Monodeep Kar

1.1k citations
48 papers · 635 · h-index 15

Impact in

Papers in

Monodeep Kar

46 papers receiving 628 citations

Peers

Monodeep Kar
Comparison fields: 5 of 30
  • Hardware and Architecture 378
  • Artificial Intelligence 388
  • Computer Vision and Pattern Recognition 186
  • Signal Processing 59
  • Electrical and Electronic Engineering 264
Replace Arvind Singh with:
Arvind Singh United States
Philippe Maurine France
Kwen‐Siong Chong Singapore
Yu‐ichi Hayashi Japan
Xuan‐Tu Tran Vietnam
Van‐Phuc Hoang Vietnam
Bo‐Cheng Lai Taiwan
Daniel Ziener Germany
Ke Chen China
Mostafa Taha Canada
Monodeep Kar relative to Arvind Singh United States Arvind Singh's profile →
Citations per field
00.5×1.6×
Arvind Singh · 1×
Citations per year

Countries citing papers authored by Monodeep Kar

Since Specialization
Citations

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

Fields of papers citing papers by Monodeep Kar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 48 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201755
2 201854
3 201850
4 201941
5 201939
6 201735
7 201935
8 202027
9 201523
10 201622
11 201921
12 202020
13 202019
14 201819
15 201618
16 201714
17 201413
18 201410
19 201810
20 20169

About Monodeep Kar

Monodeep Kar is a scholar working on Hardware and Architecture, Artificial Intelligence, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Biomedical Engineering, having authored 48 papers that have together received 635 indexed citations. Recurring topics across this work include Physical Unclonable Functions (PUFs) and Hardware Security (21 papers), Cryptographic Implementations and Security (21 papers), Chaos-based Image/Signal Encryption (12 papers), Advanced Memory and Neural Computing (9 papers), Low-power high-performance VLSI design (8 papers), Security and Verification in Computing (7 papers), Analog and Mixed-Signal Circuit Design (6 papers) and Semiconductor materials and devices (5 papers). The work is most often cited by research in Hardware and Architecture (378 citations), Artificial Intelligence (388 citations), Computer Vision and Pattern Recognition (186 citations), Signal Processing (59 citations) and Electrical and Electronic Engineering (264 citations). Monodeep Kar has collaborated with scholars based in United States, India and Switzerland. Frequent co-authors include Saibal Mukhopadhyay, Arvind Singh, Vivek De, Anand Prem Rajan, Sanu Mathew, Jong Hwan Ko, Venkata Chaitanya Krishna Chekuri, Himanshu Kaul, Amit Agarwal and Ram Krishnamurthy. Their work appears in journals such as IEEE Journal of Solid-State Circuits, IEEE Internet of Things Journal, IEEE Transactions on Power Electronics, IEEE Solid-State Circuits Letters and IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

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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