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Spectrum cascade plot for lateral and longitudinal vibrations exhibit distinctive response features for slant and transverse crack rotors.
Other response features, for example a decaying oscillatory approach to a new steady-state firing rate, appear to be more universal among neuronal models.
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The mapping between input phase and number of spikes per burst is a robust response feature for a broad range of stimulus statistics.
Understanding how specific model properties underlie these response features is important for understanding how neurons will respond to rapidly varying signals, as the temporal dynamics of the response onset and response decay to new steady-state determine what range of signal frequencies a population of neurons can respond to and faithfully encode.
Fig. 9 Each plot represents the effect of combing 8 response features on the authentication success rate for 3 different scenarios for 1, 3, and 6 questions respectively.
The main reason is that such models do not account for important response features, such as the interaction between shear, flexure and axial forces.
In general, a scale consisting of such a small number of items is not of much practical use, as a set of items, even though their common item content may be of interest (for developing further/new items, or for identifying other response features common to these items).
Fig. 8 Each plot represents the effect of response features on the authentication success rate for 3 different attack scenarios (only naive, only strong, strong and naive) for different number of questions (e.g., N =1 to 6).
In this case, we utilize eight response features (listed in Table 10) and observed different classification accuracy for different response features.
For example, for the edge response feature, we will have (16).
For improved transient response featuring low peak response and fast attenuation, the design procedure utilizes the mode equalization followed by the maximization of the damping.
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