Dear Colleagues,

Acoustic emission testing has firmly established itself as a crucial component of non-destructive testing (NDT). It encompasses a diverse array of sophisticated techniques designed to not only observe natural acoustic emission processes but also to extract valuable practical insights. The domain of acoustic emission continues to expand as its applications are increasingly recognized and realized. This Special Issue aspires to examine the state-of-the-art acoustic emission technology and its applications while offering a glimpse into the exciting future directions that this field is poised to take.

This Special Issue aims to showcase the latest innovations and breakthroughs in the field. It will be dedicated to new perspectives on topics ranging from structural health monitoring in aerospace and civil engineering to non-destructive testing in manufacturing. Also highlighting advancements in sensor technology, signal processing, and data analytics, this Special Issue aims to bridge the gap between theory and real-world implementation.

Dr. Tomaz Kek
Dr. Rúnar Unnþórsson
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• structural health monitoring (SHM)
• aerospace applications
• material characterization
• additive manufacturing
• condition-based monitoring
• miniaturization and sensor technology
• data fusion and analytics
• artificial intelligence, machine learning and deep learning