Add to ORKGMatching pursuit (MP) is an adaptive signal decomposition technique and can be applied to process Lamb waves, such as denoising, wave parameter estimation, and feature extraction, for health monitoring applications. This paper explored matching pursuit decomposition using Gaussian and chirplet dictionaries to decompose/approximate Lamb waves and extract wave parameters. While Gaussian dictionary based MP is optimal for decomposing symmetric signals, chirplet dictionary based MP is able to decompose asymmetric signals, e.g., dispersed Lamb wave. The extracted parameter, chirp rate, from the chirplet MP can be used to correlate with two Lamb wave modes, S0 and A0
Compressive sensing (CS) has emerged as a potentially viable technique for the efficient compression...
Lamb waves have multimodal and dispersion effects, which reduces their performance in damage localiz...
International audienceIn the framework of audio signal analysis, it is desired to obtain sparse repr...
ABSTRACT. Matching pursuit is an iterative method whereby a signal is decomposed into a linear combi...
The Matching Pursuit algorithm of Mallat and Zhang [MZ93] is a power-ful tool to decompose signals i...
In this work, a damage location method for plate-like structures is developed based on Lamb wave sig...
Computing the optimal expansion of a signal in a redundant dictionary of waveforms is an NP-hard pro...
A persistent problem in the analysis of Lamb wave signatures in experimental data is the fact that s...
Copyright © 2013 Yufeng Lu et al. This is an open access article distributed under the Creative Comm...
In ultrasonic imaging systems, shape, size and orientation of the reflectors and the physical proper...
Compressive sensing (CS) has emerged as a potentially viable technique for the efficient compression...
Compressive sensing (CS) has emerged as a potentially viable technique for the efficient compression...
Compressive sensing (CS) has emerged as a potentially viable technique for the efficient compression...
Compressive sensing (CS) has emerged as a potentially viable technique for the efficient compression...
International audienceIn the framework of audio signal analysis, it is desired to obtain sparse repr...
Compressive sensing (CS) has emerged as a potentially viable technique for the efficient compression...
Lamb waves have multimodal and dispersion effects, which reduces their performance in damage localiz...
International audienceIn the framework of audio signal analysis, it is desired to obtain sparse repr...
ABSTRACT. Matching pursuit is an iterative method whereby a signal is decomposed into a linear combi...
The Matching Pursuit algorithm of Mallat and Zhang [MZ93] is a power-ful tool to decompose signals i...
In this work, a damage location method for plate-like structures is developed based on Lamb wave sig...
Computing the optimal expansion of a signal in a redundant dictionary of waveforms is an NP-hard pro...
A persistent problem in the analysis of Lamb wave signatures in experimental data is the fact that s...
Copyright © 2013 Yufeng Lu et al. This is an open access article distributed under the Creative Comm...
In ultrasonic imaging systems, shape, size and orientation of the reflectors and the physical proper...
Compressive sensing (CS) has emerged as a potentially viable technique for the efficient compression...
Compressive sensing (CS) has emerged as a potentially viable technique for the efficient compression...
Compressive sensing (CS) has emerged as a potentially viable technique for the efficient compression...
Compressive sensing (CS) has emerged as a potentially viable technique for the efficient compression...
International audienceIn the framework of audio signal analysis, it is desired to obtain sparse repr...
Compressive sensing (CS) has emerged as a potentially viable technique for the efficient compression...
Lamb waves have multimodal and dispersion effects, which reduces their performance in damage localiz...
International audienceIn the framework of audio signal analysis, it is desired to obtain sparse repr...