A TinyML Deep Learning Approach for Indoor Tracking of Assets.

Avellaneda D, Mendez D, Fortino G

DOI: 10.3390/s23031542
Published: 2023 Jan 31
Container: Sensors (Basel, Switzerland)
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3390/s23031542,
  title = {A TinyML Deep Learning Approach for Indoor Tracking of Assets.},
  author = {Avellaneda D and Mendez D and Fortino G},
  year = {2023},
  journal = {Sensors (Basel, Switzerland)},
  doi = {10.3390/s23031542},
  url = {https://doi.org/10.3390/s23031542}
}

RIS

TY  - JOUR
TI  - A TinyML Deep Learning Approach for Indoor Tracking of Assets.
AU  - Avellaneda D
AU  - Mendez D
AU  - Fortino G
PY  - 2023
JO  - Sensors (Basel, Switzerland)
DO  - 10.3390/s23031542
UR  - https://doi.org/10.3390/s23031542
ER  -

APA

D, A., D, M., & G, F. (2023). A TinyML Deep Learning Approach for Indoor Tracking of Assets.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s23031542

Source records

pubmed · retrieved 2026-11-10T08:11:25.140Z