Enhanced Light Control in Transmission and Reflection through a Dynamically Deformed Multimode Fiber with Deep Learning

Pengfei Fan*, Yufei Wang, Michael Ruddlesden, Chao Zuo, Lei Su

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

We present a continual deep-learning framework for characterizing a dynamically deformed multimode fiber (MMF). It enables real-time self-adaptive focus control using transmission and reflection synchronously, addressing challenges like imaging system drift and fiber distal access.

Original languageEnglish
Title of host publication2024 Conference on Lasers and Electro-Optics, CLEO 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781957171395
DOIs
Publication statusPublished - 2024
Event2024 Conference on Lasers and Electro-Optics, CLEO 2024 - Charlotte, United States
Duration: 7 May 202410 May 2024

Publication series

Name2024 Conference on Lasers and Electro-Optics, CLEO 2024

Conference

Conference2024 Conference on Lasers and Electro-Optics, CLEO 2024
Country/TerritoryUnited States
CityCharlotte
Period7/05/2410/05/24

Keywords

  • Deep learning
  • Electro-optical waveguides
  • Fiber lasers
  • Imaging
  • Lasers and electrooptics
  • Real-time systems
  • Reflection

Fingerprint

Dive into the research topics of 'Enhanced Light Control in Transmission and Reflection through a Dynamically Deformed Multimode Fiber with Deep Learning'. Together they form a unique fingerprint.

Cite this