V. Ramasubramanian
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Signal Processing top 5%
- Radiology, Nuclear Medicine and Imaging
- Epidemiology
- Co-authors
- K.K. PaliwalAdrian GlasserT.V. SreenivasAmit KaleArthur BradleyDawn MeyerAmitava DasPete Kollbaum
- Topics
- Speech Recognition and Synthesis (34 papers)Speech and Audio Processing (33 papers)Advanced Data Compression Techniques (22 papers)
- Journals
- Scientific ReportsIEEE Transactions on Signal ProcessingIEEE Transactions on Communications
- Partner nations
- IndiaUnited StatesGermany
In The Last Decade
V. Ramasubramanian
61 papers receiving 502 citations
Peers
Comparison fields: 5 of 65
- Artificial Intelligence 245
- Computer Vision and Pattern Recognition 241
- Signal Processing 216
- Radiology, Nuclear Medicine and Imaging 103
- Epidemiology 97
Countries citing papers authored by V. Ramasubramanian
This map shows the geographic impact of V. Ramasubramanian'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 V. Ramasubramanian with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites V. Ramasubramanian more than expected).
Fields of papers citing papers by V. Ramasubramanian
This network shows the impact of papers produced by V. Ramasubramanian. 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 V. Ramasubramanian. The network helps show where V. Ramasubramanian may publish in the future.
Co-authorship network of co-authors of V. Ramasubramanian
This figure shows the co-authorship network connecting the top 25 collaborators of V. Ramasubramanian. A scholar is included among the top collaborators of V. Ramasubramanian based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with V. Ramasubramanian. V. Ramasubramanian is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 8 | |
| 5 | 13 | |
| 6 | 16 | |
| 7 | Implications of phytolith records from an Early Historic megalithic burial site at Porunthal in Southern India | 1 |
| 8 | 7 | |
| 9 | 16 | |
| 10 | 35 | |
| 11 | 12 | |
| 12 | 5 | |
| 13 | Low complexity near-optimal unit-selection algorithm for ultra low bit-rate speech coding based on n-best lattice and Viterbi search. | 1 |
| 14 | 34 | |
| 15 | 18 | |
| 16 | 1 | |
| 17 | 13 | |
| 18 | 5 | |
| 19 | An efficient approximation-elimination algorithm for fast-nearest-neighbour search | 5 |
| 20 | 42 |
About V. Ramasubramanian
V. Ramasubramanian is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 70 papers that have together received 550 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (34 papers), Speech and Audio Processing (33 papers) and Advanced Data Compression Techniques (22 papers). The work is most often cited by research in Signal Processing (216 citations), Computer Vision and Pattern Recognition (241 citations) and Ophthalmology (87 citations). V. Ramasubramanian has collaborated with scholars based in India, United States and Germany. Frequent co-authors include K.K. Paliwal, Adrian Glasser, T.V. Sreenivas, Amit Kale, Arthur Bradley, Dawn Meyer, Amitava Das, Pete Kollbaum, Dinesh Babu Jayagopi and R. Karthik. Their work appears in journals such as Scientific Reports, IEEE Transactions on Signal Processing and IEEE Transactions on Communications.
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.