On the use of windows for harmonic analysis with the discrete Fourier transform

Abstract

Ahmw-This Pw!r mak= available a concise review of data win-compromise consists of applying windows to the sampled The two operations to which we subject the data are momc mterference. We dm call attention to a number of common -= in be r p~c r h of windows den used with the fd F~-sampling and windowing. These operations can be performed transform. This paper includes a comprehensive catdog of data win-in either order. Sampling is well understood, windowing is less related to sampled windows for DFT's.

An interactive platform to guide catheter ablation in human persistent atrial fibrillation using dominant frequency, organization and phase mapping

“…In addition, longer FFT window segments would contribute to a considerable increase in the appearance of stability of DF over time, which could be potentially misleading the localization of potential AF sites [16]. In the spectral analysis strategy, an anti-leakage window has been implemented to reduce undesirable effects due to abrupt discontinuities at the beginning or the end of the segment [49]. By considering that the AF signal might have non-stationary components, relevant AF events can occur in periodic intervals that might well be important to the analysis.…”

The temperature dependence of radiationless transition rates from ab initio computations

“…54 The appealing feature of the GF approach is in the fact that, at variance with quantum dynamics, its computational cost is independent of the value of the temperature used in calculation, making thus it possible the analysis of the temperature dependence of ET rates between large redox cofactors.…”

Quantitative evaluation of convolution-based methods for medical image interpolation

“…Window functions are well known in digital filter design [14,26,37], where they are applied in order to reduce the Gibbs phenomena which appear when reducing the bandwidth of a signal, and also in spectroscopy and spectrometry [15], where they are known as apodization functions and are used to window spatial or temporal data prior to spectral Table 2. Window functions and their definitions.…”

“…In the definition of the Kaiser window, α ∈ R + is a free parameter, for which we used values of 5.0, 6.0, 7.0, and 8.0. I 0 is the zeroth-order modified Bessel function of the first kind, which can be approximated accurately by using its series expansion [15,61]. For the free parameter α ∈ R + in the definition of the Gaussian window we used values of 2.5, 3.0, 3.5, and 4.0.…”