This chapter is to provide a tutorial and pointers to results and related work on timed automata with a focus on semantical and algorithmic aspects of verification tools. We prese...
The Compressive Sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by reducing the sampling rate required to acquire and stably recover sparse s...
Laurent Jacques, Jason N. Laska, Petros Boufounos,...
Leading compressed sensing (CS) methods require m = O (k log(n)) compressive samples to perfectly reconstruct a k-sparse signal x of size n using random projection matrices (e.g., ...
A general framework for solving image inverse problems is introduced in this paper. The approach is based on Gaussian mixture models, estimated via a computationally efficient MAP...
We propose a new strategy to estimate surface normal information from highly noisy sparse data. Our approach is based on a tensor field morphologically adapted to infer normals. I...
Marcelo Bernardes Vieira, Paulo P. Martins Jr., Ar...