Abstract

Fast power transients in optical communications networks are described quantitatively by means of the wavelet transform method. Their behavior can be then analyzed using an artificial neural networks model. The proposed method permits the detection and classification of the fast power transients. Numerical simulation examples are presented.

© 2006 Optical Society of America

Automatic classification of atherosclerotic tissue in intravascular optical coherence tomography images

Ping Zhou, Tongjing Zhu, Chunliu He, and Zhiyong Li
J. Opt. Soc. Am. A 34(7) 1152-1159 (2017)

Confidentiality-preserving machine learning algorithms for soft-failure detection in optical communication networks

Moises Felipe Silva, Andrea Sgambelluri, Alessandro Pacini, Francesco Paolucci, Andre Green, David Mascarenas, and Luca Valcarenghi
J. Opt. Commun. Netw. 15(8) C212-C222 (2023)

Power distribution in two-dimensional optical network channels

Dong-Xue Wang and Mohammad A. Karim
Appl. Opt. 35(11) 1911-1916 (1996)

Performance limitations of a free-space optical communication satellite network owing to vibrations: heterodyne detection

Shlomi Arnon, Stanley R. Rotman, and Norman S. Kopeika
Appl. Opt. 37(27) 6366-6374 (1998)

Low-Power Low-Latency Optical Network Architecture for Memory Access Communication

Yue Wang, Huaxi Gu, Kang Wang, Yintang Yang, and Kun Wang
J. Opt. Commun. Netw. 8(10) 757-764 (2016)