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  1. Dynamic neural network-based methods for compensation of nonlinear effects in multimode communication lines

    Sidelnikov, O. S., Redyuk, A. A. & Sygletos, S., 2017, In: Quantum Electronics. 47, 12, p. 1147-1149 3 p.

    Research output: Contribution to journal › Article › peer-review

  2. Efficient Nonlinear Distortion Compensation Algorithms Using Machine Learning for Telecommunication Systems

    Redyuk, A. A., Shevelev, E. I., Sidelnikov, O. S., Danilko, V. R., Bazarov, T. O., Senko, M. A., Samodelkin, L. A., Nanii, O. E., Treshchikov, V. N. & Fedoruk, M. P., Dec 2025, In: Bulletin of the Lebedev Physics Institute. 52, Suppl 11, p. S1172-S1187 16 p., 8.

    Research output: Contribution to journal › Article › peer-review

  3. Enhancing long-term stability of photoacoustic gas sensor using an extremum-seeking control algorithm

    Bednyakova, A., Erushin, E., Miroshnichenko, I., Kostyukova, N., Boyko, A. & Redyuk, A., Sept 2023, In: Infrared Physics and Technology. 133, 6 p., 104821.

    Research output: Contribution to journal › Article › peer-review

  4. Equalization performance and complexity analysis of dynamic deep neural networks in long haul transmission systems

    Sidelnikov, O., Redyuk, A. & Sygletos, S., 10 Dec 2018, In: Optics Express. 26, 25, p. 32765-32776 12 p.

    Research output: Contribution to journal › Article › peer-review

  5. Extreme power fluctuations in optical communications

    Derevyanko, S. A., Redyuk, A., Vergeles, S. & Turitsyn, S., 1 Jan 2018, Frontiers in Optics, FIO 2018. OSA Publishing, Vol. Part F114-FIO 2018. (Optics InfoBase Conference Papers; vol. Part F114-FIO 2018).

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

  6. Identifying Extreme PAPR in Coherent Optical Communications

    Derevyanko, S. A., Redyuk, A., Vergeles, S. & Turitsyn, S., 14 Nov 2018, 2018 European Conference on Optical Communication, ECOC 2018. Institute of Electrical and Electronics Engineers Inc., Vol. 2018-September. 8535373. (European Conference on Optical Communication, ECOC; vol. 2018-September).

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

  7. Intelligent Feature Extraction and Event Classification in Distributed Acoustic Sensing Using Wavelet Packet Decomposition

    Kozmin, A., Borozdin, P., Chernenko, A., Gostilovich, S., Kalashev, O. & Redyuk, A., 11 Nov 2025, In: Technologies. 13, 11, 21 p., 514.

    Research output: Contribution to journal › Article › peer-review

  8. Interchannel nonlinearity compensation using a perturbative machine learning technique

    Kozulin, I. A. & Redyuk, A. A., 15 Aug 2021, In: Optics Communications. 493, 127026.

    Research output: Contribution to journal › Article › peer-review

  9. Interpretation models for data of metal-oxide gas sensors based on machine learning methods

    Kozmin, A. D. & Redyuk, A. A., 2024, In: Journal of Computational Technologies. 29, 4, p. 4-23 20 p.

    Research output: Contribution to journal › Article › peer-review

  10. Invited Article: Visualisation of extreme value events in optical communications

    Derevyanko, S., Redyuk, A., Vergeles, S. & Turitsyn, S., 1 Jun 2018, In: APL Photonics. 3, 6, 13 p., 060801.

    Research output: Contribution to journal › Article › peer-review

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