In this paper, we focus on the use of random projections as a dimensionality reduction tool for sampled manifolds in highdimensional Euclidean spaces. We show that geodesic paths ...
Over the past few years, a number of approximate inference algorithms for networked data have been put forth. We empirically compare the performance of three of the popular algori...
This paper deals with the problem of time-resolved fluorescence diffuse optical tomography. We propose a new reconstruction scheme based on a multi-resolution approximation of th...
Nicolas Ducros, Anabela da Silva, Jean-Marc Dinten...
We propose new discrete-to-continuous interpolation models for hexagonally sampled data, that generalize two families of splines developed in the literature for the hexagonal latt...
— In this paper, a low-complexity high-performance detection algorithm for multiple input multiple output (MIMO) channels with severe delay spread is proposed. This algorithm per...