Microstructure identification via detrended fluctuation analysis of ultrasound signals
Data Analysis, Statistics and Probability
2015-06-11 v2 Disordered Systems and Neural Networks
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.
Keywords
Cite
@article{arxiv.1208.2070,
title = {Microstructure identification via detrended fluctuation analysis of ultrasound signals},
author = {Paulo G. Normando and Romao S. Nascimento and Elineudo P. Moura and Andre P. Vieira},
journal= {arXiv preprint arXiv:1208.2070},
year = {2015}
}
Comments
Submitted to Phys. Rev. E