Approximate computing has been motivated by the large and growing class of applications that demonstrate inherent error resilience, such as DSP, multimedia (images/audio/video), graphics, wireless communications, and emerging workloads such as recognition, mining, and synthesis [11].
A decade ago, I collected a bunch of "fumblerules" that demonstrated the errors they intended to correct.
In addition, a comedy training video was produced in 2002, that demonstrates how many types of error occur, and the many different people involved in transfusion.
The comparison results are shown in Figure 5 and they demonstrate that error accumulates as time increases.
The results demonstrate that error type affects the reduction in significance as well as the RR estimate in the epidemiologic analysis.
In terms of accuracy, the DUQOL is the test that has demonstrated a lower error of measurement (higher accuracy in the central area of the continuum).
For stochastically generated landscapes we numerically demonstrate that the error decays faster than ϵ.
Furthermore, when (|D|) is close to (|A|), simulations demonstrate that the error has a very small magnitude (see Section 4).
We demonstrate that the error indicator is highly-correlated with the error in mean flow prediction, and can be efficiently computed through an offline/online strategy.
Table 1 and Table 2 demonstrate that the error term (u t ) was stationary in nature, where variables such as V t, and M t, as well as w t, were cointegrated with long-run equilibrium adjustments (Equation 6).
