Oscar Chang

27 papers receiving 159 citations

Peers

Oscar Chang
Comparison fields: 5 of 58
  • Signal Processing 21
  • Aerospace Engineering 43
  • Media Technology 14
  • Computer Vision and Pattern Recognition 28
  • Artificial Intelligence 40
Replace Rodrigo Moreira with:
Rodrigo Moreira Brazil
Mahdi Nikooghadam Iran
Anh-Tien Tran South Korea
Marcus de Ree United Kingdom
Charles W. Bostian United States
Baicen Xiao United States
Franco Tommasi Italy
Athanasios T. Karygiannis United States
Oscar Chang relative to Rodrigo Moreira Brazil Rodrigo Moreira's profile →
Citations per field
00.5×10×13×
Rodrigo Moreira · 1×
Citations per year

Countries citing papers authored by Oscar Chang

Since Specialization
Citations

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

Fields of papers citing papers by Oscar Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Oscar Chang. 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 Oscar Chang. The network helps show where Oscar Chang may publish in the future.

Co-authors

The 25 scholars most cited alongside Oscar Chang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Oscar Chang Line = papers co-authored together Oscar Chang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201628
2 200218
3 202416
4 200211
5 200211
6 20179
7 20209
8 20206
9 20236
10 20156
11 20106
12 20235
13 20165
14 20145
15 20215
16 20203
17 19813
18 20162
19 20152
20 20202

About Oscar Chang

Oscar Chang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Aerospace Engineering, Electrical and Electronic Engineering and Media Technology, having authored 34 papers that have together received 167 indexed citations. Recurring topics across this work include Antenna Design and Optimization (3 papers), Neural dynamics and brain function (3 papers), Robotics and Sensor-Based Localization (3 papers), Digital Imaging for Blood Diseases (2 papers), Microwave Engineering and Waveguides (2 papers), Human Pose and Action Recognition (2 papers), Spectroscopy and Chemometric Analyses (2 papers) and Experimental Learning in Engineering (2 papers). The work is most often cited by research in Signal Processing (21 citations), Aerospace Engineering (43 citations), Media Technology (14 citations), Computer Vision and Pattern Recognition (28 citations) and Artificial Intelligence (40 citations). Oscar Chang has collaborated with scholars based in Ecuador, Venezuela and Spain. Frequent co-authors include María I. Jiménez, Olivier Siohan, Leo Ramos, Hank Liao, Dmitriy Serdyuk, Diego H. Peluffo-Ordóńez, Fernando A. Gonzales-Zubiate, Dũng Trần, Kazuhito Koishida and Diego Jiménez. Their work appears in journals such as IEEE Transactions on Computers, Multimedia Tools and Applications, International Journal of Systems Science, Biosystems and Journal of Artificial Intelligence and Soft Computing Research.

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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