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Version: publishedVersion × Subject: Separation ×

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Now showing 1 - 3 of 3
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    Phase transition and anomalous low temperature ferromagnetic phase in Pr 0.6Sr 0.4MnO 3 single crystals
    (New York, NY : Springer Science + Business Media B.V., 2009) Rößler, S.; Harikrishnan, S.; Naveen Kumar, C.M.; Bhat, H.L.; Elizabeth, S.; Rößler, U.K.; Steglich, F.; Wirth, S.
    We report on the magnetic and electrical properties of Pr 0.6Sr 0.4MnO 3 single crystals. This compound undergoes a continuous paramagnetic-ferromagnetic transition with a Curie temperature T C301 K and a first-order structural transition at T S64 K. At T S, the magnetic susceptibility exhibits an abrupt jump, and a corresponding small hump is seen in the resistivity. The critical behavior of the static magnetization and the temperature dependence of the resistivity are consistent with the behavior expected for a nearly isotropic ferromagnet with short-range exchange belonging to the Heisenberg universality class. The magnetization (M-H) curves below T S are anomalous in that the virgin curve lies outside the subsequent M-H loops. The hysteretic structural transition at T S as well as the irreversible magnetization processes below T S can be explained by phase separation between a high-temperature orthorhombic and a low-temperature monoclinic ferromagnetic phase.
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    Adaptive smoothing of digital images: The R package adimpro
    (Los Angeles, Calif. : UCLA, Dept. of Statistics, 2007) Polzehl, J.; Tabelow, K.
    Digital imaging has become omnipresent in the past years with a bulk of applications ranging from medical imaging to photography. When pushing the limits of resolution and sensitivity noise has ever been a major issue. However, commonly used non-adaptive filters can do noise reduction at the cost of a reduced effective spatial resolution only. Here we present a new package adimpro for R, which implements the propagationseparation approach by (Polzehl arid Spokoiriy 2006) for smoothing digital images. This method naturally adapts to different structures of different size in the image and thus avoids oversmoothing edges and fine structures. We extend the method for imaging data with spatial correlation. Furthermore we show how the estimation of the dependence between variance and mean value can be included. We illustrate the use of the package through some examples.
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    A new perspective on the propagation-separation approach: Taking advantage of the propagation condition
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2013) Becker, Saskia; Mathé, Peter
    The Propagation-Separation approach is an iterative procedure for pointwise estimation of local constant and local polynomial functions. The estimator is defined as a weighted mean of the observations with data-driven weights. Within homogeneous regions it ensures a similar behavior as non-adaptive smoothing (propagation), while avoiding smoothing among distinct regions (separation). In order to enable a proof of stability of estimates, the authors of the original study introduced an additional memory step aggregating the estimators of the successive iteration steps. Here, we study theoretical properties of the simplified algorithm, where the memory step is omitted. In particular, we introduce a new strategy for the choice of the adaptation parameter yielding propagation and stability for local constant functions with sharp discontinuities