Task-cognizant sparse sensing for inference

Publications

  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. Coding Mask Design for Single Sensor Ultrasound Imaging
    P. van der Meulen; P. Kruizinga; J.G. Bosch; G. Leus;
    IEEE Trans. on Computational Imaging,
    Volume 6, pp. 358--373, 2020. DOI: 10.1109/TCI.2019.2948729

  6. Observing and Tracking Bandlimited Graph Processes from Sampled Measurements
    E. Isufi; P. Banelli; P. Di Lorenzo; G. Leus;
    Signal Processing,
    Volume 177, pp. 107749, December 2020.

  7. 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.

  8. 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.

  9. 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

  10. 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

  11. 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

  12. Low-Complexity Gridless 2D Harmonic Retrieval via Decoupled-ANM Covariance Reconstruction
    Yu Zhang; Yue Wang; Zhi Tian; G. Leus; Gong Zhang;
    In 29th European Signal Processing Conference (EUSIPCO 2020),
    Amsterdam (Netherlands), EURASIP, pp. 1876-1880, August 2020.
    document

  13. 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

  14. 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

  15. 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

  16. 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

  17. DOA estimation in heteroscedastic noise
    P. Gerstoft; S. Nannuru; C.F. Mecklenbrauker; G. Leus;
    Signal Processing,
    March 2019. DOI: 10.1016/j.sigpro.2019.03.014
    document

  18. 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

  19. Online Graph-Adaptive Learning With Scalability and Privacy
    Yanning Shen; G. Leus; G.B. Giannakis;
    IEEE Tr. Signal Processing,
    Volume 67, Issue 9, pp. 2471-2483, May 2019. DOI: 10.1109/TSP.2019.2904922
    document

  20. Controllability of Bandlimited Graph Processes Over Random Time Varying Graphs
    F. Gama; E. Isufi; A. Ribeiro; G. Leus;
    IEEE Trans. on Signal Processing,
    Volume 67, Issue 24, pp. 6440--6454, December 2019. DOI: 10.1109/TSP.2019.2952053

  21. Forecasting Time Series With VARMA Recursions on Graphs
    E. Isufi; A. Loukas; N. Perraudin; G. Leus;
    IEEE Trans. on Signal Processing,
    Volume 67, Issue 18, pp. 4870--4885, September 2019. DOI: 10.1109/TSP.2019.2929930

  22. 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.

  23. Aggregation Graph Neural Networks
    F. Gama; A.G. Marques; A. Ribeiro; G. Leus;
    In 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
    Brighton, UK, IEEE, pp. 4943-4947, May 2019. DOI: 10.1109/ICASSP.2019.8682975
    document

  24. 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

  25. 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

  26. 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

  27. 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

  28. Convolutional Graph Neural Networks
    F. Gama; A.G. Marques; G. Leus; A. Ribeiro;
    In Proc. of Asilomar Conf. on Signals, Systems, and Computers (Asilomar),
    Pacific Grove, California, USA, pp. 452--456, November 2019. DOI: 10.1109/IEEECONF44664.2019.9048767

  29. 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

  30. Structured ultrasound microscopy
    J. Janjic; P. Kruizinga; P. van der Meulen; G. Springeling; F. Mastik; G. Leus; J.G. Bosch; A.F.W. van der Steen; G. van Soest;
    Applied Physics Letters,
    Volume 112, Issue 25, April 2018. DOI: 10.1063/1.5026863
    document

  31. Statistical Graph Signal Processing: Stationarity and Spectral Estimation
    S. Segarra; S.P. Chepuri; A.G. Marques; G. Leus;
    In Cooperative and Graph Signal Processing,
    Academic Press, 2018. ISBN: 978-0-12-813677-5. DOI: 10.1016/B978-0-12-813677-5.00012-2
    document

  32. 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

  33. 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

  34. Graph Sampling With and Without Input Priors
    S.P. Chepuri; Y. Eldar; G. Leus;
    In 2018 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP),
    Calgary (Canada), IEEE, pp. 4564-4568, April 2018. ISSN: 2379-190X. DOI: 10.1109/ICASSP.2018.8461420
    document

  35. Aggregation Convolutional Neural Networks for Graph Signals
    F. Gama; A. Ribeiro; A. Marques; G. Leus;
    In Graph Signal Processing Workshop (GSP18),
    Lausanne (CH), June 2018.

  36. Control of graph signals over random time-varying graphs
    F. Gama; E. Isufi; G. Leus; A. Ribeiro;
    In 2018 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP),
    Calgary (Canada), IEEE, pp. 4169-4173, April 2018. ISSN: 2379-190X. DOI: 10.1109/ICASSP.2018.8462381
    document

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

  38. Observing Bandlimited Graph Processes
    E. Isufi; G. Leus; P. Banelli; P. Di Lorenzo;
    In Graph Signal Processing Workshop (GSP18),
    Lausanne (CH), June 2018.

  39. Blind Graph Topology Change Detection
    E. Isufi; G. Leus;
    In Graph Signal Processing Workshop (GSP18),
    Lausanne (CH), June 2018.

  40. 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

  41. Convolutional neural networks via node-varying graph filters
    F. Gama; G. Leus; A. Marques; A. Ribeiro;
    In IEEE Data Science Workshop (DSW18),
    Lausanne (CH), IEEE, pp. 1-5, June 2018. DOI: 10.1109/DSW.2018.8439899
    document

  42. 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.

  43. Observing Bandlimited Graph Processes from Subsampled Measurements
    E. Isufi; P. Banelli; P. Di Lorenzo; G. Leus;
    In 52nd Asilomar Conference on Signals, Systems and Computers,
    IEEE, November 2018.

  44. 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.

  45. Calibration techniques for single-sensor ultrasound imaging with a coding mask
    P. van der Meulen; P. Kruizinga; J.G. Bosch; G. Leus;
    In 52nd Asilomar Conference on Signals, Systems and Computers,
    IEEE, November 2018.
    document

  46. Stationary Graph Processes and Spectral Estimation
    A. G. Marques; S. Segarra; G. Leus; A. Ribeiro;
    IEEE Transactions on Signal Processing,
    Volume 65, Issue 22, pp. 5911-5926, November 2017. ISSN: 1053-587X. DOI: 10.1109/TSP.2017.2739099
    document

  47. Compressive 3D ultrasound imaging using a single sensor
    P. Kruizinga; P. van der Meulen; A. Fedjajevs; F. Mastik; G. Springeling; N. de Jong; J.G. Bosch; G. Leus;
    Science Advances,
    Volume 3, December 2017. ISSN: 2375-2548. DOI: 10.1126/sciadv.1701423
    document
    Youtube

  48. Model-based image reconstruction for medical ultrasound
    P. Kruizinga; P. van der Meulen; F. Mastik; N. de Jong; J. G. Bosch; G. Leus;
    The Journal of the Acoustical Society of America,
    Volume 141, Issue 5, pp. 3610-3610, June 2017. DOI: 10.1121/1.4987733

  49. Stationary graph processes: parametric power spectral estimation
    S. Segarra; A. G. Marques; G. Leus; A. Ribeiro;
    In Int. Conf. Audio Speech Signal Proc. (ICASSP),
    New Orleans (USA), IEEE, pp. 4099-4103, March 2017. DOI: 10.1109/ICASSP.2017.7952927
    document

  50. 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.

  51. Acoustical compressive 3D imaging with a single sensor
    P. Kruizinga; P. van der Meulen; F. Mastik; A. Fedjajevs; G. Springeling; N. de Jong; G. Leus; J. G. Bosch;
    In 2017 IEEE International Ultrasonics Symposium (IUS),
    pp. 1-1, September 2017. DOI: 10.1109/ULTSYM.2017.8091779
    document

  52. 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

  53. Stationary Graph Signals: Power Spectral Density Estimation and Sampling (distinguished lecture)
    G. Leus;
    In 2017 5th IEEE Global Conference on Signal and Information Processing (GlobalSIP),
    Montreal, Canada, IEEE, November 2017.

  54. 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.

  55. 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.

  56. Spatial Compression in Ultrasound Imaging
    P. van der Meulen; P. Kruizinga; J. G. Bosch; G. Leus;
    In 51st Asilomar Conf. on Signals, Systems and Computers,
    Asilomar (CA), IEEE, October 2017.

  57. Impulse response estimation method for ultrasound arrays
    P. van der Meulen; P. Kruizinga; J. G. Bosch; G. Leus;
    In 2017 IEEE International Ultrasonics Symposium (IUS),
    pp. 1-4, September 2017. DOI: 10.1109/ULTSYM.2017.8092977
    document

  58. Graph Sampling for Covariance Estimation
    S.P. Chepuri; G. Leus;
    In Subm. IEEE Journ. of Selec. Topics in Signal Proc.,
    November 2016.

  59. Sparse Sensing for Statistical Inference
    S.P. Chepuri; G. Leus;
    Boston-Delft: Foundations and Trends in Signal Processing, , 2016. ISBN-978-1-68083-236-5.. DOI: 10.1561/2000000069

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