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It can support optical circuit switching (OCS) and optical packet switching (OPS) in parallel and simultaneously, in order to optimize the performance of the network with both services.
Real-time regulation of traffic will optimise the performance of the network, with trains running in succession within an adjacent movable safety zone.
Performance of the network with an intruder is studied next.
Table 4 shows the performance of the network with a variable number of clusters.
The performance of the network with and without the intruder is investigated.
We developed an analytical model to investigate the performance of the network with mobile nodes and in non-saturated regime.
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The performance of the network elements with respect to reliability, speed and capacity utilization is measured.
Table 2 shows the performance of the network compared with the performance achieved by human subjects and the PMSP96 model.
The performance of the network is studied by comparing with the energy-driven routing protocol.
In all these results, we compare the performance of the network-coded framework with that of the traditional direct transmission.
To explore how topology affects performance within Hopfield-type associative memory neural networks (AMNNs), we studied the computational performance of the neural networks with regular lattice, random, small-world, and scale-free structures.
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