The state feedback is established using hybrid automaton formalism, which allows us to design independently a transitory state and steady state control schemes.
The possibility to use computers for counting ballots allows us to design new voting schemes that are arguably fairer than existing schemes designed for hand-counting.
Such knowledge allows us to design new tracking algorithms that use such information to compensate and reduce the impact of localization errors, depending of the localization algorithm used.
The use of this model, together with hypothesis theory, allows us to design the most powerful likelihood ratio test (LRT) when the distribution parameters (expectation and variance) are known.
We prove Theorem 1 using the following large deviation theory argument; this approach also allows us to design networks achieving optimal robustness index (alpha= 1/2) (Theorem 2).
In order to estimate socially optimal economic incentives, we have calibrated and used a model which optimises public transport services and subsequently allows us to design incentives for operators that will induce them to provide the proper level of service.
Thus, the formulation of the received signal in Equation 10, leading to the PARAFAC model, also allows us to design a time-domain CFO compensation scheme for data symbols using Equations 22 and 21.
Although new technology now allows us to design many vaccines in days, manufacturing strategies for influenza vaccines have not changed in decades.
This analysis allows us to design energy efficient protocols by predicting the energy consumption.
Such abstraction allows us to design and implement information dissemination schemes with increased efficiency.
This allows us to design algorithms that produce best-effort results under given resource constraints.
