scholarly journals Massively parallel non-stationary EEG data processing on GPGPU platforms with Morlet continuous wavelet transform

2012 ◽  
Vol 3 (3) ◽  
pp. 347-357 ◽  
Author(s):  
Ze Deng ◽  
Dan Chen ◽  
Yangyang Hu ◽  
Xiaoming Wu ◽  
Weizhou Peng ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Data Processing ◽  
Continuous Wavelet Transform ◽  
Massively Parallel ◽  
Continuous Wavelet ◽  
Eeg Data ◽  
Morlet Continuous Wavelet Transform

scholarly journals CNN-based Framework using Spatial Dropping for Enhanced Interpretation of Neural Activity in Motor Imagery Classification

2020 ◽  
Author(s):  
Diego Fabian Collazos Huertas ◽  
Andres Marino Alvarez Meza ◽  
German Castellanos Dominguez
Keyword(s):  
Wavelet Transform ◽  
Motor Imagery ◽  
Continuous Wavelet Transform ◽  
Brain Activity ◽  
Machine Learning Techniques ◽  
Continuous Wavelet ◽  
Brain Response ◽  
Power Spectral ◽  
Learning Techniques ◽  
Eeg Data

Abstract Interpretation of brain activity responses using Motor Imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra and inter subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. Obtained results in a bi-task MI database show that the thresholding strategy in combination with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with differentiated behavior between μ and β rhythms.

Relative acoustic impedance from wavelet transform

2014 ◽  
Vol 2 (1) ◽  
pp. SA107-SA118 ◽  
Author(s):  
Marcílio Castro de Matos ◽  
Rodrigo Penna ◽  
Paulo Johann ◽  
Kurt Marfurt
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Real Data ◽  
Velocity Model ◽  
Continuous Wavelet ◽  
Reconstruction Process ◽  
Seismic Wavelet ◽  
Morlet Continuous Wavelet Transform

Most deconvolution algorithms try to transform the seismic wavelet into spikes by designing inverse filters that remove an estimated seismic wavelet from seismic data. We assume that seismic trace subtle discontinuities are associated with acoustic impedance contrasts and can be characterized by wavelet transform spectral ridges, also called modulus maxima lines (WTMML), allowing us to improve seismic resolution by using the wavelet transform. Specifically, we apply the complex Morlet continuous wavelet transform (CWT) to each seismic trace and compute the WTMMLs. Then, we reconstruct the seismic trace with the inverse continuous wavelet transform from the computed WTMMLs with a broader band complex Morlet wavelet than that used in the forward CWT. Because the reconstruction process preserves amplitude and phase along different scales, or frequencies, the result looks like a deconvolution method. Considering this high-resolution seismic representation as a reflectivity approximation, we estimate the relative acoustic impedance (RAI) by filtering and trace integrating it. Conventional deconvolution algorithms assume the seismic wavelet to be stochastic, while the CWT is implicitly time varying such that it can be applied to both depth and time-domain data. Using synthetic and real seismic data, we evaluated the effectiveness of the methodology on detecting seismic events associated with acoustic impedance changes. In the real data examples, time and in-depth RAI results, show good correlation with real P-impedance band-pass data computed using more rigorous commercial inversion software packages that require well logs and low-frequency velocity model information.

scholarly journals Fringe pattern analysis using a one-dimensional modified Morlet continuous wavelet transform

2008 ◽  
Author(s):  
Abdulbasit Z. Abid ◽  
Munther A. Gdeisat ◽  
David R. Burton ◽  
Michael J. Lalor ◽  
Hussein S. Abdul-Rahman ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Fringe Pattern ◽  
Pattern Analysis ◽  
Continuous Wavelet ◽  
One Dimensional ◽  
Morlet Continuous Wavelet Transform ◽  
Fringe Pattern Analysis

COMPUTATION OF THE CONTINUOUS WAVELET TRANSFORM ON MASSIVELY PARALLEL SIMD ARRAYS

1999 ◽  
Vol 09 (04) ◽  
pp. 453-466 ◽  
Author(s):  
MANFRED FEIL ◽  
ANDREAS UHL ◽  
MARIAN VAJTERŠIC
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Massively Parallel ◽  
Continuous Wavelet

Strategies for computing the continuous wavelet transform on massively parallel SIMD arrays are introduced and discussed. The different approaches are theoretically assessed and the results of implementations on a MasPar MP-2 are compared.

scholarly journals Processing seismic ambient noise data with the continuous wavelet transform to obtain reliable empirical Green's functions

2020 ◽  
Vol 222 (2) ◽  
pp. 1224-1235
Author(s):  
Yang Yang ◽  
Chunyu Liu ◽  
Charles A Langston
Keyword(s):  
Wavelet Transform ◽  
Data Processing ◽  
Continuous Wavelet Transform ◽  
Ambient Noise ◽  
Continuous Wavelet ◽  
Denoising Method ◽  
Transient Signals ◽  
Empirical Green’S Functions ◽  
Noise Data ◽  
Correlated Signals

SUMMARY Obtaining reliable empirical Green's functions (EGFs) from ambient noise by seismic interferometry requires homogeneously distributed noise sources. However, it is difficult to attain this condition since ambient noise data usually contain highly correlated signals from earthquakes or other transient sources from human activities. Removing these transient signals is one of the most essential steps in the whole data processing flow to obtain EGFs. We propose to use a denoising method based on the continuous wavelet transform to achieve this goal. The noise level is estimated in the wavelet domain for each scale by determining the 99 per cent confidence level of the empirical probability density function of the noise wavelet coefficients. The correlated signals are then removed by an efficient soft thresholding method. The same denoising algorithm is also applied to remove the noise in the final stacked cross-correlogram. A complete data processing workflow is provided with the overall data processing procedure divided into four stages: (1) single station data preparation, (2) removal of earthquakes and other transient signals in the seismic record, (3) spectrum whitening, cross-correlation and temporal stacking and (4) remove the noise in the stacked cross-correlogram to deliver the final EGF. The whole process is automated to make it accessible for large data sets. Synthetic data constructed with a recorded earthquake and recorded ambient noise is used to test the denoising method. We then apply the new processing workflow to data recorded by the USArray Transportable Array stations near the New Madrid Seismic Zone where many seismic events and transient signals are observed. We compare the EGFs calculated from our workflow with commonly used time domain normalization method and our results show improved signal-to-noise ratios. The new workflow can deliver reliable EGFs for further studies.

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