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This detection process turns out to be much faster than your typical microscope examination.
The effective detection time is 6.2 min in this detection process.
In order to ease this detection process, the potential of microfluidics is investigated in this paper.
Since the result of this detection process is not unique, several criteria are introduced to drive the designer in identifying which constraints should be removed to minimize the impact on his/her original design intent.
This detection process explicitly results in unfairness.
Nevertheless, there are two issues that should be taken into account in the implementation of this detection process.
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Throughout this paper, the detection process is called a successive approximate ML detection (SAMD).
Generally these data sets come with spatiotemporal attributes and, therefore, could potentially be integrated with the seasonal ILI data; this should enhance the detection process (in terms of timeliness, specificity and sensitivity) of pandemic influenza.
In some sense, this knowledge could help the detection process to be easier.
This embedded signaling enhance the detection process described in the Section 4 and also significantly reduce the PAPR level.
Besides aiding in the detection process directly this image normalisation process has a second important consequence.
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