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For this hardware model, we show that frame processing times depend on the computational load of the decoder and we provide an iterative algorithm to compute jointly frame processing times and decoding delay.
Quantum spin, though of atomic nuclei rather than of electrons, would be the ideal basis for this hardware.
Charging for this hardware could help Square improve that somewhat.
Based on Equation 2, a maximum distance for this hardware is calculated.
"We're building specifically for this hardware and if it happens to work on [other headsets] that's okay".
Thus, for this hardware design constraint, the ACLR performance cannot satisfy the LTE test specification without the PAPR reduction scheme because of the non-linear distortion of active components, especially the PA.
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We made an assessment of this hardware for a few representative problems and compared the results with and without it.
For coarse-grained devices with wide-word datapaths, the area overhead for integrating this hardware support into the network can be below 30%; for conventional FPGAs, a collection of hundreds of FPGAs can be configured to route one FPGA rapidly.
That's a great way for us to distribute this hardware too.
You know what they say, a picture is worth 1,000 words, so gaze at this hardware for a while.
For a long time, this hardware challenge limited the ability of PET-MRI to compete with PET-CT or PET in terms of quantification.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com