Shih-Gu Huang

477 total citations
22 papers, 308 citations indexed

About

Shih-Gu Huang is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Applied Mathematics. According to data from OpenAlex, Shih-Gu Huang has authored 22 papers receiving a total of 308 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 8 papers in Signal Processing and 6 papers in Applied Mathematics. Recurrent topics in Shih-Gu Huang's work include Image and Signal Denoising Methods (7 papers), Digital Filter Design and Implementation (6 papers) and Mathematical Analysis and Transform Methods (6 papers). Shih-Gu Huang is often cited by papers focused on Image and Signal Denoising Methods (7 papers), Digital Filter Design and Implementation (6 papers) and Mathematical Analysis and Transform Methods (6 papers). Shih-Gu Huang collaborates with scholars based in Taiwan, United States and Singapore. Shih-Gu Huang's co-authors include Soo‐Chang Pei, Anqi Qiu, Moo K. Chung, Jing Xia, Liyuan Xu, Ilwoo Lyu, Andrey Gritsenko, Li Shen, Jian–Jiun Ding and Hernando Ombao and has published in prestigious journals such as IEEE Transactions on Signal Processing, IEEE Transactions on Medical Imaging and IEEE Transactions on Communications.

In The Last Decade

Shih-Gu Huang

21 papers receiving 305 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shih-Gu Huang Taiwan	10	97	84	66	53	47	22	308
Michael Davies United Kingdom	11	145 1.5×	56 0.7×	22 0.3×	147 2.8×	25 0.5×	27	474
Dale H. Mugler United States	9	97 1.0×	29 0.3×	35 0.5×	107 2.0×	36 0.8×	41	321
E.I. Plotkin Canada	11	174 1.8×	82 1.0×	77 1.2×	131 2.5×	27 0.6×	84	446
R. Bos Netherlands	9	30 0.3×	24 0.3×	64 1.0×	76 1.4×	34 0.7×	21	292
Dominique Pastor France	14	115 1.2×	63 0.8×	35 0.5×	193 3.6×	34 0.7×	52	462
Christoph Wiesmeyr Austria	10	54 0.6×	89 1.1×	20 0.3×	34 0.6×	33 0.7×	31	316
C.E. Davila United States	13	117 1.2×	54 0.6×	111 1.7×	318 6.0×	147 3.1×	39	599
Chunru Wan Singapore	12	136 1.4×	117 1.4×	37 0.6×	237 4.5×	10 0.2×	36	796
Ronald L. Allen United States	7	64 0.7×	46 0.5×	38 0.6×	34 0.6×	13 0.3×	10	293

Countries citing papers authored by Shih-Gu Huang

Since Specialization

Citations

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

Fields of papers citing papers by Shih-Gu Huang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shih-Gu Huang. 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 Shih-Gu Huang. The network helps show where Shih-Gu Huang may publish in the future.

Co-authorship network of co-authors of Shih-Gu Huang

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

All Works

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20 of 20 papers shown

Chung, Moo K., et al.. (2024). Topological state-space estimation of functional human brain networks. PLoS Computational Biology. 20(5). e1011869–e1011869.

Huang, Shih-Gu, Jing Xia, Liyuan Xu, & Anqi Qiu. (2022). Spatio-temporal directed acyclic graph learning with attention mechanisms on brain functional time series and connectivity. Medical Image Analysis. 77. 102370–102370. 21 indexed citations

Huang, Shih-Gu, Moo K. Chung, & Anqi Qiu. (2021). Fast mesh data augmentation via Chebyshev polynomial of spectral filtering. Neural Networks. 143. 198–208. 7 indexed citations

Huang, Shih-Gu, et al.. (2021). Revisiting convolutional neural network on graphs with polynomial approximations of Laplace–Beltrami spectral filtering. Neural Computing and Applications. 33(20). 13693–13704. 7 indexed citations

Huang, Shih-Gu, Ilwoo Lyu, Anqi Qiu, & Moo K. Chung. (2020). Fast Polynomial Approximation of Heat Kernel Convolution on Manifolds and Its Application to Brain Sulcal and Gyral Graph Pattern Analysis. IEEE Transactions on Medical Imaging. 39(6). 2201–2212. 15 indexed citations

Chung, Moo K., Shih-Gu Huang, Andrey Gritsenko, Li Shen, & Hyekyoung Lee. (2019). Statistical Inference on the Number of Cycles in Brain Networks. PubMed. 2019. 113–116. 12 indexed citations

Huang, Shih-Gu, et al.. (2019). Dynamic Functional Connectivity Using Heat Kernel. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 222–226. 3 indexed citations

Huang, Shih-Gu, et al.. (2019). Statistical model for dynamically-changing correlation matrices with application to brain connectivity. Journal of Neuroscience Methods. 331. 108480–108480. 10 indexed citations

Pei, Soo‐Chang & Shih-Gu Huang. (2019). 2-D Laguerre Distributed Approximating Functional: A Circular Low-Pass/Band-Pass Filter. IEEE Transactions on Circuits & Systems II Express Briefs. 66(5). 818–822. 6 indexed citations

10.

Huang, Shih-Gu, Andrey Gritsenko, Martin A. Lindquist, & Moo K. Chung. (2019). Circular Pearson Correlation Using Cosine Series Expansion. 1. 1774–1777. 1 indexed citations

11.

Pei, Soo‐Chang & Shih-Gu Huang. (2018). Adaptive STFT with Chirp-Modulated Gaussian Window. 4354–4358. 7 indexed citations

12.

Pei, Soo‐Chang & Shih-Gu Huang. (2016). Two-dimensional nonseparable discrete linear canonical transform based on CM-CC-CM-CC decomposition. Journal of the Optical Society of America A. 33(2). 214–214. 11 indexed citations

13.

Pei, Soo‐Chang & Shih-Gu Huang. (2016). Fast and accurate computation of normalized Bargmann transform. Journal of the Optical Society of America A. 34(1). 18–18. 1 indexed citations

14.

Pei, Soo‐Chang, Shih-Gu Huang, & Jian–Jiun Ding. (2015). Discrete Gyrator Transforms: Computational Algorithms and Applications. IEEE Transactions on Signal Processing. 63(16). 4207–4222. 6 indexed citations

15.

Pei, Soo‐Chang & Shih-Gu Huang. (2015). Fast Discrete Linear Canonical Transform Based on CM-CC-CM Decomposition and FFT. IEEE Transactions on Signal Processing. 64(4). 855–866. 25 indexed citations

16.

Pei, Soo‐Chang & Shih-Gu Huang. (2014). Instantaneous frequency estimation by group delay attractors and instantaneous frequency attractors. 471–475. 1 indexed citations

17.

Pei, Soo‐Chang & Shih-Gu Huang. (2013). Reversible Joint Hilbert and Linear Canonical Transform Without Distortion. IEEE Transactions on Signal Processing. 61(19). 4768–4781. 12 indexed citations

18.

Huang, Shih-Gu. (2009). Improvement of Active Interference Cancellation : Avoidance Technique for OFDM Cognitive Radio. IEEE Transactions on Communications. 8(12). 17 indexed citations

19.

Huang, Shih-Gu, et al.. (2009). Improvement of active interference cancellation: avoidance technique for OFDM cognitive radio. IEEE Transactions on Wireless Communications. 8(12). 5928–5937. 24 indexed citations

20.

Huang, Shih-Gu, et al.. (2008). Low Complexity Active Interference Cancellation for OFDM Cognitive Radios. 2. 1279–1283. 8 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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