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Our TRNGs that use SR latches are small enough to be implemented in embedded devices.
In addition, they were found to be small and low-power enough to be implemented in embedded devices.
Moreover, the whole system will be implemented in embedded platforms to make the proposed framework more attractive for the hands-free communication market.
The proposed algorithms could be implemented in embedded digital processors that are mostly sharing resources with the rest of the GNSS receiver operations, specially in software-defined implementations.
While the fastest and most accurate way to model an energy source is to sample (with the sampling rate determining the accuracy of the model) the I−V curve and store these samples on main memory, this approach cannot be implemented in embedded systems for most energy sources due to the high number of parameters which do not fit the main memory.
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However, the memory requirements of these least-squares algorithms do not allow them to be implemented in standalone embedded systems.
We consider our TRNGs to be small enough to be implemented in a chip for embedded devices.
Complex applications can indeed be implemented in reconfigurable hardware for mobile and embedded applications.
The embedding can be implemented in a straightforward manner by applying nowadays a standard tool in steganography - the Syndrome-Trellis Codes (STCs)[3].
The softening relation is implemented in an embedded discontinuity method, which is employed for the numerical simulation of three point bending experiments.
Demand-side programs have been implemented in developed countries embedded in contexts where populations are well registered, bureaucracies function, and accountability mechanisms are in place.
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