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Pregled bibliografske jedinice broj: 960435

Statistical Compressive Sensing For Efficient Signal Reconstruction and Classification

Ralašić, Ivan; Tafro, Azra; Seršić, Damir
Statistical Compressive Sensing For Efficient Signal Reconstruction and Classification // Proceedings of 2018 4th International Conference on Frontiers of Signal Processing / Prof. Jacques Blanc-Talon (ur.).
Poitiers: Institute of Electrical and Electronics Engineers (IEEE), 2018. str. 44-49 doi:10.1109/ICFSP.2018.8552059 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)

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Naslov
Statistical Compressive Sensing For Efficient Signal Reconstruction and Classification

Autori
Ralašić, Ivan ; Tafro, Azra ; Seršić, Damir

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of 2018 4th International Conference on Frontiers of Signal Processing / Prof. Jacques Blanc-Talon - Poitiers : Institute of Electrical and Electronics Engineers (IEEE), 2018, 44-49

ISBN
978-1-5386-7852-7

Skup
4th International Conference on Frontiers of Signal Processing (ICFSP)

Mjesto i datum
Poitiers, Francuska, 24.09.2018. - 27.09.2018

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
classification, compressive sensing, dimensional- ity reduction, Gaussian mixture models, inverse problems, signal reconstruction

Sažetak
Compressive sensing (CS) represents a signal pro- cessing technique for simultaneous signal acquisition and compression that relies on signal dimensionality reduction. Statistical compressive sensing (SCS) uses statistical models to develop an efficient sampling strategy for signals that follow some statistical distribution. In this paper, statistical model based on Gaussian mixtures is employed to design an efficient framework for the CS signal reconstruction and classification. A robust classification method based on sparse signal representation using overcomplete eigenvector dictionaries and l 1 - norm is presented. Optimal non-adaptive measurement matrix for observed Gaussian mixture model is discussed. A series of experiments to analyze the performance of the proposed method has been performed and presented in the experimental results section.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA

Projekti:
HRZZ-IP-2014-09-2625 - Iznad Nyquistove granice (BeyondLimit) (Seršić, Damir, HRZZ ) ( CroRIS)

Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Ivan Ralašić (autor)

Avatar Url Azra Tafro (autor)

Avatar Url Damir Seršić (autor)

Poveznice na cjeloviti tekst rada:

doi ieeexplore.ieee.org

Citiraj ovu publikaciju:

Ralašić, Ivan; Tafro, Azra; Seršić, Damir
Statistical Compressive Sensing For Efficient Signal Reconstruction and Classification // Proceedings of 2018 4th International Conference on Frontiers of Signal Processing / Prof. Jacques Blanc-Talon (ur.).
Poitiers: Institute of Electrical and Electronics Engineers (IEEE), 2018. str. 44-49 doi:10.1109/ICFSP.2018.8552059 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Ralašić, I., Tafro, A. & Seršić, D. (2018) Statistical Compressive Sensing For Efficient Signal Reconstruction and Classification. U: Prof. Jacques Blanc-Talon (ur.)Proceedings of 2018 4th International Conference on Frontiers of Signal Processing doi:10.1109/ICFSP.2018.8552059.
@article{article, author = {Rala\v{s}i\'{c}, Ivan and Tafro, Azra and Ser\v{s}i\'{c}, Damir}, year = {2018}, pages = {44-49}, DOI = {10.1109/ICFSP.2018.8552059}, keywords = {classification, compressive sensing, dimensional- ity reduction, Gaussian mixture models, inverse problems, signal reconstruction}, doi = {10.1109/ICFSP.2018.8552059}, isbn = {978-1-5386-7852-7}, title = {Statistical Compressive Sensing For Efficient Signal Reconstruction and Classification}, keyword = {classification, compressive sensing, dimensional- ity reduction, Gaussian mixture models, inverse problems, signal reconstruction}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Poitiers, Francuska} }
@article{article, author = {Rala\v{s}i\'{c}, Ivan and Tafro, Azra and Ser\v{s}i\'{c}, Damir}, year = {2018}, pages = {44-49}, DOI = {10.1109/ICFSP.2018.8552059}, keywords = {classification, compressive sensing, dimensional- ity reduction, Gaussian mixture models, inverse problems, signal reconstruction}, doi = {10.1109/ICFSP.2018.8552059}, isbn = {978-1-5386-7852-7}, title = {Statistical Compressive Sensing For Efficient Signal Reconstruction and Classification}, keyword = {classification, compressive sensing, dimensional- ity reduction, Gaussian mixture models, inverse problems, signal reconstruction}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Poitiers, Francuska} }

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