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Filtering and extracting features from infrasound data
University of Gävle.
KTH, School of Engineering Sciences (SCI), Physics.
KTH, School of Engineering Sciences (SCI), Physics.
2005 (English)In: 2005 14TH IEEE-NPSS Real Time Conference: Stockholm; 4 June 2005 through 10 June 2005, 2005, 451-455 p.Conference paper, Published paper (Refereed)
Abstract [en]

There are many reasons for using infrasound, i.e. low frequency sound, to monitor various events. Inherent features like its long-distance propagation and the use of simple, ground based equipment in very flexible system are some. The disadvantage is that it is a slow system due to the speed of sound. In this papr we try to show that there are several other advantages if one can extract all the features of the signal. In this way it is hoped that we can get a fingerprint of the event that caused the infrasound. Rayleigh waves and sound from epicentre may be obtained for earthquakes, pressure pulses and electro jets from aurora, core radius and funnel shape from tornados, etc. All these possibilities are suggestive for further R&D of the infrasound detection systems.

Place, publisher, year, edition, pages
2005. 451-455 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-14106DOI: 10.1109/RTC.2005.1547494Scopus ID: 2-s2.0-33751438758ISBN: 978-0-7803-9183-3 (print)OAI: oai:DiVA.org:kth-14106DiVA: diva2:329789
Note
QC 20100713Available from: 2010-07-13 Created: 2010-07-13 Last updated: 2010-11-10Bibliographically approved
In thesis
1. Feature Extraction for Low-Frequency Signal Classification
Open this publication in new window or tab >>Feature Extraction for Low-Frequency Signal Classification
2008 (English)Doctoral thesis, comprehensive summary (Other scientific)
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 highdimensional 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 lowfrequency 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. viii, 61 p.
Series
Trita-FYS, ISSN 0280-316X ; 2008:2
Keyword
infrasound, seismic signals, feature extraction, wavelets, fingerprints, mining, multimodal data space
National Category
Atom and Molecular Physics and Optics
Identifiers
urn:nbn:se:kth:diva-4661 (URN)978-91-7178-856-6 (ISBN)
Public defence
2008-03-31, FA 31, AlbaNova Universitetscentrum, Roslagstullsbacken 21, Stockholm, 10:00
Opponent
Supervisors
Note
QC 20100713Available from: 2008-03-06 Created: 2008-03-06 Last updated: 2010-07-13Bibliographically approved
2. Filtering extracting features from infrasound data
Open this publication in new window or tab >>Filtering extracting features from infrasound data
2006 (English)Licentiate thesis, comprehensive summary (Other scientific)
Abstract [en]

The goal of the research presented in this thesis is to extract features, to filter and get fingerprints from signals detected by infrasound, seismic and magnetic sensors. If this can be achieved in a real time system, then signals from various events can be detected and identified in an otherwise torrent data.

Several approaches have been analyzed. Wavelet transform methods are used together with ampligram and time scale spectrum to analyze infrasound, seismic and magnetic data. The energy distribution in the frequency domain may be seen in wavelet scalograms. A scalogram displays the wavelet coefficients as a function of the time scale and of the elapsed time. The ampligram is a useful method of presentation of the physical properties of the time series. The ampligram demonstrate the amplitude and phase of components of the signal corresponding to different spectral densities. The ampligram may be considered as an analogy to signal decomposition into Fourier components. In that case different components correspond to different frequencies. In the present case different components correspond to different wavelet coefficient magnitudes, being equivalent to spectral densities. The time scale spectrum is a forward wavelet transform of each row (wavelet coefficient magnitude) in the ampligram. The time scale spectrum reveals individual signal components and indicates the statistical properties of each component: deterministic or stochastic.

Next step is to distinguish between different sources of infrasound on-line. This will require signal classification after detection is made. The implementation of wavelet – neural network in hardware may be a first choice. In this work the Independent Component Analysis is presented to improve the quality of the infrasonic signals by removing background noise before the hardware classification. The implementation of the discrete wavelet transform in a Field Programmable Gate Array (FPGA) is also included in this thesis using Xilinx System Generator and Simulink software.

A study of using infrasound recordings together with a miniature 3-axis fluxgate magnetometer to find meteorites as soon as possible after hitting the earth is also presented in this work.

Place, publisher, year, edition, pages
Stockholm: Fysik, 2006. vi, 48 p.
Series
Trita-FYS, ISSN 0280-316X ; 2006:32
Keyword
infrasound, seismic signals, feature extraction, wavelets, fingerprints, mining, magnetometer
National Category
Physical Sciences
Identifiers
urn:nbn:se:kth:diva-3978 (URN)
Presentation
2006-05-31, Sal FA32, AlbaNova, Roslagstullsbacken 21, Stockholm, 10:00
Opponent
Supervisors
Note
QC 20101110Available from: 2006-05-19 Created: 2006-05-19 Last updated: 2010-11-10Bibliographically approved

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