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Hardware implementation of 1D wavelet transform on an FPGA for infrasound signal classification
KTH, Skolan för teknikvetenskap (SCI), Fysik.
KTH, Skolan för teknikvetenskap (SCI), Fysik.
2008 (engelsk)Inngår i: IEEE Transactions on Nuclear Science, ISSN 0018-9499, E-ISSN 1558-1578, Vol. 55, nr 1, s. 9-13Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Infrasound is a low frequency acoustic phenomenon that typically ranges from 0.01 to 20 Hz. The data collected from infrasound microphones are presented online by the infrasound monitoring system operating in Northern Europe, i.e., the Swedish-Finnish Infrasound Network (SFIN). Processing the continuous flow of data to extract optimal feature information is important for real-time signal classification. Performing wavelet decomposition on the real-time signals is an alternative. The purpose of this paper is to present the design and FPGA implementation of discrete wavelet transforms (DWT) for real-time infrasound data processing; our approach uses only two FIR filters, a high-pass and a low-pass filter. A compact implementation was realized with pipelining techniques and multiple use of generalized building blocks. The design was described in VHDL and the FPGA implementation and simulation were performed on the QUARTUS II platform.

sted, utgiver, år, opplag, sider
2008. Vol. 55, nr 1, s. 9-13
Emneord [en]
discrete wavelet transform (DWT), FPGA, filtering, infrasound
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-14105DOI: 10.1109/TNS.2007.914322ISI: 000253224700003Scopus ID: 2-s2.0-39049131227OAI: oai:DiVA.org:kth-14105DiVA, id: diva2:329784
Merknad
QC 20100713Tilgjengelig fra: 2010-07-13 Laget: 2010-07-13 Sist oppdatert: 2017-12-12bibliografisk kontrollert
Inngår i avhandling
1. Feature Extraction for Low-Frequency Signal Classification
Åpne denne publikasjonen i ny fane eller vindu >>Feature Extraction for Low-Frequency Signal Classification
2008 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Stockholm: KTH, 2008. s. viii, 61
Serie
Trita-FYS, ISSN 0280-316X ; 2008:2
Emneord
infrasound, seismic signals, feature extraction, wavelets, fingerprints, mining, multimodal data space
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-4661 (URN)978-91-7178-856-6 (ISBN)
Disputas
2008-03-31, FA 31, AlbaNova Universitetscentrum, Roslagstullsbacken 21, Stockholm, 10:00
Opponent
Veileder
Merknad
QC 20100713Tilgjengelig fra: 2008-03-06 Laget: 2008-03-06 Sist oppdatert: 2010-07-13bibliografisk kontrollert

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