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Although quantization and thermal noise in PLL-based architectures can be addressed by applying noise shaping and oversampling, offset and 1/f noise limit the resolution at high oversampling ratios.
A recent example of using dynamic causal modelling to test hypotheses based upon predictive coding architectures can be found in Brown and Friston (2012).
Although straightforward hardware and time redundancy based concurrent error detection (CED) architectures can be used to thwart such attacks, they entail significant overhead (either area or performance).
Hence, different architectures can be compared based on their sensitivity to attacks, and the method can be used to guide decision both in architecting the system-of-systems and in planning updates and modifications, accounting for the impact of interdependencies on the robustness of the system-of-systems.
Additionally, these architectures can be implemented with different technologies.
Given its plug-in based architecture it can be extended with new functionalities.
One possible architecture can be based on a wideband pseudorandom demodulator and a low rate sampler[14, 15].
Then, the final architecture can be based on a standard embedded (e.g. ARM) processor with a FPU and one or more OP coprocessors.
Such systems will frequently store large amounts of data, and in order to provide the necessary computing power and data bandwidth, a parallel system based on a shared-nothing architecture can be necessary.
The maximum current density achieved in a flow battery with a flow field architecture can be predicted based on the 100% amount of electrolyte flow reactant consumption through the porous electrode beneath flow channel and landing/rib.
Histologic diagnosis based on resected tumors is usually straightforward when the architecture can be adequately assessed.
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