Tag: Eigenmodes

Temporal Sparse Wavenumber Analysis Repository

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.

June 3, 2025 0
Data-driven matched field processing

Data-Driven Matched Field Processing (DDMFP) Repository

Data-Driven Matched Field Processing (DDMFP) is an innovative signal processing framework designed for localizing acoustic sources in complex environments, such as those encountered in structural health monitoring (SHM) using Lamb waves. Traditional matched field processing (MFP) techniques rely heavily on accurate physical models of the propagation medium, which can be challenging to obtain in real-world scenarios. DDMFP circumvents this limitation by constructing localization models directly from measured data, enhancing robustness and accuracy in complex, multimodal propagation environments.

May 17, 2025 0

Listening to the Plate: How Lamb Waves Quietly Reveal the Structure of Materials

Guided waves like Lamb modes are reshaping how we inspect, model, and understand solid materials — all by listening to vibrations within the structure itself.

If you’ve never heard of Lamb waves, you’re not alone. Though they’ve been known to physicists and engineers for over a century, they remain surprisingly underdiscussed outside specialized fields like non-destructive testing, ultrasonics, and solid mechanics. But behind the scenes, Lamb waves are playing a crucial role — helping us understand how materials behave, age, and break, all through the language of wave motion.

May 16, 2025 0

©  2025 SmartDATA Lab. Built using WordPress and OnePage Express Theme.