CT The traditional approach to worst-case static-timing analysis is becoming unacceptably conservative due to an ever-increasing number of circuit and process effects. We propose a...
A selective sampling algorithm is a learning algorithm for classification that, based on the past observed data, decides whether to ask the label of each new instance to be classi...
The k-means algorithm is the method of choice for clustering large-scale data sets and it performs exceedingly well in practice. Most of the theoretical work is restricted to the c...
ion of Assembler Programs for Symbolic Worst Case Execution Time Analysis Tobias Schuele Tobias.Schuele@informatik.uni-kl.de Klaus Schneider Klaus.Schneider@informatik.uni-kl.de Re...