The signal variations induced by respiration and cardiac motion decrease the statistical significance in functional MRI data analysis. Significant components of these fluctuations are aliased into the activation spectrum in standard multi-slice imaging protocols. A method of estimation and removal physiological noise in image space is reported. Based on reordering the data from slice ordering to time ordering, the aliased physiological information is available in multi-slice magnitude images. Then physiological noise can be estimated and removed adaptively using signal projecting technique with the actual functional signal preserved.
At present, the conventional automatic localization method of the power quality disturbance signal of the distribution network mainly extracts the feature vector of the disturbance signal. It constructs the target localization function, which leads to poor localization accuracy because the noise part of the disturbance signal is ignored. In this regard, the automatic localization method of PV access power quality disturbance signal based on big data is proposed. By using the wavelet decomposition algorithm, the noise part of the power quality disturbance signal is removed, and the measurement matrix and reconstruction function are combined with compressing and reconstructing the disturbance signal. Finally, the automatic positioning of the disturbance signal is achieved by extracting the characteristics of the disturbance signal data and clustering analysis processing. In the experiment, the designed signal localization method is tested for the localization effect. The results can prove that when the proposed method is used to automatically localize the disturbed signal, the localization results are consistent with the spatial feature distribution of the signal and have a more desirable automatic localization effect.
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