activity
20172023
most citedAdvances in Distributed Graph Filtering

109 citations · 287 across the 16 of their papers we have counts for

collaborators

20 papers

eess.SP2023

Sensor Selection using the Two-Target Cramér-Rao Bound for Angle of Arrival Estimation

Costas A. Kokke, Mario Coutiño, Laura Anitori +2

Sensor selection is a useful method to help reduce data throughput, as well as computational, power, and hardware requirements, while still maintaining acceptable performance. Alth…

eess.SP2023

Revisiting Matching Pursuit: Beyond Approximate Submodularity

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. Although greedy methods have been widel…

eess.SP2023

Run-Time Monitors Design for Adaptive Radar Systems: A Practical Framework

Pepijn Cox, Mario Coutino, Giuseppe Papari +2

Adaptivity in multi-function radar systems is rapidly increasing, especially when moving towards fully adaptive, cognitive radar systems. However, the large number of available sys…

eess.SP2023

Kernel Design Meets Clutter Cancellation for Irregular Waveforms

Pepijn B. Cox, Mario A. Coutino, Wim L. van Rossum

Efficient clutter filtering for pulsed radar systems remains an open issue when employing pulse-to-pulse modulation and irregular pulse interval waveforms within the coherent proce…

cs.LG2021

Learning Time-Varying Graphs from Online Data

Alberto Natali, Elvin Isufi, Mario Coutino +1

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 the…

eess.SP2020★ 5 cited

Node-Adaptive Regularization for Graph Signal Reconstruction

Maosheng Yang, Mario Coutino, Geert Leus +1

A critical task in graph signal processing is to estimate the true signal from noisy observations over a subset of nodes, also known as the reconstruction problem. In this paper, w…