IJERT-EMS
IJERT-EMS

Compressive Sensing Reconstruction for Sparse Signals with Convex Optimization


Compressive Sensing Reconstruction for Sparse Signals with Convex Optimization
Authors : Ahmed Mohamed Awadallah, Guangming Shi, Guanghui Zhao
Publication Date: 03-09-2014

Authors

Author(s):  Ahmed Mohamed Awadallah, Guangming Shi, Guanghui Zhao

Published in:   International Journal of Engineering Research & Technology

License:  This work is licensed under a Creative Commons Attribution 4.0 International License.

Website: www.ijert.org

Volume/Issue:   Vol. 3 - Issue 9 (September - 2014)

e-ISSN:   2278-0181

Abstract

The theory of compressive sampling (CS), also known as compressed sensing. It is a modern sensing scheme that goes against the common theory in data acquisition. The CS theory claims that one can recover images or signals from fewer samples or measurements than the traditional methods use. To achieve this recovery, CS theory depends on two basic principles: the first is the sparsity of signal, which relates to the signals of interest, and the incoherence, which relates to the sensing method. In this paper we will give a simple review on the CS theory and the analog to information (AIC) system will be discussed briefly supported with two examples of signal reconstruction from undersampled signals. Simulation results show the powerful of the CS reconstruction for both sparse in time and spars in frequency signals.

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