Data-Driven and Advanced Signal Processing Approaches for Acoustic and Ultrasonic Sensing
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".
Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 6071
Special Issue Editors
Interests: acoustic sensors; physics-based signal processing; active and passive sensing; multimodal sensing; piezoelectric transducers
Special Issue Information
Dear Colleagues,
Acoustic/vibrational sensors have attracted significant attention for tasks such as noninvasive damage detection, in situ process monitoring, 3D imaging, and material characterization. This is due to the fact that acoustic measurements in both the time and frequency domain are highly sensitive to the material and geometry of their environment, and they possess signatures that are information-rich. A challenging task associated with practical applications is developing methodologies to extract meaningful information from data. This is further augmented by the challenges associated with the generation of large-scale datasets from multiple sensors/sensor streams. Thus, state-of-the-art techniques, both-physics-based and data-driven, are being developed to address the above challenges. This Special Issue covers all topics related to analyzing and interpreting acoustic/vibration measurements by applying advanced signal processing techniques including data-driven techniques such as machine learning and deep learning, as well as physics- or statistics-driven processing techniques.
Dr. Vamshi Krishna Chillara
Dr. John Greenhall
Guest Editors
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Keywords
- acoustics
- ultrasonics
- signal processing
- statistical signal processing
- data-driven sensing
- machine Learning
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