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Feature Extraction for Low-Frequency Signal Classification
University of Gävle, Department of Technology and Built Environment, Ämnesavdelningen för elektronik.
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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 thesis 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). The dimensionality of the feature space can range from a few to thousands. For processing high-dimensional data we use multi-modal data space to find low-dimensional structures. The advantage of this space is that multiple metrics of similarity are converted into one single Euclidean space.

The overall goal of our research is a system for automatic identification and classification of low-frequency signals in real-time that is easy to implement in hardware. In this thesis we present our design and implementation of the discrete wavelet transform (DWT) on FPGAs for processing a continuous flow of data to obtain optimal extraction of feature information. FPGA simulation and implementation has been realized by using the polyphase structure, the filter reuse method and techniques such as pipelining and basic operations on the QUARTUS II platform. VHDL has been used to describe the functionality of the discrete wavelet transform and ModelSim has been used for the functional verification.

Advancements in electronics provide a vital new option for implementation of low-frequency smart sensors that can perform signal processing close to the sensors and transmit the data wirelessly. These smart sensors can improve the efficiency of an automatic classification system and reduce the cost of actual infrasound microphones. The design of a digital wireless data acquisition system using a QF4512 programmable signal converter from Quickfilter Technologies, a MSP430 microcontroller from Texas Instruments and a F2M03GLA Bluetooth module from Free2move for infrasonic records is also presented in this thesis. The digital wireless data acquisition system has passed extensive laboratory and field tests (e.g. with man-made explosions).

A study of using a miniature 3-axis fluxgate magnetometer to get fingerprints from ferrous objects is also presented in this thesis. In this experiment, distinguishing features of iron samples of four different shapes were determined using wavelet methods. Systematic differences were observed between the signatures of the four shaped iron samples.

Place, publisher, year, edition, pages
Stockholm: KTH , 2008. , 61 p.
Series
Trita-FYS, ISSN 0280-316X ; 2008:2
Keyword [en]
Infrasound, feature extraction, wavelets, data acquisition
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hig:diva-1657ISBN: 978-91-7178-856-6 (print)OAI: oai:DiVA.org:hig-1657DiVA: diva2:118319
Public defence
(English)
Available from: 2008-05-27 Created: 2008-05-27 Last updated: 2009-04-22Bibliographically approved

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http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4661

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Chilo, José
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