Hwa-Joon Oh

447 total citations
21 papers, 217 citations indexed

About

Hwa-Joon Oh is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Hardware and Architecture. According to data from OpenAlex, Hwa-Joon Oh has authored 21 papers receiving a total of 217 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Electrical and Electronic Engineering, 9 papers in Artificial Intelligence and 6 papers in Hardware and Architecture. Recurrent topics in Hwa-Joon Oh's work include Neural Networks and Applications (9 papers), Advanced Memory and Neural Computing (7 papers) and CCD and CMOS Imaging Sensors (5 papers). Hwa-Joon Oh is often cited by papers focused on Neural Networks and Applications (9 papers), Advanced Memory and Neural Computing (7 papers) and CCD and CMOS Imaging Sensors (5 papers). Hwa-Joon Oh collaborates with scholars based in United States, South Korea and Germany. Hwa-Joon Oh's co-authors include S.H. Dhong, Silvia Melitta Mueller, Christian Jacobi, Hiroaki Nishikawa, Osamu Takahashi, B. Flachs, F.M.A. Salam, Yukio Watanabe, J. Leenstra and S. Asano and has published in prestigious journals such as IEEE Journal of Solid-State Circuits, Computers & Electrical Engineering and Analog Integrated Circuits and Signal Processing.

In The Last Decade

Hwa-Joon Oh

14 papers receiving 202 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Hwa-Joon Oh United States 6 125 91 68 66 36 21 217
Ranjani Narayan India 9 177 1.4× 64 0.7× 147 2.2× 36 0.5× 41 1.1× 46 261
Tanguy Risset France 9 193 1.5× 50 0.5× 163 2.4× 60 0.9× 14 0.4× 36 282
Mingjun Wang United States 3 271 2.2× 149 1.6× 215 3.2× 19 0.3× 14 0.4× 5 390
Kahng United States 6 151 1.2× 309 3.4× 36 0.5× 51 0.8× 19 0.5× 9 355
Kiran Bondalapati United States 6 151 1.2× 73 0.8× 111 1.6× 23 0.3× 9 0.3× 8 214
Louis Monier United States 9 111 0.9× 103 1.1× 120 1.8× 71 1.1× 17 0.5× 16 267
D. Lanneer Belgium 12 259 2.1× 97 1.1× 101 1.5× 31 0.5× 18 0.5× 20 316
Marius Strum Brazil 8 120 1.0× 147 1.6× 139 2.0× 39 0.6× 8 0.2× 42 242
Samuel Bayliss United Kingdom 10 210 1.7× 76 0.8× 129 1.9× 26 0.4× 10 0.3× 18 288
D.C. Cronquist United States 6 368 2.9× 122 1.3× 245 3.6× 24 0.4× 21 0.6× 8 406

Countries citing papers authored by Hwa-Joon Oh

Since Specialization
Citations

This map shows the geographic impact of Hwa-Joon Oh'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 Hwa-Joon Oh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hwa-Joon Oh more than expected).

Fields of papers citing papers by Hwa-Joon Oh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hwa-Joon Oh. 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 Hwa-Joon Oh. The network helps show where Hwa-Joon Oh may publish in the future.

Co-authorship network of co-authors of Hwa-Joon Oh

This figure shows the co-authorship network connecting the top 25 collaborators of Hwa-Joon Oh. A scholar is included among the top collaborators of Hwa-Joon Oh 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 Hwa-Joon Oh. Hwa-Joon Oh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
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Oh, Hwa-Joon, et al.. (2025). Deep Learning-Based Autonomous Vehicle on SoC. 1–5.
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Oh, Hwa-Joon, et al.. (2006). A Fully Pipelined Single-Precision Floating-Point Unit in the Synergistic Processor Element of a CELL Processor. IEEE Journal of Solid-State Circuits. 41(4). 759–771. 46 indexed citations
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Takahashi, Osamu, S.H. Dhong, B. Flachs, et al.. (2005). The circuit design of the synergistic processor element of a CELL processor. International Conference on Computer Aided Design. 111–117. 2 indexed citations
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Flachs, B., S. Asano, S.H. Dhong, et al.. (2005). A streaming processing unit for a CELL processor. 134–135. 93 indexed citations
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Mueller, Silvia Melitta, et al.. (2005). The Vector Floating-Point Unit in a Synergistic Processor Element of a CELL Processor. 20. 59–67. 33 indexed citations
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Takahashi, Osamu, S.H. Dhong, B. Flachs, et al.. (2005). The circuits and physical design of the synergistic processor element of a CELL processor. 20–23. 13 indexed citations
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Salam, F.M.A. & Hwa-Joon Oh. (2002). Real-time tracking control using modular neural chips with on-chip learning. Proceedings of International Conference on Neural Networks (ICNN'96). 2. 914–919.
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Oh, Hwa-Joon & F.M.A. Salam. (2002). Design of a temporal learning chip for signal generation and classification. 1. 689–692. 2 indexed citations
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Oh, Hwa-Joon & F.M.A. Salam. (2002). Analog CMOS implementation of neural network for adaptive signal processing. 6. 503–506. 5 indexed citations
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Oh, Hwa-Joon & F.M.A. Salam. (2002). 4×4×2 neural network design using modular neural chips with on-chip learning. 4. 2070–2073.
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Oh, Hwa-Joon & F.M.A. Salam. (2002). A modular analog chip for feed-forward networks with on-chip learning. 766–769. 2 indexed citations
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Oh, Hwa-Joon, et al.. (1999). Design of a Temporal Learning Chip for Signal Generation and Classification. Analog Integrated Circuits and Signal Processing. 18(2-3). 229–242. 6 indexed citations
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Oh, Hwa-Joon, et al.. (1999). A 50-neuron CMOS analog chip with on-chip digital learning: design, development, and experiments. Computers & Electrical Engineering. 25(5). 357–378. 4 indexed citations
20.
Oh, Hwa-Joon. (1996). Analog CMOS implementation of artificial neural networks for temporal signal learning. Michigan State University Libraries. 1 indexed citations

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.

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