As Internet of Things (loT) devices become both cheaper and more powerful, researchers are increasingly finding solutions to their scientific curiosities both financially and com- putationally feasible. When operating with restricted power or communications budgets, however, devices can only send highly- compressed data. Such circumstances are common for devices placed away from electric grids that can only communicate via satellite, a situation particularly plausible for environmental sensor networks. These restrictions can be further complicated by potential variability in the communications budget, for ex-ample a solar-powered device needing to expend less energy when transmitting data on a cloudy day. We propose a novel, topology-based, lossy compression method well-equipped for these restrictive yet variable circumstances. This technique, Topological Signal Compression, allows sending compressed sig-nals that utilize the entirety of a variable communications budget. To demonstrate our algorithm's capabilities, we per-form entropy calculations as well as a classification exercise on increasingly topologically simplified signals from the Free- Spoken Digit Dataset and explore the stability of the resulting performance against common baselines.


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    Titel :

    Topological Simplification of Signals for Inference and Approximate Reconstruction


    Beteiligte:
    Koplik, Gary (Autor:in) / Borggren, Nathan (Autor:in) / Voisin, Sam (Autor:in) / Angeloro, Gabrielle (Autor:in) / Hineman, Jay (Autor:in) / Johnson, Tessa (Autor:in) / Bendich, Paul (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    04.03.2023


    Format / Umfang :

    7037330 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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