1. Вступительное слово

    Федорук, М. П. & Moskvichev, V. V., 2025, In: Вычислительные технологии. 30, 4, p. 5-6 2 p.

    Research output: Contribution to journalEditorialpeer-review

  2. Using Computer Vision and Deep Learning for Nanoparticle Recognition on Scanning Probe Microscopy Images: Modified U-net Approach

    Liz, M. F., Nartova, A. V., Matveev, A. V. & Okunev, A. G., 14 Nov 2020, Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020. Institute of Electrical and Electronics Engineers Inc., p. 13-16 4 p. 9303184. (Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

  3. Using Computer Vision and Deep Learning for Cells Recognition

    Kudinov, V. Y., Mashukov, M. Y., Maslova, E. A., Orishchenko, K. E., Okunev, A. G. & Matveev, A. V., 14 Nov 2020, Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020. Institute of Electrical and Electronics Engineers Inc., p. 17-20 4 p. 9303201. (Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

  4. Transfer Learning for Transformer-Based Modeling of Nonlinear Pulse Evolution in Er-Doped Fiber Amplifiers

    Bednyakova, A. E., Gemuzov, A. S., Mishevsky, M. S., Saraeva, K. P., Redyuk, A. A., Mkrtchyan, A. A., Nasibulin, A. G. & Gladush, Y. G., 12 Dec 2025, In: Laser and Photonics Reviews. 14 p., e02014.

    Research output: Contribution to journalArticlepeer-review

  5. Particle Recognition on Transmission Electron Microscopy Images Using Computer Vision and Deep Learning for Catalytic Applications

    Nartova, A. V., Mashukov, M. Y., Astakhov, R. R., Kudinov, V. Y., Matveev, A. V. & Okunev, A. G., Feb 2022, In: Catalysts. 12, 2, 135.

    Research output: Contribution to journalArticlepeer-review

  6. Nonlinear Fourier Transform as a Tool for Analyzing the Soliton Dynamics in Systems Obeying the Haus–Ginzburg–Landau Equation

    Chekhovskoy, I. S., Shtyrina, O. V. & Fedoruk, M. P., Dec 2025, In: Bulletin of the Lebedev Physics Institute. 52, Suppl 11, p. S1151-S1160 10 p., 6.

    Research output: Contribution to journalArticlepeer-review

  7. Method for Compensating for Nonlinear Distortions in Long-Haul Soliton Fiber-Optic Communication Lines

    Patrin, G. A., Chekhovskoy, I. S., Sidelnikov, O. S., Shtyrina, O. V. & Fedoruk, M. P., Dec 2025, In: Bulletin of the Lebedev Physics Institute. 52, Suppl 11, p. S1161-S1171 11 p., 7.

    Research output: Contribution to journalArticlepeer-review

  8. 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, p. 2896-2913 18 p.

    Research output: Contribution to journalArticlepeer-review

  9. iOk Platform for Automatic Search and Analysis of Objects in Images Using Artificial Intelligence in the Study of Supported Catalysts

    Nartova, A. V., Matveev, A. V., Mashukov, M. Y., Belotserkovskii, V. A., Sankova, N. N., Kudinov, V. Y. & Okunev, A. G., Aug 2023, In: Kinetics and Catalysis. 64, 4, p. 458-465 8 p.

    Research output: Contribution to journalArticlepeer-review

ID: 17403078