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Many applications rely on scanned data, which can come from a variety of sources: optical scanners, coordinate measuring machines, or medical imaging.
This anomaly has arisen because many applications rely on non-standard calibration genes, which themselves often change in value during experimental manipulation.
However, many applications rely on passing vehicles to upload their collected information via wireless network, which imposes new challenges as the uplink data update will have to compete with the downlink data dissemination for the limited wireless bandwidth.
Furthermore, many applications rely on features derived from specific human poses, such as systems based on features derived from the human face which is only visible when a person is facing the detecting camera.
Determining whether the number of vehicles reporting an event is above a threshold is an important mechanism for VANETs, because many applications rely on a threshold number of notifications to reach agreement among vehicles, to determine the validity of an event, or to prevent the abuse of emergency alarms.
Many applications rely on the longitudinal monitoring of an individual outside the clinical settings, leveraging on the multiple data sources provided by the current smartphones.
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However, many vehicular applications rely on continuous and detailed location information of the vehicles, which has the potential to infringe the users' location privacy.
The electrical properties and mobility are very important because many potential applications rely on electrical behavior.
That's only by building and selecting pre-optimized versions of popular open-source libraries that many popular applications rely on.
Many biomedical applications rely on the ability to co-express multiple proteins within the same cell.
Many of the applications rely on the use of natural fluorophores.
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