Li-Ye Niu

433 total citations
11 papers, 343 citations indexed

About

Li-Ye Niu is a scholar working on Electrical and Electronic Engineering, Biomedical Engineering and Cognitive Neuroscience. According to data from OpenAlex, Li-Ye Niu has authored 11 papers receiving a total of 343 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Electrical and Electronic Engineering, 7 papers in Biomedical Engineering and 4 papers in Cognitive Neuroscience. Recurrent topics in Li-Ye Niu's work include Plasmonic and Surface Plasmon Research (6 papers), Advanced Memory and Neural Computing (5 papers) and Neural dynamics and brain function (4 papers). Li-Ye Niu is often cited by papers focused on Plasmonic and Surface Plasmon Research (6 papers), Advanced Memory and Neural Computing (5 papers) and Neural dynamics and brain function (4 papers). Li-Ye Niu collaborates with scholars based in China. Li-Ye Niu's co-authors include Qi Wang, Wan‐Ming Zhao, Jianying Jing, Xue-Zhou Wang, Xi Zhuo Jiang, Ying Wei, Wenbo Liu, Yue Liu, Aisong Zhu and Cheng Zhan and has published in prestigious journals such as IEEE Transactions on Instrumentation and Measurement, Optics Communications and Engineering Applications of Artificial Intelligence.

In The Last Decade

Li-Ye Niu

10 papers receiving 336 citations

Peers

Li-Ye Niu

EEE

BE

MB

EOMM

BIOEN

Mahin Khan Mahadi Bangladesh

Reefat Inum Bangladesh

Jingkai Zhou Singapore

Sadegh Kamaei Switzerland

Ahmet Yaşlı Türkiye

Myunghan Bae South Korea

D. Tsiokos Greece

Kuldeep Choudhary India

Debdatta Panigrahi Japan

Evgeny Pikhay Israel

Mahin Khan Mahadi Bangladesh

Li-Ye Niu

137 ×0.5

EEE

158 ×0.7

BE

51 ×0.6

MB

87 ×1.9

EOMM

7 ×0.2

BIOEN

Citations per year, relative to Li-Ye Niu Li-Ye Niu (= 1×) peers Mahin Khan Mahadi

Countries citing papers authored by Li-Ye Niu

Since Specialization

Citations

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

Fields of papers citing papers by Li-Ye Niu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Li-Ye Niu. 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 Li-Ye Niu. The network helps show where Li-Ye Niu may publish in the future.

Co-authorship network of co-authors of Li-Ye Niu

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

All Works

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11 of 11 papers shown

1.

Niu, Li-Ye, Ying Wei, Liping Zhao, & Keli Hu. (2024). Deep spiking neural networks based on model fusion technology for remote sensing image classification. Engineering Applications of Artificial Intelligence. 142. 109873–109873. 2 indexed citations

2.

Niu, Li-Ye, et al.. (2023). Research Progress of spiking neural network in image classification: a review. Applied Intelligence. 53(16). 19466–19490. 28 indexed citations

3.

Niu, Li-Ye, Ying Wei, & Yue Liu. (2023). Event-driven spiking neural network based on membrane potential modulation for remote sensing image classification. Engineering Applications of Artificial Intelligence. 123. 106322–106322. 16 indexed citations

4.

Niu, Li-Ye & Ying Wei. (2023). CIRM-SNN: Certainty Interval Reset Mechanism Spiking Neuron for Enabling High Accuracy Spiking Neural Network. Neural Processing Letters. 55(6). 7561–7582.

5.

Niu, Li-Ye, et al.. (2021). High-Accuracy Spiking Neural Network for Objective Recognition Based on Proportional Attenuating Neuron. Neural Processing Letters. 54(2). 1055–1073. 3 indexed citations

6.

Zhan, Cheng, Qi Wang, Aisong Zhu, et al.. (2020). Au-nanoshells modified surface field enhanced LRSPR biosensor with low LOD for highly sensitive hIgG sensing. Optics & Laser Technology. 134. 106656–106656. 22 indexed citations

7.

Wang, Qi, et al.. (2020). Enhanced sensitivity of bimetallic optical fiber SPR sensor based on MoS2 nanosheets. Optics and Lasers in Engineering. 128. 105997–105997. 69 indexed citations

8.

Wang, Qi, et al.. (2019). Barium titanate film based fiber optic surface plasmon sensor with high sensitivity. Optics & Laser Technology. 124. 105899–105899. 30 indexed citations

9.

Wang, Qi, Jianying Jing, Xue-Zhou Wang, Li-Ye Niu, & Wan‐Ming Zhao. (2019). A D-Shaped Fiber Long-Range Surface Plasmon Resonance Sensor With High Q-Factor and Temperature Self-Compensation. IEEE Transactions on Instrumentation and Measurement. 69(5). 2218–2224. 87 indexed citations

10.

Niu, Li-Ye, et al.. (2019). High quality factor D-type fiber surface plasmon resonance (SPR) sensor based on the modification of gold nanoshells. Instrumentation Science & Technology. 48(1). 63–74. 11 indexed citations

11.

Niu, Li-Ye, et al.. (2019). Sensitivity enhanced D-type large-core fiber SPR sensor based on Gold nanoparticle/Au film co-modification. Optics Communications. 450. 287–295. 75 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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