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S-STEM: Attracting and Cultivating AI Scholars through Multi-faceted Mentoring, Research Experiences, and Cohort Building
Project Team: Xiaohui Yuan (PI), Xuan Guo (co-I), David Keathly (co-I), Amar Maharjan (senior personnel), Kirill Mozokov (senior personnel), Gergely Zaruba (senior personnel)
Sponsor: NSF (2424803) 2025.1-2030.12
Project site: AI Scholar
 
This project studies factors that enable students' success in Computer Science and Engineering. We will answer scholarly calls to use qualitative research approaches to explore new understandings of student issues pertinent to STEM education. The project team will implement a suite of support programs such as an AI certificate, seminars, faculty-led research experience, and industry alliances. We will also integrate and extend the existing programs at UNT to provide our students with multi-faceted mentoring, cohort, and research experience.

  • to be updated

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Irrigation Status and Type Changes
Investigators: L. Lu and X. Yuan
Sponsor: NASA and USGS, 2022.8-2025.7
 
This project aims to enhance the collection and estimation of water-use data for crop irrigation -- the largest freshwater-use category in the US -- to better understand the availability and use of water resources that are needed to meet the Nation's water demands. Irrigated agriculture represents 20% of the total cultivated land in the US. It is also the largest freshwater user that accounted for 42% of total freshwater withdrawals and 70% of the total fresh groundwater withdrawals nationwide. A detailed characterization of the irrigation status and system type for each crop field is a fundamental first step for more accurate water-use estimates.

This project will 1) include classification of all four surface and one sprinkle irrigation type, and 2) scale up the application spatially and temporally to generate decadal-long irrigation type maps.

  • Abolfazl Meyarian, Xiaohui Yuan, Lu Liang, Wencheng Wang, and Lichuan Gu, Gradient Convolutional Neural Network for Classification of Agricultural Fields with Contour Levee, International Journal of Remote Sensing, 43(1), pp. 75-94, 2022
  • Lu Liang, Abolfazl Meyarian, Xiaohui Yuan, Benjamin R. K. Runkle, George Mihaila, Yuchu Qin, Jacob Daniels, Michele L. Reba, James R, Rigby, The First Fine-Resolution Mapping of Contour Levee Irrigation Using Deep Bi-Stream Convolutional Neural Networks, International Journal of Applied Earth Observation and Geoinformation, 105, 102631(1-10), December 2021
 
Accurate Human Pose Estimation and Tracking
Investigators: X. Yuan, M. Elhoseny
Sponsor: Physmodo, Inc.
 
Accurately tracking individual's free movements enables a better understanding of a person's activity level, inter-person interaction, habits, etc. In this project, we aim at achieving a precise evaluation of human free movement in 3D space to quantify a person's energy expenditure and emotional status.

  • Xiaohui Yuan and Amar Maharjan, Non-Rigid Point Set Registration: Recent Trends and Challenges, Artificial Intelligence Review, Oct., 2022 (accepted)
  • Amar Maharjan and Xiaohui Yuan, Registration of Human Point Set using Automatic Key Point Detection and Region-aware Features, IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, Jan. 4-8, 2022
  • A. Maharjan, X. Yuan, Q. Lu, Y. Fan, T. Chen, Non-Rigid Registration of Point Clouds using Landmarks and Stochastic Neighbor Embedding, Journal of Electronic Imaging, 30(3), pp. 031202(1-15), Jan. 2021
  • A. Maharjan, X. Yuan, Point Set Registration of Large Deformation Using Auxiliary Landmarks, International Conference on Urban Intelligence and Applications, 86-98, Taiyuan, China, Aug. 14-16, 2020
  • L. Kong, X. Yuan, A. M. Maharjan, A Hybrid Framework for Automatic Joint Detection of Human Poses in Depth Frames, Pattern Recognition, 77, 216-225, May 2018
  • X. Yuan, D. Li, D. Mohapatra, M. Elhoseny, Automatic Removal of Complex Shadows from Indoor Videos using Transfer Learning and Dynamic Thresholding, Computers and Electrical Engineering, 70, 813-825, August 2018
  • X. Yuan, L. Kong, D. Feng, Z. Wei, Automatic Feature Point Detection and Tracking of Human Action in Time-of-Flight Videos, IEEE/CAA Journal of Automatica Sinica, 4(4), 677-685, Oct. 2017
  • Y. Zhou, J. Han, X. Yuan, Z. Wei, and R. Hong, Inverse Sparse Group Lasso Model for Robust Object Tracking, IEEE Transactions on Multimedia, 19(8), 1798-1810, Aug. 2017
  • Y. Liu, Z. Xie, X. Yuan, J. Chen, W. Song, Multi-level Structured Hybrid Forest for Joint Head Detection and Pose Estimation, Neurocomputing, 266, 206-215, Aug. 2017
 

Completed

Human Trusted Decision Fusion using Classifier Ensemble and Subgroup Feature Selection (WP AFB, 2011.5 - 2011.8, 2012.5 - 2012.8, 2013.6-2013.8)
Infusing Advanced Sensor Network Research into Cross-disciplinary Undergraduate Education (NSF, 2009.4 - 2011.12)
Computer-aided Diagnosis for Gastrointestinal Bleeding using Wireless Capsule Endoscopy(Texas ARP, 2008.6 - 2010.6)
A New Tool for Economic and Environmental Planning - Expanding the Boundaries of LiDAR (NSF/SGER #0722106, 2007.7 - 2008.6)
US/China Digital Government Collaboration: A New Tool for Economic and Environmental Planning - Expanding the Boundaries of LiDAR (NSF/SGER #0737861, 2007.9 - 2008.8)
Fusing LiDAR and Infrared to Model and Simulate Hydrological Events (ORAU, 2008.7 - 2009.6)