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Finally, we evaluate the detector performance and precision of shower reconstructions.
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The real range distributed target data and high-resolution sea clutter are used to evaluate the detector, and the experimental results show that it attains a better detection performance in comparison with several existing detectors.
Performance criterion such as the false alarm probability and the true detection probability are used for evaluating the watermark detector performance.
Section 7 evaluates the proposed detector performance for several mobile scenarios, and finally, Section 8 summarizes the main conclusions of the article.
The detector performance, as being evaluated in test beam at CERN, will be discussed for the first five modules.
The detector performance of with-memory signaling is compared with the detector performance for no-memory signaling.
In the end, the detector performance decides the outcome of an experiment, and even the most sophisticated data analysis cannot make up for a bad detector.
The number of voxels which have to be considered depends on the detector performance.
The goal is to evaluate the performance of the detector in detecting the anomalous region in the validation data for different values of K. We cluster the spectral targets in the normalized training data to eight different clusters using the K-means clustering algorithm and form a dictionary D comprising of the cluster centroids.
For a fixed τ=0. 1 and ε=0. 1, we evaluate the performance of the detector as the number of measurements K increases under the AK and AU cases respectively, by comparing the pseudo-ROC (receiver operating characteristic) curves obtained by plotting the empirical FDR against 1−FNR, where FNR is the false nondiscovery rate.
However, to make this claim more precise, we evaluate the experimental performance of the detectors by receiver operating characteristic (ROC) curve where the detection rate is plotted versus the false alarm probability in Figure 6.
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