Memory-based pruning of deep neural networks for IoT devices applied to flood detection (2021)
- Authors:
- USP affiliated authors: NONATO, LUIS GUSTAVO - ICMC ; UEYAMA, JO - ICMC ; RANIERI, CAETANO MAZZONI - ICMC
- Unidade: ICMC
- DOI: 10.3390/s21227506
- Subjects: ENCHENTES URBANAS; REDES NEURAIS; INTERNET DAS COISAS
- Keywords: deep neural networks; semantic segmentation; random pruning; flood detection; user preference
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
- Licença: cc-by
-
ABNT
FERNANDES JUNIOR, Francisco Erivaldo et al. Memory-based pruning of deep neural networks for IoT devices applied to flood detection. Sensors, v. 21, n. 22, p. 1-18, 2021Tradução . . Disponível em: https://doi.org/10.3390/s21227506. Acesso em: 28 dez. 2025. -
APA
Fernandes Junior, F. E., Nonato, L. G., Ranieri, C. M., & Ueyama, J. (2021). Memory-based pruning of deep neural networks for IoT devices applied to flood detection. Sensors, 21( 22), 1-18. doi:10.3390/s21227506 -
NLM
Fernandes Junior FE, Nonato LG, Ranieri CM, Ueyama J. Memory-based pruning of deep neural networks for IoT devices applied to flood detection [Internet]. Sensors. 2021 ; 21( 22): 1-18.[citado 2025 dez. 28 ] Available from: https://doi.org/10.3390/s21227506 -
Vancouver
Fernandes Junior FE, Nonato LG, Ranieri CM, Ueyama J. Memory-based pruning of deep neural networks for IoT devices applied to flood detection [Internet]. Sensors. 2021 ; 21( 22): 1-18.[citado 2025 dez. 28 ] Available from: https://doi.org/10.3390/s21227506 - Using digital image processing to estimate the depth of urban streams
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Informações sobre o DOI: 10.3390/s21227506 (Fonte: oaDOI API)
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