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- W1520233742 abstract "Electrical Impedance Tomography (EIT) calculates internal conductivity from surface measurements;image reconstruction is most commonly formulated as an inverse problem using regularization techniques. Regularization adds prior information to adress the solution ill-conditioning. This paper presents a novel approach to understand and quantify this information. We ask: how many bits of information (in the Shannon sense) do we get from an EIT data frame. We define the term information in measurements (IM) as the: decrease in uncertainty about the contents of a medium, due to a set of measurements. Before the measurements, we know the prior information (inter-class model, q). The measured data tell us about the medium (which, corrupted by noise, gives the intra-class model, p). The measurement information is given by the relative entropy (or Kullback-Leibler divergence)." @default.
- W1520233742 created "2016-06-24" @default.
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- W1520233742 date "2007-10-26" @default.
- W1520233742 modified "2023-09-25" @default.
- W1520233742 title "Information Content of EIT Measurements" @default.
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- W1520233742 doi "https://doi.org/10.1007/978-3-540-73841-1_94" @default.
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