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0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems

Branco, Sérgio ; Carvalho, João G ; Reis, Marco S ; Lopes, Nuno V ; Cabral, Jorge

Sensors (Basel, Switzerland), 2022-05, Vol.22 (10), p.3657 [Periódico revisado por pares]

Switzerland: MDPI AG

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  • Título:
    0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems
  • Autor: Branco, Sérgio ; Carvalho, João G ; Reis, Marco S ; Lopes, Nuno V ; Cabral, Jorge
  • Assuntos: Algorithms ; Complexity ; Data analysis ; Datasets ; Dimensional analysis ; embedded intelligence ; Embedded systems ; Homology ; Information management ; intelligent resource-scarce embedded systems ; persistent homology ; Software ; TinyML ; topological data analysis
  • É parte de: Sensors (Basel, Switzerland), 2022-05, Vol.22 (10), p.3657
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: Persistent Homology (PH) analysis is a powerful tool for understanding many relevant topological features from a given dataset. PH allows finding clusters, noise, and relevant connections in the dataset. Therefore, it can provide a better view of the problem and a way of perceiving if a given dataset is equal to another, if a given sample is relevant, and how the samples occupy the feature space. However, PH involves reducing the problem to its simplicial complex space, which is computationally expensive and implementing PH in such Resource-Scarce Embedded Systems (RSES) is an essential add-on for them. However, due to its complexity, implementing PH in such tiny devices is considerably complicated due to the lack of memory and processing power. The following paper shows the implementation of 0-Dimensional Persistent Homology Analysis in a set of well-known RSES, using a technique that reduces the memory footprint and processing power needs of the 0-Dimensional PH algorithm. The results are positive and show that RSES can be equipped with this real-time data analysis tool.
  • Editor: Switzerland: MDPI AG
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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