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Collaborative Research: CyberTraining: Pilot: Research Workforce Development for Deep Learning Systems in Advanced GPU Cyberinfrastructure (open access)

Collaborative Research: CyberTraining: Pilot: Research Workforce Development for Deep Learning Systems in Advanced GPU Cyberinfrastructure

Data management plan for the grant, "Collaborative Research: CyberTraining: Pilot: Research Workforce Development for Deep Learning Systems in Advanced GPU Cyberinfrastructure." This project aims to develop a novel set of interactive training materials, including hands-on lecture modules, invited research talks from renowned researchers, and an interdisciplinary collaborative project in an intensive workshop, integrating a wide variety of advanced and inter-connected techniques employed by research workforce for deep learning (DL) systems in advanced GPU cyberinfrastructure (CI). Specifically, this project focuses on training seniors, graduate students, and researchers on how advanced GPU CI can be efficiently utilized and improved to enable high-performance DL systems for data-intensive DL applications in geoscience (GS) and computer science and engineering (CSE) research. The goal is to foster future CI users and contributors to adopt, develop, and improve advanced GPU CI for DL systems in their research.
Date: 2022-12-01/2024-11-30
Creator: Shu, Tong
Object Type: Text
System: The UNT Digital Library
OptiFit: Computer-Vision-Based Smartphone Application to Measure the Foot from Images and 3D Scans (open access)

OptiFit: Computer-Vision-Based Smartphone Application to Measure the Foot from Images and 3D Scans

Article asserts that the foot is a vital organ, as it stabilizes the impact forces between the human skeletal system and the ground. The authors present an instep girth measurement algorithm, and they used a pixel per metric algorithm for measurement; these algorithms were accordingly integrated with the application.
Date: December 6, 2022
Creator: Rafiq, Riyad Bin; Hoque, Kazi Miftahul; Kabir, Muhammad Ashad; Ahmed, Sayed & Laird, Craig
Object Type: Article
System: The UNT Digital Library