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Editorial Introduction to JSTQE Special Issue on Machine Learning in Photonic Communication and Measurement Systems

  • Darko Zibar
  • , Sergei Turitsyn
  • , Bahram Jalali
  • , Keisuke Kojima
  • , Marija Furdek
  • Aston University
  • California State University Los Angeles
  • Chalmers University of Technology

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

The papers in this special section focus on machine learning in photonic communication and measurement systems. From being a niche field within computer science, the field of machine learning has gone mainstream within the last couple of years. The reason is that researchers around the world that work within photonics systems and components design have realized that machine learning brings a new set of highly-effective tools that can be used to: 1) design novel components and systems 2) optimize transmission systems and 3) obtain more accurate measurements. Indeed, using machine learning to design the next generation of components and systems as well as measurement systems is an emerging line of research in the photonics community.
Original languageEnglish
Article number9851897
JournalIEEE Journal of Selected Topics in Quantum Electronics
Volume28
Issue number4
Pages (from-to)3-3
ISSN1558-4542
DOIs
Publication statusPublished - 1 Aug 2022

Keywords

  • Special issues and sections
  • Machine learning
  • Neural networks
  • Optical beams
  • Optical filters
  • Optical fibers
  • Optical diffraction
  • Optical coupling

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