StableLev: Data-Driven Stability Enhancement forMulti-Particle Acoustic Levitation
Acoustic levitation technique often encounters challenges with maintaining stable positions and movements of particles in mid-air. Here, we present StableLev, a data-driven pipeline for the detection and amendment of instabilities in multi-particle levitation. With our curation of a first-of-its-kind comprehensive dataset, we design an AutoEncoder model to detect anomalies in the simulation data that correspond to actual particle drops. To amend these instabilities, the acoustic field is reconstructed at the identified problematic areas, leading to enhanced particle stability. Our work provides new insights into multi-particle levitation and enhances its robustness, which will be valuable in a wide range of applications.
Authors: Lei Gao, Giorgos Christopoulos, Prateek Mittal, Ryuji Hirayama, Sriram Subramanian
The full paper is available at: https://doi.org/10.1145/3613904.3642286
The dataset is available at: https://github.com/Lei-Oriana/StableLev