Data management plan for the grant "Developing a cylindrical-type wireless high temperature pressure sensor for rocket propulsion system test."

Data management plan for the grant, "A Novel Hybrid-Mode Curved-shape SAW (CsSAW) Sensor For The In-Situ/RealTime Torque And Temperature Measurement of Rotor Shaft in Harsh Environments."

Data management plan for the grant "Self -powered Wireless Hybrid Density/Level Sensing with Differential Pressure Sensors for Safeguarding and Monitoring of Electrochemical Processing of Nuclear Spent Fuel."

Data management plan for research material for the grant "Multi-modal Surface Acoustic Wave Sensing System for Pressure and Temperature monitoring of Spent Fuel Canisters." This project is a collaboration of ​University of North Texas, Oak Ridge National Laboratory, and National Energy Technology Laboratory to develop a multi-modal wireless passive SAW (Surface Acoustic Wave) sensor array, which are deployed on the outside surface of stainless steel canisters used for nuclear waste, to monitor the strain of the canister and thus determine the inside pressure to ensure safety and security. In addition, the SAW strain sensor could also measure the surface temperature and potentially monitor helium gas leaks.​

Data management plan for the grant, "High-Contrast Elasto-Metamaterial Photonics for Adaptable and Stable Lasing."

Data management plan for the grant, "Protective Thermal Electro-Chromic Coatings (ProTECC) for Lunar Exploration."

Data management plan for the grant, "Tunable Plasmonic Multispectral Meta surfaces." This project investigates the fundamental light-matter interactions and light emission tunability of 2D materials augmented by plasmonic gratings. A key application of our fundamental research is the development of the next generation of optical transistor that use photons to transmit signals containing digital information.

Data management plan for the grant, "CAREER: Reinventing Network-on-Chips of GPU-Accelerated Systems." Research seeking to reinvent on-chip networks for GPU-accelerated systems to remove a communication bottleneck. A major outcome of the project is a set of techniques that enable the development of effective and efficient network-on-chip architectures. Graphics processing units (GPUs) have rapidly evolved to become high-performance accelerators for data-parallel computing. To fully take advantage of the computing power of GPUs, on-chip networks need to provide timely data movement to satisfy the requests of data by the processing cores. Currently, there exists a big gap between the fast-growing processing power of the GPU processing cores and the slow-increasing on-chip network bandwidth. Because of this, GPU-accelerated systems are interconnect-dominated and the on-chip network becomes their performance bottleneck.

Data management plan for the grant, "REU Site: Interdisciplinary Research Experience on Accelerated Deep Learning through A Hardware-Software Collaborative Approach." This Research Experiences for Undergraduates (REU) Site Program at the University of North Texas will enhance the knowledge and research skills of a diverse cohort of undergraduate students through empowering, innovative, and interdisciplinary research experiences in developing Deep Learning applications and systems. The program aims to 1) expose undergraduate students to real-world and cutting-edge research focused on accelerated deep learning through combined hardware and software development; 2) encourage more undergraduate students to continue their academic careers and seek graduate degrees in computer science, computer engineering, and related disciplines; 3) develop research skills and improve communication and collaborative skills in undergraduate students.

Data management plan for the grant, "CAREER: Understanding the Role of Nanoprecipitates in Advanced Metastable Titanium Alloys."

Data management plan for the grant, "Collaborative Research: DMREF: AI-enabled Automated design of ultrastrong and ultraelastic metallic alloys"

Data management plan for the grant, "Understanding the Role of Residual Stress Gradients on Plastic Strain Recovery in Nanocrystalline Thin Films."