Akhilesh Prasad
- Applied Mathematics top 0.5%
- Computer Vision and Pattern Recognition top 2%
- Signal Processing top 2%
- Mathematical Physics top 10%
- Organic Chemistry
- Co-authors
- Manish KumarSantanu MannaR. S. PathakTanuj KumarC. GeibelNanhai SinghPraveen KumarR. K. Sharma
- Topics
- Mathematical Analysis and Transform Methods (108 papers)Image and Signal Denoising Methods (56 papers)Mathematical functions and polynomials (43 papers)
- Partner nations
- IndiaGermanyUnited States
In The Last Decade
Akhilesh Prasad
114 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 63
- Applied Mathematics 844
- Computer Vision and Pattern Recognition 596
- Signal Processing 314
- Mathematical Physics 96
- Organic Chemistry 57
Countries citing papers authored by Akhilesh Prasad
This map shows the geographic impact of Akhilesh Prasad'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 Akhilesh Prasad with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Akhilesh Prasad more than expected).
Fields of papers citing papers by Akhilesh Prasad
This network shows the impact of papers produced by Akhilesh Prasad. 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 Akhilesh Prasad. The network helps show where Akhilesh Prasad may publish in the future.
Co-authorship network of co-authors of Akhilesh Prasad
This figure shows the co-authorship network connecting the top 25 collaborators of Akhilesh Prasad. A scholar is included among the top collaborators of Akhilesh Prasad 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 Akhilesh Prasad. Akhilesh Prasad 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 | 7 | |
| 3 | 8 | |
| 4 | 1 | |
| 5 | 7 | |
| 6 | 0 | |
| 7 | 0 | |
| 8 | 1 | |
| 9 | 5 | |
| 10 | 12 | |
| 11 | 1 | |
| 12 | 8 | |
| 13 | 16 | |
| 14 | 1 | |
| 15 | 5 | |
| 16 | Pseudo-differential operator associated with the fractional Fourier transform | 8 |
| 17 | Continuous Fractional Power Bessel Wavelet Transform on Zemanian Type Spaces | 1 |
| 18 | 70 | |
| 19 | The fractional Fourier transform and its applications | 96 |
| 20 | 2 |
About Akhilesh Prasad
Akhilesh Prasad is a scholar working on Applied Mathematics, Signal Processing and Computer Vision and Pattern Recognition, having authored 126 papers that have together received 1.1k indexed citations. Recurring topics across this work include Mathematical Analysis and Transform Methods (108 papers), Image and Signal Denoising Methods (56 papers) and Mathematical functions and polynomials (43 papers). The work is most often cited by research in Applied Mathematics (844 citations), Signal Processing (314 citations) and Computer Vision and Pattern Recognition (596 citations). Akhilesh Prasad has collaborated with scholars based in India, Germany and United States. Frequent co-authors include Manish Kumar, Santanu Manna, R. S. Pathak, Tanuj Kumar, C. Geibel, Nanhai Singh, Praveen Kumar, R. K. Sharma, Z. Hossain and Santosh Kumar Singh. Their work appears in journals such as Physical Review B, Journal of Materials Science and Journal of Physics Condensed Matter.
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.