Mohammad Eshghi

2.0k citations
137 papers · 1.5k · h-index 19

Impact in

Papers in

Mohammad Eshghi

129 papers receiving 1.4k citations

Peers

Mohammad Eshghi
Comparison fields: 5 of 100
  • Computational Theory and Mathematics 441
  • Computer Vision and Pattern Recognition 374
  • Artificial Intelligence 532
  • Signal Processing 134
  • Hardware and Architecture 81
Replace Sergio Callegari with:
Sergio Callegari Italy
Fabio Pareschi Italy
Chik How Tan Singapore
Günhan Dündar Türkiye
Sattar Mirzakuchaki Iran
Shuting Cai China
Yoshifumi Nishio Japan
Radu Dogaru Romania
P.P. Chaudhuri India
Osama S. Faragallah Egypt
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Citations per field
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Citations per year

Countries citing papers authored by Mohammad Eshghi

Since Specialization
Citations

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

Fields of papers citing papers by Mohammad Eshghi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Mohammad Eshghi, 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 Mohammad Eshghi Line = papers co-authored together Mohammad Eshghi links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 137 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2009127
2 201374
3 201673
4 200970
5 201166
6 201763
7 200959
8 201543
9
Design and Optimization of Reversible BCD Adder/Subtractor Circuit for Quantum and Nanotechnology Based Systems
200840
10 200833
11 200927
12 201826
13 201325
14 201425
15 201025
16 201224
17 201123
18 201321
19 201721
20 201817

About Mohammad Eshghi

Mohammad Eshghi is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition and Biomedical Engineering, having authored 137 papers that have together received 1.5k indexed citations. Recurring topics across this work include Low-power high-performance VLSI design (23 papers), Advancements in Semiconductor Devices and Circuit Design (17 papers), Analog and Mixed-Signal Circuit Design (16 papers), Advanced Adaptive Filtering Techniques (15 papers), Quantum-Dot Cellular Automata (14 papers), Speech and Audio Processing (14 papers), Quantum Computing Algorithms and Architecture (13 papers) and Digital Filter Design and Implementation (11 papers). The work is most often cited by research in Computational Theory and Mathematics (441 citations), Computer Vision and Pattern Recognition (374 citations), Artificial Intelligence (532 citations), Signal Processing (134 citations) and Hardware and Architecture (81 citations). Mohammad Eshghi has collaborated with scholars based in Iran, United States and Japan. Frequent co-authors include Majid Mohammadi, Shahram Etemadi Borujeni, Mohammad Hossein Moaiyeri, Majid Haghparast, Majid Mohammadi, Keivan Navi, Babak Majidi, Somayeh Timarchi, Reza Faghih Mirzaee and Azadeh Sharafi. Their work appears in journals such as Quantum Information Processing, IEEE Access, Optical Fiber Technology, Journal of Applied Sciences and Microelectronics Reliability.

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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