Gölz, Martin (2024)
Spatial inference via multiple hypothesis testing.
In: Science Talks, 9
doi: 10.1016/j.sctalk.2024.100313
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
The identification of the spatial regions of interesting, different, or anomalous signal behavior is a central task in many applications. Evidence about the signal state in different locations can be gathered by large-scale heterogeneous wireless sensor networks. Efficient extraction, exchange and fusion of the local information from the nodes are essential while operating in the congested radio frequency spectrum and to ensure long sensor battery lifetime. Our proposed spatial inference methods are based on multiple hypothesis testing. They allow to control the accuracy of the estimated areas of anomalous signal behavior in terms of false positives. All required statistical models are learned from the data, which makes our methods applicable to a wide range of practical problems.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Gölz, Martin |
| Art des Eintrags: | Bibliographie |
| Titel: | Spatial inference via multiple hypothesis testing |
| Sprache: | Englisch |
| Publikationsjahr: | März 2024 |
| Verlag: | Elsevier |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Science Talks |
| Jahrgang/Volume einer Zeitschrift: | 9 |
| DOI: | 10.1016/j.sctalk.2024.100313 |
| Kurzbeschreibung (Abstract): | The identification of the spatial regions of interesting, different, or anomalous signal behavior is a central task in many applications. Evidence about the signal state in different locations can be gathered by large-scale heterogeneous wireless sensor networks. Efficient extraction, exchange and fusion of the local information from the nodes are essential while operating in the congested radio frequency spectrum and to ensure long sensor battery lifetime. Our proposed spatial inference methods are based on multiple hypothesis testing. They allow to control the accuracy of the estimated areas of anomalous signal behavior in terms of false positives. All required statistical models are learned from the data, which makes our methods applicable to a wide range of practical problems. |
| Zusätzliche Informationen: | Art.No.: 100313 |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Signalverarbeitung |
| Hinterlegungsdatum: | 05 Mär 2024 15:39 |
| Letzte Änderung: | 23 Mai 2024 16:35 |
| PPN: | 518554104 |
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| Suche nach Titel in: | TUfind oder in Google |
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