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We compare this mechanism with our mechanism in Section 6.
By the mechanism in Section 3.4, the network could optimize the topology in the ZigBee hierarchical structure.
We give the mobile embedded image fusion mechanism in Section 3. In Section 4, the fusion algorithm analysis and verification was completed.
In the step of Pico UE grouping, fast adjustment introduced in Section 4 and refinement mechanism in Section 5 are utilized to separate pico UEs into two groups for achieving better performance.
Subsequently, we present the network model and assumptions used by our mechanism in Section 3. In Section 4, the proposed mechanism is presented in detail, followed by the performance evaluation using NS-3 in Section 5. Finally, we conclude the paper in Section 6. Channel allocation problem constitutes an interesting and well-investigated research issue of CR networks and VANETs.
V is a |V| by |L| matrix where V ij =1 if link j is incident from node i; otherwise, V ij =0. 1 is a vector with all 1. η is a vector with η l =1-ω =1-ω le ω l is the channel whereoad on the channel of link l which is estimated by the proposed cognitive mechanism in Section 3.2.
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We test for these specific mechanisms in section 5. We are not the first to study the channels through which aid induces migration.
This is followed by the presentation of the different IEEE 802.15.5 hop-1 and hop-2 resource allocation mechanisms in Section VI.
Analyzing overall compliance matrix for different flexure mechanisms in Section 3, it is known that the resultant mechanism with three symmetric planes can lead to no parasitic motion theoretically because of its diagonal compliance matrix form.
We describe the current target decompositions based on scattering mechanisms in Section "Target decomposition based on scattering mechanisms" and present the framework of our proposed Kernel KSVD algorithm for the learning-based decomposition in Section "A novel learning-based target decomposition method based on Kernel KSVD for PolSAR image".
We provide the system model of the downlink NOMA and introduce the SIC detection mechanisms in Section 2. Section 3 presents the limited feedback model of the NOMA system in terms of RBF and ZFBF and introduces the proposed user selection schemes for both beamforming schemes.
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