N. Ranganathan

246 papers receiving 3.8k citations

Peers

N. Ranganathan
Comparison fields: 5 of 99
  • Hardware and Architecture 761
  • Computational Theory and Mathematics 1.3k
  • Artificial Intelligence 1.4k
  • Electrical and Electronic Engineering 2.4k
  • Computer Vision and Pattern Recognition 588
Replace Chip-Hong Chang with:
Chip-Hong Chang Singapore
Rob A. Rutenbar United States
Israel Koren United States
Massimo Alioto Singapore
Peter Y. K. Cheung United Kingdom
Earl E. Swartzlander United States
G.A. Jullien Canada
Mehdi B. Tahoori Germany
Selim G. Akl Canada
Ray C. C. Cheung Hong Kong
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Citations per year

Countries citing papers authored by N. Ranganathan

Since Specialization
Citations

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

Fields of papers citing papers by N. Ranganathan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 266 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2006218
2 2004170
3 2010154
4 2008146
5 2012124
6 2009113
7 2009110
8 200990
9 199582
10 201173
11 201169
12 199163
13 201159
14 200656
15 200755
16 199352
17 201151
18 201050
19 200646
20 200445

About N. Ranganathan

N. Ranganathan is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence, Hardware and Architecture, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 266 papers that have together received 4.1k indexed citations. Recurring topics across this work include Low-power high-performance VLSI design (71 papers), Embedded Systems Design Techniques (42 papers), VLSI and FPGA Design Techniques (39 papers), Parallel Computing and Optimization Techniques (36 papers), Quantum-Dot Cellular Automata (32 papers), Quantum Computing Algorithms and Architecture (31 papers), Algorithms and Data Compression (27 papers) and Advanced Data Compression Techniques (22 papers). The work is most often cited by research in Hardware and Architecture (761 citations), Computational Theory and Mathematics (1.3k citations), Artificial Intelligence (1.4k citations), Electrical and Electronic Engineering (2.4k citations) and Computer Vision and Pattern Recognition (588 citations). N. Ranganathan has collaborated with scholars based in United States, Singapore and India. Frequent co-authors include Himanshu Thapliyal, Saraju P. Mohanty, Saurabh Kotiyal, Matthew Morrison, Levent Yobaş, Wee‐Liat Ong, Sanjukta Bhanja, K. L. Pey, Mario Kovač and N. Balasubramanian. Their work appears in journals such as IEEE Transactions on Very Large Scale Integration (VLSI) Systems, IEEE Transactions on Computers, Journal of Mathematical Analysis and Applications, Journal of Micromechanics and Microengineering and ACM Transactions on Design Automation of Electronic 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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