Kernel Principal Component Analysis (KPCA) is a popular generalization of linear PCA that allows non-linear feature extraction. In KPCA, data in the input space is mapped to highe...
— Quadratic systems play an important role in the modeling of a wide class of nonlinear processes (electrical, robotic, biological, etc.). For such systems it is of mandatory imp...
Francesco Amato, Francesco Calabrese, Carlo Cosent...
Recent advances in Dynamic Power Management (DPM) techniques have resulted in designs that support a rich set of power management options, both at the hardware and software levels...
Shrirang M. Yardi, Karthik Channakeshava, Michael ...
Many techniques for the verification of reactive systems rely on the analysis of their reachable state spaces. In this paper, a new algorithm for the symbolic generation of the sta...
We describe an empirical study on the feature space of interest points for natural images. Although local features have been widely used in image analysis as building blocks of va...