000134484 001__ 134484
000134484 005__ 20190316234516.0
000134484 022__ $$a1053-587X
000134484 02470 $$2ISI$$a000274472600013
000134484 0247_ $$2doi$$a10.1109/TSP.2009.2034908
000134484 037__ $$aARTICLE
000134484 245__ $$aDistributed Sampling of Signals Linked by Sparse Filtering: Theory and Applications
000134484 269__ $$a2010
000134484 260__ $$c2010
000134484 336__ $$aJournal Articles
000134484 520__ $$aWe study the distributed sampling and centralized reconstruction of two correlated signals, modeled as the input and output of an unknown sparse filtering operation. This is akin to a Slepian-Wolf setup, but in the sampling rather than the lossless compression case. Two different scenarios are considered: In the case of universal reconstruction, we look for a sensing and recovery mechanism that works for all possible signals, whereas in what we call almost sure reconstruction, we allow to have a small set (with measure zero) of unrecoverable signals. We derive achievability bounds on the number of samples needed for both scenarios. Our results show that, only in the almost sure setup can we effectively exploit the signal correlations to achieve effective gains in sampling efficiency. In addition to the above theoretical analysis, we propose an efficient and robust distributed sampling and reconstruction algorithm based on annihilating filters. Finally, we evaluate the performance of our method in one synthetic scenario, and two practical applications, including the distributed audio sampling in binaural hearing aids and the efficient estimation of room impulse responses. The numerical results confirm the effectiveness and robustness of the proposed algorithm in both synthetic and practical setups.
000134484 6531_ $$aDistributed sampling
000134484 6531_ $$afinite rate of innovation
000134484 6531_ $$aannihilating filter
000134484 6531_ $$aiterative denoising
000134484 6531_ $$acompressed
000134484 6531_ $$asensing
000134484 6531_ $$acompressive sampling
000134484 6531_ $$asparse reconstruction
000134484 6531_ $$aYule-Walker system
000134484 6531_ $$aNCCR-MICS
000134484 6531_ $$aNCCR-MICS/CL1
000134484 700__ $$0242499$$aHormati, Ali$$g171232
000134484 700__ $$0244019$$aRoy, Olivier$$g118640
000134484 700__ $$aLu, Yue M.
000134484 700__ $$0240184$$aVetterli, Martin$$g107537
000134484 773__ $$j58$$k3$$q1095-1109$$tIEEE Transactions on Signal Processing
000134484 8560_ $$fmihailo.kolundzija@epfl.ch
000134484 8564_ $$s1099626$$uhttps://infoscience.epfl.ch/record/134484/files/05290053.pdf$$yPublisher's version$$zPublisher's version
000134484 8564_ $$s3117827$$uhttps://infoscience.epfl.ch/record/134484/files/HormatiRLV09_data_rr.zip
000134484 909C0 $$0252056$$pLCAV$$xU10434
000134484 909CO $$ooai:infoscience.tind.io:134484$$pIC$$particle$$qGLOBAL_SET
000134484 917Z8 $$x171232
000134484 917Z8 $$x171232
000134484 917Z8 $$x190838
000134484 917Z8 $$x222073
000134484 917Z8 $$x190838
000134484 937__ $$aLCAV-ARTICLE-2009-005
000134484 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000134484 980__ $$aARTICLE