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In order to improve the measurement accuracy of phase shift method, we present a novel dual-frequency combined analog encoding phase unwrapping model and 3D measurement method.
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To test how the performance deterioration during one session is affected by the proposed covariate shift adaption method, we did a linear regression (least squares regression by Matlab's polyfit function) on the accuracy throughout a session and used the slope of the regression line as a measure for performance deterioration.
Using the proposed screen column-shift method, actually, we can generate more encoding patterns.
To evaluate the performance of the pcanorm-method proposed in this paper and to compare it to other previously described covariate shift adaption methods, we trained the classifier after using the respective covariate shift adaption method on session 1 of the data and tested it on session 2, referenced to as S1S2-validation later.
Therefore, we propose a frequency shift method that increases the frequency level of the heart sound to provide an accurate transcription.
To evaluate the advantage of different covariate shift adaption methods we performed an offline analysis on data recorded for another study [14].
Using phylogenetic and function shift analysis methods, we identify new directions for the investigation of this broad and complex group of proteins.
As for the phase shift method, the difference between the two operations is slight, but we suggest that removal should be chosen because the integral for the phase-shift operator of the zeroed traces would bring in the sloped aliasing.
In order to avoid the measurement difficulties, we use low magnitude variable frequency AC test current (so called Frequency Shift Method – FSM).
We corrected the sample surface topology through a cross-correlation maximization-based shift method.
The TMM was validated by the wavelength shift method.
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