A. Mamonov

20 papers receiving 343 citations

Peers

A. Mamonov
Comparison fields: 5 of 51
  • Gastroenterology 60
  • Computational Mathematics 6
  • Computer Vision and Pattern Recognition 115
  • Oncology 138
  • Radiology, Nuclear Medicine and Imaging 116
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Guozheng Yan China
Adérito Araújo Portugal
Hsuan-Ting Chang Taiwan
Farah Deeba Canada
Entao Liu China
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Countries citing papers authored by A. Mamonov

Since Specialization
Citations

This map shows the geographic impact of A. Mamonov'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 A. Mamonov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A. Mamonov more than expected).

Fields of papers citing papers by A. Mamonov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by A. Mamonov. 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 A. Mamonov. The network helps show where A. Mamonov may publish in the future.

Co-authors

The 19 scholars most cited alongside A. Mamonov, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with A. Mamonov Line = papers co-authored together A. Mamonov links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014198
2 201344
3 201927
4 201015
5 201611
6 20149
7 20109
8 20199
9 20228
10 20227
11 20076
12 20236
13 20194
14 20243
15 20252
16 20072
17 20241
18
Clustering of graph vertex subset via Krylov subspace model reduction.
20181
19
Improving the Noise Immunity of the Modem with the Optimal Finite Signals that do not Cause Intersymbol Interference in a Linear Communication Channel
20191
20 20151

About A. Mamonov

A. Mamonov is a scholar working on Geophysics, Statistical and Nonlinear Physics, Electrical and Electronic Engineering, Ocean Engineering and Computational Mechanics, having authored 21 papers that have together received 365 indexed citations. Recurring topics across this work include Seismic Imaging and Inversion Techniques (6 papers), Model Reduction and Neural Networks (6 papers), Seismic Waves and Analysis (4 papers), Numerical methods in inverse problems (3 papers), Geophysical Methods and Applications (3 papers), Geophysical and Geoelectrical Methods (2 papers), Image Retrieval and Classification Techniques (2 papers) and Gastrointestinal Bleeding Diagnosis and Treatment (2 papers). The work is most often cited by research in Gastroenterology (60 citations), Computational Mathematics (6 citations), Computer Vision and Pattern Recognition (115 citations), Oncology (138 citations) and Radiology, Nuclear Medicine and Imaging (116 citations). A. Mamonov has collaborated with scholars based in United States, Russia and France. Frequent co-authors include Isabel N. Figueiredo, Pedro Figueiredo, Yen‐Hsi Richard Tsai, Kui Ren, Liliana Borcea, Vladimir Druskin, Mikhail Zaslavsky, Maxim A. Olshanskii, Sunil Kumar and Luís Pinto. Their work appears in journals such as Inverse Problems, SIAM Journal on Imaging Sciences, IEEE Transactions on Medical Imaging, Computer Methods in Applied Mechanics and Engineering and SIAM Journal on Scientific Computing.

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.

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