ir. M.A. Coutino

PhD student
Circuits and Systems (CAS), Department of Microelectronics

PhD thesis (Apr 2021): Advances in graph signal processing: Graph filtering and network identification
Promotor: Geert Leus

Expertise: Array signal processing, Sensor networks, Optimization, Numerical Lineal Algebra

Themes: Signal processing for communication

Biography

Mario CoutiƱo Minguez finished his MSc thesis in Aug 2016 in the CAS group (while working at Bang & Olufsen, Denmark) and started in Sep 2016 as a PhD student on the ASPIRE project. He defended his PhD thesis in April 2021, and joined TNO (The Hague).

Task-cognizant sparse sensing for inference

Low-cost sparse sensing designed for specific tasks

  1. Online Time-Varying Topology Identification via Prediction-Correction Algorithms
    Alberto Natali; Mario Coutino; Elvin Isufi; Geert Leus;
    In submitted to Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP),
    June 2021.

  2. Advances in graph signal processing: Graph filtering and network identification
    M. Coutino;
    PhD thesis, TU Delft, Fac. EEMCS, April 2021. ISBN:978-94-6416-560-9. DOI: 10.4233/uuid:3654933b-8a8a-4a45-9a54-323e51641f5f
    document

  3. Submodularity in Action: From Machine Learning to Signal Processing Applications
    E. Tohidi; R. Amiri; M. Coutino; D. Gesbert; G. Leus; A. Karbasi;
    IEEE Signal Processing Magazine,
    Volume 37, Issue 5, pp. 120-133, 2020. DOI: 10.1109/MSP.2020.3003836
    document

  4. State-Space Network Topology Identification From Partial Observations
    M. Coutino; E. Isufi; T. Maehara; G. Leus;
    IEEE Transactions on Signal and Information Processing over Networks,
    Volume 6, pp. 211-225, 2020. DOI: 10.1109/TSIPN.2020.2975393
    document

  5. Towards a General Framework for Fast and Feasible k-Space Trajectories for MRI Based on Projection Methods
    S. Sharma; M. Coutino; S.P. Chepuri; G. Leus; K.V.S. Hari;
    Magnetic Resonance Imaging,
    Volume 72, pp. 122--134, October 2020.

  6. Fast Spectral Approximation of Structured Graphs with Applications to Graph Filtering
    M. Coutino; S.P. Chepuri; T. Maehara; G. Leus;
    Algorithms,
    Volume 13, Issue 9, pp. 214, August 2020.

  7. Node varying regularization for graph signals
    Maosheng Yang; M. Coutino; E. Isufi; G. Leus;
    In 29th European Signal Processing Conference (EUSIPCO 2020),
    Amsterdam (Netherlands), EURASIP, pp. 845-849, August 2020.
    document

  8. State-space based network topology identification
    M. Coutino; E. Isufi; T. Maehara; G. Leus;
    In 29th European Signal Processing Conference (EUSIPCO 2020),
    Amsterdam (Netherlands), EURASIP, pp. 1055-1059, August 2020.
    document

  9. Privacy-Preserving Distributed Graph Filtering
    Qiongxiu Li; M. Coutino; G. Leus; M. Graesboll Christensen;
    In 29th European Signal Processing Conference (EUSIPCO 2020),
    Amsterdam (Netherlands), EURASIP, pp. 2155-2159, August 2020.
    document

  10. Blind calibration for arrays with an aberration layer in ultrasound imaging
    P. van der Meulen; M. Coutino; P. Kruizinga; J.G. Bosch; G. Leus;
    In 29th European Signal Processing Conference (EUSIPCO 2020),
    Amsterdam (Netherlands), EURASIP, pp. 1270-1274, August 2020.
    document

  11. Joint blind calibration and time-delay estimation for multiband ranging
    T. Kazaz; M. Coutino; G.J.M. Janssen; A.J. van der Veen;
    In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
    IEEE, pp. 4846-4850, 2020.
    document

  12. Topology-Aware Joint Graph Filter and Edge Weight Identification for Network Processes
    Alberto Natali; Mario Coutino; Geert Leus;
    In 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP),
    Espoo (Finland), September 2020. DOI: 10.1109/MLSP49062.2020.9231913
    document

  13. Self-Driven Graph Volterra Models for Higher-Order Link Prediction
    M. Coutino; G. V. Karanikolas; G. Leus; G.B. Giannakis;
    In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
    pp. 3887-3891, 2020. DOI: 10.1109/ICASSP40776.2020.9053655
    document

  14. Learning connectivity and higher-order interactions in radial distribution grids
    Qiuling Yang; M. Coutino; Gang Wang; G.B. Giannakis; G. Leus;
    In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
    pp. 5555-5559, 2020. DOI: 10.1109/ICASSP40776.2020.9054665
    document

  15. Advances in Distributed Graph Filtering
    M. Coutino; E. Isufi; G. Leus;
    IEEE Tr. Signal Processing,
    Volume 67, Issue 9, pp. 2320-2333, May 2019. DOI: 10.1109/TSP.2019.2904925
    document

  16. Sparse Antenna and Pulse Placement for Colocated MIMO Radar
    E. Tohidi; M. Coutino; S.P. Chepuri; H. Behroozi; M.M. Nayebi; G. Leus;
    IEEE Tr. Signal Processing,
    Volume 67, Issue 3, pp. 579-593, February 2019. DOI: 10.1109/TSP.2018.2881656
    document

  17. Sparse Sampling for Inverse Problems With Tensors
    G. Ortiz-Jimenez; M. Coutino; S.P. Chepuri; G. Leus;
    IEEE Trans. on Signal Processing,
    Volume 67, Issue 12, pp. 3272--3286, June 2019.

  18. Asynchronous Distributed Edge-Variant Graph Filters
    Mario Coutino; Geert Leus;
    In 2019 IEEE Data Science Workshop (DSW),
    IEEE, pp. 115--119, 2019. ISBN: 978-1-7281-0709-7. DOI: 10.1109/DSW.2019.8755577
    Abstract: ... 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 to provide guarantees for asynchronous graph filtering. To alleviate the possible reduction of convergence speed due to asynchronous updates, we also show how a slight modification to the graph filter recursion, through operator splitting, can be performed to obtain faster convergence. Finally, through numerical experiments the performance of the discussed methods is illustrated.

    document

  19. Learning Sparse Hypergraphs from Dyadic Relational Data
    M. Coutino; S.P. Chepuri; G. Leus;
    In Proc. of IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP),
    Le Gosier, Guadeloupe, pp. 216--220, December 2019. DOI: 10.1109/CAMSAP45676.2019.9022661

  20. Design Strategies for Sparse Control of Random Time-Varying Networks
    M. Coutino; E. Isufi; F. Gama; A. Ribeiro; G. Leus;
    In Proc. of Asilomar Conf. on Signals, Systems, and Computers (Asilomar),
    Pacific Grove, California, USA, pp. 184--188, November 2019. DOI: 10.1109/IEEECONF44664.2019.9049024

  21. On Distributed Consensus by a Cascade Of Generalized Graph Filters
    M. Coutino; G. Leus;
    In Proc. of Asilomar Conf. on Signals, Systems, and Computers (Asilomar),
    Pacific Grove, California, USA, pp. 46--50, November 2019. DOI: 10.1109/IEEECONF44664.2019.9048983

  22. Phase-based distance determination for wireless sensor networks
    T. Kazaz; M. Coutino; G.J.M. Janssen; G.J.T. Leus; A.J. van der Veen;
    Patent, USPTO 621 81 5,1 64, March 2019.

  23. Submodular Sparse Sensing for Gaussian Detection With Correlated Observations
    M. Coutino; S. P. Chepuri; G. Leus;
    IEEE Transactions on Signal Processing,
    Volume 66, Issue 15, pp. 4025-4039, August 2018. ISSN: 1053-587X. DOI: 10.1109/TSP.2018.2846220
    document

  24. Subset selection for kernel-based signal reconstruction
    M. Coutino; S.P. Chepuri; G. Leus;
    In 2018 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP),
    Calgary (Canada), IEEE, pp. 4014-4018, April 2018. ISSN: 2379-190X. DOI: 10.1109/ICASSP.2018.8461510
    document

  25. Distributed Analytical Graph Identification
    S.P. Chepuri; M. Coutino; A. G. Marques; G. Leus;
    In 2018 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP),
    Calgary (Canada), IEEE, pp. 4064-4068, April 2018. ISSN: 2379-190X. DOI: 10.1109/ICASSP.2018.8461484
    document

  26. Edge-Variant Graph Filters
    G. Leus; M. Coutino; E. Isufi;
    In Graph Signal Processing Workshop (GSP18),
    Lausanne (CH), IEEE, June 2018.

  27. Sparsest network support estimation: a submodular approach
    M. Coutino; S.P. Chepuri; G. Leus;
    In IEEE Data Science Workshop (DSW18),
    Lausanne (CH), IEEE, pp. 200-204, June 2018. DOI: 10.1109/DSW.2018.8439890
    document

  28. Joint Ranging and Clock Synchronization for a Dense Heterogeneous IoT Networks
    T. Kazaz; M. Coutino; G. Leus; A.J. van der Veen; G. Janssen;
    In 52nd Asilomar Conference on Signals, Systems and Computers,
    Asilomar, CA, IEEE, pp. 2169-2173, November 2018. DOI: 10.1109/ACSSC.2018.8645210
    document

  29. Sampling and Reconstruction of Signals on Product Graphs
    G. Ortiz-Jimenez; M. Coutino; S.P. Chepuri; G. Leus;
    In Proc. of the IEEE Global Conference on Signal and Information Processing (GlobalSIP 2018),
    Anaheim, California, USA, November 2018.

  30. On the Limits of Finite-Time Distributed Consensus through Successive Local Linear Operations
    M. Coutino; E. Isufi; G. Leus;
    In 52nd Asilomar Conference on Signals, Systems and Computers,
    IEEE, November 2018.

  31. Greedy alternative for room geometry estimation from acoustic echoes: a subspace-based method
    M. Coutino; M.B. Moller; J.K. Nielsen; R. Heusdens;
    In Int. Conf. Audio Speech Signal Proc. (ICASSP),
    New Orleans (USA), IEEE, pp. 366-370, March 2017. DOI: 10.1109/ICASSP.2017.7952179
    document

  32. Sparse Sensing for Composite Matched Subspace Detection
    M. Coutino; S. P. Chepuri; G. Leus;
    In 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP),
    Curacao, IEEE, December 2017. ISBN 978-1-5386-1250-7.

  33. Near-Optimal Greedy Sensor Selection for MVDR Beamforming with Modular Budget Constraint
    M. Coutino; S.P. Chepuri; G.J.T. Leus;
    In 25th European Signal Processing Conference (EUSIPCO 2017),
    Kos (Greece), EURASIP, pp. 2035-2039, August 2017. ISBN 978-0-9928626-7-1. DOI: 10.23919/EUSIPCO.2017.8081556
    document

  34. DOA Estimation and Beamforming Using Spatially Under-Sampled AVS Arrays
    K. Nambur Ramamohan; M. M. Coutino; S.P. Chepuri; D. Fernandez Comesana; G. Leus;
    In 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP),
    Curacao, IEEE, December 2017. ISBN 978-1-5386-1250-7.

  35. Distributed Edge-Variant Graph Filters
    M. Coutino; E. Isufi; G. Leus;
    In 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP),
    Curacao, IEEE, December 2017. ISBN 978-1-5386-1250-7.

  36. Direction of arrival estimation based on information geometry
    M. Coutino; R. Pribic; G. Leus;
    In Int. Conf. Audio Speech Signal Proc. (ICASSP),
    Shanghai (China), IEEE, March 2016.
    document

  37. Stochastic resolution analysis of co-prime arrays in radar
    R. Pribic; M. Coutino; G. Leus;
    In IEEE Stat. Signal Proc. Workshop,
    June 2016. DOI: 10.1109/SSP.2016.7551757
    document

  38. Bound on the estimation grid size for sparse reconstruction in direction of arrival estimation
    M. Coutino; R. Pribic; G. Leus;
    In IEEE Stat. Signal Proc. Workshop,
    June 2016. DOI: 10.1109/SSP.2016.7551781
    document

BibTeX support

Last updated: 13 Jun 2021

Mario Coutino

Alumnus