М. В. Терешонок

504 total citations
38 papers, 349 citations indexed

About

М. В. Терешонок is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, М. В. Терешонок has authored 38 papers receiving a total of 349 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Electrical and Electronic Engineering, 18 papers in Artificial Intelligence and 13 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in М. В. Терешонок's work include Neural Networks and Applications (7 papers), Advanced Memory and Neural Computing (5 papers) and Advanced Signal Processing Techniques (4 papers). М. В. Терешонок is often cited by papers focused on Neural Networks and Applications (7 papers), Advanced Memory and Neural Computing (5 papers) and Advanced Signal Processing Techniques (4 papers). М. В. Терешонок collaborates with scholars based in Russia, Moldova and United Kingdom. М. В. Терешонок's co-authors include N. V. Klenov, I. I. Soloviev, A. M. Popov, A. V. Bogatskaya, S. V. Bakurskiy, M. Yu. Kupriyanov, A. A. Golubov, V. S. Stolyarov, E. A. Volkova and Anatolie Sidorenko and has published in prestigious journals such as Journal of Applied Physics, Sensors and Journal of Physics D Applied Physics.

In The Last Decade

М. В. Терешонок

35 papers receiving 344 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
М. В. Терешонок Russia	11	193	171	148	40	25	38	349
Ankur Agrawal United States	9	108 0.6×	222 1.3×	180 1.2×	15 0.4×	4 0.2×	20	439
Oliver Sander Germany	11	153 0.8×	118 0.7×	74 0.5×	45 1.1×	38 1.5×	84	466
Vladimir Lončar United States	10	53 0.3×	97 0.6×	118 0.8×	38 0.9×	3 0.1×	30	376
С. Н. Андрианов Russia	6	74 0.4×	82 0.5×	91 0.6×	11 0.3×	12 0.5×	61	293
Norman Lay United States	9	82 0.4×	84 0.5×	129 0.9×	7 0.2×	7 0.3×	37	359
Stephan Häfner Germany	15	285 1.5×	26 0.2×	303 2.0×	35 0.9×	8 0.3×	41	651
Simon Rommel Netherlands	19	1.0k 5.4×	76 0.4×	136 0.9×	27 0.7×	8 0.3×	134	1.2k
Jean Barbier Italy	10	104 0.5×	134 0.8×	18 0.1×	33 0.8×	8 0.3×	32	463
Boris Kryzhanovsky Russia	9	36 0.2×	82 0.5×	130 0.9×	75 1.9×	10 0.4×	60	303
Leandro Stefanazzi Argentina	10	226 1.2×	70 0.4×	59 0.4×	18 0.5×	22 0.9×	28	330

Countries citing papers authored by М. В. Терешонок

Since Specialization

Citations

This map shows the geographic impact of М. В. Терешонок's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by М. В. Терешонок with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites М. В. Терешонок more than expected).

Fields of papers citing papers by М. В. Терешонок

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by М. В. Терешонок. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by М. В. Терешонок. The network helps show where М. В. Терешонок may publish in the future.

Co-authorship network of co-authors of М. В. Терешонок

This figure shows the co-authorship network connecting the top 25 collaborators of М. В. Терешонок. A scholar is included among the top collaborators of М. В. Терешонок based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with М. В. Терешонок. М. В. Терешонок is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Терешонок, М. В., et al.. (2025). Increasing Neural-Based Pedestrian Detectors’ Robustness to Adversarial Patch Attacks Using Anomaly Localization. Journal of Imaging. 11(1). 26–26. 1 indexed citations

Терешонок, М. В., et al.. (2025). Parallel Algorithm for NP-Hard Problem of Channel Resource Allocation Optimization in Ad Hoc and Sensor Networks. Future Internet. 17(8). 362–362.

Bogatskaya, A. V., et al.. (2024). Multilayer Bolometric Structures for Efficient Wideband Communication Signal Reception. Nanomaterials. 14(2). 141–141. 2 indexed citations

Терешонок, М. В., et al.. (2024). Cross-Layer Methods for Ad Hoc Networks—Review and Classification. Future Internet. 16(1). 29–29. 3 indexed citations

Klenov, N. V., et al.. (2024). Neural network search for optimal pulse trains for qubit dynamics control. T-Comm. 18(7). 56–63.

Bogatskaya, A. V., et al.. (2023). Peculiarities of Resonant Absorption of Electromagnetic Signals in Multilayer Bolometric Sensors. Sensors. 23(3). 1549–1549. 2 indexed citations

Терешонок, М. В., et al.. (2023). Low-Pass Image Filtering to Achieve Adversarial Robustness. Sensors. 23(22). 9032–9032. 1 indexed citations

Klenov, N. V., S. V. Bakurskiy, I. I. Soloviev, et al.. (2022). Tunable superconducting neurons for networks based on radial basis functions. Beilstein Journal of Nanotechnology. 13. 444–454. 18 indexed citations

Терешонок, М. В., et al.. (2022). Noise Immunity and Robustness Study of Image Recognition Using a Convolutional Neural Network. Sensors. 22(3). 1241–1241. 14 indexed citations

10.

Bogatskaya, A. V., et al.. (2022). Issues with Modeling a Tunnel Communication Channel through a Plasma Sheath. Sensors. 22(1). 398–398. 9 indexed citations

11.

Klenov, N. V., et al.. (2022). A Survey on Symmetrical Neural Network Architectures and Applications. Symmetry. 14(7). 1391–1391. 14 indexed citations

12.

Klenov, N. V., et al.. (2021). Dynamic Processes in a Superconducting Adiabatic Neuron with Non-Shunted Josephson Contacts. Symmetry. 13(9). 1735–1735. 7 indexed citations

13.

Терешонок, М. В., et al.. (2021). MATHEMATICAL MODELS AND RECOGNITION METHODS FOR MOBILE SUBSCRIBERS MUTUAL PLACEMENT. T-Comm. 15(4). 49–56. 4 indexed citations

14.

Klenov, N. V., et al.. (2021). Superconducting Neural Networks: from an Idea to Fundamentals and, Further, to Application. Nanobiotechnology Reports. 16(6). 811–820. 7 indexed citations

15.

Klenov, N. V., et al.. (2020). Learning cell for superconducting neural networks. Superconductor Science and Technology. 34(1). 15006–15006. 21 indexed citations

16.

Bogatskaya, A. V., E. A. Volkova, N. V. Klenov, М. В. Терешонок, & A. M. Popov. (2020). Toward the Nonstationary Theory of a Telecommunication Channel Through a Plasma Sheath. IEEE Transactions on Antennas and Propagation. 68(6). 4831–4838. 23 indexed citations

17.

Soloviev, I. I., N. V. Klenov, S. V. Bakurskiy, et al.. (2018). Adiabatic superconducting artificial neural network: Basic cells. Journal of Applied Physics. 124(15). 53 indexed citations

18.

Klenov, N. V., et al.. (2016). Adiabatic superconducting cells for ultra-low-power artificial neural networks. Beilstein Journal of Nanotechnology. 7. 1397–1403. 38 indexed citations

19.

Klenov, N. V., et al.. (2016). The use of artificial neural networks for classification of signal sources in cognitive radio systems. Programming and Computer Software. 42(3). 121–128. 14 indexed citations

20.

Klenov, N. V., et al.. (2015). Methods for the automatic recognition of digital modulation of signals in cognitive radio systems. Moscow University Physics Bulletin. 70(6). 448–456. 13 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact