Duc Hoang
Impact in
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- Particle Detector Development and Performance
- Particle physics theoretical and experimental studies
Papers in ⓘ
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- Time Series Analysis and Forecasting 1
- Co-authors
- J. Ngadiuba (2 shared papers)Giuseppe Di Guglielmo (2 shared papers)Vladimir Lončar (2 shared papers)Dylan Rankin (2 shared papers)Z. Wu (2 shared papers)M. Pierini (2 shared papers)J. Duarte (2 shared papers)Andrey A. Kurekin (1 shared paper)
- Journals
- Journal of Instrumentation (1 paper)IEEE Transactions on Geoscience and Remote Sensing (1 paper)IEEE Transactions on Applied Superconductivity (1 paper)DSpace@MIT (Massachusetts Institute of Technology) (1 paper)
- Partner nations
- United StatesSwitzerlandSerbia
In The Last Decade
Duc Hoang
4 papers receiving 137 citations
Peers
Comparison fields: 5 of 39
- Nuclear and High Energy Physics 35
- Hardware and Architecture 16
- Oceanography 28
- Computer Vision and Pattern Recognition 28
- Artificial Intelligence 40
Countries citing papers authored by Duc Hoang
This map shows the geographic impact of Duc Hoang'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 Duc Hoang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Duc Hoang more than expected).
Fields of papers citing papers by Duc Hoang
This network shows the impact of papers produced by Duc Hoang. 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 Duc Hoang. The network helps show where Duc Hoang may publish in the future.
Co-authors
The 24 scholars most cited alongside Duc Hoang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 50 | |
| 2 | Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML | 2020 | 44 |
| 3 | 2018 | 37 | |
| 4 | 2021 | 7 |
About Duc Hoang
Duc Hoang is a scholar working on Environmental Chemistry, Signal Processing, Oceanography, Statistical and Nonlinear Physics and Nuclear and High Energy Physics, having authored 4 papers that have together received 138 indexed citations. Recurring topics across this work include Particle Detector Development and Performance (1 paper), Time Series Analysis and Forecasting (1 paper), Computational Physics and Python Applications (1 paper), Numerical Methods and Algorithms (1 paper), Neural Networks and Applications (1 paper), Arctic and Antarctic ice dynamics (1 paper), Model Reduction and Neural Networks (1 paper) and Superconducting Materials and Applications (1 paper). The work is most often cited by research in Nuclear and High Energy Physics (35 citations), Hardware and Architecture (16 citations), Oceanography (28 citations), Computer Vision and Pattern Recognition (28 citations) and Artificial Intelligence (40 citations). Duc Hoang has collaborated with scholars based in United States, Switzerland and Serbia. Frequent co-authors include J. Ngadiuba, Giuseppe Di Guglielmo, Vladimir Lončar, Dylan Rankin, Z. Wu, M. Pierini, J. Duarte, Andrey A. Kurekin, Francesco Nencioli and Jean-Christophe Poisson. Their work appears in journals such as Journal of Instrumentation, IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Applied Superconductivity and DSpace@MIT (Massachusetts Institute of 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.