Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/830EF95D-00E8-4B2B-AE34-656BBFD853DE | - |
| Αναγνωριστικό | https://doi.org/10.1109/TSP.2009.2021450 | - |
| Αναγνωριστικό | https://ieeexplore.ieee.org/document/4838901 | - |
| Γλώσσα | en | - |
| Μέγεθος | 12 pages | en |
| Τίτλος | Computationally efficient spatial interpolators based on spartan spatial random fields | en |
| Δημιουργός | Elogne Samuel N. | en |
| Δημιουργός | Christopoulos Dionysios | en |
| Δημιουργός | Χριστοπουλος Διονυσιος | el |
| Εκδότης | Institute of Electrical and Electronics Engineers | en |
| Περίληψη | This paper addresses the spatial interpolation of scattered data in d dimensions. The problem is approached using the theory of Spartan spatial random fields (SSRFs), focusing on a specific Gaussian SSRF, i.e., the fluctuation-gradient-curvature (FGC) model. A family of spatial interpolators (predictors) is formulated by maximizing the FGC-SSRF probability density function at each prediction point, conditioned by the data. An analytical expression for the general uniform bandwidth Spartan (GUBS) predictor is derived. The linear weights of this predictor involve weighted summations of kernel functions over the sample and prediction points. Approximations for the sums are obtained at the asymptotic limit of a dense sampling network, leading to simplified explicit expressions of the weights. An asymptotic locally adaptive Spartan (ALAS) predictor is defined by means of a kernel family that involves a tunable local parameter. The relevant equations are fully developed in d=2. Using simulated data in two dimensions, we show that the ALAS prediction accuracy is comparable to that of ordinary kriging (OK), which is an optimal spatial linear predictor (SOLP). The numerical complexity of the ALAS predictor increases linearly with the sample size, in contrast with the cubic dependence of OK. For large data sets, the ALAS predictor is shown to be orders of magnitude faster than OK at the cost of a slightly higher prediction dispersion. The performance of the ALAS predictor and OK are compared on a data set of rainfall measurements using cross validation measures. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2015-09-26 | - |
| Ημερομηνία Δημοσίευσης | 2009 | - |
| Θεματική Κατηγορία | Kernel | en |
| Θεματική Κατηγορία | Interpolation | el |
| Θεματική Κατηγορία | Scattering | en |
| Θεματική Κατηγορία | Probability density function | en |
| Θεματική Κατηγορία | Data analysis | en |
| Θεματική Κατηγορία | Bandwidth | en |
| Θεματική Κατηγορία | Sampling methods | en |
| Θεματική Κατηγορία | Equations | en |
| Θεματική Κατηγορία | Predictive models | en |
| Θεματική Κατηγορία | Accuracy | en |
| Βιβλιογραφική Αναφορά | D. T. Hristopulos and S. N. Elogne, " Computationally efficient spatial interpolators based on spartan spatial random fields," IEEE Trans. Sign. Proc., vol. 57, no.9, pp. 3475-3487, Sep. 2009. doi: 10.1109/TSP.2009.2021450 | en |
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