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arXiv:1603.01446math.AT

Sheaves are the canonical datastructure for sensor integration

Michael Robinson

Abstract

A sensor integration framework should be sufficiently general to accurately represent all information sources, and also be able to summarize information in a faithful way that emphasizes important, actionable information. Few approaches adequately address these two discordant requirements. The purpose of this expository paper is to explain why sheaves are the canonical data structure for sensor integration and how the mathematics of sheaves satisfies our two requirements. We outline some of the powerful inferential tools that are not available to other representational frameworks.

arXiv comments
42 pages; 18 figures
Journal reference
DOI
Subjects
math.ATAlgebraic Topology

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Linked by 3 identifiable commenters in 3 comments across 3 threads, from Feb 2017 to Dec 2022. Found in 1 HN submission from Feb 2017 to Feb 2017. Open submission discussions.

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Discussed in comments3 threads

  1. Introduction to Homotopy Type Theory 1 comment

    • Michael Robinson is the person I know working on this topic — and who I borrowed the phrasing from. https://www.drmichaelrobinson.net/sheaftutorial/ https://arxiv.org/abs/1603.01446

      zmgsabstView on HNParent context

  2. Local Topological Analysis of Complex Systems (2017) [video] 1 comment

    • Thanks for posting the sheaf talks a while back, been getting into em. Also found this he made https://arxiv.org/abs/1603.01446

      pizzaView on HNParent context

  3. Sheaf Theory: The Mathematics of Data Fusion (audio starts at 10:44) [video] 1 comment

    • A sheaf is a combinatorial, high-dimensional data structure on a topological space. You can use it to stitch together images, spatio-temporal target tracking, inference, data fusion, IoT time-series, continuous functions -- all kinds of things. Crazy cool stuff! Applied sheaf…

      espeedView on HNParent context

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