Abstract

New developments in radiation and photonic detectors improve resolution, sensitivity, size, and rate, all of which contribute to a gigantic increase in the data production rate. Moving data analysis and compression adjacent or even embedded within the detector hardware will reduce the data volumes generated, thereby reducing material cost, power, and data management requirements. Several solutions are already being developed both on the hardware and on the software side to facilitate the use of machine learning as a real-time data analysis solution.

© 2022 Optica Publishing Group

Machine learning methods for identification and classification of events in ϕ-OTDR systems: a review

Deus F. Kandamali, Xiaomin Cao, Manling Tian, Zhiyan Jin, Hui Dong, and Kuanglu Yu
Appl. Opt. 61(11) 2975-2997 (2022)

At the intersection of optics and deep learning: statistical inference, computing, and inverse design

Deniz Mengu, Md Sadman Sakib Rahman, Yi Luo, Jingxi Li, Onur Kulce, and Aydogan Ozcan
Adv. Opt. Photon. 14(2) 209-290 (2022)

Machine learning in analytical spectroscopy for nuclear diagnostics [Invited]

Ashwin P. Rao, Phillip R. Jenkins, Ryan E. Pinson, John D. Auxier II, Michael B. Shattan, and Anil K. Patnaik
Appl. Opt. 62(6) A83-A109 (2023)

Advances in machine learning optimization for classical and quantum photonics

M. Sanchez, C. Everly, and P. A. Postigo
J. Opt. Soc. Am. B 41(2) A177-A190 (2024)

Machine learning techniques for quality of transmission estimation in optical networks

Yvan Pointurier
J. Opt. Commun. Netw. 13(4) B60-B71 (2021)