Compression of Biomedical Signals With Mother Wavelet Optimization and Best-Basis Wavelet Packet Selection

2007 ◽  
Vol 54 (12) ◽  
pp. 2186-2192 ◽  
Author(s):  
L. Brechet ◽  
M.-F. Lucas ◽  
C. Doncarli ◽  
D. Farina
Keyword(s):  
Wavelet Packet ◽  
Mother Wavelet ◽  
Biomedical Signals ◽  
Basis Wavelet

INPUT-OUTPUT RELATION OF LINEAR SYSTEMS BY USING COMPLEX WAVELET PACKET TRANSFORM

2010 ◽  
Vol 08 (04) ◽  
pp. 521-535 ◽  
Author(s):  
MASAAKI OSAKE ◽  
KAZUSHI NAKANO ◽  
TETSUYA TABARU ◽  
SEIICHI SHIN
Keyword(s):  
Numerical Experiment ◽  
Linear Systems ◽  
Hilbert Transform ◽  
Linear Relation ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Frequency Resolution ◽  
Input Output ◽  
Mother Wavelet ◽  
Complex Wavelet

This paper is concerned with an input-output relation of linear systems when using the complex wavelet packet transform. In general, the linear relation between input-output is guaranteed by the following two conditions: (1) the mother wavelet has a better frequency resolution, and (2) the real and imaginary parts in the mother wavelet consist of a Hilbert transform pair. The complex wavelet satisfying the above conditions has been used for demonstrating the linear relation. In this paper, a complex wavelet packet transform is applied for improving the frequency resolution. The validity of our approach is shown through a numerical experiment.

Selection of a mother wavelet as identification pattern for the detection of cracks in shafts

2021 ◽  
pp. 107754632110260
Author(s):  
Marta Zamorano ◽  
María Jesus Gómez Garcia ◽  
Cristina Castejón
Keyword(s):  
Standard Method ◽  
Wavelet Packet ◽  
Rotation Frequency ◽  
Wavelet Function ◽  
Classification Systems ◽  
Mother Wavelet ◽  
Decomposition Level ◽  
Rotating Shaft ◽  
Critical Rotation ◽  
Selection Of

Nowadays, there are many methods to detect and diagnose defects in mechanical components during operation. The newest methods that can be found in the literature are based on intelligent classification systems and evaluation of patterns to obtain a diagnosis; however, there is not any standard method to assess features. Wavelet packet transform allows to obtain interesting patterns for evaluating the condition of rotating elements. To perform this calculation, it is necessary to select a series of parameters that affect the resulting pattern. These parameters are the decomposition level and the mother wavelet function. A detailed methodology for the selection of the mother wavelet is proposed, which is the aim of this work, to obtain the most suitable patterns in the diagnostic task. This proposed methodology is applied to data obtained from a rotating shaft with a crack located at the change of section. These signals were measured at low rotation frequency (below the critical rotation frequency) and without eccentricity, where detection becomes more complex.

Multi-Category Classification of Tool Conditions Using Wavelet Packets and ART2 Network

1998 ◽  
Vol 120 (4) ◽  
pp. 807-816 ◽  
Author(s):  
Y. M. Niu ◽  
Y. S. Wong ◽  
G. S. Hong ◽  
T. I. Liu
Keyword(s):  
Tool Wear ◽  
Frequency Band ◽  
Wavelet Packet ◽  
Divide And Conquer ◽  
Time Frequency ◽  
Continuous Components ◽  
Wide Range ◽  
Ae Signal ◽  
Basis Wavelet ◽  
Tool Cutting

This paper proposes a new approach for multi-category identification of turning tool conditions. It uses the time-frequency feature information of the AE signal obtained from best-basis wavelet packet analysis. By applying the philosophy of divide-and-conquer and a local wavelet packet extraction technique, acoustic emission (AE) signals from turning process have been separated into transient and continuous components. The transient and continuous AE components are used respectively for transient tool conditions and tool wear identification. For transient tool condition identification, a 16-element feature vector derived from the frequency band value of wavelet packet coefficients in the time-frequency phase plane is used to identify tool fracture, chipping and chip breakage through an ART2 network. To identify tool wear status, spectral and statistical analysis techniques have been employed to extract three primary features: the frequency band power at 300 kHz–600 kHz, the skew and kurtosis. The mean and standard deviation within a moving window of the primary features are then computed to give three secondary features. The six features form the inputs to an ART2 neural network to identify fresh and worn state of the tool. Cutting experimental results have shown that this approach is highly successful in identifying both the transient and progressive tool wear states over a wide range of turning conditions.

Complexity-constrained best-basis wavelet packet algorithm for image compression

1998 ◽  
Vol 145 (6) ◽  
pp. 391 ◽  
Author(s):  
D. Marpe ◽  
H.L. Cycon ◽  
W. Li
Keyword(s):  
Image Compression ◽  
Wavelet Packet ◽  
Basis Wavelet

Advances in Time-Frequency Analysis of Biomedical Signals

1996 ◽  
Vol 24 (1) ◽  
pp. 1-72 ◽  
Author(s):  
Zhiyue Lin ◽  
Jian De Z. Chen
Keyword(s):  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Biomedical Signals ◽  
Time Frequency

New solutions for visualization of biomedical signals in telemedicine systems

2019 ◽  
Vol 11 (2) ◽  
Author(s):  
Y.G. Gorshkov
Keyword(s):  
Biomedical Signals

Detection of Cardiac Abnormality from PCG Signal Using Wavelet Packet

2015 ◽  
Vol 3 (1) ◽  
pp. 12-16
Author(s):  
Tripti Singh ◽  
◽  
Abhishek Misal ◽  
Keyword(s):  
Wavelet Packet ◽  
Cardiac Abnormality
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