Neil Zeghidour
Papers
2
Total Citations
11
H-Index
2
About
Neil Zeghidour is a researcher at the forefront of non-destructive evaluation and robotic inspection, specializing in ultrasonic guided wave sensing for structural health monitoring. His work addresses the critical challenge of autonomously mapping large metal structures—such as storage tanks and ship hulls—using Lamb waves. Zeghidour’s major contribution lies in developing novel frameworks that combine grid-based and feature-based spatial representations from ultrasonic echoes, enabling robots to simultaneously localize themselves and map their environment. His 2022 paper on learning the propagation properties of rectangular metal plates for Lamb wave-based mapping (8 citations) demonstrates how machine learning can enhance the accuracy of these inspections. In his most cited work, "Combined Grid and Feature-based Mapping of Metal Structures with Ultrasonic Guided Waves" (3 citations), he pioneers a joint approach that recovers both occupancy grids and geometric features from a single ultrasonic signal. This innovation is pivotal for automating the inspection of safety-critical infrastructure, reducing human risk and improving reliability. Zeghidour’s research sits at the intersection of robotics, acoustics, and signal processing, offering practical solutions for real-world industrial maintenance.
Research Focus
Key Achievements
Top Papers
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