Abstract
We describe an algorithm for simulating ultrasound propagation in random one-dimensional media, mimicking different microstructures by choosing physical properties such as domain sizes and mass densities from probability distributions. By combining a detrended fluctuation analysis (DFA) of the simulated ultrasound signals with tools from the pattern-recognition literature, we build a Gaussian classifier which is able to associate each ultrasound signal with its corresponding microstructure with a very high success rate. Furthermore, we also show that DFA data can be used to train a multilayer perceptron which estimates numerical values of physical properties associated with distinct microstructures.
- Received 21 December 2012
DOI:https://doi.org/10.1103/PhysRevE.87.043304
©2013 American Physical Society
Erratum
Erratum: Microstructure identification via detrended fluctuation analysis of ultrasound signals [Phys. Rev. E 87, 043304 (2013)]
Paulo G. Normando, Romão S. Nascimento, Elineudo P. Moura, and André P. Vieira
Phys. Rev. E 94, 059903 (2016)
Focus
Ultrasound Signal Reveals Microstructure
Published 5 April 2013
A model for analyzing materials using ultrasound shows that the seemingly random fluctuations in the data may contain information about the microscopic structure.
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