Task-cognizant sparse sensing for inference

Publications

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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.

  13. 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

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

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

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

  17. 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

  18. 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

  19. 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.

  20. 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.

  21. 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.

  22. 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

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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.

  28. 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

  29. 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

  30. 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.

  31. 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.

  32. 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.

  33. 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.

  34. 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

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

  36. 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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