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When you look across the spectrum of Windows machines, there's inconsistency in correct color profiles.
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These windows are essentially multiplying the data samples in time and will result in a convolution of the true spectrum with the spectrum of the window.
Power spectra of these windows were averaged and converted to a logarithmic scale.
where W is the spectrum of the analysis window w: W = ∫ - ∞ + ∞ w ( t ) exp - j ω t dt.
The variance of the derivative of the spectrum of each time window was also computed as another feature to capture this difference.
A frequency spectrum of corresponding time window by DFT operation is shown in Fig. 11 b, with a mean frequency at 3.3365 MHz.
Based on the above observations on the frequency characteristics of P-waves and noise and within the spirit of [10], so-called Mel Scale, subband energy features were extracted from the spectrum of each time window using a fast Fourier transform.
where W R, N = sin ( N ω / 2 ) sin ( ω / 2 ) is the spectrum of the rectangular window, Ω F s, N = 2 π F s / N, and F s is the sampling frequency.
For the purpose of the spectrum windowing, the Hanning window has been chosen due to its relatively high OOB interference attenuation.
An analogous calculation was performed on the baseline awake period by taking a triggered spectrum of an equal number of windows spaced 4 s apart.
aFor example, to estimate the clean power spectrum of the 2i th window W2i, the estimated clean power spectra of the 2 i-1 th window W2 i-1), the 2 i-2 th window W2 i-2),... were used.
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