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This section shows that previously developed solutions either do not optimize exposure or either the optimization is done for a low median or average field strength in the entire building, whereas our solution progresses the work in [10] and is able to minimize the average whole-body exposure of the actual persons that are present in the different rooms of the building (user-centric approach).
Parameter optimization is done on the hybrid models, optimizing the parameters for that single set of MRL reactions alone in the context of the remaining ARL network.
Moreover, parameter optimization is done sequentially so that we first optimize parameters and for individual data sources.
The optimization is done by the employment of the biogeography-based optimizer algorithm.
The optimization is done in terms of aggregate throughput, yielding that the optimal carrier sensing range is approximately equal to the interference range.
The optimization is done off-line on a number of training scenarios and optimal FLCs are found.
Noise optimization is done using suitable filters.
The optimization is done iteratively by a Newton Raphson approach.
The optimization is done iteratively: Update operation can be additive, multiplicative or more complex.
Conventionally, this optimization is done by inserting relays between sensors and destination.
The drawback of all these works is that the optimization is done offline.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com