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Unrolling of Simplicial ElasticNet for Edge Flow Signal Reconstruction

Liu, Chengen (author), Leus, G.J.T. (author), Isufi, E. (author)

The edge flow reconstruction task consists of retreiving edge flow signals from corrupted or incomplete measurements. This is typically solved by a regularized optimization problem on higher-order networks such as simplicial complexes and the corresponding regularizers are chosen based on prior knowledge. Tailoring this prior to the setting...

journal article 2023

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Self-Calibration of Acoustic Scalar and Vector Sensor Arrays

Ramamohan, Krishnaprasad Nambur (author), Chepuri, Sundeep Prabhakar (author), Comesana, Daniel Fernandez (author), Leus, G.J.T. (author)

In this work, we consider the self-calibration problem of joint calibration and direction-of-Arrival (DOA) estimation using acoustic sensor arrays. Unlike many previous iterative approaches, we propose solvers that can be readily used for both linear and non-linear arrays for jointly estimating the sensor gain, phase errors, and the source...

journal article 2023

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A Cascaded Structure for Generalized Graph Filters

Coutino, Mario (author), Leus, G.J.T. (author)

One of the main challenges of graph filters is the stability of their design. While classical graph filters allow for a stable design using optimal polynomial approximation theory, generalized graph filters tend to suffer from the ill-conditioning of the involved system matrix. This issue, accentuated for increasing graph filter orders,...

journal article 2022

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Learning Time-Varying Graphs from Online Data

Natali, A. (author), Isufi, E. (author), Coutino, Mario (author), Leus, G.J.T. (author)

This work proposes an algorithmic framework to learn time-varying graphs from online data. The generality offered by the framework renders it model-independent, i.e., it can be theoretically analyzed in its abstract formulation and then instantiated under a variety of model-dependent graph learning problems. This is possible by phrasing (time...

journal article 2022

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Computational Array Signal Processing via Modulo Non-Linearities

Fernandez-Menduina, Samuel (author), Krahmer, Felix (author), Leus, G.J.T. (author), Bhandari, Ayush (author)

Conventional literature on array signal processing (ASP) is based on the "capture first, process" later philosophy and to this end, signal processing algorithms are typically decoupled from the hardware. This poses fundamental limitations because if the sensors result in information loss, the algorithms may no longer be able to achieve their...

journal article 2022

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Graphon Filters: Graph Signal Processing in the Limit

Morency, M.W. (author), Leus, G.J.T. (author)

Graph signal processing is an emerging field which aims to model processes that exist on the nodes of a network and are explained through diffusion over this structure. Graph signal processing works have heretofore assumed knowledge of the graph shift operator. Our approach is to investigate the question of graph filtering on a graph about...

journal article 2021

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The Dual Graph Shift Operator: Identifying the Support of the Frequency Domain

Leus, G.J.T. (author), Segarra, Santiago (author), Ribeiro, Alejandro (author), Marques, Antonio G. (author)

Contemporary data is often supported by an irregular structure, which can be conveniently captured by a graph. Accounting for this graph support is crucial to analyze the data, leading to an area known as graph signal processing (GSP). The two most important tools in GSP are the graph shift operator (GSO), which is a sparse matrix accounting...

journal article 2021

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Compressed-Domain Detection and Estimation for Colocated MIMO Radar

Tohidi, Ehsan (author), Hariri, Alireza (author), Behroozi, Hamid (author), Nayebi, Mohammad Mahdi (author), Leus, G.J.T. (author), Petropulu, Athina P. (author)

This article proposes a compressed-domain signal processing (CSP) multiple-input multiple-output (MIMO) radar, a MIMO radar approach that achieves substantial sample complexity reduction by exploiting the idea of CSP. CSP MIMO radar involves two levels of data compression followed by target detection at the compressed domain. First,...

journal article 2020

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State-Space Network Topology Identification from Partial Observations

Coutino, Mario (author), Isufi, E. (author), Maehara, Takanori (author), Leus, G.J.T. (author)

In this article, we explore the state-space formulation of a network process to recover from partial observations the network topology that drives its dynamics. To do so, we employ subspace techniques borrowed from system identification literature and extend them to the network topology identification problem. This approach provides a unified...

journal article 2020

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Observing and tracking bandlimited graph processes from sampled measurements

Isufi, E. (author), Banelli, Paolo (author), Di Lorenzo, Paolo (author), Leus, G.J.T. (author)

A critical challenge in graph signal processing is the sampling of bandlimited graph signals; signals that are sparse in a well-defined graph Fourier domain. Current works focused on sampling time-invariant graph signals and ignored their temporal evolution. However, time can bring new insights on sampling since sensor, biological, and...

journal article 2020

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Fast spectral approximation of structured graphs with applications to graph filtering

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

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Convolutional Neural Network Architectures for Signals Supported on Graphs

Gama, F. (author), Marques, Antonio G. (author), Leus, G.J.T. (author), Ribeiro, Alejandro (author)

Two architectures that generalize convolutional neural networks (CNNs) for the processing of signals supported on graphs are introduced. We start with the selection graph neural network (GNN), which replaces linear time invariant filters with linear shift invariant graph filters to generate convolutional features and reinterprets pooling as a...

journal article 2019

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Controllability of bandlimited graph processes over random time varying graphs

Gama, F. (author), Isufi, E. (author), Ribeiro, Alejandro (author), Leus, G.J.T. (author)

Controllability of complex networks arises in many technological problems involving social, financial, road, communication, and smart grid networks. In many practical situations, the underlying topology might change randomly with time, due to link failures such as changing friendships, road blocks or sensor malfunctions. Thus, it leads to...

journal article 2019

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Advances in Distributed Graph Filtering

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

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Sparse Sampling for Inverse Problems with Tensors

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

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Doa Estimation in heteroscedastic noise

Gerstoft, Peter (author), Nannuru, Santosh (author), Mecklenbrauker, Christoph F. (author), Leus, G.J.T. (author)

The paper considers direction of arrival (DOA) estimation from long-term observations in a very noisy environment. The concern is to derive methods obtaining reasonable DOAs at very low SNR. The noise is assumed zero-mean Gaussian and its variance varies in time and space, causing stationary data models to fit poorly over long observation...

journal article 2019

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Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies

Di Lorenzo, Paolo (author), Banelli, Paolo (author), Isufi, E. (author), Barbarossa, Sergio (author), Leus, G.J.T. (author)

The goal of this paper is to propose novel strategies for adaptive learning of signals defined over graphs, which are observed over a (randomly) time-varying subset of vertices. We recast two classical adaptive algorithms in the graph signal processing framework, namely, the least mean squares (LMS) and the recursive least squares (RLS)...

journal article 2018

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Filter Design for Autoregressive Moving Average Graph Filters

Liu, J. (author), Isufi, E. (author), Leus, G.J.T. (author)

In the field of signal processing on graphs, graph filters play a crucial role in processing the spectrum of graph signals. This paper proposes two different strategies for designing autoregressive moving average (ARMA) graph filters on both directed and undirected graphs. The first approach is inspired by Prony's method, which...

journal article 2018

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Blind Graph Topology Change Detection

Isufi, E. (author), Mahabir, Ashvant S.U. (author), Leus, G.J.T. (author)

This letter investigates methods to detect graph topological changes without making any assumption on the nature of the change itself. To accomplish this, we merge recently developed tools in graph signal processing with matched subspace detection theory and propose two blind topology change detectors. The first detector exploits the prior...

journal article 2018

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Uniaxial Acoustic Vector Sensors for direction-of-arrival estimation

Nambur Ramamohan, K. (author), Comesaña, Daniel Fernandez (author), Leus, G.J.T. (author)

In this paper, a specific reduced-channel Acoustic Vector Sensor (AVS) is proposed comprising one omni-directional microphone and only one particle velocity transducer, such that it can have an arbitrary orientation. Such a reduced transducer configuration is referred to as a Uniaxial AVS (U-AVS). The DOA performance of an array of U-AVSs is...

journal article 2018