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dc.creatorBaraniuk, Richard G.
Duarte, Marco F.
Davenport, Mark A.
Wakin, Michael B.
dc.date.accessioned 2015-05-04T19:06:04Z
dc.date.available 2015-05-04T19:06:04Z
dc.date.issued 2013-07-09
dc.identifier.urihttps://hdl.handle.net/1911/80128
dc.description.abstract The recently introduced theory of Compressive Sensing (CS) enables a new method for signal recovery from incomplete information (a reduced set of “compressive” linear measurements), based on the assumption that the signal is sparse in some dictionary. Such compressive measurement schemes are desirable in practice for reducing the costs of signal acquisition, storage, and processing. However, the current CS framework considers only a certain task (signal recovery) and only in a certain model setting (sparsity). We show that compressive measurements are in fact information scalable, allowing one to answer a broad spectrum of questions about a signal when provided only with a reduced set of compressive measurements. These questions range from complete signal recovery at one extreme down to a simple binary detection decision at the other. (Questions in between include, for example, estimation and classification.) We provide techniques such as a “compressive matched filter” for answering several of these questions given the available measurements, often without needing to first reconstruct the signal. In many cases, these techniques can succeed with far fewer measurements than would be required for full signal recovery, and such techniques can also be computationally more efficient. Based on additional mathematical insight, we discuss information scalable algorithms in several model settings, including sparsity (as in CS), but also in parametric or manifold-based settings and in model-free settings for generic statements of detection, classification, and estimation problems.
dc.format.extent 27 pp
dc.language.iso eng
dc.title Method and apparatus for signal detection- classification and estimation from compressive measurements
dc.type Utility patent
dc.digitization.specificationsThis patent information was downloaded from the US Patent and Trademark website (http://www.uspto.gov/) as image-PDFs. The PDFs were OCRed for access purposes.
dc.contributor.publisher United States Patent and Trademark Office
dc.type.genre patents
dc.type.dcmi Text
dc.date.filed 2006-10-25
dc.identifier.patentID US8483492B2
dc.contributor.assignee Rice University
dc.identifier.citation Baraniuk, Richard G., Duarte, Marco F., Davenport, Mark A. and Wakin, Michael B., "Method and apparatus for signal detection- classification and estimation from compressive measurements." Patent US8483492B2. issued 2013-07-09. Retrieved from https://hdl.handle.net/1911/80128.


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