This paper surveys recent work in applying ideas from graphical models and message passing algorithms to solve large scale regularized regression problems. In particular, the focu...
Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-N...
Dror Baron, Shriram Sarvotham, Richard G. Baraniuk
In this paper we present a new compressed sensing model and reconstruction method for multi-detector signal acquisition. We extend the concept of the famous single-pixel camera to...
Torsten Edeler, Kevin Ohliger, Stephan Hussmann, A...
This paper develops a new class of algorithms for signal recovery in the distributed compressive sensing (DCS) framework. DCS exploits both intra-signal and inter-signal correlati...
Stephen R. Schnelle, Jason N. Laska, Chinmay Hegde...
In this paper we introduce a virtual locomotion system that allows navigation within any large-scale virtual environment (VE) by real walking. In contrast to [5] we have developed...
Frank Steinicke, Timo Ropinski, Gerd Bruder, Klaus...