Bayesian sparse inversion based on nonstationary deconvolution using hyperbolic smoothing methods

Author(s):  
Xuejing Hao ◽  
Baoli Wang ◽  
Xingyao Yin
2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.


1987 ◽  
Author(s):  
B. W. Silverman ◽  
C. Jennison
Keyword(s):  

1993 ◽  
Vol 11 (1) ◽  
pp. 177-184 ◽  
Author(s):  
M. Aydin ◽  
H. Hora

Smoothing of laser-plasma interaction by ISI, RPP, SSD, etc. was mainly directed to overcome lateral nonuniformity of irradiation. While these problems are in no way less important, we derived numerically the model of the Laue rippling and hydrorelaxation model for explanation of the measured temporal pulsation in the 10- to 40-ps range and how the smoothing schemes suppress these pulsations. The partial standing wave fields of the normally coherent laser-irradiated plasma corona is then suppressed by smoothing and conclusion for tests for this model, e.g., by the “question mark experiment” is given. The result provides a physics solution of the laser interaction problem for direct-drive inertial fusion energy


2014 ◽  
Vol 30 (2) ◽  
pp. 391-403 ◽  
Author(s):  
Helder Manoel Venceslau ◽  
Daniela Cristina Lubke ◽  
Adilson Elias Xavier

2009 ◽  
Vol 34 (2) ◽  
pp. 303-319 ◽  
Author(s):  
Alfred Auslender ◽  
Miguel A. Goberna ◽  
Marco A. López

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