Manoj Alwani
- Computer Vision and Pattern Recognition top 5%
- Electrical and Electronic Engineering
- Artificial Intelligence top 10%
- Hardware and Architecture top 5%
- Computer Networks and Communications
- Co-authors
- Han ChenPeter MilderMichael FerdmanAnil Kumar TiwariVashisht MadhavanYang WangMonjur RahmanFatema Khatun
- Topics
- Image and Signal Denoising Methods (2 papers)Advanced Neural Network Applications (2 papers)Structural Health Monitoring Techniques (1 paper)
- Journals
- Global Health Action2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Civil Engineering Systems
- Partner nations
- IndiaUnited StatesNorway
In The Last Decade
Manoj Alwani
7 papers receiving 458 citations
Hit Papers
Peers
Comparison fields: 5 of 47
- Computer Vision and Pattern Recognition 345
- Electrical and Electronic Engineering 253
- Artificial Intelligence 139
- Hardware and Architecture 93
- Computer Networks and Communications 42
Countries citing papers authored by Manoj Alwani
This map shows the geographic impact of Manoj Alwani'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 Manoj Alwani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Manoj Alwani more than expected).
Fields of papers citing papers by Manoj Alwani
This network shows the impact of papers produced by Manoj Alwani. 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 Manoj Alwani. The network helps show where Manoj Alwani may publish in the future.
Co-authorship network of co-authors of Manoj Alwani
This figure shows the co-authorship network connecting the top 25 collaborators of Manoj Alwani. A scholar is included among the top collaborators of Manoj Alwani 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 Manoj Alwani. Manoj Alwani is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 25 | |
| 3 | Fused-layer CNN acceleratorsbreakdown → | 406 |
| 4 | Enhancement of fog degraded images on the basis of histrogram classification | 1 |
| 5 | 30 | |
| 6 | 1 | |
| 7 | 1 |
About Manoj Alwani
Manoj Alwani is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Signal Processing, having authored 7 papers that have together received 465 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (2 papers), Advanced Neural Network Applications (2 papers) and Structural Health Monitoring Techniques (1 paper). The work is most often cited by research in Computational Mathematics (12 citations), Computer Vision and Pattern Recognition (345 citations) and Hardware and Architecture (93 citations). Manoj Alwani has collaborated with scholars based in India, United States and Norway. Frequent co-authors include Han Chen, Peter Milder, Michael Ferdman, Anil Kumar Tiwari, Vashisht Madhavan, Yang Wang, Monjur Rahman, Fatema Khatun, Ian May and Ingrid K. Friberg. Their work appears in journals such as Global Health Action, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and Civil Engineering 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.