Pradip Sircar

57 papers receiving 942 citations

Peers

Pradip Sircar
Comparison fields: 5 of 85
  • Signal Processing 473
  • Cognitive Neuroscience 376
  • Experimental and Cognitive Psychology 178
  • Computer Vision and Pattern Recognition 199
  • Control and Systems Engineering 199
Replace G. Castellanos-Domínguez with:
G. Castellanos-Domínguez Colombia
Abhay Upadhyay India
Rishi Raj Sharma India
Sugata Munshi India
Abhijit Bhattacharyya India
Ali Hassan Pakistan
Mingjiang Wang China
Masatoshi Nakamura Japan
Zhu Liang Yu China
M. Kemal Kıymık Türkiye
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Citations per field
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Citations per year

Countries citing papers authored by Pradip Sircar

Since Specialization
Citations

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

Fields of papers citing papers by Pradip Sircar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 18 scholars most cited alongside Pradip Sircar, 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 Pradip Sircar Line = papers co-authored together Pradip Sircar links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 57 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020145
2 2007101
3 200690
4 202073
5 200968
6 201648
7 202040
8 201137
9 199631
10 199626
11 199723
12 201922
13 200622
14 200621
15 201920
16 201517
17 199712
18 201712
19 201912
20 200811

About Pradip Sircar

Pradip Sircar is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Control and Systems Engineering, Artificial Intelligence and Civil and Structural Engineering, having authored 57 papers that have together received 993 indexed citations. Recurring topics across this work include Blind Source Separation Techniques (31 papers), Image and Signal Denoising Methods (16 papers), Speech and Audio Processing (15 papers), Machine Fault Diagnosis Techniques (13 papers), EEG and Brain-Computer Interfaces (8 papers), Structural Health Monitoring Techniques (8 papers), Control Systems and Identification (6 papers) and Neural Networks and Applications (5 papers). The work is most often cited by research in Signal Processing (473 citations), Cognitive Neuroscience (376 citations), Experimental and Cognitive Psychology (178 citations), Computer Vision and Pattern Recognition (199 citations) and Control and Systems Engineering (199 citations). Pradip Sircar has collaborated with scholars based in India, United States and Singapore. Frequent co-authors include Ram Bilas Pachori, Rahul Sharma, Sudipta Mukhopadhyay, Nishchal K. Verma, Adrish Banerjee, Pushpendra Singh, U. Rajendra Acharya, Narendra Kumar, Tapan K. Sarkar and Atul Sharma. Their work appears in journals such as Signal Processing, Digital Signal Processing, Biomedical Signal Processing and Control, SpringerPlus and International Journal of Speech Technology.

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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