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We evaluate our method on a generated dataset from the TPC-D benchmark and a real video distribution application.
The study follows AEM along the various trajectories of implementation (design, distribution, application).
The networks, revealing very different application mixtures, are investigated in terms of session duration and data volume, packet size distribution, application usage frequency and data volume, as well as user mobility.
We have evaluated PrivHab under the scope of a realistic podcast distribution application in remote rural areas, where these programs have to be recorded into a physical format and distributed to the local radio stations.
But the trends are clear, and the movement toward distribution, application centricity, automation and scale appears inevitable.
Currently, we are gathering data from a crowd by free distribution application on the market and by testing user suitability and battery consumption.
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This study provides a first basis for the mechanical design of hydrogen distribution applications.
The products will initially be targeted at automotive under-hood and electrical power distribution applications.
Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications.
End of the numerous experiment we finally concluded that, ANFIS could be used to modeling the illumination distribution applications perfectly.
In this and other similar media distribution applications, a server or distribution center sends a media/video to a group peers with different bandwidth resources and display capacities.
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