A fuzzy-logic-based threshold function for signal recovery using discrete wavelet transform

2002 ◽  
Author(s):  
Wenbo Mei ◽  
Lik-Kwan Shark
Keyword(s):  
Fuzzy Logic ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Threshold Function ◽  
Discrete Wavelet ◽  
Signal Recovery

Assessment of Power Quality based on Fuzzy Logic and Discrete Wavelet Transform for Nonstationary Disturbances

2010 ◽  
Author(s):  
Pampa Sinha ◽  
Sudipta Nath ◽  
Swapan Paruya ◽  
Samarjit Kar ◽  
Suchismita Roy
Keyword(s):  
Fuzzy Logic ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Power Quality ◽  
Discrete Wavelet

scholarly journals Classification of fetal abnormalities based on CTG signal

2014 ◽  
Vol 11 (2) ◽  
pp. 681-689
Author(s):  
Baghdad Science Journal
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Discrete Wavelet ◽  
Artificial Neural

The fetal heart rate (FHR) signal processing based on Artificial Neural Networks (ANN),Fuzzy Logic (FL) and frequency domain Discrete Wavelet Transform(DWT) were analysis in order to perform automatic analysis using personal computers. Cardiotocography (CTG) is a primary biophysical method of fetal monitoring. The assessment of the printed CTG traces was based on the visual analysis of patterns that describing the variability of fetal heart rate signal. Fetal heart rate data of pregnant women with pregnancy between 38 and 40 weeks of gestation were studied. The first stage in the system was to convert the cardiotocograghy (CTG) tracing in to digital series so that the system can be analyzed ,while the second stage ,the FHR time series was transformed using transform domains Discrete Wavelet Transform(DWT) in order to obtain the system features .At the last stage the approximation coefficients result from the Discrete Wavelet Transform were fed to the Artificial Neural Networks and to the Fuzzy Logic, then compared between two results to obtain the best for classifying fetal heart rate.

scholarly journals Detection of artifacts in the EEG signal with using wavelet transform

2011 ◽  
Vol 16 (4) ◽  
pp. 126-130
Author(s):  
A.A. Popov ◽  
A.M. Kanajkin ◽  
K.A. Roshchina ◽  
O.R. CHertov ◽  
V.A. SHashkov
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Signal Recovery ◽  
Eeg Signal

The paper considers the task of cleaning up the EEG signal from artifacts. Method for identifying electrooculogram and signal recovery after its removal using discrete wavelet transform of the electroencephalogram is proposed. The developed method showed nice results on examined examples of real signals at localization and removal of artifacts

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