Robust Similarity Measure for Acoustical Leak Detection in the Presence of Different Noise, Ph.D. Dissertation Sharif University of Technology ; Amjadi, Ahmad (Supervisor) ; Bahrampour, Alireza (Supervisor)
Abstract
Precise leak detection by acoustic signal analysis calls for robust similarity measures to estimate the time delay between recorded leakage signals and minimize the probability of false alarms, in the face of dispersive propagation, multiple reflections, and unknown correlated, uncorrelated and impulsive background noise. Providing evidence that higher order modes and multi-reflected signals behave like sets of correlated noise, this thesis uses a regression model to optimize residual complexity in the presence of both correlated and uncorrelated noise. This optimized residual complexity (ORC) is highly robust since it takes into account both the level and complexity of noise and signal,...
Cataloging briefRobust Similarity Measure for Acoustical Leak Detection in the Presence of Different Noise, Ph.D. Dissertation Sharif University of Technology ; Amjadi, Ahmad (Supervisor) ; Bahrampour, Alireza (Supervisor)
Abstract
Precise leak detection by acoustic signal analysis calls for robust similarity measures to estimate the time delay between recorded leakage signals and minimize the probability of false alarms, in the face of dispersive propagation, multiple reflections, and unknown correlated, uncorrelated and impulsive background noise. Providing evidence that higher order modes and multi-reflected signals behave like sets of correlated noise, this thesis uses a regression model to optimize residual complexity in the presence of both correlated and uncorrelated noise. This optimized residual complexity (ORC) is highly robust since it takes into account both the level and complexity of noise and signal,...
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