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Spectral Analysis for Signal Detection and Classification : Reducing Variance and Extracting Features

Reinhold, Isabella LU (2021)
Abstract
Spectral analysis encompasses several powerful signal processing methods. The papers in this thesis present methods for finding good spectral representations, and methods both for stationary and non-stationary signals are considered. Stationary methods can be used for real-time evaluation, analysing shorter segments of an incoming signal, while non-stationary methods can be used to analyse the instantaneous frequencies of fully recorded signals. All the presented methods aim to produce spectral representations that have high resolution and are easy to interpret. Such representations allow for detection of individual signal components in multi-component signals, as well as separation of close signal components. This makes feature extraction... (More)
Spectral analysis encompasses several powerful signal processing methods. The papers in this thesis present methods for finding good spectral representations, and methods both for stationary and non-stationary signals are considered. Stationary methods can be used for real-time evaluation, analysing shorter segments of an incoming signal, while non-stationary methods can be used to analyse the instantaneous frequencies of fully recorded signals. All the presented methods aim to produce spectral representations that have high resolution and are easy to interpret. Such representations allow for detection of individual signal components in multi-component signals, as well as separation of close signal components. This makes feature extraction in the spectral representation possible, relevant features include the frequency or instantaneous frequency of components, the number of components in the signal, and the time duration of the components. Two methods that extract some of these features automatically for two types of signals are presented in this thesis. One adapted to signals with two longer duration frequency modulated components that detects the instantaneous frequencies and cross-terms in the Wigner-Ville distribution, the other for signals with an unknown number of short duration oscillations that detects the instantaneous frequencies in a reassigned spectrogram. This thesis also presents two multitaper methods that reduce the influence of noise on the spectral representations. One is designed for stationary signals and the other for non-stationary signals with multiple short duration oscillations. Applications for the methods presented in this thesis include several within medicine, e.g. diagnosis from analysis of heart rate variability, improved ultrasound resolution, and interpretation of brain activity from the electroencephalogram. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Prof. Aviyente, Selin, Michigan State University, USA
organization
alternative title
Spektralanalys för detektion och klassificering av signaler : Minska varians och extrahera egenskaper
publishing date
type
Thesis
publication status
published
subject
keywords
Multifönster, Tids-frekvensanalys, Instantaneous frequency, Multitaper, Signal resolution, Reassignment method, Time-frequency analysis
pages
190 pages
publisher
Lund University / Centre for Mathematical Sciences /LTH
defense location
Lecture hall MH:Riesz, Centre of Mathematical Sciences, Sölvegatan 18, Faculty of Engineering LTH, Lund University, Lund
defense date
2021-05-19 13:15:00
ISBN
978-91-7895-803-0
978-91-7895-804-7
language
English
LU publication?
yes
id
56e107ad-ec15-4785-84ce-c27838024e82
date added to LUP
2021-04-19 14:55:32
date last changed
2021-04-23 10:07:32
@phdthesis{56e107ad-ec15-4785-84ce-c27838024e82,
  abstract     = {Spectral analysis encompasses several powerful signal processing methods. The papers in this thesis present methods for finding good spectral representations, and methods both for stationary and non-stationary signals are considered. Stationary methods can be used for real-time evaluation, analysing shorter segments of an incoming signal, while non-stationary methods can be used to analyse the instantaneous frequencies of fully recorded signals. All the presented methods aim to produce spectral representations that have high resolution and are easy to interpret. Such representations allow for detection of individual signal components in multi-component signals, as well as separation of close signal components. This makes feature extraction in the spectral representation possible, relevant features include the frequency or instantaneous frequency of components, the number of components in the signal, and the time duration of the components. Two methods that extract some of these features automatically for two types of signals are presented in this thesis. One adapted to signals with two longer duration frequency modulated components that detects the instantaneous frequencies and cross-terms in the Wigner-Ville distribution, the other for signals with an unknown number of short duration oscillations that detects the instantaneous frequencies in a reassigned spectrogram. This thesis also presents two multitaper methods that reduce the influence of noise on the spectral representations. One is designed for stationary signals and the other for non-stationary signals with multiple short duration oscillations. Applications for the methods presented in this thesis include several within medicine, e.g.  diagnosis from analysis of heart rate variability, improved ultrasound resolution, and interpretation of brain activity from the electroencephalogram.},
  author       = {Reinhold, Isabella},
  isbn         = {978-91-7895-803-0},
  language     = {eng},
  month        = {04},
  publisher    = {Lund University / Centre for Mathematical Sciences /LTH},
  school       = {Lund University},
  title        = {Spectral Analysis for Signal Detection and Classification : Reducing Variance and Extracting Features},
  url          = {https://lup.lub.lu.se/search/ws/files/96848090/Reinhold_thesis_electronic.pdf},
  year         = {2021},
}