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Figure 9 False recognition probability versus SNR.
The trend of recognition probability emerges upward from vertical direction.
We define the error recognition probability as Perror = P SCLD| OFDM) + P OFDM| SCLD).
The recognition probability of eight typed signal is satisfied in all SNRs.
In general, high coherence ensures high recognition probability in template-based classifiers [61].
The proposed algorithm is evaluated through confusion matrix and false recognition probability.
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We present a specific algorithm to simulate color recognition probabilities for 20 different whitish powders by using two similar detectors.
The curves depict the false recognition probabilities (FRP) of the code length and synchronization position estimations on different SNRs.
As assumed, the competetive sensor structure providing just a sampling bandwidth between 420 nm and 630 nm achieves recognition probabilities of 62.5% with a reduced readout time of only 6.1 ms.
For the sensor providing sensitivity maxima reaching from 450 nm to 600 nm with sampling peaks in the range between 400 nm and 670 nm, the simulation discloses enhanced recognition probabilities of more than 70.2%, requiring a readout time of at least 15.5 ms.
As we can see from the comparison of Figures 4 and 5, the detection probabilities are slightly greater than the recognition probabilities for the absence of y(t) in the detection problem. Figure 5 P D vs. P F in the detection problem.
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