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Statistical Compressive Sensing For Efficient Signal Reconstruction and Classification (CROSBI ID 667070)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

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

Podaci o odgovornosti

Ralašić, Ivan ; Tafro, Azra ; Seršić, Damir

engleski

Statistical Compressive Sensing For Efficient Signal Reconstruction and Classification

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.

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

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Podaci o prilogu

44-49.

2018.

objavljeno

10.1109/ICFSP.2018.8552059

Podaci o matičnoj publikaciji

Proceedings of 2018 4th International Conference on Frontiers of Signal Processing

Prof. Jacques Blanc-Talon

Poitiers: Institute of Electrical and Electronics Engineers (IEEE)

978-1-5386-7852-7

Podaci o skupu

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

predavanje

24.09.2018-27.09.2018

Poitiers, Francuska

Povezanost rada

Računarstvo

Poveznice