Subband correlation for EEG data in the dual tree complex wavelet transform domain for the detection of epilepsy and seizure

2014 ◽  
Author(s):  
Anindya Bijoy Das ◽  
Mohammed Imamul Hassan Bhuiyan
Keyword(s):  
Wavelet Transform ◽  
Transform Domain ◽  
Complex Wavelet Transform ◽  
Eeg Data ◽  
Complex Wavelet

Compound Fault Feature Separation of Rolling Bearing Based on Complex Wavelet and Energy Operator Demodulation

2012 ◽  
Vol 226-228 ◽  
pp. 765-771
Author(s):  
Yang Yang ◽  
Jian Yu Zhang ◽  
Sui Zheng Zhang
Keyword(s):  
Wavelet Transform ◽  
Wavelet Coefficient ◽  
Energy Operator ◽  
Rolling Bearing ◽  
Difficult Problem ◽  
Transform Domain ◽  
Complex Wavelet Transform ◽  
Transform Coefficients ◽  
Complex Wavelet ◽  
Fault Information

Compound fault feature separation is a difficult problem in diagnosis field of mechanical system. For the rolling bearing with compound fault on outer and inner race, feature separation technology based on complex wavelet transform and energy operator demodulation is introduced. Through continuous wavelet transform, coefficients of mixed fault signal can be achieved in different wavelet transform domain (i.e. real, imaginary, modulus and phase domain). Furthermore, wavelet power spectrum contours and time average wavelet energy spectrum are applied to extract the scales which hold rich fault information, and the wavelet coefficient slice of specific scale is also drawn. For wavelet coefficients in different domain, spectrum analysis and energy operator demodulation can be used successfully to separate mixed fault. The comparison of feature extraction effect between complex wavelet and real wavelet transform shows that complex wavelet transform is obviously better than the latter.

scholarly journals Reduced-reference image quality metric based on statistic model in complex wavelet transform domain

2019 ◽  
Vol 74 ◽  
pp. 218-230
Author(s):  
Xinwen Xie ◽  
Philippe Carré ◽  
Clency Perrine ◽  
Yannis Pousset ◽  
Nanrun Zhou ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Quality ◽  
Reference Image ◽  
Transform Domain ◽  
Complex Wavelet Transform ◽  
Quality Metric ◽  
Statistic Model ◽  
Image Quality Metric ◽  
Complex Wavelet

scholarly journals Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain

2021 ◽  
Vol 3 (3) ◽  
pp. 218-233
Author(s):  
R. Dhaya
Keyword(s):  
Wavelet Transform ◽  
Sparse Representation ◽  
Structural Characteristics ◽  
Threshold Value ◽  
Transform Domain ◽  
Wireless Multimedia ◽  
Complex Wavelet Transform ◽  
Denoising Method ◽  
Suggested Technique ◽  
Complex Wavelet

In recent years, there has been an increasing research interest in image de-noising due to an emphasis on sparse representation. When sparse representation theory is compared to transform domain-based image de-noising, the former indicates that the images have more information. It contains structural characteristics that are quite similar to the structure of dictionary-based atoms. This structure and the dictionary-based method is highly unsuccessful. However, image representation assumes that the noise lack such a feature. The dual-tree complex wavelet transform incorporates an increase in transform data density to reduce the effects of sparse data. This technique has been developed to decrease the image noise by selecting the best-predicted threshold value derived from wavelet coefficients. For our experiment, Discrete Cosine Transform (DCT) and Complex Wavelet Transform (CWT) are used to examine how the suggested technique compares the conventional DCT and CWT on sets of realistic images. As for image quality measures, DT-CWT has leveraged superior results. In terms of processing time, DT-CWT gave better results with a wider PSNR range. Further, the proposed model is tested with a standard digital image named Lena and multimedia sensor images for the denoising algorithm. The suggested denoising technique has delivered minimal effect on the MSE value.

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