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Coutino, Mario (author), Chepuri, Sundeep Prabhakar (author), Maehara, Takanori (author), Leus, G.J.T. (author)
To analyze and synthesize signals on networks or graphs, Fourier theory has been extended to irregular domains, leading to a so-called graph Fourier transform. Unfortunately, different from the traditional Fourier transform, each graph exhibits a different graph Fourier transform. Therefore to analyze the graph-frequency domain properties of...
journal article 2020
document
Tohidi, E. (author), Amiri, Rouhollah (author), Coutino, Mario (author), Gesbert, David (author), Leus, G.J.T. (author), Karbasi, Amin (author)
Submodularity is a discrete domain functional property that can be interpreted as mimicking the role of well-known convexity/concavity properties in the continuous domain. Submodular functions exhibit strong structure that lead to efficient optimization algorithms with provable near-optimality guarantees. These characteristics, namely,...
journal article 2020
document
Yang, M. (author), Coutino, Mario (author), Isufi, E. (author), Leus, G.J.T. (author)
While regularization on graphs has been successful for signal reconstruction, strategies for controlling the bias-variance trade-off of such methods have not been completely explored. In this work, we put forth a node varying regularizer for graph signal reconstruction and develop a minmax approach to design the vector of regularization...
conference paper 2020
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van der Meulen, P.Q. (author), Coutino, Mario (author), Kruizinga, P. (author), Bosch, J.G. (author), Leus, G.J.T. (author)
We consider the scenario of finding the transfer function of an aberrating layer in front of an ultrasound array. We are interested in blindly estimating this transfer function without prior knowledge of the unknown ultrasound sources or ultrasound contrast image. The algorithm gives an exact solution if the matrix representing the aberration...
conference paper 2020
document
Kazaz, T. (author), Coutino, Mario (author), Janssen, G.J.M. (author), Leus, G.J.T. (author), van der Veen, A.J. (author)
Synchronization and ranging in internet of things (IoT) networks are challenging due to the narrowband nature of signals used for communication between IoT nodes. Recently, several estimators for range estimation using phase difference of arrival (PDoA) measurements of narrowband signals have been proposed. However, these estimators are based...
conference paper 2019
document
Coutino, Mario (author), Leus, G.J.T. (author)
As the size of the sensor network grows, synchronization starts to become the main bottleneck for distributed computing. As a result, efforts in several areas have been focused on the convergence analysis of asynchronous computational methods. In this work, we aim to cross-pollinate distributed graph filters with results in parallel computing...
conference paper 2019
document
Coutino, Mario (author), Isufi, E. (author), Maehara, Takanori (author), Leus, G.J.T. (author)
In this work, we explore the limits of finite-time distributed consensus through the intersection of graph filters and matrix function theory. We focus on algorithms capable to compute the consensus exactly through filtering operations over a graph, and that have been proven to converge in finite time. In this context, we show that there...
conference paper 2019
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Tohidi, E. (author), Coutino, Mario (author), Chepuri, S.P. (author), Behroozi, Hamid (author), Nayebi, Mohammad Mahdi (author), Leus, G.J.T. (author)
Multiple-input multiple-output (MIMO) radar is known for its superiority over conventional radar due to its antenna and waveform diversity. Although higher angular resolution, improved parameter identifiability, and better target detection are achieved, the hardware costs (due to multiple transmitters and multiple receivers) and high-energy...
journal article 2019
document
Coutino, Mario (author), Isufi, E. (author), Leus, G.J.T. (author)
Graph filters are one of the core tools in graph signal processing. A central aspect of them is their direct distributed implementation. However, the filtering performance is often traded with distributed communication and computational savings. To improve this tradeoff, this paper generalizes state-of-the-art distributed graph filters to...
journal article 2019
document
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
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
document
Coutino, Mario (author), Isufi, E. (author), Leus, G.J.T. (author)
The main challenges distributed graph filters face in practice are the communication overhead and computational complexity. In this work, we extend the state-of-the-art distributed finite impulse response (FIR) graph filters to an edge-variant (EV) version, i.e., a filter where every node weights the signals from its neighbors with different...
conference paper 2018
document
Coutino, Mario (author), Chepuri, S.P. (author), Leus, G.J.T. (author)
In this work, we introduce subset selection strategies for signal reconstruction based on kernel methods, particularly for the case of kernel-ridge regression. Typically, these methods are employed for exploiting known prior information about the structure of the signal of interest. We use the mean squared error and a scalar function of the...
conference paper 2018
document
Chepuri, S.P. (author), Coutino, Mario (author), Marques, Antonio G. (author), Leus, G.J.T. (author)
An analytical algebraic approach for distributed network identification is presented in this paper. The information propagation in the network is modeled using a state-space representation. Using the observations recorded at a single node and a known excitation signal, we present algorithms to compute the eigenfrequencies and eigenmodes of...
conference paper 2018
document
Coutino, Mario (author), Chepuri, S.P. (author), Leus, G.J.T. (author)
In this work, we address the problem of identifying the underlying network structure of data. Different from other approaches, which are mainly based on convex relaxations of an integer problem, here we take a distinct route relying on algebraic properties of a matrix representation of the network. By describing what we call possible...
conference paper 2018
document
Ortiz-Jimenez, Guillermo (author), Coutino, Mario (author), Chepuri, S.P. (author), Leus, G.J.T. (author)
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. Specifically, we leverage the product structure of the underlying domain and sample nodes from the graph factors. The...
conference paper 2018
document
Pribic, R (author), Coutino, Mario (author), Leus, G.J.T. (author)
Resolution from co-prime arrays and from a full ULA of the size equal to the virtual size of co-prime arrays is investigated. We take into account not only the resulting beam width but also the fact that fewer measurements are acquired by co-prime arrays. This fact is relevant in compressive acquisition typical for compressive sensing. Our...
conference paper 2016
document
Coutino, Mario (author), Pribic, R (author), Leus, G.J.T. (author)
A bound for sparse reconstruction involving both the signal-to-noise ratio (SNR) and the estimation grid size is presented. The bound is illustrated for the case of a uniform linear array (ULA). By reducing the number of possible sparse vectors present in the feasible set of a constrained ℓ1-norm minimization problem, ambiguities in the...
conference paper 2016
document
Coutino, Mario (author), Pribic, R (author), Leus, G.J.T. (author)
In this paper, a new direction of arrival (DOA) estimation approach is devised using concepts from information geometry (IG). The proposed method uses geodesic distances in the statistical manifold of probability distributions parametrized by their covariance matrix to estimate the direction of arrival of several sources. In order to obtain a...
conference paper 2016
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