The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator
manually interpreting data with the support of rudimentary automation tools. Recently, many …

Ultrasonic Testing (UT) has seen increasing application of machine learning (ML) in recent
years, promoting higher-level automation and decision-making in flaw detection and …

Manual interpretation from technicians with prior expertise knowledge is still needed for
ultrasonic array tomography when detecting subsurface damages in reinforced concrete …

In ultrasonic testing, diffraction artifacts generated around defects increase the challenge of
quantitatively characterizing defects. In this paper, we propose a label-enhanced semi …

This research work aims to implement an automated segmentation process to extract the
endoplasmic reticulum (ER) network in fluorescence microscopy images (FMI) using …

The use of machine learning (ML) in acoustics has received much attention in the last
decade. ML is unique in that it can be applied to all areas of acoustics. ML has …

Components with complex surfaces can pose a challenge for achieving phased array
ultrasonic testing. Although coupling issues can be addressed by immersing or adding …

Ultrasonic phased array imaging using full-matrix capture (FMC) has raised great interest
among various communities, including the nondestructive testing community, as it makes full …

Artificial intelligence (AI) continues to change paradigms in the field of medicine with new
applications that are applicable to daily life. The field of ultrasonography, which has been …

Phased array-based full-matrix ultrasonic imaging has been the golden standard for the non-
destructive evaluation of critical components. However, the piezoelectric phased array …