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This architecture uses CNN to extract dense descriptors from images.
This architecture uses the good thermal stability of PI as the top and bottom structure layers.
This architecture uses supplementary electronic RAM buffers when the traffic volume increases.
This architecture uses the routing pattern of AWG, and its symmetric nature, to simplify switch operation significantly.
As in much of the prior work [4, 11, 15], this architecture uses a feedback channel to implement a Slepian-Wolf codec.
Although, the distributed implementation proposed in [6] reduces the execution time to M/K instead of M clock cycles, this architecture uses MK memory locations instead of 2 M in the straightforward implementation.
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We demonstrate the benefit of this architecture using the example of a long-term tracking system.
Here, the development of effective channel estimation and hybrid beamforming solutions for this architecture using phase shifters and analog-to-digital converters (ADCs) with limited resolution presents major research challenges [78].
We demonstrate this architecture using a light emitting organic field-effect transistor (LEOFET) operated at a dual-transduction mode, as a proof-of-concept.
This is the architecture used for ARM multi-core processing on the Linux system.
The construction of this deep architecture uses restricted Boltzmann machine as a building unit, and uses greedy layer-wise training for model construction.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com