Tae‐Gyu Chang

69 papers receiving 1.1k citations

Hit Papers

Surface Electromyography Signal Processing and Classifica...20132026201720212013200400600

Peers

Tae‐Gyu Chang
Comparison fields: 5 of 101
  • Biomedical Engineering 715
  • Cognitive Neuroscience 438
  • Electrical and Electronic Engineering 212
  • Signal Processing 175
  • Cellular and Molecular Neuroscience 165
Replace Beth Jelfs with:
Beth Jelfs United Kingdom
Hans Hallez Belgium
David E. Thompson United States
Christine Servière France
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Shuai Cao China
Muhammad Ibn Ibrahimy Malaysia
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Zhizeng Luo China
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Citations per field
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Citations per year

Countries citing papers authored by Tae‐Gyu Chang

Since Specialization
Citations

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

Fields of papers citing papers by Tae‐Gyu Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tae‐Gyu Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Tae‐Gyu Chang. A scholar is included among the top collaborators of Tae‐Gyu Chang 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 Tae‐Gyu Chang. Tae‐Gyu Chang 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
#WorkIndexed citations
1 1
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Izbor i postavljanje senzora za otkrivanje sudara vozila
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3
A high speed current dq PI controller for PMSM drive
3
4 2
5 12
6 2
7 1
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A new structure for the flexible intelligent electronic device (IED)
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9 4
10 1
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A Power Reduction Technique based on the Microscopic Dynamic Voltage Scaling(DVS) of Multimedia Signal Processors
0
12 1
13
WCDMA Reverse Link Beamforming Algorithm and Its Performance Simulation
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14
Power-line phase measurement algorithm based on the sliding-DFT
1
15 2
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An efficient search of binary tree for huffman decoding based on numeric interpretation of codewords
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18 4
19 8
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Development of an expert system for multichannel eeg signal analysis
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About Tae‐Gyu Chang

Tae‐Gyu Chang is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Cognitive Neuroscience, having authored 77 papers that have together received 1.2k indexed citations. Recurring topics across this work include Advanced Data Compression Techniques (10 papers), Speech and Audio Processing (9 papers) and Advanced Adaptive Filtering Techniques (9 papers). The work is most often cited by research in Cognitive Neuroscience (438 citations), Human-Computer Interaction (92 citations) and Signal Processing (175 citations). Tae‐Gyu Chang has collaborated with scholars based in South Korea, Malaysia and United States. Frequent co-authors include Mamun Bin Ibne Reaz, Rafi Hassan Chowdhury, Mohd Helmi Ali, Kalaivani Chellappan, José C. Prı́ncipe, Sen M. Kuo, Sunit Gala, Labonnah Farzana Rahman, Jubayer Jalil and Mohammad Arif Sobhan Bhuiyan. Their work appears in journals such as Journal of Power Sources, IEEE Transactions on Signal Processing and Expert Systems with Applications.

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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