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We developed the notion of working patterns that can fulfill the requirements with generic transmission energy consumption model and variable communication ranges and sensing ranges, and adopted a column generation based approach to solve the problem where a column corresponds to a feasible working pattern, with initial working patterns being obtained through either a random selection algorithm.
The selective and adaptive features of the clonal selection algorithm allow the classifier to evolve its pattern recognition antibodies towards the goal of matching the training data.
Moreover, the use of reconfigurable antennas also introduce the need for efficient selection algorithm to leverage the radiation pattern diversity resulting from the different antenna states to improve diversity gain.
Figure 6 Illustration of unit selection algorithm.
Figure 3 The AWPP station selection algorithm.
Figure 1 presents the detail of the selection algorithm.
Figure 5 presents a flowchart illustrating the snapshot selection algorithm.
Data were then used for multivariate pattern analysis employing a method that combines machine learning with an iterative, multivariate voxel selection algorithm.
The proposed template selection algorithm takes advantage of the FPGA area-time measures of the enumerated patterns, which can be easily inferred from the FPGA-aware enumeration strategy.
To identify functional connectivity patterns, we used a biophysical model to simulate larval dispersal, and then prioritized highly-connected patches using a reserve selection algorithm.
Algorithm 1: Random selection algorithm.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com