Welcome to WISP

Wave Motion and Signal Processing

Modern society relies on wave motion. Communication systems, medical ultrasonic, structural evaluation, RADAR, MEMS devices, and many other applications rely on our ability to effectively analyze wave motion.

Yet, in complex and noisy environments (for example, to assess the structural integrity of composite aircraft frames or to medically image tumors around dense tissue), complex wave motion limits our capabilities. Traditional analysis methods exhibit significant errors in these environments and fail to meet the rigorous standards to many fields, including medical and structural monitoring.

Our research group focuses on reducing the data analysis limitations created by complex wave motion as well as leveraging the complex wave motion to improve traditional data analysis methods. Complex environments often provide a plethora a new information about a problem or situation. Multiple reflections, for example, often carry multiple perspectives of a tumor in a tissue or a crack in a bridge. We create signal processing algorithms to optimally use this information that is normally consider a "problem" or "noise" in traditional data analysis.

Selected Projects

Big Data Structural Health Monitoring
Big Data Structural
Health Monitoring
This project statistically identifies barely visible, critically important trends in large (gigabyte to terabyte size) structural health monitoring data sets while addressing three primary challenges: (1) distorting environmental conditions, (2) poor scaling to large data sets, and (3) a lack of current standard statistics for reliability.
Big Data Structural Health Monitoring
Structural Monitoring of Composite Aircrafts
This project integrates physics-based models of anisotropy with sparse recovery tools to model, predictive, and leverage the wave properties of composite materials. These models are used to accurate detect and locate composite damage (e.g., delaminations and fiber damage) that are generally undetectable with traditional inspection methods.
Big Data Structural Health Monitoring
Hybrid Noise Reduction and Noise Cancellation
This project combines noise reduction and active noise cancellation methods to remove very high, non-stationary noise from a dynamic, outdoor environment. The algorithms will help communicate information across high noise environments or communication channels.