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One major concern is the number of reflection coefficients to be computed for feature extraction.
In Fig. 5, classification performance obtained by varying number of reflection coefficients is presented.
The number of reflection coefficients to be used in the feature matrix directly dictates the feature dimension.
where refi is the number of reflection while a signal is transmitted between RS i and RSi+1.
In that case, a few number of reflection coefficients, say less than six, are sufficient to consider as feature.
In order to investigate the number of reflection coefficients to be considered as feature, detailed statistical analysis on first few reflection coefficients is performed.
Similar(44)
The total number of reflections measured was 7187, of which 3461, were observed, I > 2σ(I).
The total number of reflections measured was 6967, of which 2513 were observed, |F|2 ≥ 2σ|F|2.
The power of signal is reduced, corresponding to the transmission distance and the number of reflections.
Radiation from the emitter can reach the receiver after any number of reflections (see Figure 2).
The time of the calculation depends simulatenously on the number of reflections and the number of parameters.
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