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To this end — based on an existing parametric structural model — the impulse response functions of acceleration, velocity and displacement have been computed.
We model the impulse response as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential stability.
In this cluster-based channel model the impulse response is given by (17).
Also, it is hard to efficiently model the impulse responses lasting above milliseconds long with hundreds of coefficients.
Using a sampling frequency of 8 kHz, the impulse responses were truncated to 128 samples such that the adaptive filters could, in theory, perfectly model the impulse responses with the chosen K=128.
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In the model, the impulses start abruptly at the points (t_{i}) and their action continues on a finite time interval ([t_{i},s_{i}]).
The choice of the uniform distribution to model the impulses is motivated by the fact that we do not have any information about the amplitude of the impulsive noise other than the fact that in any practical equipment, there is a certain limit on how large the amplitude entering the device can be.
For example the impulse model proposed by Chechik and Koller (2009) (is designed to capture the typical impulse-like response to a stimulus, using a function with biologically meaningful parameters.
This model provides a simple and computationally efficient way to generate the event-related basis function for an experiment by just convolving the developed hemodynamic model with the impulse approximation of the experimental stimuli.
In this paper, the time-variant model of the impulse response, which consists of a direct sound and a reflection with a modulated delay time, is applied to simulate such a time-variant sound field.
In this paper, we introduced a more realistic model for the impulse noise called General Fixed-Valued Impulse Noise (GFN).
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