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Here we suggest an algorithm for candidate selection for electroencephalographic (EEG -based BCI homEEG -based
In the genetic algorithm, new candidates for the solution are created by two mechanisms i.e. crossover and mutation.
Since there is an unknown parameter f p in the measurement matrix Q, we perform the OMP algorithm for each candidate for f p and retain the candidate which minimizes the mean squares error between the corresponding sparse representation and observation vector, i.e. f ^ p = arg min f ∈ ϒ y - Q ^ f a ^ f 2 (18).
The algorithm selects a candidate for specialization randomly (Line 7) and iteratively creates child partitions based on the given taxonomy trees (Line 8).
In fact, random walk with restart (RWR) is a well-known ranking algorithm for candidate gene prioritization [ 28].
Compared with three representative constraint handling techniques and latest optimization algorithms, experimental results have indicated that the proposed algorithm is an effective candidate for real-world problems.
For the cases considered in this paper, a dramatic improvement in the reconstruction accuracy is achieved, which indicates that the proposed algorithm is a promising candidate for solving CS inverse problem.
The sensitivity analysis of different parameters is also carried out which shows that the multiobjective particle swarm optimization algorithm is a better candidate for solving the multiobjective problem of deploying the sensors.
We believe that the proposed algorithm is a good candidate for robust beamforming.
Although the VF2 isomorphism algorithm is an ideal candidate for a parallel approach, going parallel is not always the best solution.
The results verified that the proposed algorithm is a prominent candidate for the design of disjoint pilot sequences for systems that utilize multiple transmit antennas and provide superior BER performance over the partially equi-spaced pilot symbols.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com