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Because strontium is more abundant in the ocean than in fresh water, a high strontium concentration signals time spent in the ocean.
A cluster computer processes these signals to determine the exact positions based on the signals' Time Of Arrival (TOA) at the back end.
A general observation is that for small datasets and clean signals, time, frequency, and time-frequency-based methods report similar accuracies.
We developed a program, EEG Detection Analysis for Behavioral States (EEG-DABS) that advances Fast Fourier Transforms through ECoG signals time series, separating it into (user defined) frequency bands and normalizes them to reduce variability.
In practice, due to the finite speeds of the switching and transmission of signals, time delays do exist in a working network and thus should be incorporated into the model equation.
In practice, due to the finite speeds of the switching and transmission of signals, time delays do exist in a working network and thus should be incorporated into the model equation [3 12].
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Due to time-varying spectra of these signals, time-frequency analysis has been used in their analysis.
Also, due to the multicomponent nature of these signals, time-frequency representations without cross-terms should be used.
Experiments demonstrate that this algorithm allows identification of audio signals time-scaled up to ±15%, which notably outperforms most fixed-length framed methods.
In analyzing non-stationary and multi-component signals, time-frequency-based techniques were shown to outperform classical techniques based on either time or frequency domains [1] (Chapter 1).
Namely, for an efficient analysis of nonstationary signals, such are radar, sonar, biomedical, seismic, and multimedia signals, time-frequency representations are required.
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