Statistical Partial Wavefield Imaging (SPWI) is a powerful signal processing algorithm designed to detect and localize structural damage using sparse ultrasonic sensor arrays. Unlike traditional imaging methods that require dense sensor grids or full wavefield measurements, SPWI extracts high-quality damage localization images from partial and limited measurements, making it ideal for real-world Structural Health Monitoring (SHM) applications.
K-SVD Dictionary Learning for Damage Detection is a baseline-free, data-driven approach for detecting structural damage using guided ultrasonic waves. Developed by Supreet Alguri and Dr. Joel B. Harley, this method eliminates the need for pristine baseline measurements—a major limitation in many real-world Structural Health Monitoring (SHM) systems.
This CodeOcean capsule provides a reproducible implementation of the algorithm, as described in the paper:
Temporal Sparse Wavenumber Analysis (TSWA) is a novel technique that reconstructs high-resolution spatiotemporal wavefields using fewer temporal samples than traditional methods typically require. Developed by Soroosh Sabeti and Dr. Joel B. Harley, TSWA makes it possible to retrieve accurate guided wave information from temporally undersampled data, which is crucial in applications where high-speed sensing, data storage, or power consumption are limiting factors.