve production efficiency and product quality by deploying advanced sensing and control systems. Wearable sensors are emerging as a promising solution for achieving this goal, as they can provide continuous and unobtrusive monitoring of workers' activities in the manufacturing line. This paper presents a novel wearable sensing prototype that combines IMU and body capacitance sensing modules to recognize worker activities in the manufacturing line. To handle these multimodal sensor data, we propose and compare early, and late sensor data fusion approaches for multi-channel time-series convolutional neural networks and deep convolutional LSTM. We evaluate the proposed hardware and neural network model by collecting and annotating sensor data using the proposed sensing prototype and Apple Watches in the testbed of the manufacturing line. Experimental results demonstrate that our proposed methods achieve superior performance compared to the baseline methods, indicating the potential of the proposed approach for real-world applications in manufacturing industries. Furthermore, the proposed sensing prototype with a body capacitive sensor and feature fusion method improves by 6.35%, yielding a 9.38% higher macro F1 score than the proposed sensing prototype without a body capacitive sensor and Apple Watch data, respectively.

cs.LG↗