Darshan Hegde
Impact in
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- Autonomous Vehicle Technology and Safety
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- Advanced Neural Network Applications
- Video Surveillance and Tracking Methods
- Face and Expression Recognition
Papers in
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- IoT-based Smart Home Systems 1
- Low-power high-performance VLSI design 1
- Advancements in Semiconductor Devices and Circuit Design 1
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- Autonomous Vehicle Technology and Safety 1
- Co-authors
- Nemanja Djuric (1 shared paper)Yi Shi (1 shared paper)Fang‐Chieh Chou (1 shared paper)Carlos Vallespi-Gonzalez (1 shared paper)Sunil Kumar (1 shared paper)
- Journals
- International Research Journal of Modernization in Engineering Technology and Science (1 paper)International Journal of Advanced Research in Science Communication and Technology (1 paper)2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (1 paper)
- Partner nations
- United StatesIndia
In The Last Decade
Darshan Hegde
2 papers receiving 46 citations
Peers
Comparison fields: 5 of 26
- Automotive Engineering 22
- Computer Vision and Pattern Recognition 31
- Building and Construction 5
- Artificial Intelligence 11
- Ocean Engineering 5
Countries citing papers authored by Darshan Hegde
This map shows the geographic impact of Darshan Hegde'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 Darshan Hegde with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Darshan Hegde more than expected).
Fields of papers citing papers by Darshan Hegde
This network shows the impact of papers produced by Darshan Hegde. 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 Darshan Hegde. The network helps show where Darshan Hegde may publish in the future.
Co-authors
The 5 scholars most cited alongside Darshan Hegde, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
About Darshan Hegde
Darshan Hegde is a scholar working on Electrical and Electronic Engineering, Automotive Engineering, Computer Vision and Pattern Recognition, Neurology and Environmental Engineering, having authored 4 papers that have together received 46 indexed citations. Recurring topics across this work include IoT-based Smart Home Systems (1 paper), Low-power high-performance VLSI design (1 paper), Autonomous Vehicle Technology and Safety (1 paper), Brain Tumor Detection and Classification (1 paper), Remote Sensing and LiDAR Applications (1 paper), Advanced Neural Network Applications (1 paper), Advancements in Semiconductor Devices and Circuit Design (1 paper) and Analog and Mixed-Signal Circuit Design (1 paper). The work is most often cited by research in Automotive Engineering (22 citations), Computer Vision and Pattern Recognition (31 citations), Building and Construction (5 citations), Artificial Intelligence (11 citations) and Ocean Engineering (5 citations). Darshan Hegde has collaborated with scholars based in United States and India. Frequent co-authors include Nemanja Djuric, Yi Shi, Fang‐Chieh Chou, Carlos Vallespi-Gonzalez and Sunil Kumar. Their work appears in journals such as International Research Journal of Modernization in Engineering Technology and Science, International Journal of Advanced Research in Science Communication and Technology and 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
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.