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Parameters are determined from frequency response measurements.
In addition, the signals from frequency band L2 are weaker in signal strength than the signals from frequency band L1.
Parametric best linear approximation models are estimated from frequency response function measurements using random phase multisines.
A new numerical optimization method to obtain these mode shapes from frequency response data is described.
A numerical analysis is introduced to calculate the coupling strength from frequency.
GPS and GLONASS data from frequency bands L1 and L2 were analyzed independently with both methods.
The interleaved allocation is usually considered to benefit from frequency diversity (IEEE 802.16) [9].
In the conversion from frequency to depth, we also considered the effect of topography.
It can learn high-level features from frequency distribution of measured signals for diagnosis tasks.
The filtered data are converted back from frequency domain to time domain.
This strategy is more flexible and thus better benefits from frequency and multi-user diversity.
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