Department of Microelectronics
Faculty of Electrical Engineering, Mathematics, and Computer Science

ir. M.A. Coutino

PhD student
Signal Processing Systems (SPS), 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: Autonomous sensor systems, XG - Next Generation Sensing and Communication

Biography

Mario Coutiño 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).

Projects history

Task-cognizant sparse sensing for inference

Low-cost sparse sensing designed for specific tasks

  1. Transmit and Receive Sensor Selection Using the Multiplicity in the Virtual Array
    I. van der Werf; C. Kokke; R. Heusdens; R. C. Hendriks; G. Leus; M. Coutino;
    In 32nd European Signal Processing Conference (EUSIPCO 2024),
    2024.

  2. Phase-based distance determination for wireless networks
    A.J. van der Veen; T. Kazaz; G.J.M. Janssen; G.J.T. Leus; Mario Coutino;
    Patent, US 11,889,459 B2, 2024.

  3. Sensor Selection for Angle of Arrival Estimation Based on the Two-Target Cramér-Rao Bound
    Costas Kokke; Mario Coutino; Laura Anitori; Richard Heusdens; Geert Leus;
    In 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
    pp. 1--5, June 2023. DOI: 10.1109/ICASSP49357.2023.10094942
    Keywords: ... Acoustics,array processing,Array signal processing,Computational modeling,cramér-rao bound,Estimation,Hardware,multi-target estimation,sensor selection,Sensors,sparse sensing,Throughput.

    Abstract: ... Sensor selection is a useful method to help reduce data throughput, as well as computational, power, and hardware requirements, while still maintaining acceptable performance. Although minimizing the Cramér-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown source models. In this work, we propose to tackle the sensor selection problem for angle of arrival estimation using the worst-case Cramér-Rao bound of two uncorrelated sources. To do so, we cast the problem as a convex semi-definite program and retrieve the binary selection by randomized rounding. Through numerical examples related to a linear array, we illustrate the proposed method and show that it leads to the natural selection of elements at the edges plus the center of the linear array. This contrasts with the typical solutions obtained from minimizing the single-target Cramér-Rao bound.

  4. Sensor Selection Using the Two-Target Cramér-Rao Bound for Angle of Arrival Estimation
    Costas A. Kokke; Mario Coutino; Laura Anitori; Richard Heusdens; Geert Leus;
    Submitted to ISCS2023, July 2023. A 2-page abstract of Sensor Selection for Angle of Arrival Estimation Based on the Two-Target Cramér-Rao Bound.. DOI: 10.48550/arXiv.2307.16478
    Keywords: ... Electrical Engineering and Systems Science - Signal Processing.

    Abstract: ... Sensor selection is a useful method to help reduce data throughput, as well as computational, power, and hardware requirements, while still maintaining acceptable performance. Although minimizing the Cramér-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown source models. We propose to tackle the sensor selection problem for angle of arrival estimation using the worst-case Cramér-Rao bound of two uncorrelated sources. We cast the problem as a convex semi-definite program and retrieve the binary selection by randomized rounding. Through numerical examples related to a linear array, we illustrate the proposed method and show that it leads to the selection of elements at the edges plus the center of the linear array.

  5. Learning Time-Varying Graphs From Online Data
    Natali, A.; Isufi, E.; Coutino, M.; Leus, G.;
    IEEE Open Journal of Signal Processing,
    Volume 3, pp. 212--228, 2022. DOI: 10.1109/OJSP.2022.3178901

  6. A Cascaded Structure for Generalized Graph Filters
    Coutino, M.; Leus, G.;
    IEEE Transactions on Signal Processing,
    Volume 70, pp. 3499--3513, 2022. DOI: 10.1109/TSP.2021.3099630

  7. Single-Pulse Estimation of Target Velocity on Planar Arrays
    Costas Kokke; Mario Coutino; Richard Heusdens; Geert Leus; Laura Anitori;
    In 2022 30th European Signal Processing Conference (EUSIPCO),
    pp. 1811--1815, August 2022. ISSN: 2076-1465. DOI: 10.23919/EUSIPCO55093.2022.9909976
    Keywords: ... Adaptation models,array signal processing,Cramér-Rao bound,Data models,Doppler processing,Estimation,Parallel processing,Planar arrays,pulse-Doppler radar,Signal processing algorithms,Transceivers,velocity estimation.

    Abstract: ... Doppler velocity estimation in pulse-Doppler radar is done by evaluating the target returns of bursts of pulses. While this provides convenience and accuracy, it requires multiple pulses. In adaptive and cognitive radar systems, the ability to adapt on consecutive pulses, instead of bursts, brings potential performance benefits. Hence, with radar transceiver arrays growing increasingly larger in their number of elements over the years, it may be time to re-evaluate how Doppler velocity can be estimated when using large planar arrays. In this work, we present variance bounds on the estimation of velocity using the Doppler shift as it appears in the array model. We also propose an efficient method of performing the velocity estimation and we verify its performance using Monte Carlo simulations.

  8. A Momentum-Guided Frank-Wolfe Algorithm
    Li, Bingcong; Coutino, M.; Giannakis, G.B.; Leus, G.;
    IEEE Transactions on Signal Processing,
    Volume 69, pp. 3597--3611, 2021. DOI: 10.1109/TSP.2021.3087910

  9. Node-Adaptive Regularization for Graph Signal Reconstruction
    Yang, Maosheng; Coutino, M.; Leus, G.; Isufi, E.;
    IEEE Open Journal of Signal Processing,
    Volume 2, pp. 85--98, 2021. DOI: 10.1109/OJSP.2021.3056897

  10. Online Graph Learning From Time-Varying Structural Equation Models
    Natali, A.; Isufi, E.; Coutino, M.; Leus, G.;
    In Proc. of Asilomar Conference on Signals, Systems, and Computers (Asilomar),
    Monterey, California, USA, pp. 1579--1585, October 2021. DOI: 10.1109/IEEECONF53345.2021.9723163

  11. Topological Volterra Filters
    Leus, G.; Yang, Maosheng; Coutino, M.; Isufi, E.;
    In Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
    Toronto, Ontario, Canada, pp. 5385--5399, June 2021. DOI: 10.1109/ICASSP39728.2021.9414275

  12. Online Time-Varying Topology Identification Via Prediction-Correction Algorithms
    Natali, A.; Coutino, M.; Isufi, E.; Leus, G.;
    In Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
    Rio de Janeiro, Brazil, pp. 5400--5404, July 2021. DOI: 10.1109/ICASSP39728.2021.9415053

  13. 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

  14. 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

  15. 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

  16. 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.

  17. 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.

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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

  28. 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.

  29. 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

  30. 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

  31. 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

  32. 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

  33. 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.

  34. 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

  35. 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

  36. 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

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

  38. 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

  39. 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

  40. 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.

  41. 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.

  42. 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

  43. 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.

  44. 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

  45. 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.

  46. 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.

  47. 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

  48. 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

  49. 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

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Last updated: 13 Jun 2021

Mario Coutino

Alumnus