2021-07-02 10:00:00 2021-07-02 11:00:00 US/East-Indiana Fabrication of self-powered devices and the application in human-computer interface Shengjie Gao, Ph.D. Candidate https://purdue-edu.zoom.us/j/5548351721
Fabrication of self-powered devices and the application in human-computer interface
|Event Date:||July 2, 2021|
|Sponsor:||Dr. Wenzhuo Wu|
|Time:||9:00 am EDT
|School or Program:||Industrial Engineering
The development of computation resources, sensing technologies, and artificial intelligence have been the critical driving force for the development of next-generation smart devices in this Internet of Things (IoT) era. The requirement of sustainable and reliable sensing operation is becoming more and more important due to the exponential increment in the number of sensors with different functions deployed everywhere. Such a sensing network provides a more convenient way for connection and communication, making the boundary among humans, machines, and the external environment gradually blurred. Self-powered sensors which can directly utilize the external input energy to drive the operation of their own are highly desirable compared with the current sensing technology with a limited life cycle caused by powered shortage. The invention of Triboelectric Nanogenerators (TENG) and Piezoelectric Nanogenerators (PENG) provide a novel direction for self-powered sensing technology, which can directly convert mechanical input to an electrical signal that can be further processed and understood by computers and humans.
In this presentation, the research efforts have led to the design and fabrication of self-powered devices as well as system integration in the Human-Computer Interface (HCI). Materials engineering was carried out to boost the performance of TENG output. A system-level demonstration of using TENG for HCI application was also carried out. With the help of artificial intelligence technology such as machine learning and deep learning, more in-depth information was successfully extracted from the general TENG signal. The combination between Finite Element Analysis (FEA) and deep learning provides a more powerful platform for the development and verification of TENG-based sensing devices with an improved working reliability. The presented concepts and results in this research show the potential for the implementation of novel self-powered sensing technology in the future development for smart sensors, virtual/augmented reality (VR/AR) and other HCI-related areas.