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First, very accurate performance models (not constrained to be convex, posynomial, or even in explicit form) can be exploited to estimate implementation margins.
Considerable research has been undertaken to estimate implementation factors such as area, power, and speed up that are subsequently used in HW/SW partitioning tools with different focuses related to granularity, architecture model, communication topology, and so on.
Start from the beginning and estimate implementation dates to the completion of the project.
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Along the same lines, to avoid undue burden, this project restricted estimated implementation costs to investment in the intervention being implemented.
We have proposed an original solution for estimating implementation effort that extends the concept of the cyclomatic complexity.
Comparing with other global REDD+ implementation cost studies, Nepstad et al. [27] estimated implementation costs to US$0.58tCO2O2 in the Amazon region based on historical data.
Finally we conclude in Section 5. Most research about estimating implementation effort is found in the software domain, especially within the COCOMO project [8].
We would therefore like to remind the reader that this paper proposes a methodology for estimating implementation effort and the validation of the model concentrates on illustrating its usefulness.
The estimated implementation costs ranged between US$2.144 – 19.77 tCO2, with opportunity costs at US$1.900 – 13.40 tCO2.
Table 1 summarizes the estimated implementation costs of the stand-alone surveys and the integrated survey by cost category in the Nouna HDSS in Burkina Faso.
Reducing the intervention from 4 to 2 contact days, in a scale up scenario, cut the estimated implementation cost from $2,293 to $1,097 per village.
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