<< Chapter < Page Chapter >> Page >
Introduction module in the Sparse Signal Recovery collection.

Introduction

As we progress into the era of information overload, it becomes increasingly important to find ways to extract information efficiently from data sets. One of the key concerns in signal processing is the accurate decoding and interpretation of an input in a minimal period of time. In the distant past, the information theorists' objective primarily involved reducing the signal elements to be processed. Now, with a multitude of extraction options, we are also concerned about the computation time required to interpret each element.

In this project, we investigate a few methods by which we can “accurately” reconstruct an arbitrarily complex signal using a minimum number of iterations. The input signals we probe are deemed sparse – that is, they are constructed using a number of basis vectors that is small relative to the length of the signal. This generally means that the signals consist mostly of zero values, with spikes at a few selected positions. We compare the signal-to-noise ratios achieved by our recovery methods after specified numbers of iterations upon a variety of input signals, and deduce a few conclusions about the most efficient and most feasible recovery methods.

Get Jobilize Job Search Mobile App in your pocket Now!

Get it on Google Play Download on the App Store Now




Source:  OpenStax, Sparse signal recovery in the presence of noise. OpenStax CNX. Dec 14, 2009 Download for free at http://cnx.org/content/col11144/1.1
Google Play and the Google Play logo are trademarks of Google Inc.

Notification Switch

Would you like to follow the 'Sparse signal recovery in the presence of noise' conversation and receive update notifications?

Ask