Sparse signal representations using the tunable Q-factor wavelet transform
TLDR
The tunable Q-factor wavelet transform (TQWT) is a fully-discrete wavelet Transform for which the Q-Factor, Q, of the underlying wavelet and the asymptotic redundancy, r, ofThe transform are easily and independently specified, and the specified parameters Q and r can be real-valued.Abstract:
The tunable Q-factor wavelet transform (TQWT) is a fully-discrete wavelet transform for which the Q-factor,
Q, of the underlying wavelet and the asymptotic redundancy (over-sampling rate), r, of the transform are easily
and independently specified. In particular, the specified parameters Q and r can be real-valued. Therefore,
by tuning Q, the oscillatory behavior of the wavelet can be chosen to match the oscillatory behavior of the
signal of interest, so as to enhance the sparsity of a sparse signal representation. The TQWT is well suited
to fast algorithms for sparsity-based inverse problems because it is a Parseval frame, easily invertible, and can
be efficiently implemented using radix-2 FFTs. The TQWT can also be used as an easily-invertible discrete
approximation of the continuous wavelet transform.read more
Citations
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Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform
TL;DR: In this paper, an ensemble super-wavelet transform (ESW) is proposed for investigating vibration features of motor bearing faults, which is based on the combination of tunable Q-factor wavelet transform and Hilbert transform.
Journal ArticleDOI
Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis
TL;DR: The analysis results of simulation signals and experimental signals indicate that the proposed time-series decomposition approach can decompose the analyzed signals accurately and effectively.
Journal ArticleDOI
An open access database for the evaluation of respiratory sound classification algorithms.
Bruno Rocha,Dimitris Filos,Luis Mendes,Gorkem Serbes,Sezer Ulukaya,Yasemin P. Kahya,Niksa Jakovljevic,Tatjana Loncar Turukalo,Ioannis M. Vogiatzis,Eleni Perantoni,Evangelos Kaimakamis,Pan Tells Natsiavas,Ana Oliveira,Cristina Jácome,Alda Marques,Nicos Maglaveras,Nicos Maglaveras,Rui Pedro Paiva,Ioanna Chouvarda,Paulo Carvalho +19 more
TL;DR: A public respiratory sound database is described, which was compiled for an international competition, the first scientific challenge of the IFMBE's International Conference on Biomedical and Health Informatics.
Journal ArticleDOI
Early fault feature extraction of rolling bearing based on ICD and tunable Q-factor wavelet transform
TL;DR: In this paper, a combination of intrinsic characteristic-scale decomposition (ICD) and TQWT is proposed to diagnose the early fault of rolling bearings, which has significant advantages on computation efficiency and alleviation of mode mixing.
Journal ArticleDOI
Exponential Wavelet Iterative Shrinkage Thresholding Algorithm for compressed sensing magnetic resonance imaging
TL;DR: Experiments indicated that the proposed EWISTA showed better reconstruction performance than the state-of-the-art algorithms such as FCSA, ISTA, FISTA, SisTA, and EWT-ISTA.
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