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Bibliographic Details
Main Authors: Brogan, Joel, Kotevska, Olivera, Torres, Anibely, Jha, Sumit, Adams, Mark
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2409.01532
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Table of Contents:
  • Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in the field of signal classification and detection; however, these methods aren't designed to handle the low signal-to-noise ratios inherent within non-vision signal processing tasks. While they are powerful, they are currently not the method of choice in the inherently noisy and dynamic critical infrastructure domain, such as smart-grid sensing, anomaly detection, and non-intrusive load monitoring.