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A block diagram of the method is displayed in Fig. 2. In each sampled frame of the thermal video, key anatomical points on the face, called landmarks, were detected using a framework that combines two artificial neural networks (Section 2.5.1).
The control method is displayed in Fig. 6.
The overall process of the proposed facial expression recognition method is displayed in Figure 1.
The complexity, taps selected, and the SSNR of each tap-selection method is displayed in Table 1.
Finally, one successful field case with the proposed method is displayed, and the results illustrate its great potential for low-frequency recovery and high-resolution estimation.
The approximate solution is plotted in Figure 8 with values of parameters listed in its caption, and the absolute error using J-GL-C method is displayed in Figure 9. From the presented results, it can be concluded that the numerical solutions are in excellent agreement with the exact solutions.
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The usefulness and capability of the proposed method are displayed through two cases, which are considered general enough to be the representatives of nonlinear and non-stationary dynamic response of vehicle track coupling system.
The experimental results of the above four method are displayed in Figures 7(a)–7 d), respectively.
The RMSEs of DOA and range estimations for the proposed method are displayed in Figure3, and compared with the high-order ESPRIT method and the near-field CRB.
In the third row, the restoration results by the proposed method are displayed, with the PSNRs 32.7622 dB (left) and 29.6473 dB (right) severally.
Plots of survival curves obtained by Kaplan-Meier method were displayed for TgOvPrP4 mice overexpressing 1.5 or 6× the PrPARQ ovine prion protein in each of the six inoculum groups.
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