Min Fu

2.2k total citations
67 papers, 1.7k citations indexed

About

Min Fu is a scholar working on Media Technology, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Min Fu has authored 67 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Media Technology, 14 papers in Electrical and Electronic Engineering and 13 papers in Computer Vision and Pattern Recognition. Recurrent topics in Min Fu's work include Remote-Sensing Image Classification (13 papers), Remote Sensing and Land Use (12 papers) and Underwater Vehicles and Communication Systems (10 papers). Min Fu is often cited by papers focused on Remote-Sensing Image Classification (13 papers), Remote Sensing and Land Use (12 papers) and Underwater Vehicles and Communication Systems (10 papers). Min Fu collaborates with scholars based in China, United States and France. Min Fu's co-authors include Yi Zuo, Ju Lu, Xinzhu Yu, Xuefeng Liu, Roger N. Rosenberg, Doris Lambracht‐Washington, Diana C. Chen, Joel Kubby, Oscar Azucena and Xiaodong Tao and has published in prestigious journals such as Nature, The Science of The Total Environment and Trends in Neurosciences.

In The Last Decade

Min Fu

56 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Min Fu China 19 419 392 322 200 187 67 1.7k
Hua Han China 21 229 0.5× 325 0.8× 639 2.0× 200 1.0× 123 0.7× 116 2.3k
Jing Yuan China 29 565 1.3× 697 1.8× 453 1.4× 109 0.5× 135 0.7× 177 3.0k
Xiangning Li China 27 960 2.3× 861 2.2× 729 2.3× 278 1.4× 306 1.6× 168 3.4k
Leigh A. Johnston Australia 30 404 1.0× 424 1.1× 597 1.9× 213 1.1× 159 0.9× 108 2.7k
Ting Zhao China 17 436 1.0× 298 0.8× 325 1.0× 40 0.2× 75 0.4× 52 1.2k
V. Srinivasa Chakravarthy India 24 446 1.1× 247 0.6× 609 1.9× 152 0.8× 122 0.7× 118 2.1k
Xiaoli Qi China 27 408 1.0× 809 2.1× 102 0.3× 142 0.7× 110 0.6× 83 2.3k
J. Kaufhold United States 11 276 0.7× 160 0.4× 232 0.7× 74 0.4× 221 1.2× 21 1.2k
Hideki Hayakawa Japan 25 400 1.0× 522 1.3× 193 0.6× 289 1.4× 178 1.0× 79 2.0k

Countries citing papers authored by Min Fu

Since Specialization
Citations

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

Fields of papers citing papers by Min Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Min Fu. 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 Min Fu. The network helps show where Min Fu may publish in the future.

Co-authorship network of co-authors of Min Fu

This figure shows the co-authorship network connecting the top 25 collaborators of Min Fu. A scholar is included among the top collaborators of Min Fu 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 Min Fu. Min Fu 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.
Fu, Min, et al.. (2025). HySwinFormer: A hybrid deep learning architecture for fine-grained classification of marine microalgae. The Science of The Total Environment. 998. 180245–180245.
2.
Li, Shanshan, et al.. (2025). A novel recursive transformer-based U-Net architecture for enhanced multi-scale medical image segmentation. Computers in Biology and Medicine. 196(Pt A). 110658–110658.
3.
4.
Xie, Zhihui, Min Fu, & Xuefeng Liu. (2023). Detection of Fittings Based on the Dynamic Graph CNN and U-Net Embedded with Bi-Level Routing Attention. Electronics. 12(22). 4611–4611. 1 indexed citations
5.
Liu, Qiuyue, Min Fu, & Xuefeng Liu. (2023). Shadow Enhancement Using 2D Dynamic Stochastic Resonance for Hyperspectral Image Classification. Remote Sensing. 15(7). 1820–1820. 5 indexed citations
6.
Fu, Min, et al.. (2023). A Facial Feature and Lip Movement Enhanced Audio-Visual Speech Separation Model. Sensors. 23(21). 8770–8770.
7.
Lambracht‐Washington, Doris, et al.. (2023). DNA Aβ42 immunization via needle-less Jet injection in mice and rabbits as potential immunotherapy for Alzheimer's disease. Journal of the Neurological Sciences. 446. 120564–120564. 3 indexed citations
8.
Liu, Xuefeng, et al.. (2018). Hyperspectral Image Classification Based on Parameter-Optimized 3D-CNNs Combined with Transfer Learning and Virtual Samples. Remote Sensing. 10(9). 1425–1425. 32 indexed citations
9.
Rosenberg, Roger N., Min Fu, & Doris Lambracht‐Washington. (2018). Active full-length DNA Aβ42 immunization in 3xTg-AD mice reduces not only amyloid deposition but also tau pathology. Alzheimer s Research & Therapy. 10(1). 115–115. 34 indexed citations
10.
11.
Rosenberg, Roger N., Min Fu, & Doris Lambracht‐Washington. (2018). Intradermal active full-length DNA Aβ42 immunization via electroporation leads to high anti-Aβ antibody levels in wild-type mice. Journal of Neuroimmunology. 322. 15–25. 4 indexed citations
12.
Hussain, Rehana Z., William A. Miller-Little, Doris Lambracht‐Washington, et al.. (2017). Laquinimod has no effects on brain volume or cellular CNS composition in the F1 3xTg-AD/C3H mouse model of Alzheimer's disease. Journal of Neuroimmunology. 309. 100–110. 5 indexed citations
13.
Yuan, Yuan, Min Fu, & Xiaoqiang Lu. (2015). Substance Dependence Constrained Sparse NMF for Hyperspectral Unmixing. IEEE Transactions on Geoscience and Remote Sensing. 53(6). 2975–2986. 51 indexed citations
14.
Fu, Min, Xinzhu Yu, Ju Lu, & Yi Zuo. (2012). Repetitive motor learning induces coordinated formation of clustered dendritic spines in vivo. Nature. 483(7387). 92–95. 354 indexed citations
15.
Lambracht‐Washington, Doris, Bao‐Xi Qu, Min Fu, et al.. (2012). A peptide prime-DNA boost immunization protocol provides significant benefits as a new generation Aβ42 DNA vaccine for Alzheimer disease. Journal of Neuroimmunology. 254(1-2). 63–68. 26 indexed citations
16.
Fu, Min, et al.. (2011). Autocrine motility factor/phosphoglucose isomerase regulates ER stress and cell death through control of ER calcium release. Cell Death and Differentiation. 18(6). 1057–1070. 41 indexed citations
17.
Tao, Xiaodong, Oscar Azucena, Min Fu, et al.. (2011). Adaptive optics microscopy with direct wavefront sensing using fluorescent protein guide stars. Optics Letters. 36(17). 3389–3389. 53 indexed citations
18.
Tao, Xiaodong, Oscar Azucena, Min Fu, et al.. (2011). Adaptive optics confocal microscopy using direct wavefront sensing. Optics Letters. 36(7). 1062–1062. 135 indexed citations
19.
Lambracht‐Washington, Doris, et al.. (2010). Analysis of three plasmid systems for use in DNA Aβ42 immunization as therapy for Alzheimer's disease. Vaccine. 28(32). 5280–5287. 27 indexed citations
20.
Fu, Min, Meng Wu, Jifeng Wang, Yanjiang Qiao, & Wang Zhao. (2007). Disruption of the intracellular Ca2+ homeostasis in the cardiac excitation–contraction coupling is a crucial mechanism of arrhythmic toxicity in aconitine-induced cardiomyocytes. Biochemical and Biophysical Research Communications. 354(4). 929–936. 57 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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