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Introduction

Module by: Graham de Wit, Nicholas Newton, Grant Cathcart. E-mail the authors

Summary: 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.

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Definition of a lens

Lenses

A lens is a custom view of the content in the repository. You can think of it as a fancy kind of list that will let you see content through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual member, a community, or a respected organization.

What are tags? tag icon

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

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