While process variations are becoming more significant with each new IC technology generation, they are often modeled via linear regression models so that the resulting performanc...
Xin Li, Jiayong Le, Padmini Gopalakrishnan, Lawren...
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. In many cases, regression algori...
Abstract. The recovery of the mixture of an N-dimensional signal generated by N independent processes is a well studied problem (see e.g. [1,10]) and robust algorithms that solve t...
Harold W. Gutch, Takanori Maehara, Fabian J. Theis
We propose a variance-component probabilistic model for sparse signal reconstruction and model selection. The measurements follow an underdetermined linear model, where the unknown...
We present an adaptive, feedback-based, energy estimation model for battery-powered embedded devices such as sensor network gateways and hand-held computers. Our technique maps ha...