Cargando…
An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity
Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and batter...
Autores principales: | Signoretti, Gabriel, Silva, Marianne, Andrade, Pedro, Silva, Ivanovitch, Sisinni, Emiliano, Ferrari, Paolo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8235329/ https://www.ncbi.nlm.nih.gov/pubmed/34204300 http://dx.doi.org/10.3390/s21124153 |
Ejemplares similares
-
TinyML
por: Warden, Pete
Publicado: (2019) -
A TinyML Soft-Sensor Approach for Low-Cost Detection and Monitoring of Vehicular Emissions
por: Andrade, Pedro, et al.
Publicado: (2022) -
DDD TinyML: A TinyML-Based Driver Drowsiness Detection Model Using Deep Learning
por: Alajlan, Norah N., et al.
Publicado: (2023) -
TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications
por: Alajlan, Norah N., et al.
Publicado: (2022) -
Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles
por: de Prado, Miguel, et al.
Publicado: (2021)