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In this framework, processing and analysis are applied to each resulting locally stationary segment separately.
The final estimate of the structural system parameters is a time duration weighted average of the estimates in each stationary segment.
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A non-stationary signal can be divided into a series of quasi stationary segments.
Otherwise, nonstationarity must be dealt with by windowing time series data; that is, dividing it into approximately stationary segments.
Hereinafter, we assumed that the different stationary segments of the signal are already known and just the PDF of the non-stationary signal should be estimated.
Shen et al. [8, 9] investigated time-varying bispectral analysis of non-stationary EEG data considering piece-wise third order stationary segments and non-Gaussian autoregressive modelling.
In this manner, an entire non-stationary received signal s can be divided onto a series of quasi stationary segments x 1,…,x M using a sliding window function ψ.
The stationary segments are reconstructed by a single coefficient, the nonstationary parts of DC components with ratio, while the ratio for nonstationary segments of AC sequences is An example with the original and corresponding reconstructed sequence is shown in Figure 11.
The goal is to estimate the instants { n k } k = 1 K where the given time series {y n } is split into K + 1 (stationary) segments, and also the constant AR coefficients per segment, i.e., { a k } k = 0 K.
The calculation was performed on stationary segments of the time series, with at least 120 points.
We carefully avoided non-stationary segments, since stationarity is a prerequisite for entropy analyses [8], [9].
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