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Lili Shen

Lili Shen

 

 

Tianjin International Engineering Institute

+86-13820771311

sll@tju.edu.cn

Dept

School of Communication Engineering

Title

Associate Professor

Address

D515 Room, Building 26

 

Biography

Dr. Shen Lili, received his Ph.D. in Information and Communication Engineering, from Tianjin University. She is currently an associate professor of the School of Electrical and Information Engineering at Tianjin University.

She has published more than 50 refereed journal and conference papers. Dr. Shen has been awarded 6 Chinese patents, with another 3 Chinese patents pending.

 

Education

Ph D (Information and Communication Engineering), Tianjin University, 2010

MS (Communication and Information System), Xidian University, 2004

BS (Communication Engineering), Xidian University, 2001

Research Interests

My interests are focused on image processing and EEG classification.

The research about image processing mainly includes image assessment, image inpainting, image restoration, object detection and recognition.

EEG classification and recognition involve stereoscopic image ranking, motor imagery decoding, emotion recognition and 3D perception.

(1) The National Natural Science Foundation of China, “Research on 3D image quality assessment by EEG embedded cross-modal learning”, Project Director.

In this project, we focus on 3D image quality assessment via multimodal learning, including image quality ranking and EEG classification.

(2) The National High Technology Research Development Program China (863 project), “The key technology and application system of 3D content”, Sub-project in charge. We conducted cognitive feature measurement and quality evaluation for 3D content.

(3) The National Natural Science Foundation of China, “Research on perception of viewing comfort theory and assessment method for stereoscopic visual information”, Project Director.

We revealed the transformation rules of 3D information, analyzed parameters through multi-factors analysis, studied the mismatches between different viewpoints in stereoscopic information. Finally, we provided a deeper understanding in stereoscopic information and construct systems for rating viewing comfort.

(4) The Science and Technology Found of Tianjin Eye Hospital, “EEG detection system based on dynamic random dot stereograms” Project Director.

In this study, we will implement detection of stereoacuity test. We hope this study will make a substantial contribution to find patients in a timely manner.

(5) Enterprise Post-doctoral Program of Tianjin, “Research on extraction and quality evaluation of echo information for marine monitoring”, Project Director

    

Selected Publications (2020-2022)

(1) Shen Lili, Zhao Bo et al, Channel recombination and projection network for blind image quality measurement, IEEE transaction on instrumentation and measurement, 2022,71, pp.2513512 (SCI: 000846867100011)

(2) Shen Lili, Sun Mingyang et al, Multiscale temporal self-attention and dynamical graph convolution hybrid network for EEG-based stereogram recognition, IEEE transaction on neural systems and rehabilitation engineering, 2022,30, pp.1191-1202 (SCI: 000797424600001)

(3) Shen Lili, Chen Xiongfei et al, No-reference stereoscopic image quality assessment based on global and local content characteristics, Neurocomputing, 2021, 424, pp.132-142 (SCI: 000611084200014)

(4) Shen Lili, You Liang et al, Group multi-scale attention pyramid network for traffic sign detection, Neurocomputing, 2021, 452, pp.1-14 (SCI: 000663092000001)

(5) Shen Lili, Zhang Chuhe et al, Saliency-based feature fusion convolutional network for blind image quality assessment, Signal, image and video processing, 2021, 16, pp. 419-427 (SCI: 000691961800001)

(6) Shen Lili, Xia Yu et al, A multiscale siamese convolutional neural network with cross-channel fusion for motor imagery decoding, Journal of neuroscience methods, 2022, 367, pp.109426 (SCI: 000788129500003)

(7) Shen Lili, Liu Sicong et al, Asymmetric two-stream network for screen content image quality assessment based on region features, Journal of electronic imaging, 2022, 31, pp.013006 (SCI: 000691961800001)

(8) Shen Lili, Zhao Wei et al, Parallel sequence-channel projection convolutional neural network for EEG-based emotion recognition, IEEE access, 2020, pp.222966-222976 (SCI: 000603714000001)

(9) Shen Lili, Liu Zhijian et al, EEG based dynamic RDS recognition with frequency domain selection and bispectrum feature optimizationJournal of Neuroscience Methods, 2020 ,337, pp. 108650 (SCI: 000526108300004)

(10) Shen Lili, Dong Xinxin, Li Yueping Analysis and classification of hybrid EEG features based on the depth DRDS videosJournal of Neuroscience Methods, 2020, 338, pp. 108690 (SCI: 000526108700012)

(11) Shen Lili, Hang Ning et al, Feature-segmentation strategy based convolutional neural network for no-reference image quality assessmentMultimedia tools and applications, 2020, 79(17-18), pp.11891-11904 (SCI: 000530872400028)

(12) Shen Lili, Wang Dan, Xu Ke, Study on EEG of stereoscopic deep motion perception, Journal of Tianjin University, 2021, 54(4), pp.356-361 (EI: 20210910003337)

(13) Shen Lili, Geng Xiaoquan, Study on EEG of Stereoscopic Deep Motion PerceptionJournal of University of Electronic Science and Technology of China2020, 49(4), pp. 603-608 (EI: 20203309035853)

(14) Shen Lili, Wang Ying, No-reference stereoscopic image quality assessment based on singular value decomposition, Journal of Tianjin University, 2020, 53(6), pp.641-646 (EI: 20202008666016)

(15) Shen Lili, Xing Yang, Visual fatigue assessment of stereoscopic images in lateral motion based on EEG, Journal of Tianjin University, 2020,53(3), pp.259-264 (EI20201808589997)

 

 

Selected Awards and Honors

(1) The First Prize for Advance on Science and Technology of Tianjin in 2018

(2) The First Prize for Technology Invention of Tianjin in 2017

(3) The Third Prize for Advance on Science and Technology of Chinese Journal of Electronics in 2016

 

Teaching Experience

(1) Electronic Circuit Analysis and Design

(2) Engineering Practice 1: Communication System

(3) Quality Assessment for Visual Information