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qIf not known, or published by the provider, Δt can in principle be measured by transferring a known data volume and see what the incurred costs is.
ANNs represent complex, nonlinear functions with many parameters that are adjusted (calibrated or trained) in such a way that the ANN's output becomes similar to measured output on a known data set.
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To the best of our knowledge, there is no central database to encompass all experiment datasets due to the fact that large-scale data handling is a challenge for any known data publication platform.
Tortoise [58] and FSL diffusion toolbox (FDT) [59] are popular choices for eddy current correction and motion correction in dMRI data, and the recently proposed DTIPrep [60] offers a thorough solution for all known data quality problems of dMRI.
Encoding the nodes by binary vectors, χE can be represented by an Ordered Binary Decision Diagram (OBDD) which is a well known data structure for Boolean functions.
The solution is based on LEACH by Heinzelman et al.; a well known data aggregation technique for wireless sensor networks.
These a priori known data streams can be canceled by each receiving node prior to detection by performing self- and known-interference cancellation.
The pivotal requirement to address this question is the knowledge of a well known data set.
The algorithm is based on a well known data mining approach called frequent itemset.
The DeBi algorithm is based on a well known data mining approach called Maximal Frequent Item Set [ 28].
They essentially divide the atmosphere into millions of cubes, several miles on a side, each filled with a wealth of known data — temperature, humidity, wind speed, and more.
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