Wavelet transform analysis of transient signals [electronic resource] : the seismogram and the electrocardiogram.
In this dissertation I quantitatively demonstrate how the wavelet transform can be an effective mathematical tool for the analysis of transient signals. The two key signal processing applications of the wavelet transform, namely feature identification and representation (i.e., compression), are show...
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Online Access |
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| Format: | Government Document Electronic eBook |
| Language: | English |
| Published: |
Washington, D.C. : Oak Ridge, Tenn. :
United States. Dept. of Energy ; distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy,
1997.
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| Subjects: |
MARC
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| 245 | 0 | 0 | |a Wavelet transform analysis of transient signals |h [electronic resource] : |b the seismogram and the electrocardiogram. |
| 260 | |a Washington, D.C. : |b United States. Dept. of Energy ; |a Oak Ridge, Tenn. : |b distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, |c 1997. | ||
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| 500 | |a "DE98052159" | ||
| 500 | |a "GC0402000" | ||
| 500 | |a Anant, K.S. | ||
| 520 | 3 | |a In this dissertation I quantitatively demonstrate how the wavelet transform can be an effective mathematical tool for the analysis of transient signals. The two key signal processing applications of the wavelet transform, namely feature identification and representation (i.e., compression), are shown by solving important problems involving the seismogram and the electrocardiogram. The seismic feature identification problem involved locating in time the P and S phase arrivals. Locating these arrivals accurately (particularly the S phase) has been a constant issue in seismic signal processing. In Chapter 3, I show that the wavelet transform can be used to locate both the P as well as the S phase using only information from single station three-component seismograms. This is accomplished by using the basis function (wave-let) of the wavelet transform as a matching filter and by processing information across scales of the wavelet domain decomposition. The ̀pick̀ time results are quite promising as compared to analyst picks. The representation application involved the compression of the electrocardiogram which is a recording of the electrical activity of the heart. Compression of the electrocardiogram is an important problem in biomedical signal processing due to transmission and storage limitations. In Chapter 4, I develop an electrocardiogram compression method that applies vector quantization to the wavelet transform coefficients. The best compression results were obtained by using orthogonal wavelets, due to their ability to represent a signal efficiently. Throughout this thesis the importance of choosing wavelets based on the problem at hand is stressed. In Chapter 5, I introduce a wavelet design method that uses linear prediction in order to design wavelets that are geared to the signal or feature being analyzed. The use of these designed wavelets in a test feature identification application led to positive results. The methods developed in this thesis; the feature identification methods of Chapter 3, the compression methods of Chapter 4, as well as the wavelet design methods of Chapter 5, are general enough to be easily applied to other transient signals. | |
| 536 | |b W-7405-ENG-48. | ||
| 650 | 7 | |a Signals. |2 local. | |
| 650 | 7 | |a P Waves. |2 local. | |
| 650 | 7 | |a S Waves. |2 local. | |
| 650 | 7 | |a Seismographs. |2 local. | |
| 650 | 7 | |a Electrocardiograms. |2 local. | |
| 650 | 7 | |a Wave Functions. |2 local. | |
| 650 | 7 | |a Instrumentation, Including Nuclear And Particle Detectors. |2 edbsc. | |
| 710 | 2 | |a Lawrence Livermore National Laboratory. |4 res. | |
| 710 | 2 | |a United States. |b Department of Energy. |4 spn. | |
| 710 | 2 | |a United States. |b Department of Energy. |b Office of Scientific and Technical Information. |4 dst. | |
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