Contributors
Project Description
The long-term vision of the PI is to become a leader in interactive cyber-physical human systems enabled by radio frequency (RF-CPHS) though integrating artificial intelligence (AI)/ML into fully-adaptive RF transceiver operation to empower innovative CPHS applications involving autonomous vehicles, health monitoring, safety/security and HCI. This vision builds on the PI’s unique, multi-disciplinary background in radar signal processing, AI/ML, and human dynamics, and track-record in innovation, as exemplified by her patent for radar-based sign language recognition. This study will focus on the design of cyber-physical human systems using RF-sensor data acquired from daily human movements in a non-contact fashion. These activities will require more advanced machine learning and signal processing to discriminate, which is the focus of the data analysis aspect of this study. Specifically, we will focus on the development of AI/ML algorithms from sensors (radar) to control devices (personal assistants, avatars in a simulation, or autonomous vehicles, like a remote-controlled car or drone). In addition, to improving AI/ML for using radar as a sensor to enable human interaction with a variety of cyber-physical systems, this study seeks to understand how universities can prepare more students, especially women and minorities for careers in RF sensing-enabled CPS. To accomplish this goal the PI will organize Multidisciplinary Research Internships, new courses for undergraduate and graduate students, undergraduate research experiences, and outreach activities at the Alabama Institute of the Deaf and Blind, local K-12 schools, and HBCUs. by 1) Preparing students for roles in multi-disciplinary teams via internships that include training and mentoring by faculty outside of electrical engineering, 2) Developing undergraduate/graduate RF electives with hands-on, experiential learning, and 3) Stimulating interest in RF and STEM fields via outreach activities for K-12 and HBCU students.