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References and Acknowledgements

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

Summary: References and Acknowledgements for the Sparse Signal Recovery in the Presence of Noise collection.

References and Acknowledgements

We wish to thank our mentor, JP Slavinsky. Without your guidance and support, this project would not have been possible.

Many thanks also to Dr. Rich Baraniuk – for creating Connexions, and (arguably more importantly), for presenting inspiring signal processing lectures throughout the semester.

J D Haupt's presentation to the Electrical Engineering department on sparse signal reconstruction was integral to the development of our algorithm, although it concentrated on efficiency rather than arbitrary reconstruction.

The algorithms and techniques discussed in the following papers were used for comparison against our algorithm:

Haupt, J. and R. Nowak. "Signal Reconstruction From Noisy Random Projections." IEEE Trans. Info. Theory, vol.52, no.9, pp.4036-4048, 2006. http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1683924&isnumber=35459

J. Tropp and A. Gilbert, "Signal Recovery from Partial Information via Orthogonal Matching Pursuit." IEEE Trans. Info. Theory, vol. 53, no. 12, pp. 4655--4666, 2007. http://www.math.lsa.umich.edu/~annacg/papers/TG05-signal-recovery-rev-v2.pdf

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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.

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