Low-Frequency Signal Classification

The development of the International Monitoring
System (IMS) for the verification of the
Comprehensive Nuclear-Test-Ban Treaty Organization
(CTBTO) has led to a rapid revival of interest in
infrasound. Furthermore, the installation of low-
frequency sensors at seismic sites has increased in
recent years, providing researchers with large and
heterogeneous data-sets in near real-time. New
techniques are needed to better process all of this
data and to extract meaningful information quickly
for various applications. In particular, there is a
need to find distinct features in the infrasonic
signals that allow one to distinguish low level
nuclear tests from seismic events.
In this book three methods for feature extraction
from infrasound and other types of low frequency
signal data are discussed: (1) discrete wavelets
transforms (DWTs); (2) time scale spectra (TSSs)
using continuous wavelet transforms (CWTs); and (3)
empirical mode decomposition (EMD).

Autorentext
José Chilo has got his Ph D in physics from the Royal Institute
of Technology in 2008 where he conducted research on signal and
data processing. He is presently project manager for the
research project called "M2M Communications in Electromagnetic
Disturbed Environments" and lecturer in electronics at
University of Gävle in Sweden.

Klappentext
The development of the International Monitoring System (IMS) for the verification of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) has led to a rapid revival of interest in infrasound. Furthermore, the installation of low- frequency sensors at seismic sites has increased in recent years, providing researchers with large and heterogeneous data-sets in near real-time. New techniques are needed to better process all of this data and to extract meaningful information quickly for various applications. In particular, there is a need to find distinct features in the infrasonic signals that allow one to distinguish low level nuclear tests from seismic events. In this book three methods for feature extraction from infrasound and other types of low frequency signal data are discussed: (1) discrete wavelets transforms (DWTs); (2) time scale spectra (TSSs) using continuous wavelet transforms (CWTs); and (3) empirical mode decomposition (EMD).