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Deep learning with synthetic photonic lattices for equalization in optical transmission systems. / Pankov, Artem V.; Sidelnikov, Oleg S.; Vatnik, Ilya D. et al.

Real-Time Photonic Measurements, Data Management, and Processing IV. ed. / Ming Li; Bahram Jalali; Mohammad Hossein Asghari. SPIE, 2019. 111920N (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11192).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Pankov, AV, Sidelnikov, OS, Vatnik, ID, Sukhorukov, AA & Churkin, DV 2019, Deep learning with synthetic photonic lattices for equalization in optical transmission systems. in M Li, B Jalali & MH Asghari (eds), Real-Time Photonic Measurements, Data Management, and Processing IV., 111920N, Proceedings of SPIE - The International Society for Optical Engineering, vol. 11192, SPIE, Real-Time Photonic Measurements, Data Management, and Processing IV 2019, Hangzhou, China, 22.10.2019. https://doi.org/10.1117/12.2537462

APA

Pankov, A. V., Sidelnikov, O. S., Vatnik, I. D., Sukhorukov, A. A., & Churkin, D. V. (2019). Deep learning with synthetic photonic lattices for equalization in optical transmission systems. In M. Li, B. Jalali, & M. H. Asghari (Eds.), Real-Time Photonic Measurements, Data Management, and Processing IV [111920N] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11192). SPIE. https://doi.org/10.1117/12.2537462

Vancouver

Pankov AV, Sidelnikov OS, Vatnik ID, Sukhorukov AA, Churkin DV. Deep learning with synthetic photonic lattices for equalization in optical transmission systems. In Li M, Jalali B, Asghari MH, editors, Real-Time Photonic Measurements, Data Management, and Processing IV. SPIE. 2019. 111920N. (Proceedings of SPIE - The International Society for Optical Engineering). doi: 10.1117/12.2537462

Author

Pankov, Artem V. ; Sidelnikov, Oleg S. ; Vatnik, Ilya D. et al. / Deep learning with synthetic photonic lattices for equalization in optical transmission systems. Real-Time Photonic Measurements, Data Management, and Processing IV. editor / Ming Li ; Bahram Jalali ; Mohammad Hossein Asghari. SPIE, 2019. (Proceedings of SPIE - The International Society for Optical Engineering).

BibTeX

@inproceedings{16a016ad8a394b34a5074b41a1af78d1,
title = "Deep learning with synthetic photonic lattices for equalization in optical transmission systems",
abstract = "In this work we propose a new physical realization of optical neural network (ONN) based on a recently appeared technological platform of synthetic photonic lattices (SPL), and demonstrate its capabilities for deep learning. The system operates with time series of optical pulses with ability to easily control their parameters and possesses the architecture that well suits the ONN paradigm. We have also shown that such an ONN can be potentially utilized for signal processing in optical communication lines for signal distortion compensation.",
keywords = "Deep learning, Optical Transmission Systems, Synthetic Photonic Lattices",
author = "Pankov, {Artem V.} and Sidelnikov, {Oleg S.} and Vatnik, {Ilya D.} and Sukhorukov, {Andrey A.} and Churkin, {Dmitriy V.}",
year = "2019",
month = nov,
day = "20",
doi = "10.1117/12.2537462",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Ming Li and Bahram Jalali and Asghari, {Mohammad Hossein}",
booktitle = "Real-Time Photonic Measurements, Data Management, and Processing IV",
address = "United States",
note = "Real-Time Photonic Measurements, Data Management, and Processing IV 2019 ; Conference date: 22-10-2019 Through 23-10-2019",

}

RIS

TY - GEN

T1 - Deep learning with synthetic photonic lattices for equalization in optical transmission systems

AU - Pankov, Artem V.

AU - Sidelnikov, Oleg S.

AU - Vatnik, Ilya D.

AU - Sukhorukov, Andrey A.

AU - Churkin, Dmitriy V.

PY - 2019/11/20

Y1 - 2019/11/20

N2 - In this work we propose a new physical realization of optical neural network (ONN) based on a recently appeared technological platform of synthetic photonic lattices (SPL), and demonstrate its capabilities for deep learning. The system operates with time series of optical pulses with ability to easily control their parameters and possesses the architecture that well suits the ONN paradigm. We have also shown that such an ONN can be potentially utilized for signal processing in optical communication lines for signal distortion compensation.

AB - In this work we propose a new physical realization of optical neural network (ONN) based on a recently appeared technological platform of synthetic photonic lattices (SPL), and demonstrate its capabilities for deep learning. The system operates with time series of optical pulses with ability to easily control their parameters and possesses the architecture that well suits the ONN paradigm. We have also shown that such an ONN can be potentially utilized for signal processing in optical communication lines for signal distortion compensation.

KW - Deep learning

KW - Optical Transmission Systems

KW - Synthetic Photonic Lattices

UR - http://www.scopus.com/inward/record.url?scp=85078327501&partnerID=8YFLogxK

U2 - 10.1117/12.2537462

DO - 10.1117/12.2537462

M3 - Conference contribution

AN - SCOPUS:85078327501

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Real-Time Photonic Measurements, Data Management, and Processing IV

A2 - Li, Ming

A2 - Jalali, Bahram

A2 - Asghari, Mohammad Hossein

PB - SPIE

T2 - Real-Time Photonic Measurements, Data Management, and Processing IV 2019

Y2 - 22 October 2019 through 23 October 2019

ER -

ID: 23577340