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We can see that with each additional fading coefficient considered, a straightforward graph expansion is carried out, effectively nesting the F−1 diversity achieving graph in the code capable of full-diversity performance on the channel with F fading coefficients.
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(2) There is a still room improving more on the replication factor and load imbalance and achieving efficient graph clustering.
In the proposed method, the minimization is achieved by graph cut algorithm.
Initially, a mesh free formulation for rectangular domains is developed and a full decomposition of matrix equations is achieved using graph product rules.
The final co-segmentation results are achieved by graph cuts with iteratively updating unary term of the foreground appearance model and background appearance model.
The code graph achieving the requirements on stopping sets among V 1,⋯,V 4 containing information variable nodes is presented in (12) [12].
The algorithm works on blocks of varying size (in the number of input lists) and sacrifices access time for better compression ratio, achieving more succinct graph representation than other algorithms reported in the literature.
Similarly, multiple scales can be achieved in graph-based visualizations by interactive visualization of associations, initially showing a general view and also allowing interactive browsing of specific relations [ 108].
We observe that the learned graph achieves comparable spatial accuracy to the true graph, while the adjacency graph has lower accuracy.
Furthermore, given that the cost of physical level topologies is an important aspect from a design perspective, we also compare the cost of synthetically generated geographic graphs and find that the synthetic Gabriel graphs achieve the smallest cost among all the graph models that we consider.
(3) We establish the graph conditions so that the distributed consensus can be achieved when the graph topology is time-varying and with noisy communication links.
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