S. Narayanan

24 papers receiving 393 citations

Peers

S. Narayanan
Comparison fields: 5 of 52
  • Signal Processing 186
  • Experimental and Cognitive Psychology 105
  • Artificial Intelligence 270
  • Computer Vision and Pattern Recognition 106
  • Computer Networks and Communications 63
Replace Michael Pucher with:
Michael Pucher Austria
Eric Castelli France
Yijun Zhou China
Valentin Enescu Belgium
Milan Gnjatović Serbia
Petar Aleksic United States
David Huggins-Daines United States
Viktor Rozgić United States
Julien Pinquier France
Xiaobo Pi United States
S. Narayanan relative to Michael Pucher Austria Michael Pucher's profile →
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Citations per year

Countries citing papers authored by S. Narayanan

Since Specialization
Citations

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

Fields of papers citing papers by S. Narayanan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200368
2 200668
3 200661
4 200654
5 200639
6 200426
7
Multimodal Sensing for Pediatric Obesity Applications
200824
8 200622
9 200918
10 200214
11 200913
12 20098
13 20036
14
Factors Affecting the Design of Direct Methanol Fuel Cell Systems
19986
15 20035
16 20065
17 20094
18 20054
19 20062
20 20131

About S. Narayanan

S. Narayanan is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Experimental and Cognitive Psychology, having authored 25 papers that have together received 452 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (10 papers), Speech and Audio Processing (8 papers), Phonetics and Phonology Research (5 papers), Fuel Cells and Related Materials (4 papers), Speech and dialogue systems (3 papers), Music and Audio Processing (3 papers), Advancements in Solid Oxide Fuel Cells (2 papers) and Context-Aware Activity Recognition Systems (2 papers). The work is most often cited by research in Signal Processing (186 citations), Experimental and Cognitive Psychology (105 citations), Artificial Intelligence (270 citations), Computer Vision and Pattern Recognition (106 citations) and Computer Networks and Communications (63 citations). S. Narayanan has collaborated with scholars based in United States, Germany and China. Frequent co-authors include Sankaranarayanan Ananthakrishnan, Joseph Tepperman, C.S. Raghavendra, Murtaza Bulut, May D. Wang, Antonio Bonafonte, Alan W. Black, Harald Höge, Hermann Ney and Zhigang Deng. Their work appears in journals such as SAE technical papers on CD-ROM/SAE technical paper series, IEEE Transactions on Visualization and Computer Graphics, IEEE Signal Processing Letters, IEEE Transactions on Audio Speech and Language Processing and PubMed.

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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