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Now, in the aftermath of the cold war, we need to rethink and refurbish this architecture for the next half century or more.
In this paper we highlight and demonstrate the main purpose of this architecture for a "touch" sensing applications.
Our goal has been to apply this architecture for field security applications by incorporating low-power design with compact packaging.
The simulation results show the effectiveness (in terms of degradation of learned patterns and learning capacity) of this architecture for adaptive modeling.
We construct the small-world network by following the Watts Strogatz approach, and use this architecture for classifying the diabetes, and compare its performance with that of the regular or the conventional FFANN.
Finally, we describe layers designed for the task of robot control and a system that we have implemented that uses this architecture for the control of a Nomad 200 mobile robot.
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In this section, the architecture for a V-RAN using virtualisation of radio resources is discussed.
Within this article, we will elaborate the architecture for this TWCS.
This architecture accounts for the seemingly diverse features of motor learning.
This includes challenges faced while implementing this architecture setup for production summarized as 'data infrastructure integration activities'.
This architecture had for purpose to jointly optimize the energy cost and reduce the battery aging effects.
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