Ling-Hua Chang

1.3k total citations
13 papers, 39 citations indexed

About

Ling-Hua Chang is a scholar working on Computational Mechanics, Electrical and Electronic Engineering and Molecular Biology. According to data from OpenAlex, Ling-Hua Chang has authored 13 papers receiving a total of 39 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computational Mechanics, 7 papers in Electrical and Electronic Engineering and 4 papers in Molecular Biology. Recurrent topics in Ling-Hua Chang's work include Sparse and Compressive Sensing Techniques (7 papers), Indoor and Outdoor Localization Technologies (3 papers) and Coding theory and cryptography (3 papers). Ling-Hua Chang is often cited by papers focused on Sparse and Compressive Sensing Techniques (7 papers), Indoor and Outdoor Localization Technologies (3 papers) and Coding theory and cryptography (3 papers). Ling-Hua Chang collaborates with scholars based in Taiwan, China and Canada. Ling-Hua Chang's co-authors include Jwo-Yuh Wu, Vahid Shah‐Mansouri, Suyang Duan, Vincent W. S. Wong, Chun-Yu Lai, Yunghsiang S. Han, Pei‐Chun Wong, Jialin Wu, Po‐Ning Chen and Chun‐Hung Liu and has published in prestigious journals such as IEEE Transactions on Information Theory, Biomedicines and Microscopy and Microanalysis.

In The Last Decade

Ling-Hua Chang

9 papers receiving 39 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ling-Hua Chang Taiwan 4 22 18 12 8 5 13 39
Ganghua Yang China 3 9 0.4× 21 1.2× 5 0.4× 4 0.5× 9 32
Kihyun Kim South Korea 3 9 0.4× 21 1.2× 24 2.0× 28 3.5× 18 55
Hui Qian China 3 7 0.3× 9 0.5× 8 0.7× 4 0.5× 16 20
Fabien Delalondre Switzerland 5 6 0.3× 7 0.4× 8 0.7× 25 3.1× 2 0.4× 8 54
Alexander Lier Germany 5 12 0.5× 12 0.7× 24 2.0× 3 0.4× 2 0.4× 13 70
Dalia El Badawy Switzerland 5 12 0.5× 10 0.6× 3 0.3× 5 0.6× 5 33
C. Reimers Austria 5 3 0.1× 6 0.3× 17 1.4× 2 0.3× 2 0.4× 11 43
T. Andeen United States 4 4 0.2× 34 1.9× 27 2.3× 2 0.3× 2 0.4× 7 67
Maya Kabkab United States 2 31 1.4× 5 0.3× 10 0.8× 1 0.1× 1 0.2× 5 49
Michalina Pacholska Switzerland 4 5 0.2× 14 0.8× 2 0.2× 4 0.5× 5 33

Countries citing papers authored by Ling-Hua Chang

Since Specialization
Citations

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

Fields of papers citing papers by Ling-Hua Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ling-Hua Chang

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

All Works

13 of 13 papers shown
1.
Chang, Ling-Hua, Po‐Ning Chen, Fady Alajaji, & Yunghsiang S. Han. (2022). Decoder Ties Do Not Affect the Error Exponent of the Memoryless Binary Symmetric Channel. IEEE Transactions on Information Theory. 68(6). 3501–3510.
2.
Wong, Pei‐Chun, et al.. (2021). Two-Step Approach Using Degradable Magnesium to Inhibit Surface Biofilm and Subsequently Kill Planktonic Bacteria. Biomedicines. 9(11). 1677–1677. 1 indexed citations
3.
Wong, Pei‐Chun, et al.. (2021). Detection and Identification of the Stages of DH5-Alpha Escherichia coli Biofilm Formation on Metal by Using an Artificial Intelligence System. Microscopy and Microanalysis. 27(5). 1218–1225. 4 indexed citations
4.
Chang, Ling-Hua, Po‐Ning Chen, Fady Alajaji, & Yunghsiang S. Han. (2020). The Asymptotic Generalized Poor-Verdú Bound Achieves the BSC Error Exponent at Zero Rate. 2131–2136. 1 indexed citations
5.
Wu, Jwo-Yuh, et al.. (2019). Sparse Subspace Clustering With Sequentially Ordered and Weighted L1-Minimization. 24. 3387–3391. 1 indexed citations
6.
Chang, Ling-Hua, et al.. (2018). On the Maximum Size of Block Codes Subject to a Distance Criterion. IEEE Transactions on Information Theory. 65(6). 3751–3757.
7.
Wu, Jwo-Yuh, et al.. (2018). Enhanced Noisy Sparse Subspace Clustering via Reweighted L1-Minimization. 17. 1–6. 3 indexed citations
8.
9.
Chang, Ling-Hua, et al.. (2017). An Exact Spectrum Formula for the Maximum Size of Finite Length Block Codes..
10.
Chang, Ling-Hua & Jwo-Yuh Wu. (2014). An improved RIP-based performance guarantee for sparse signal recovery via orthogonal matching pursuit. 7. 28–31. 6 indexed citations
11.
12.
Shah‐Mansouri, Vahid, Suyang Duan, Ling-Hua Chang, Vincent W. S. Wong, & Jwo-Yuh Wu. (2013). Compressive sensing based asynchronous random access for wireless networks. 884–888. 12 indexed citations
13.

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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