Demystifying Band-Limited Extrapolation
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU140729" target="_blank" >RIV/00216305:26220/21:PU140729 - isvavai.cz</a>
Výsledek na webu
<a href="https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2021_sbornik_1.pdf" target="_blank" >https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2021_sbornik_1.pdf</a>
DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Demystifying Band-Limited Extrapolation
Popis výsledku v původním jazyce
Extrapolation of band-limited signals gained scientific attention over the last 60 years. The famous methods: Gerchberg-Papoulis algorithm, Prolate spheroidal wave functions (PSWFs), and sinc interpolation—they all promise excellent results. But when it comes to their practical implementation, users may find themselves struggling with many unanswered questions. Especially PSWFs became viewed as mysterious. They are hard to compute and even harder to apply. In theory they promise excellent extrapolation capabilities—something which is contrary to our intuition. This paradox is resolved if we admit that the real-world data contain noise. In this paper we review the above-mentioned methods and try to provide a brief assessment of their capabilities by considering the effects of noise and the length of signal observation.
Název v anglickém jazyce
Demystifying Band-Limited Extrapolation
Popis výsledku anglicky
Extrapolation of band-limited signals gained scientific attention over the last 60 years. The famous methods: Gerchberg-Papoulis algorithm, Prolate spheroidal wave functions (PSWFs), and sinc interpolation—they all promise excellent results. But when it comes to their practical implementation, users may find themselves struggling with many unanswered questions. Especially PSWFs became viewed as mysterious. They are hard to compute and even harder to apply. In theory they promise excellent extrapolation capabilities—something which is contrary to our intuition. This paradox is resolved if we admit that the real-world data contain noise. In this paper we review the above-mentioned methods and try to provide a brief assessment of their capabilities by considering the effects of noise and the length of signal observation.
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
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OECD FORD obor
10102 - Applied mathematics
Návaznosti výsledku
Projekt
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Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2021
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů