0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems
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 datase...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/10/3657 |
| _version_ | 1827666695378960384 |
|---|---|
| author | Sérgio Branco João G. Carvalho Marco S. Reis Nuno V. Lopes Jorge Cabral |
| author_facet | Sérgio Branco João G. Carvalho Marco S. Reis Nuno V. Lopes Jorge Cabral |
| author_sort | Sérgio Branco |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:55:19Z |
| format | Article |
| id | doaj.art-c86031f256494fb596f08c6706f56cd8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:55:19Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c86031f256494fb596f08c6706f56cd82023-11-23T12:58:56ZengMDPI AGSensors1424-82202022-05-012210365710.3390/s221036570-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded SystemsSérgio Branco0João G. Carvalho1Marco S. Reis2Nuno V. Lopes3Jorge Cabral4Algoritmi Center, University of Minho, 4800-058 Guimarães, PortugalAlgoritmi Center, University of Minho, 4800-058 Guimarães, PortugalCIEPQPF, Department of Chemical Engineering, University of Coimbra, Rua Sílvio Lima, Pólo II—Pinhal de Marrocos, 3030-790 Coimbra, PortugalDTx—Digital Transformation CoLab, University of Minho, 4800-058 Guimarães, PortugalAlgoritmi Center, University of Minho, 4800-058 Guimarães, PortugalPersistent 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.https://www.mdpi.com/1424-8220/22/10/3657persistent homologytopological data analysisembedded intelligenceintelligent resource-scarce embedded systemsTinyML |
| spellingShingle | Sérgio Branco João G. Carvalho Marco S. Reis Nuno V. Lopes Jorge Cabral 0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems Sensors persistent homology topological data analysis embedded intelligence intelligent resource-scarce embedded systems TinyML |
| title | 0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems |
| title_full | 0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems |
| title_fullStr | 0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems |
| title_full_unstemmed | 0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems |
| title_short | 0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems |
| title_sort | 0 dimensional persistent homology analysis implementation in resource scarce embedded systems |
| topic | persistent homology topological data analysis embedded intelligence intelligent resource-scarce embedded systems TinyML |
| url | https://www.mdpi.com/1424-8220/22/10/3657 |
| work_keys_str_mv | AT sergiobranco 0dimensionalpersistenthomologyanalysisimplementationinresourcescarceembeddedsystems AT joaogcarvalho 0dimensionalpersistenthomologyanalysisimplementationinresourcescarceembeddedsystems AT marcosreis 0dimensionalpersistenthomologyanalysisimplementationinresourcescarceembeddedsystems AT nunovlopes 0dimensionalpersistenthomologyanalysisimplementationinresourcescarceembeddedsystems AT jorgecabral 0dimensionalpersistenthomologyanalysisimplementationinresourcescarceembeddedsystems |