Seok‐Bum Ko

3.6k total citations
192 papers, 2.4k citations indexed

About

Seok‐Bum Ko is a scholar working on Electrical and Electronic Engineering, Computational Theory and Mathematics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Seok‐Bum Ko has authored 192 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 95 papers in Electrical and Electronic Engineering, 56 papers in Computational Theory and Mathematics and 38 papers in Computer Vision and Pattern Recognition. Recurrent topics in Seok‐Bum Ko's work include Low-power high-performance VLSI design (50 papers), Numerical Methods and Algorithms (45 papers) and Digital Filter Design and Implementation (32 papers). Seok‐Bum Ko is often cited by papers focused on Low-power high-performance VLSI design (50 papers), Numerical Methods and Algorithms (45 papers) and Digital Filter Design and Implementation (32 papers). Seok‐Bum Ko collaborates with scholars based in Canada, India and South Korea. Seok‐Bum Ko's co-authors include S. Venkatachalam, Younhee Choi, Hao Zhang, S. Deivalakshmi, Khan A. Wahid, Gong Yong Jin, Hyuk-Jae Lee, Qiao Zhang, Han Liu and Dongdong Chen and has published in prestigious journals such as IEEE Access, IEEE Transactions on Computers and Lara D. Veeken.

In The Last Decade

Seok‐Bum Ko

177 papers receiving 2.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Seok‐Bum Ko Canada 23 1.0k 527 511 502 485 192 2.4k
Khan A. Wahid Canada 30 538 0.5× 266 0.5× 1.0k 2.0× 259 0.5× 490 1.0× 201 3.3k
Miriam Leeser United States 24 742 0.7× 352 0.7× 484 0.9× 435 0.9× 150 0.3× 184 2.1k
Tughrul Arslan United Kingdom 24 1.5k 1.4× 261 0.5× 290 0.6× 422 0.8× 644 1.3× 430 3.1k
Xuan Zeng China 26 2.0k 2.0× 336 0.6× 182 0.4× 323 0.6× 583 1.2× 330 3.2k
Domenico Grimaldi Italy 28 561 0.5× 57 0.1× 195 0.4× 313 0.6× 647 1.3× 176 2.4k
Rajib Kar India 28 1.3k 1.3× 251 0.5× 182 0.4× 927 1.8× 488 1.0× 372 3.4k
Keck Voon Ling Singapore 28 633 0.6× 109 0.2× 271 0.5× 373 0.7× 192 0.4× 194 2.7k
Reza R. Adhami United States 19 420 0.4× 110 0.2× 544 1.1× 162 0.3× 266 0.5× 103 1.5k
Abdellatif Mtibaa Tunisia 21 463 0.5× 213 0.4× 838 1.6× 398 0.8× 50 0.1× 179 1.7k
Houjun Wang China 19 636 0.6× 72 0.1× 140 0.3× 195 0.4× 200 0.4× 195 1.5k

Countries citing papers authored by Seok‐Bum Ko

Since Specialization
Citations

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

Fields of papers citing papers by Seok‐Bum Ko

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Seok‐Bum Ko. 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 Seok‐Bum Ko. The network helps show where Seok‐Bum Ko may publish in the future.

Co-authorship network of co-authors of Seok‐Bum Ko

This figure shows the co-authorship network connecting the top 25 collaborators of Seok‐Bum Ko. A scholar is included among the top collaborators of Seok‐Bum Ko 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 Seok‐Bum Ko. Seok‐Bum Ko 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
1.
Ko, Seok‐Bum, et al.. (2025). The multi-GPU Wetland DEM Ponding Model. Computers & Geosciences. 199. 105912–105912.
2.
Bae, Gihyun, et al.. (2024). Proposed package type for evaluating reliability of HBM Memory. Solid-State Electronics. 216. 108923–108923.
3.
Ko, Seok‐Bum, et al.. (2024). Energy Efficient FPGA-Based Binary Transformer Accelerator for Edge Devices. 1–5. 3 indexed citations
4.
Ko, Seok‐Bum, et al.. (2024). Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images. Physical and Engineering Sciences in Medicine. 48(1). 3–18.
5.
Ko, Seok‐Bum, et al.. (2023). An Optimized Hardware Implementation of Modular Multiplication of Binary Ring LWE. IEEE Transactions on Emerging Topics in Computing. 11(3). 817–821. 9 indexed citations
6.
Zhang, Hao, et al.. (2022). Segmentation for document layout analysis: not dead yet. International Journal on Document Analysis and Recognition (IJDAR). 25(2). 67–77. 8 indexed citations
7.
Zhang, Hao & Seok‐Bum Ko. (2022). Efficient Approximate Posit Multipliers for Deep Learning Computation. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 13(1). 201–211. 11 indexed citations
8.
Ahmed, Suhaib, et al.. (2021). QCA based cost efficient coplanar 1 × 4 RAM design with set/reset ability. International Journal of Numerical Modelling Electronic Networks Devices and Fields. 35(1). 9 indexed citations
9.
Choi, Younhee, et al.. (2021). Capsule GAN for prostate MRI super-resolution. Multimedia Tools and Applications. 81(3). 4119–4141. 20 indexed citations
10.
Lakshminarayanan, G., et al.. (2020). High-Speed Architecture for Successive Cancellation Decoder With Split-g Node Block. IEEE Embedded Systems Letters. 13(3). 118–121. 1 indexed citations
11.
Ko, Seok‐Bum, et al.. (2020). A Novel Architecture for Early Detection of Negative Output Features in Deep Neural Network Accelerators. IEEE Transactions on Circuits & Systems II Express Briefs. 67(12). 3332–3336. 10 indexed citations
12.
Ko, Seok‐Bum, et al.. (2020). Improving the Detection of Explosives in a MOX Chemical Sensors Array With LSTM Networks. IEEE Sensors Journal. 20(23). 14302–14309. 20 indexed citations
13.
Wang, Yi, Hao Zhang, Kum Ju Chae, et al.. (2020). Novel convolutional neural network architecture for improved pulmonary nodule classification on computed tomography. Multidimensional Systems and Signal Processing. 31(3). 1163–1183. 20 indexed citations
14.
Ko, Seok‐Bum, et al.. (2020). IoT-Based Intelligent Residential Kitchen Fire Prevention System. Journal of Electrical Engineering and Technology. 15(6). 2823–2832. 6 indexed citations
15.
Zhang, Hao & Seok‐Bum Ko. (2020). Design of Power Efficient Posit Multiplier. IEEE Transactions on Circuits & Systems II Express Briefs. 67(5). 861–865. 32 indexed citations
16.
Ko, Seok‐Bum, et al.. (2020). Area-Efficient Nano-AES Implementation for Internet-of-Things Devices. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 29(1). 136–148. 42 indexed citations
17.
Zhang, Hao, Dongdong Chen, & Seok‐Bum Ko. (2019). Efficient Multiple-Precision Floating-Point Fused Multiply-Add with Mixed-Precision Support. IEEE Transactions on Computers. 68(7). 1035–1048. 50 indexed citations
18.
Lakshminarayanan, G., et al.. (2019). A Self-Adaptive Mapping Approach for Network on Chip With Low Power Consumption. IEEE Access. 7. 84066–84081. 13 indexed citations
19.
Venkatachalam, S., et al.. (2019). Approximate Restoring Dividers Using Inexact Cells and Estimation From Partial Remainders. IEEE Transactions on Computers. 69(4). 468–474. 19 indexed citations
20.
Venkatachalam, S. & Seok‐Bum Ko. (2017). Design of Power and Area Efficient Approximate Multipliers. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 25(5). 1782–1786. 266 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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