Dates: 
Apr 26 2017 - 1:00pm

Dates: 

April 26 2017 - 1:00pm

Date: April 26, 2017

Time: 1:00 PM

Location: 234 Larsen Hall

Gainesville, FL

Find our location:

https://goo.gl/maps/iCmpj7Gbehn

Dr. Joel B. Harley, University of Utah

BIOGRAPHY:
Joel B. Harley is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Utah, Salt Lake City, UT. His interests include the integration of complex wave propagation models with novel signal processing, machine learning, and data science methods for applications in wave analysis, particularly for structural health monitoring and nondestructive evaluation applications. 
Dr. Harley is a recipient of a 2017 Air Force Young Investigator Award, a 2016 University of Utah ECE Teaching Award, a 2014 Carnegie Mellon A. G. Jordan Award (for academic excellence and exceptional service to the community), the 2009 National Defense Science and Engineering Graduate (NDSEG) Fellowship, the 2009 National Science Foundation (NSF) Graduate Research Fellowship, the 2009 Department of Homeland Security Graduate Fellowship (declined), and the 2008 Lamme/Westinghouse Electrical and Computer Engineering Graduate Fellowship. He has published more than 40 technical journal and conference papers, including four best student papers. He is a student representative advisor for the IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society, the president of the Utah chapter of the IEEE Signal Processing Society, a member of the IEEE Signal Processing Society, and a member of the Acoustical Society of America.

SEMINAR TALK TITLE:  Data Science, Ultrasonics, and Structural Monitoring

ABSTRACT:  
There is considerable interest in engineering and the sciences to sense and monitor the properties and integrity of physical structures both small (e.g., solar cells) and large (e.g., bridges). By predicting future states, sensing technology can reduce maintenance costs and prevent catastrophic failures. Furthermore, this technology has applications in many fields, including mechanical and aerospace engineering, civil engineering, medicine, and seismology. Yet, extracting useful information from physical structures is a challenging task. Structures come in a nearly infinite number of shapes and sizes, and the data extracted from them can significantly change over time with, for example, temperature and humidity. As a result, structural data is often complex and time-varying. 
This presentation focuses on how tools from signal processing, statistics, and machine learning allow us to decipher data from physical structures. Specifically, we apply these techniques to ultrasonic guided wave data (i.e., from waves that are guided by the geometry of their environment). Ultrasonic guided waves have been of significant interest for monitoring structures due to their sensitivity to material variations and capability to interrogate large areas at once. Yet, their complexity and dependence on material properties make data analysis a challenge. In this talk, we show how to integrate physical models of guided waves with data analysis techniques to learn generalized models for guided waves, predict wave behavior, detect damage, and locate structural changes. This allows us to create efficient and effective structural monitoring tools.

You are invited to attend the ECE Seminar presented by Joel B. Harley, University of Utah