Maria Valueva

690 total citations · 1 hit paper
26 papers, 462 citations indexed

About

Maria Valueva is a scholar working on Computer Vision and Pattern Recognition, Information Systems and Artificial Intelligence. According to data from OpenAlex, Maria Valueva has authored 26 papers receiving a total of 462 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Vision and Pattern Recognition, 14 papers in Information Systems and 9 papers in Artificial Intelligence. Recurrent topics in Maria Valueva's work include Cryptography and Residue Arithmetic (13 papers), Cryptographic Implementations and Security (6 papers) and Image and Signal Denoising Methods (5 papers). Maria Valueva is often cited by papers focused on Cryptography and Residue Arithmetic (13 papers), Cryptographic Implementations and Security (6 papers) and Image and Signal Denoising Methods (5 papers). Maria Valueva collaborates with scholars based in Russia, Mexico and Bulgaria. Maria Valueva's co-authors include Pavel Lyakhov, N.I. Chervyakov, Georgii Valuev, Nikolay Nagornov, Dmitrii Kaplun, Maxim Deryabin, Peter Boyvalenkov, Mikhail Babenko, Aleksandr Sinitca and Jorge M. Cortés-Mendoza and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Neurocomputing.

In The Last Decade

Maria Valueva

22 papers receiving 437 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Application of the residue number system to reduce hardware costs of the convolutional neural network implementation

2020 330 citations Maria Valueva, Nikolay Nagornov et al. Mathematics and Computers in Simulation profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Maria Valueva Russia	7	143	116	72	66	46	26	462
Nikolay Nagornov Russia	8	157 1.1×	139 1.2×	53 0.7×	63 1.0×	59 1.3×	30	543
Georgii Valuev Russia	7	129 0.9×	94 0.8×	56 0.8×	50 0.8×	44 1.0×	14	412
T Anjali India	14	94 0.7×	131 1.1×	55 0.8×	102 1.5×	26 0.6×	117	554
Pooja Asopa India	4	113 0.8×	101 0.9×	33 0.5×	40 0.6×	60 1.3×	5	458
Xiangli Yang China	3	322 2.3×	215 1.9×	47 0.7×	52 0.8×	49 1.1×	7	670
Zonghai Zhu China	10	308 2.2×	169 1.5×	31 0.4×	69 1.0×	29 0.6×	21	570
Jiangbo Qian China	13	180 1.3×	207 1.8×	58 0.8×	73 1.1×	23 0.5×	97	573
Zhaowei Liu China	12	131 0.9×	90 0.8×	49 0.7×	81 1.2×	44 1.0×	43	457
Bhabani Shankar Prasad Mishra India	12	125 0.9×	93 0.8×	80 1.1×	48 0.7×	16 0.3×	67	461

Countries citing papers authored by Maria Valueva

Since Specialization

Citations

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

Fields of papers citing papers by Maria Valueva

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maria Valueva

This figure shows the co-authorship network connecting the top 25 collaborators of Maria Valueva. A scholar is included among the top collaborators of Maria Valueva 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 Maria Valueva. Maria Valueva is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Valuev, Georgii, Maria Valueva, Mikhail Babenko, & Andrei Tchernykh. (2023). Digital Filter Architecture Based on Modified Winograd Method F(2× 2, 5× 5) and Residue Number System. IEEE Access. 11. 26807–26819.

Boyvalenkov, Peter, et al.. (2023). Residue number systems with six modules and efficient circuits based on power-of-two diagonal modulus. Computers & Electrical Engineering. 110. 108854–108854.

Valueva, Maria, Georgii Valuev, Mikhail Babenko, Andrei Tchernykh, & Jorge M. Cortés-Mendoza. (2022). Method for Convolutional Neural Network Hardware Implementation Based on a Residue Number System. Proceedings of the Institute for System Programming of RAS. 34(3). 61–74. 1 indexed citations

Valueva, Maria, Georgii Valuev, Mikhail Babenko, Andrei Tchernykh, & Jorge M. Cortés-Mendoza. (2022). Method for Convolutional Neural Network Hardware Implementation Based on a Residue Number System. Programming and Computer Software. 48(8). 735–744. 1 indexed citations

Lyakhov, Pavel, et al.. (2022). High Performance Parallel Pseudorandom Number Generator on Cellular Automata. Symmetry. 14(9). 1869–1869. 3 indexed citations

Lyakhov, Pavel, Georgii Valuev, Maria Valueva, Dmitrii Kaplun, & Aleksandr Sinitca. (2021). Single Image Super-Resolution Method Based on Bilinear Interpolation and U-Net Combination. 1–4. 2 indexed citations

Valueva, Maria, Pavel Lyakhov, Georgii Valuev, & Nikolay Nagornov. (2021). Digital Filter Architecture With Calculations in the Residue Number System by Winograd Method F (2 × 2, 2 × 2). IEEE Access. 9. 143331–143340. 5 indexed citations

Chervyakov, N.I., Pavel Lyakhov, Mikhail Babenko, et al.. (2020). A Division Algorithm in a Redundant Residue Number System Using Fractions. Applied Sciences. 10(2). 695–695. 2 indexed citations

Chervyakov, N.I., Pavel Lyakhov, Nikolay Nagornov, Maria Valueva, & Dmitrii Kaplun. (2020). High-Performance Hardware 3D Medical Imaging using Wavelets in the Residue Number System. 1–4. 5 indexed citations

10.

Chervyakov, N.I., Pavel Lyakhov, Maxim Deryabin, et al.. (2020). Residue Number System-Based Solution for Reducing the Hardware Cost of a Convolutional Neural Network. Neurocomputing. 407. 439–453. 15 indexed citations

11.

Lyakhov, Pavel, Maria Valueva, Georgii Valuev, & Nikolay Nagornov. (2020). High-Performance Digital Filtering on Truncated Multiply-Accumulate Units in the Residue Number System. IEEE Access. 8. 209181–209190. 16 indexed citations

12.

Lyakhov, Pavel, Maria Valueva, Nikolay Nagornov, N.I. Chervyakov, & Dmitrii Kaplun. (2020). Low-Bit Hardware Implementation of DWT for 3D Medical Images Processing. 2017 december. 1396–1399. 3 indexed citations

13.

Valueva, Maria, Nikolay Nagornov, Pavel Lyakhov, Georgii Valuev, & N.I. Chervyakov. (2020). Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Mathematics and Computers in Simulation. 177. 232–243. 330 indexed citations breakdown →

14.

Kaplun, Dmitrii, et al.. (2019). Hardware Implementation of Video Processing Device using Residue Number System. 701–704. 2 indexed citations

15.

Chervyakov, N.I., Pavel Lyakhov, Nikolay Nagornov, Maria Valueva, & Georgii Valuev. (2019). Hardware implementation of a convolutional neural network using calculations in the residue number system. Computer Optics. 43(5). 7 indexed citations

16.

Chervyakov, N.I., et al.. (2018). Area-Efficient FPGA Implementation of Minimalistic Convolutional Neural Network Using Residue Number System. SHILAP Revista de lepidopterología.

17.

Chervyakov, N.I., et al.. (2018). Area-Efficient FPGA Implementation of Minimalistic Convolutional Neural Network Using Residue Number System. 112–118. 6 indexed citations

18.

Chervyakov, N.I., Pavel Lyakhov, & Maria Valueva. (2017). Increasing of convolutional neural network performance using residue number system. 21. 135–140. 19 indexed citations

19.

Chervyakov, N.I., et al.. (2017). Efficiency analysis of the image impulse noise cleaning using median filters with weighted central element. 141–146. 6 indexed citations

20.

Chervyakov, N.I., et al.. (2016). High-speed smoothing filter in the Residue Number System. 121–126. 4 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.

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