Sentence examples for modeling a signal from inspiring English sources

As mentioned before, the DFT coefficients suffer several shortcomings for modeling a signal and exposing the signal structure.

However, some shortcomings of modeling a signal in the DFT domain can easily degrade the performance of a VAD in a noise environment.

The most important mathematical tool in the analysis of continuous signals is Fourier analysis, which can be used to model a signal as a sum of simpler sine waves.

This extreme flexibility enables to model a signal as accurately as possible with the minimum number of TF functions providing a compact approximation of the signal.

That is, when the number of iterations needed to model a signal is very low compared to the length of the signal, we just need bits.

The inherent variability introduced in the number of TF functions required to model a signal and thereby the compression is one of the highlights of using ATFT.

The inherent variability introduced in the number of TF functions required to model a signal is one of the highlights of using ATFT.

The time complexity of the ATFT algorithm increases with the increase in the number of iterations required to model a signal, which in turn depends on the nature of the signal.

For ER and WOF data, onset times of events were used to model a signal response containing a regressor for each response type, which was convolved with a double-gamma function to model the predicted hemodynamic response.

A signaling Petri net is an extension of the Petri net formalism to model a signaling network.

The most common way to model a signalling network is to write down its biochemistry and subsequently translate it to a mathematical form, typically a system of differential equations (Aldridge et al., 2006).