Study and Discussion on the Theory of Compressive Sensing

Ying Zhu; Yong Xing Jia; Yuan Wang

doi:10.4028/www.scientific.net/AMM.239-240.1462

Paper Titles

Novel Compact Genetic Algorithm for WTA Problem
p.1437

The Main Steam Temperature Cascade PID Controller Parameters Tuning Optimization Based on Ant Colony Algorithm
p.1442

Auto-Clustering Algorithm for Heterogeneous Information Network Using Improved Particle Swarm Optimization
p.1448

GLCM Inspired Fingerprints Segmentation Algorithm with Adaptive Block Size
p.1456

Study and Discussion on the Theory of Compressive Sensing
p.1462

A Method for Extracting QR Code from Complex Background Based on Morphology
p.1466

Multi-Modal Medical Image Registration of by Combining Improved Mean Shift and RPM
p.1472

Application Study of Fuzzy Comprehensive Evaluation Method on Ammunition Management in Rear Depot
p.1476

Based on SVD MEMS Microstructure Amplitude Measurements of the New Algorithm
p.1480

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 239-240Study and Discussion on the Theory of Compressive...

Study and Discussion on the Theory of Compressive Sensing

Abstract:

Compressive sensing(CS)is a novel information theory proposed recently.It broke through the restrictions of the traditional Nyquist sampling theorem on the sampling frequency,which can only use fewer sampling signals to describe the original signals. This article introduces the theory of CS including three main process like sparse representation, mesurement matric design and signal reconstruction.Then it also discusses the issures requiring improvement about the algorithm,the hardware implementation ,finds the reasons and gives some advices in follow-up studies.

You might also be interested in these eBooks

Measurement Technology and its Application

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 239-240)

Pages:

1462-1465

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.239-240.1462

Citation:

Cite this paper

Online since:

December 2012

Authors:

Ying Zhu, Yong Xing Jia, Yuan Wang

Keywords:

Compressive Sensing (CS), Measurement Matrix, Signal Reconstruction, Sparse Representation

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] G. Shi, D. Liu, D. Gao. et al. Advances in theory and application of compressed sensing, J. ACTA ELECTRONICA SINICA, 2009, 37(5)：1070-1081. (In Chinese).

Google Scholar

[2] E. J. Candes, M. B. Wakin. An introduction to compressive sampling, J. IEEE. Signal Proc. Mag, 2008, 25(2)：21-30.

DOI: 10.1109/msp.2007.914731

Google Scholar

[3] S. Li, D. We, A survery on compressive sensing, J. ACTA AUTOMATICA SINICA, 2009, 35(11)：1369-1377. (In Chinese).

DOI: 10.3724/sp.j.1004.2009.01369

Google Scholar

[4] Zhang Chunmei, Yin Zhongke, Xiao Mingxia. Signal complete representation and sparse decomposition based on redundancy dictionary, J. Chinese Science Bulletin, 2006, 51(6): 628-633. (In Chinese) Reference to a book.

DOI: 10.1007/bf02899633

Google Scholar

[5] E. J. Candes, Compressive sampling /Proceedings of International Congress of Mathematicians. Zürich, Switzerland: European Mathematical Society Publishing House, (2006).

Google Scholar