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Section 2 formulates the problem of tracking a target in the presence of unknown model parameters.
Section 2 formulates the problem of CFO and I/ Qmismatch estimation in OFDM systems.
Markov Random Field (MRF) model formulates the problem of dense matching as a Bayes decision task.
Section 2 introduces the calculation of fractional derivatives and formulates the problem of optimization through genetic algorithms (GAs).
This section formally formulates the problem of filtering of depth maps and specifies the notations used hereafter.
This paper formulates the problem of distributed throughput-efficient sensing in cognitive radio networks as a dynamic coalition formation game based on a Markovian model.
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The Nobel Prize-winning American economist Harry M. Markowitz provided a boost for nonlinear optimization in 1958 when he formulated the problem of finding an efficient investment portfolio as a nonlinear optimization problem with a quadratic objective function.
We formulate the problem of distributed tracking as a sequential Monte Carlo estimation problem.
We formulate the problem of power allocation for each part of the system.
In Section 3, we formulate the problem of power allocation in the secondary network.
We also propose and formulate the problem of off-line reordering.
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