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dc.contributor.advisor Makela, Jonathan.
dc.contributor.author Harding, Brian
dc.date.accessioned 2019-04-10T12:03:41Z
dc.date.available 2019-04-10T12:03:41Z
dc.date.issued 2013
dc.identifier.citation Harding, B. (2013).==$Ionospheric imaging with compressed sensing$==(Thesis for the degree of Master of Science in Electrical and Computer Engineering). University of Illinois, United States. es_ES
dc.identifier.uri http://hdl.handle.net/20.500.12816/4452
dc.description.abstract Compressed sensing is a novel theory of sampling and reconstruction that has emerged in the past several years. It seeks to leverage the inherent sparsity of natural images to reduce the number of necessary measurements to a sub-Nyquist level. We discuss how ideas from compressed sensing can benet ionospheric imaging in two ways. Compressed sensing suggests signal reconstruction techniques that take advantage of sparsity, oering us new ways of interpreting data, especially for undersampled problems. One example is radar imaging. We explain how compressed sensing can be used for radar imaging and show results that suggest improved performance over existing techniques. In addition to benetting the way we use data, compressed sensing can improve how we gather data, allowing us to shift complexity from sensing to reconstruction. One example is airglow imaging, wherein we propose replacing CCD-based imagers with single-pixel, compressive imagers. This will reduce the cost of airglow imagers and allow access to spatial information at infrared wavelengths. We show preliminary simulation results suggesting this technique may be feasible for airglow imaging. es_ES
dc.description.uri Tesis es_ES
dc.format application/pdf es_ES
dc.language.iso eng es_ES
dc.publisher University of Illinois es_ES
dc.rights info:eu-repo/semantics/openAccess es_ES
dc.rights.uri https://creativecommons.org/licences/by/4.0/ es_ES
dc.subject Ionosphere es_ES
dc.subject Image Processing es_ES
dc.subject Compression detection es_ES
dc.subject Radar es_ES
dc.title Ionospheric imaging with compressed sensing es_ES
dc.type info:eu-repo/semantics/masterThesis es_ES
dc.subject.ocde http://purl.org/pe-repo/ocde/ford#1.05.01 es_ES
dc.subject.ocde http://purl.org/pe-repo/ocde/ford#2.02.00 es_ES
thesis.degree.name Maestría en Ciencias en Ingeniería Eléctrica e Informática es_ES
thesis.degree.grantor University of Illinois at Urbana-Champaign es_ES
thesis.degree.level Maestría es_ES
thesis.degree.discipline Ingeniería Eléctrica e Informática es_ES

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