Titlebook: Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint; Mark K. Hinders Book 2020 The Editor(s) (if appl

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978-3-030-49397-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Classification of RFID Tags with Wavelet Fingerprinting,lected for a variety of reader frequencies, tag orientations, and ambient conditions, and pattern classification techniques are applied to automatically identify these unique RF signatures. We show that identically programmed RFID tags can be distinguished using features generated from DWFP representations of the raw RF signals.

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uding in the medical, vehicle and wireless technology.Struct.This book discusses various applications of machine learning using a new approach, the dynamic wavelet fingerprint technique, to identify features for machine learning and pattern classification in time-domain signals. Whether for medical

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Book 2020r machine learning and pattern classification in time-domain signals. Whether for medical imaging or structural health monitoring, it develops analysis techniques and measurement technologies for the quantitative characterization of materials, tissues and structures by non-invasive means.?.Intellige

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Automatic Detection of Flaws in Recorded Music,ly detect time-localized clicks, pops, and crackles in both cylinders and digital recordings. We also found that while other extra-musical noises, such as coughing, did not leave a traceable mark on the fingerprint, they were distinguishable from samples without the error.

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