In this paper, following the Compressed Sensing (CS) paradigm, we study the problem of recovering sparse or compressible signals from uniformly quantized measurements. We present ...
The problem addressed is source localization via time-differenceof-arrival estimation in a multipath channel. Solving this localization problem typically implies cross-correlating...
Ciprian R. Comsa, Alexander M. Haimovich, Stuart C...
Level-set methods have been shown to be an effective way to solve optimisation problems that involve closed curves. They are well known for their capacity to deal with flexible top...
In this paper, we present a novel approach to learning semantic localized patterns with binary projections in a supervised manner. The pursuit of these binary projections is refor...
We propose a novel boosting algorithm which improves on current algorithms for weighted voting classification by striking a better balance between classification accuracy and the ...