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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. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. Learned perturbation-based digital backpropagation with low complexity for nonlinearity compensation

    Редюк, А. А., Шевелев, Е. И., Данилко, В. Р., Bazarov, T., Senko, M., Samodelkin, L., Nanii, O., Treshchikov, V. & Федорук, М. П., 5 Dec 2025, In: OSA Continuum. 4, 12, 18 p., 2896-2913.

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

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