Principal component analysis has proven to be useful for understanding geometric variability in populations of parameterized objects. The statistical framework is well understood ...
This paper addresses the diagnosability problem of distributed discrete event systems. Until now, the problem of diagnosability has always been solved by considering centralised ap...
This paper presents a generic approach to statically analyze Java programs in order to detect potential errors (bugs). We discuss a framework that supports our approach and carrie...
A method is presented for the analysis of dynamic positron emission tomography (PET) data using sparse Bayesian learning. Parameters are estimated in a compartmental framework usin...
Jyh-Ying Peng, John A. D. Aston, R. N. Gunn, Cheng...
In this paper, an adaptive sampling method is proposed for the statistical SRAM cell analysis. The method is composed of two components. One part is the adaptive sampler that manip...