Ying Yao

782 total citations
24 papers, 585 citations indexed

About

Ying Yao is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Cellular and Molecular Neuroscience. According to data from OpenAlex, Ying Yao has authored 24 papers receiving a total of 585 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Electrical and Electronic Engineering, 6 papers in Computer Vision and Pattern Recognition and 4 papers in Cellular and Molecular Neuroscience. Recurrent topics in Ying Yao's work include Advanced Memory and Neural Computing (4 papers), Neuroscience and Neural Engineering (4 papers) and EEG and Brain-Computer Interfaces (4 papers). Ying Yao is often cited by papers focused on Advanced Memory and Neural Computing (4 papers), Neuroscience and Neural Engineering (4 papers) and EEG and Brain-Computer Interfaces (4 papers). Ying Yao collaborates with scholars based in China, United States and Taiwan. Ying Yao's co-authors include Gayatri E. Perlin, Amir M. Sodagar, K.D. Wise, M.N. Gulari, K. Najafi, Khalil Najafi, Kensall D. Wise, Silong Peng, Peng Shan and Qiaoyun Wang and has published in prestigious journals such as Proceedings of the IEEE, IEEE Access and IEEE Journal of Solid-State Circuits.

In The Last Decade

Ying Yao

22 papers receiving 574 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ying Yao China 8 392 320 212 207 38 24 585
Sohmyung Ha South Korea 18 352 0.9× 989 3.1× 147 0.7× 566 2.7× 10 0.3× 127 1.3k
Karsten Seidl Germany 16 603 1.5× 365 1.1× 412 1.9× 248 1.2× 9 0.2× 69 830
Sabah M. Ahmed Egypt 17 65 0.2× 244 0.8× 214 1.0× 172 0.8× 86 2.3× 50 933
Gemma Taverni Switzerland 7 198 0.5× 264 0.8× 149 0.7× 173 0.8× 96 2.5× 11 551
Ahnsei Shon United States 6 91 0.2× 141 0.4× 66 0.3× 241 1.2× 23 0.6× 9 478
Sergey A. Lobov Russia 13 265 0.7× 369 1.2× 338 1.6× 170 0.8× 26 0.7× 43 712
Ralph Etienne-Cummings United States 11 366 0.9× 155 0.5× 447 2.1× 466 2.3× 28 0.7× 25 701
Fan Wang China 14 44 0.1× 493 1.5× 85 0.4× 159 0.8× 41 1.1× 114 859
Hongmiao Zhang China 8 155 0.4× 128 0.4× 375 1.8× 199 1.0× 16 0.4× 35 568
Utkarsh Singhal United States 5 238 0.6× 243 0.8× 95 0.4× 307 1.5× 40 1.1× 9 560

Countries citing papers authored by Ying Yao

Since Specialization
Citations

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

Fields of papers citing papers by Ying Yao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ying Yao

This figure shows the co-authorship network connecting the top 25 collaborators of Ying Yao. A scholar is included among the top collaborators of Ying Yao 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 Ying Yao. Ying Yao 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.
Yao, Ying & Gang Liu. (2025). Enhanced Power Inspection System Utilizing Metaverse Technology. 764–769.
2.
Lou, Ping, et al.. (2023). Multi-Source Energy Storage Stations Control Strategy Considering Implicit Linearization of The Power Flow Manifold. Journal of Physics Conference Series. 2584(1). 12055–12055.
3.
Zhang, Jin, et al.. (2022). Wi-Phrase: Deep Residual-Multihead Model for WiFi Sign Language Phrase Recognition. IEEE Internet of Things Journal. 9(18). 18015–18027. 6 indexed citations
4.
Yao, Ying, et al.. (2021). PSigmoid: Improving squeeze-and-excitation block with parametric sigmoid. Applied Intelligence. 51(10). 7427–7439. 16 indexed citations
5.
Shan, Peng, et al.. (2021). A nonlinear calibration transfer method based on joint kernel subspace. Chemometrics and Intelligent Laboratory Systems. 210. 104247–104247. 5 indexed citations
6.
Shan, Peng, et al.. (2019). Stacked ensemble extreme learning machine coupled with Partial Least Squares-based weighting strategy for nonlinear multivariate calibration. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy. 215. 97–111. 16 indexed citations
7.
Shan, Peng, et al.. (2019). Principal component analysis or kernel principal component analysis based joint spectral subspace method for calibration transfer. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy. 227. 117653–117653. 14 indexed citations
8.
Yao, Ying, et al.. (2019). Rectified Exponential Units for Convolutional Neural Networks. IEEE Access. 7. 101633–101640. 28 indexed citations
9.
Shan, Peng, et al.. (2018). Interval LASSO regression based extreme learning machine for nonlinear multivariate calibration of near infrared spectroscopic datasets. Analytical Methods. 10(25). 3011–3022. 6 indexed citations
10.
Chen, Liang-Chia, et al.. (2017). Accurate submicron edge detection using the phase change of a nano-scale shifting laser spot. Optics & Laser Technology. 92. 109–119. 4 indexed citations
11.
Li, Jing, et al.. (2017). A review on signature-based detection for network threats. 1117–1121. 2 indexed citations
12.
Yao, Ying, et al.. (2016). Prediction Model of Driving Energy Consumption Based on PCA and BP Network. 16(5). 204. 7 indexed citations
13.
Yao, Ying, et al.. (2013). BOF Endpoint Prediction Based on Multi-Neural Network Model. Applied Mechanics and Materials. 441. 666–669. 5 indexed citations
14.
Ji, Qing, et al.. (2010). DBR光纤激光器有效腔长测量新方法. Chinese Optics Letters. 8(4). 398–398. 2 indexed citations
15.
Wu, Chunya, et al.. (2009). An integrated driving circuit implemented with p-type LTPS TFTs for AMOLED. Optoelectronics Letters. 5(2). 104–107. 4 indexed citations
16.
Sodagar, Amir M., Gayatri E. Perlin, Ying Yao, Khalil Najafi, & Kensall D. Wise. (2009). An Implantable 64-Channel Wireless Microsystem for Single-Unit Neural Recording. IEEE Journal of Solid-State Circuits. 44(9). 2591–2604. 164 indexed citations
17.
Wise, K.D., Amir M. Sodagar, Ying Yao, et al.. (2008). Microelectrodes, Microelectronics, and Implantable Neural Microsystems. Proceedings of the IEEE. 96(7). 1184–1202. 251 indexed citations
18.
Sodagar, Amir M., Gayatri E. Perlin, Ying Yao, Kensall D. Wise, & Khalil Najafi. (2007). An Implantable Microsystem for Wireless Multi-Channel Cortical Recording. TRANSDUCERS 2007 - 2007 International Solid-State Sensors, Actuators and Microsystems Conference. 69–72. 26 indexed citations
19.
Liu, Lihui, Qida Zhao, Hao Zhang, et al.. (2004). Study on an optical filter constituted by concatenated Hi‐Bi fiber loop mirrors. Microwave and Optical Technology Letters. 43(1). 23–26. 6 indexed citations
20.
Yao, Ying, Raj Acharya, & Sargur N. Srihari. (1994). Image enhancement using mathematical morphology with adaptive structuring elements. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 2180. 198–198. 11 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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