Statistical Compressive Sensing For Efficient Signal Reconstruction and Classification (CROSBI ID 667070)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
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