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

Sensors

João G. Carvalho¹  Jorge Cabral¹  Sérgio Branco¹  Marco S. Reis²  Nuno V. Lopes³ 

[1] Algoritmi Center, University of Minho, 4800-058 Guimarães, Portugal;CIEPQPF, Department of Chemical Engineering, University of Coimbra, Rua Sílvio Lima, Pólo II—Pinhal de Marrocos, 3030-790 Coimbra, Portugal;DTx—Digital Transformation CoLab, University of Minho, 4800-058 Guimarães, Portugal;

关键词: persistent homology;    topological data analysis;    embedded intelligence;    intelligent resource-scarce embedded systems;    TinyML;   

DOI  :  10.3390/s22103657

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown