Sensing Heterogeneous Information Network Environment

1. Spatio-temporal field estimation using Kriged Kalman Filter (KKF) with sparsity-enforcing sensor placement
   V. Roy; A. Simonetto; G. Leus;
   Sensors,
   Volume 8, Issue 6, 2018. DOI: 10.3390/s18061778
   document

2. Spatio-temporal environment monitoring leveraging sparsity
   V. Roy;
   PhD thesis, TU Delft, Fac. EEMCS, October 2018. ISBN: 978-94-6380-073-0. DOI: 10.4233/uuid:f6e26091-5885-4949-b1b0-a388e1bff3d3
   document

3. Dynamic rainfall monitoring using microwave links
   V. Roy; S. Gishkori; G. Leus;
   EURASIP J. Adv. Signal Processing,
   pp. 1-17, 2016. DOI: 10.1186/s13634-016-0367-6
   document

4. Spatio-temporal sensor management for environmental field estimation
   V. Roy; A. Simonetto; G. Leus;
   Signal Processing,
   Volume 128, pp. 369-381, November 2016. DOI: 10.1016/j.sigpro.2016.05.011
   document

5. Correlation-aware sparsity-enforcing sensor placement for spatio-temporal field estimation
   V. Roy; G. Leus;
   In Proc. Int. Conf. Acoustics, Speech, Signal Proc. (ICASSP 2015),
   Brisbane (Australia), IEEE, pp. 2389-2393, May 2015. DOI: 10.1109/ICASSP.2015.7178399
   document

6. Spatial Rainfall Mapping From Path-Averaged Rainfall Measurements Exploiting Sparsity
   V. Roy; S. Gishkori; G. Leus;
   In Proc. IEEE Global Conference on Signal and Information Processing,
   Atlanta (GA), IEEE, December 2014.

7. Sparsity-Enforcing Sensor Selection for DOA Estimation
   V. Roy; S.P. Chepuri; G. Leus;
   In Proc. 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2013),
   Saint Martin (French West Indies), IEEE, December 2013.
   document

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