This paper proposes a disturbance-based control parametrization under the Model Predictive Control framework for constrained linear discrete time systems with bounded additive dis...
In modern circuit design, it is difficult to provide reliable parametric yield prediction since the real distribution of process data is hard to measure. Most existing approaches ...
Matrix factorization algorithms are frequently used in the machine learning community to find low dimensional representations of data. We introduce a novel generative Bayesian pro...
Even with todays hardware improvements, performance problems are still common in many software systems. An approach to tackle this problem for component-based software architectur...
Process variability has a detrimental impact on the performance of memories and other system components, which can lead to parametric yield loss at the system level due to timing ...
Antonis Papanikolaou, T. Grabner, Miguel Miranda, ...