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Description
  • Method of constructing a least-squares model based on measured data with task to estimate this model using as few measurements as possible. A method is presented using minimization procedure that tests potential future measurement points and selects an optimal or sub-optimal data point with a good impact on the model variance.
  • Method of constructing a least-squares model based on measured data with task to estimate this model using as few measurements as possible. A method is presented using minimization procedure that tests potential future measurement points and selects an optimal or sub-optimal data point with a good impact on the model variance. (en)
Title
  • Measurement And Modeling In Sensor Networks
  • Measurement And Modeling In Sensor Networks (en)
skos:prefLabel
  • Measurement And Modeling In Sensor Networks
  • Measurement And Modeling In Sensor Networks (en)
skos:notation
  • RIV/61989100:27240/10:86076003!RIV12-GA0-27240___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA102/08/1429)
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  • 269991
http://linked.open...ai/riv/idVysledku
  • RIV/61989100:27240/10:86076003
http://linked.open...riv/jazykVysledku
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  • Model estimation, least-squares model, measuring minimalization (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [2D7A21F39774]
http://linked.open...v/mistoKonaniAkce
  • Sibiu
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  • Piscataway, NJ
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  • 9th RoEduNet IEEE International Conference, RoEduNet 2010
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  • Pokorný, Miroslav
  • Šmíd, Jan
  • Kotzian, Jiří
  • Bencúr, Andrej
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http://linked.open...ain/vavai/riv/wos
  • 000290548400073
http://linked.open.../riv/zahajeniAkce
issn
  • 2068-1038
number of pages
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  • IEEE Conference Publishing Services
https://schema.org/isbn
  • 978-1-4244-7335-9
http://localhost/t...ganizacniJednotka
  • 27240
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