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Moving window average

The simplest smoothing procedure is a moving window average, in which each of 2N data points is replaced by the mean of itself and its nearest neighbours ... [Pg.71]

The distributions of (R)tw obtained for a polypeptide immobilized on the attractive surface are presented in Fig. 11. Surface-immobilization is seen once again not to affect the behavior of the folded state. In this case, surface immobilization is also seen to have a relatively minor effect on the behavior at the midpoint. However, the behavior in unfolded states a and fi is clearly influenced by surface immobilization in this case. More specifically, the (R)tw distributions are hardly affected by the time-window-averaging for values of Tw that were seen to significantly modify the (R)tw distributions in the freely diffusing and repulsive surface-immobilized cases. This can be attributed to the slower dynamics of the unfolded polypeptide on the attractive surface. At the same time, conformations with a small value of R are still seen to move faster than conformations with a large value of R, as for the freely diffusing polypeptide. [Pg.92]

Comparison of Equations (2.25) and (2.28) makes it readily apparent that Agb(/) and Lc are fundamentally different concepts, even though they are both referred to as lacunarities. Indeed, whereas Agb(/) depends on the side length / of the moving windows or structuring element, Chappard et al. s [46] lacunarity Lc does not depend on /, as a result of the averaging of cv(/) over the number of circle radii k in... [Pg.39]

For the purpose of hypothesis testing (Fisher 1930 Romano 2005), the residuals r t) are averaged over a moving window and the mean (/x) is subjected to a hypothesis test at a chosen significance level a, as follows ... [Pg.3827]

Fig. 40.22. Distortion (hJhn) of a Gaussian peak for various window sizes (indicated within parentheses). (a) Moving average, (b) Polynomial smoothing. Fig. 40.22. Distortion (hJhn) of a Gaussian peak for various window sizes (indicated within parentheses). (a) Moving average, (b) Polynomial smoothing.
An example is the relatively simple moving average filter. In case of a digitized signal, the values of a fixed (odd) number of data points (a window) are added and divided by the number of points. The result is a new value of the center point. Then the window shifts one point and the procedure, which can be considered as a convolution of the sipal with a rectangular pulse function, repeats. Of course, other functions like a triangle, an exponential and a Gaussian, can be used. [Pg.74]


See other pages where Moving window average is mentioned: [Pg.132]    [Pg.76]    [Pg.72]    [Pg.132]    [Pg.76]    [Pg.72]    [Pg.373]    [Pg.392]    [Pg.402]    [Pg.3605]    [Pg.2591]    [Pg.51]    [Pg.168]    [Pg.406]    [Pg.120]    [Pg.127]    [Pg.136]    [Pg.139]    [Pg.149]    [Pg.144]    [Pg.192]    [Pg.61]    [Pg.168]    [Pg.314]    [Pg.235]    [Pg.219]    [Pg.510]    [Pg.3773]    [Pg.216]    [Pg.324]    [Pg.324]    [Pg.34]    [Pg.382]    [Pg.539]    [Pg.539]    [Pg.541]    [Pg.542]    [Pg.543]    [Pg.544]    [Pg.776]    [Pg.18]    [Pg.103]    [Pg.303]    [Pg.140]    [Pg.195]    [Pg.197]    [Pg.197]   
See also in sourсe #XX -- [ Pg.71 ]




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