Searched for: subject%3A%22submodular%255C+optimization%22
(1 - 5 of 5)
document
Li, Tianyi (author)
In this work, we deal with the problem of reconstructing a complete bandlimited graph signal from partially sampled noisy measurements. For a known graph structure, some efficient centralized algorithms are proposed to partition the nodes of the graph into disjoint subsets such that sampling the graph signal from any of these subsets leads to a...
master thesis 2023
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Ortiz-Jimenez, Guillermo (author), Coutino, Mario (author), Chepuri, S.P. (author), Leus, G.J.T. (author)
We consider the problem of designing sparse sampling strategies for multidomain signals, which can be represented using tensors that admit a known multilinear decomposition. We leverage the multidomain structure of tensor signals and propose to acquire samples using a Kronecker-structured sensing function, thereby circumventing the curse of...
journal article 2019
document
Ortiz Jimenez, Guillermo (author)
In this era of data deluge, we are overwhelmed with massive volumes of extremely complex datasets. Data generated today is complex because it lacks a clear geometric structure, comes in great volumes, and it often contains information from multiple domains. In this thesis, we address these issues and propose two theoretical frameworks to handle...
master thesis 2018
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Coutino, Mario (author), Chepuri, S.P. (author), Leus, G.J.T. (author)
Detection of a signal under noise is a classical signal processing problem. When monitoring spatial phenomena under a fixed budget, i.e., either physical, economical or computational constraints, the selection of a subset of available sensors, referred to as sparse sensing, that meets both the budget and performance requirements is highly...
journal article 2018
document
Coutino, Mario (author), Chepuri, S.P. (author), Leus, G.J.T. (author)
In this paper, we propose sensor selection strategies, based on convex and greedy approaches, for designing sparse samplers for composite detection. Particularly, we focus our attention on sparse samplers for matched subspace detectors. Differently from previous works, that mostly rely on random matrices to perform compression of the sub...
conference paper 2018
Searched for: subject%3A%22submodular%255C+optimization%22
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