An ionogram is a graph that shows the distance
that a vertically transmitted wave, of a given frequency, travels
before returning to the earth. The ionogram is shaped by making
a trace of this distance, which is called virtual height, against the
frequency of the transmitted wave. Along with the echoes of the
ionosphere, ionograms usually contain a large amount of noise
of different nature, that must be removed in order to extract
useful information. In the present work, we propose to use a
convolutional neural network model to improve the quality of
the information obtained from digital ionograms, compared to
that using image processing and machine learning techniques, in
the generation of electronic density profiles. A data set of more
than 900,000 ionograms from 5 ionospheric observation stations
is available to use. / Trabajo de investigación
| Identifer | oai:union.ndltd.org:PUCP/oai:tesis.pucp.edu.pe:20.500.12404/14984 |
| Date | 12 September 2019 |
| Creators | De la Jara Sánchez, César |
| Contributors | Olivares Poggi, César Augusto |
| Publisher | Pontificia Universidad Católica del Perú, PE |
| Source Sets | Pontificia Universidad Católica del Perú |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess, http://creativecommons.org/licenses/by-nc-sa/2.5/pe/ |
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