Machine learning-enhanced random laser sensing: unveiling scattering anisotropy for optical property characterization

Ni D, Klämpfl F, Schmidt M, Hohmann M (2025)

Publication Type: Conference contribution

Publication year: 2025

Journal

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Publisher: SPIE

Book Volume: 13935

Conference Proceedings Title: Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Event location: Munich, DEU

ISBN: 9781510698079

DOI: 10.1117/12.3098437

Abstract

We apply machine learning to enhance diffuse reflectance spectroscopy-random laser sensing, revealing that anisotropy factor g dominates random laser emission in the subdiffusive regime.

Authors with CRIS profile

Dongqin Ni Lehrstuhl für Photonische Technologien (LPT) Florian Klämpfl Lehrstuhl für Photonische Technologien (LPT) Michael Schmidt Lehrstuhl für Photonische Technologien (LPT) Martin Hohmann Lehrstuhl für Photonische Technologien (LPT)

How to cite

APA:

Ni, D., Klämpfl, F., Schmidt, M., & Hohmann, M. (2025). Machine learning-enhanced random laser sensing: unveiling scattering anisotropy for optical property characterization. In Davide Contini, Yoko Hoshi, Thomas D. O'Sullivan (Eds.), Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Munich, DEU: SPIE.

MLA:

Ni, Dongqin, et al. "Machine learning-enhanced random laser sensing: unveiling scattering anisotropy for optical property characterization." Proceedings of the 10th Diffuse Optical Spectroscopy and Imaging, Munich, DEU Ed. Davide Contini, Yoko Hoshi, Thomas D. O'Sullivan, SPIE, 2025.

BibTeX: Download