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b The compared area overlap (AO) Fig. 14 The compared results using our proposed probability map- and local entropy-based methods.
Red contour denotes the detected nuclear boundary by our method, and green contour denotes the boundary by the local entropy-based method Fig. 15 The compared results using our proposed probability map- and local entropy-based methods.
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Simulation results show the system throughput and outage probability improvement over other algorithms by using our proposed algorithm.
Three images with segmented results are shown in Fig. 11, where red, green, and blue lines denote the segmented results using local entropy, our proposed probability map, and manual segmentation (ground-truth segmentation), respectively.
The daily signals under the modified Cusum signals with the decision interval set at 2 are shown in Figure 4(a) alongside our proposed probability of alert.
Based on an extensive set of simulations using our proposed matrix structure under various configurations, we show that the proposed decoding latency is faster than that of RaptorQ codes, while maintaining an at-par decoding-failure probability.
Our personal experiences with eliciting probabilities using our verbal-numerical probability scale were very good [ 11].
The calculated probability was used as the exact value to search the optimal q in the proposed probability functions.
Three main concepts are used in our proposed method.
Two platforms are used to evaluate our proposed method.
Figure 11 shows that the proposed selective soft combining scheme under the total error probability minimization criterion provides better probability detection than the non-selective soft combining scheme using the proposed total error probability minimization and NP criteria for the considered SNR values.
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