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We describe and compare two different approaches: the first one is based on the generalized likelihood ratio test (GLRT) detector directly applied on the array snapshots, and the second one uses a digital beamformer as a spatial filter to mitigate the interferences and acquire the signal using the beamformer output.
The first one rests on Analytic Redundancy Relations (ARR) while the second one uses non-linear observers (extended Luenberger observers, high gain observers, sliding mode observers).
While the first protocol is based on an adaptive hybrid forwarding scheme, the second one uses a mobile sink to collect the sensed data from all the sensors in the network.
The first one is based on the gradient back-propagation with momentum (BPM) [ 12] and the second one uses the Levenberg-Marquardt algorithm (LM) [ 13].
The second one uses interval analysis.
The second one uses C-Based Design flow.
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Two sets of experiments, using different emulsifier agents, were run: the first one using Triton X-100 and the second one using Triton X-114.
The algorithm is made of two stages, the second one using an iterative method.
One of them should be analyzed using a BA fiber, and the second one using a MA/MMA fiber.
The second one used the methane production model to show the benefits in engineering applications of the learning law proposed in this paper.
When composed by two surfactants (the second one used as the co-surfactant), the extractive performance of such systems is considerably boosted.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com