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While most of the literature explains the neural basis of selective attention by means of an increase in neural gain, a number of papers propose enhancement in neural selectivity as an alternative or a complementary mechanism.
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Also, we propose enhancements to the frame layer rate control to better allocate the source bits.
To better manage the rate and improve coding efficiency, we propose enhancements to the H.264/AVC frame layer rate control, which take into consideration the effects of using FMO for video transmission.
The proposed enhancement method removes the scatter and preserves colors.
These limitations are addressed in the proposed enhancement, RDM TELIC as described next.
We now investigate the performance of the proposed enhancement as compared to the HM-based adaptive relaying scheme.
Experimental results show the superiority of the proposed enhancement algorithm compared to the best fingerprint enhancement procedures reported in the literature.
In this section, given a specific DSRC environment, performance of IEEE 802.11a for DSRC and performance of the proposed enhancement are derived and compared.
Since there is no possible current packet collision after the first transmission, PRRs after the first packet transmission in the proposed enhancement are (38).
To solve this problem, the proposed enhancement algorithm estimates the bright channel at each pixel as g_{b}(x)=max_{c}g^{c}(x).
Results show that the proposed enhancement of CNN architecture, combined with transfer learning improves pose classification accuracy for both the synthetic and the real silhouette images.
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