|
Most published authors (scientific articles only) of the journal
scientific articles published in peer review journal, serial, conference publications, indexed in international bibliographical databases and/or having DOI index
|
1. |
V. V. Kotlyar |
123 |
2. |
A. A. Kovalev |
73 |
3. |
S. N. Khonina |
68 |
4. |
L. L. Doskolovich |
52 |
5. |
A. G. Nalimov |
49 |
6. |
S. S. Stafeev |
48 |
7. |
N. L. Kazanskii |
45 |
8. |
V. V. Myasnikov |
29 |
9. |
S. I. Kharitonov |
28 |
10. |
R. V. Skidanov |
27 |
11. |
A. P. Porfirev |
26 |
12. |
S. V. Karpeev |
24 |
13. |
D. A. Bykov |
23 |
14. |
E. S. Kozlova |
22 |
15. |
S. G. Volotovsky |
21 |
16. |
A. V. Kupriyanov |
20 |
17. |
V. V. Podlipnov |
20 |
18. |
Yu. V. Vizilter |
17 |
19. |
A. V. Volyar |
17 |
20. |
S. A. Degtyarev |
16 |
21. |
M. A. Moiseev |
16 |
22. |
A. V. Ustinov |
16 |
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40 most published authors of the journal |
|
Most cited authors of the journal |
1. |
N. L. Kazanskii |
605 |
2. |
S. N. Khonina |
539 |
3. |
V. V. Kotlyar |
509 |
4. |
S. I. Kharitonov |
269 |
5. |
A. G. Nalimov |
268 |
6. |
A. A. Kovalev |
263 |
7. |
L. L. Doskolovich |
260 |
8. |
S. S. Stafeev |
228 |
9. |
A. V. Kupriyanov |
208 |
10. |
V. V. Podlipnov |
201 |
11. |
R. V. Skidanov |
188 |
12. |
A. V. Nikonorov |
181 |
13. |
A. V. Volyar |
168 |
14. |
V. V. Myasnikov |
159 |
15. |
S. V. Karpeev |
153 |
16. |
Ya. E. Akimova |
151 |
17. |
S. G. Volotovsky |
150 |
18. |
E. S. Kozlova |
149 |
19. |
N. A. Ivliev |
148 |
20. |
M. V. Bretsko |
147 |
|
40 most cited authors of the journal |
|
Most cited articles of the journal |
1. |
MIDV-500: a dataset for identity document analysis and recognition on mobile devices in video stream V. V. Arlazarov, K. B. Bulatov, T. S. Chernov, V. L. Arlazarov Computer Optics, 2019, 43:5, 818–824 |
71 |
2. |
Detection of objects in the images: from likelihood relationships towards scalable and efficient neural networks N. A. Andriyanov, V. E. Dementiev, A. G. Tashlinskiy Computer Optics, 2022, 46:1, 139–159 |
66 |
3. |
Image restoration in diffractive optical systems using deep learning and deconvolution A. V. Nikonorov, M. V. Petrov, S. A. Bibikov, V. V. Kutikova, A. A. Morozov, N. L. Kazanskiy Computer Optics, 2017, 41:6, 875–887 |
64 |
4. |
Injectional multilens molding parameters optimization N. L. Kazanskiy, I. S. Stepanenko, A. I. Khaimovich, S. V. Kravchenko, E. V. Byzov, M. A. Moiseev Computer Optics, 2016, 40:2, 203–214 |
59 |
5. |
Addressed fiber Bragg structures in quasi-distributed microwave-photonic sensor systems O. G. Morozov, A. Zh. Sakhabutdinov Computer Optics, 2019, 43:4, 535–543 |
51 |
6. |
On the use of a multi-raster input of one-dimensional signals in two-dimensional optical correlators M. S. Kuzmin, V. V. Davydov, S. A. Rogov Computer Optics, 2019, 43:3, 391–396 |
49 |
7. |
Russian traffic sign images dataset V. I. Shakhuro, A. S. Konushin Computer Optics, 2016, 40:2, 294–300 |
48 |
8. |
A vector optical vortex generated and focused using a metalens V. V. Kotlyar, A. G. Nalimov Computer Optics, 2017, 41:5, 645–654 |
47 |
9. |
Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches E. V. Myasnikov Computer Optics, 2017, 41:4, 564–572 |
46 |
10. |
Achievements in the development of plasmonic waveguide sensors for measuring the refractive index N. L. Kazanskiy, M. Butt, S. A. Degtyarev, S. N. Khonina Computer Optics, 2020, 44:3, 295–318 |
43 |
11. |
Optical elements based on silicon photonics M. Butt, S. N. Khonina, N. L. Kazanskiy Computer Optics, 2019, 43:6, 1079–1083 |
42 |
12. |
Method for forecasting changes in time series parameters in digital information management systems Yu. A. Kropotov, A. Yu. Proskuryakov, A. A. Belov Computer Optics, 2018, 42:6, 1093–1100 |
39 |
13. |
Vegetation type recognition in hyperspectral images using a conjugacy indicator S. A. Bibikov, N. L. Kazanskiy, V. A. Fursov Computer Optics, 2018, 42:5, 846–854 |
38 |
14. |
Modeling the performance of a spaceborne hyperspectrometer based on the Offner scheme N. L. Kazanskiy, S. I. Kharitonov, L. L. Doskolovich, A. V. Pavelev Computer Optics, 2015, 39:1, 70–76 |
38 |
15. |
Characteristics of sharp focusing of vortex Laguerre-Gaussian beams D. A. Savelyev, S. N. Khonina Computer Optics, 2015, 39:5, 654–662 |
37 |
16. |
Investigation of algorithms for coagulate arrangement in fundus images A. S. Shirokanev, D. V. Kirsh, N. Yu. Ilyasova, A. V. Kupriyanov Computer Optics, 2018, 42:4, 712–721 |
36 |
17. |
Reconstruction of anatomical structures using statistical shape modeling N. A. Smelkina, R. N. Kosarev, A. V. Nikonorov, I. M. Bairikov, K. N. Ryabov, E. V. Avdeev, N. L. Kazanskiy Computer Optics, 2017, 41:6, 897–904 |
35 |
18. |
Crop growth monitoring through Sentinel and Landsat data based NDVI time-series M. Boori, K. Choudhary, A. V. Kupriyanov Computer Optics, 2020, 44:3, 409–419 |
32 |
19. |
U-Net-bin: hacking the document image binarization contest P. V. Bezmaternykh, D. A. Ilin, D. P. Nikolaev Computer Optics, 2019, 43:5, 825–832 |
31 |
20. |
Using coupled photonic crystal cavities for increasing of sensor sensitivity A. V. Egorov, N. L. Kazanskiy, P. G. Serafimovich Computer Optics, 2015, 39:2, 158–162 |
31 |
|
40 most cited articles of the journal |
|
Most requested articles of the journal |
|
|
1. |
Distribution of the complex amplitude and intensity in a 3D scattering pattern formed by the optical system for an on-axis point object S. N. Koreshev, D. S. Smorodinov, O. V. Nikanorov, M. A. Frolova Computer Optics, 2018, 42:3, 377–384 | 36 |
2. |
A non-coherent holographic correlator based on a digital micromirror device V. G. Rodin Computer Optics, 2018, 42:3, 347–353 | 29 |
3. |
An efficient algorithm for non-rigid object registration A. Yu. Makovetskii, S. M. Voronin, V. I. Kober, A. V. Voronin Computer Optics, 2020, 44:1, 67–73 | 29 |
4. |
A method for connecting antenna radiators to RoF systems using an optical device and calculating its parameters A. Kh. Sultanov, I. L. Vinogradova, I. K. Meshkov, A. V. Andrianova, G. I. Abdrakhmanova, A. A. Ishmiyarov, L. Z. Yantilina Computer Optics, 2015, 39:5, 728–737 | 27 |
5. |
Backward flow of energy for an optical vortex with arbitrary integer topological charge V. V. Kotlyar, A. A. Kovalev, A. G. Nalimov Computer Optics, 2018, 42:3, 408–413 | 26 |
6. |
Comparative analysis of neural network models performance on low-power devices for a real-time object detection task A. Zagitov, E. Chebotareva, A. Toschev, E. Magid Computer Optics, 2024, 48:2, 242–252 | 26 |
7. |
Modeling the performance of a spaceborne hyperspectrometer based on the Offner scheme N. L. Kazanskiy, S. I. Kharitonov, L. L. Doskolovich, A. V. Pavelev Computer Optics, 2015, 39:1, 70–76 | 24 |
8. |
Methodology of automatic registration of 3D measurements of bulk materials in granaries N. V. Astapenko, K. T. Koshekov, A. N. Kolesnikov Computer Optics, 2018, 42:3, 510–520 | 24 |
9. |
Geometic-optical calculation of the focal spot of a harmonic diffractive lens S. I. Kharitonov, S. G. Volotovsky, S. N. Khonina Computer Optics, 2016, 40:3, 331–337 | 21 |
10. |
Investigation of temperature-induced lasing dynamics in broad-area VCSESL D. A. Anchikov, A. A. Krents, S. V. Krestin, N. E. Molevich, A. V. Pakhomov Computer Optics, 2015, 39:5, 721–727 | 19 |
|
Total publications: |
1232 |
Scientific articles: |
1219 |
Authors: |
1622 |
Citations: |
6620 |
Cited articles: |
922 |
 |
Impact Factor Web of Science |
|
for 2024:
1.200 |
|
for 2023:
1.100 |
 |
Scopus Metrics |
|
2024 |
SJR |
0.265 |
|
2023 |
CiteScore |
4.200 |
|
2023 |
SNIP |
0.575 |
|
2023 |
SJR |
0.251 |
|
2022 |
SJR |
0.321 |
|
2021 |
SJR |
0.508 |
|
2020 |
SJR |
0.491 |
|
2019 |
SJR |
0.586 |
|
2018 |
CiteScore |
2.370 |
|
2018 |
SJR |
0.535 |
|
2017 |
CiteScore |
1.790 |
|
2017 |
SNIP |
1.681 |
|
2017 |
SJR |
0.457 |
|
2016 |
CiteScore |
1.610 |
|
2016 |
SNIP |
1.495 |
|
2016 |
SJR |
0.348 |
|
2015 |
CiteScore |
1.220 |
|
2015 |
SNIP |
1.261 |
|
2015 |
IPP |
1.185 |
|
2015 |
SJR |
0.445 |
|
2014 |
CiteScore |
0.730 |
|
2014 |
SNIP |
0.846 |
|
2014 |
IPP |
0.656 |
|
2014 |
SJR |
0.285 |
|
2013 |
SNIP |
0.397 |
|
2013 |
IPP |
0.341 |
|
2013 |
SJR |
0.198 |
|