A Novel Deep Neural Network-Based Framework for Subsurface Analysis in Non-Stationary Thermal Wave Imaging

Enhanced depth resolution and deeper depth scanning are the most pivotal challenges in thermal wave imaging-based non-destructive evaluation. In the recent past, sweep frequency stimulation and traditional signal processing have been successful in analyzing non-linearity, which has resulted in a hig...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Sensing and imaging Jg. 26; H. 1; S. 117
Hauptverfasser:	Swapna, M. N., Suman, M., Prasanthi, Y. N., Ghali, V. S., Mulaveesala, R.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 05.09.2025 Springer Nature B.V
Schlagworte:	Artificial neural networks Data compression Deep learning Defects Electrical Engineering Engineering Frequency modulation Imaging Low carbon steels Microwaves Neural networks Nondestructive testing Orthogonality Radiology RF and Optical Engineering Signal processing Signal to noise ratio Sweep frequency Thermal imaging Thermal response Thermal wave imaging Thermography Encoded features Constrained autoencoder Weights with unit norm length Orthogonal weights
ISSN:	1557-2072, 1557-2064, 1557-2072
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!