Ming‐Hwa Sheu

184 total papers · 1.5k total citations
126 papers, 1.0k citations indexed

About

Ming‐Hwa Sheu is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Ming‐Hwa Sheu has authored 126 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 55 papers in Electrical and Electronic Engineering, 53 papers in Computer Vision and Pattern Recognition and 28 papers in Signal Processing. Recurrent topics in Ming‐Hwa Sheu's work include Low-power high-performance VLSI design (28 papers), Analog and Mixed-Signal Circuit Design (16 papers) and Image and Signal Denoising Methods (14 papers). Ming‐Hwa Sheu is often cited by papers focused on Low-power high-performance VLSI design (28 papers), Analog and Mixed-Signal Circuit Design (16 papers) and Image and Signal Denoising Methods (14 papers). Ming‐Hwa Sheu collaborates with scholars based in Taiwan, Argentina and China. Ming‐Hwa Sheu's co-authors include Jin‐Fa Lin, Yin‐Tsung Hwang, Shih‐Chang Hsia, Chung‐Chi Lin, Huann‐Keng Chiang, Ming‐Der Shieh, Chi‐Chia Sun, Yihua Wang, Chung‐Ho Chen and Chuan‐Yu Chang and has published in prestigious journals such as IEEE Access, Sensors and IEEE Journal of Solid-State Circuits.

In The Last Decade

Ming‐Hwa Sheu

110 papers receiving 946 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Ming‐Hwa Sheu	565	317	229	183	149	126	1.0k
Jen‐Shiun Chiang	229 0.4×	416 1.3×	155 0.7×	130 0.7×	44 0.3×	123	844
Luc Claesen	386 0.7×	341 1.1×	120 0.5×	135 0.7×	209 1.4×	109	1.1k
Xiaoming Xiong	251 0.4×	474 1.5×	135 0.6×	237 1.3×	109 0.7×	123	1.2k
Saeid Nooshabadi	515 0.9×	296 0.9×	187 0.8×	139 0.8×	53 0.4×	163	981
Susmita Sur‐Kolay	369 0.7×	158 0.5×	130 0.6×	384 2.1×	138 0.9×	98	1.1k
Min Yao	189 0.3×	211 0.7×	143 0.6×	353 1.9×	54 0.4×	104	969
Cong‐Kha Pham	361 0.6×	172 0.5×	159 0.7×	340 1.9×	107 0.7×	190	901
Xue Liu	192 0.3×	210 0.7×	78 0.3×	190 1.0×	72 0.5×	73	800
Talal Bonny	172 0.3×	422 1.3×	141 0.6×	299 1.6×	61 0.4×	113	1.2k
Yuan Cao	702 1.2×	221 0.7×	83 0.4×	161 0.9×	62 0.4×	100	1.2k

Countries citing papers authored by Ming‐Hwa Sheu

Since Specialization

Citations

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

Fields of papers citing papers by Ming‐Hwa Sheu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming‐Hwa Sheu. 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 Ming‐Hwa Sheu. The network helps show where Ming‐Hwa Sheu may publish in the future.

Co-authorship network of co-authors of Ming‐Hwa Sheu

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

All Works

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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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