no code implementations • 12 May 2023 • Ehsan Tohidi, Mario Coutino, David Gesbert
We study the problem of selecting a subset of vectors from a large set, to obtain the best signal representation over a family of functions.
no code implementations • 20 Feb 2023 • Pepijn Cox, Mario Coutino, Giuseppe Papari, Ahmad Mouri Sardarabadi, Laura Anitori
The proposed framework can be used by radar practitioners and researchers for applying run-time-verification to adaptive, re-configurable radar systems.
no code implementations • 21 Oct 2021 • Alberto Natali, Elvin Isufi, Mario Coutino, Geert Leus
This work proposes an algorithmic framework to learn time-varying graphs from online data.
no code implementations • 22 Oct 2020 • Alberto Natali, Mario Coutino, Elvin Isufi, Geert Leus
Signal processing and machine learning algorithms for data supported over graphs, require the knowledge of the graph topology.
no code implementations • 7 Jul 2020 • Alberto Natali, Mario Coutino, Geert Leus
Therefore, in this paper, we focus on the joint identification of coefficients and graph weights defining the graph filter that best models the observed input/output network data.
no code implementations • 19 Jun 2020 • Bingcong Li, Mario Coutino, Georgios B. Giannakis, Geert Leus
We unveil the connections between Frank Wolfe (FW) type algorithms and the momentum in Accelerated Gradient Methods (AGM).
no code implementations • 17 Jun 2020 • Ehsan Tohidi, Rouhollah Amiri, Mario Coutino, David Gesbert, Geert Leus, Amin Karbasi
We introduce a variety of submodular-friendly applications, and elucidate the relation of submodularity to convexity and concavity which enables efficient optimization.
2 code implementations • 30 Jun 2018 • Guillermo Ortiz-Jiménez, Mario Coutino, Sundeep Prabhakar Chepuri, Geert Leus
In this paper, we consider the problem of subsampling and reconstruction of signals that reside on the vertices of a product graph, such as sensor network time series, genomic signals, or product ratings in a social network.
2 code implementations • 28 Jun 2018 • Guillermo Ortiz-Jiménez, Mario Coutino, Sundeep Prabhakar Chepuri, Geert Leus
We consider the problem of designing sparse sampling strategies for multidomain signals, which can be represented using tensors that admit a known multilinear decomposition.
Information Theory Signal Processing Information Theory