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Refinado por: assunto: Chemistry remover assunto: Chemistry, Analytical remover assunto: Feature Compression remover
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1
BBNet: A Novel Convolutional Neural Network Structure in Edge-Cloud Collaborative Inference
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BBNet: A Novel Convolutional Neural Network Structure in Edge-Cloud Collaborative Inference

Zhou, Hongbo ; Zhang, Weiwei ; Wang, Chengwei ; Ma, Xin ; Yu, Haoran

Sensors (Basel, Switzerland), 2021-06, Vol.21 (13), p.4494 [Periódico revisado por pares]

MDPI

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2
Fast and Accurate Lane Detection via Graph Structure and Disentangled Representation Learning
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Fast and Accurate Lane Detection via Graph Structure and Disentangled Representation Learning

He, Yulin ; Chen, Wei ; Li, Chen ; Luo, Xin ; Huang, Libo

Sensors (Basel, Switzerland), 2021-07, Vol.21 (14), p.4657 [Periódico revisado por pares]

Basel: MDPI AG

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3
An Efficient Compression Method for Lightning Electromagnetic Pulse Signal Based on Convolutional Neural Network and Autoencoder
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An Efficient Compression Method for Lightning Electromagnetic Pulse Signal Based on Convolutional Neural Network and Autoencoder

Guo, Jinhua ; Wang, Jiaquan ; Xiao, Fang ; Zhou, Xiao ; Liu, Yongsheng ; Ma, Qiming

Sensors (Basel, Switzerland), 2023-04, Vol.23 (8), p.3908 [Periódico revisado por pares]

Switzerland: MDPI AG

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4
On the use of a self organising map as feature compressor in the building of calibration models: Application to FTIR-spectrophotometry
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On the use of a self organising map as feature compressor in the building of calibration models: Application to FTIR-spectrophotometry

Llobet, Eduard ; Anaimi, Mohamed ; Pruñonosa, Alba ; Gras, Eloi

Sensors and actuators. B, Chemical, 2011-11, Vol.158 (1), p.252-258 [Periódico revisado por pares]

Elsevier B.V

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5
Feature selection versus feature compression in the building of calibration models from FTIR-spectrophotometry datasets
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Feature selection versus feature compression in the building of calibration models from FTIR-spectrophotometry datasets

Vergara, Alexander ; Llobet, Eduard

Talanta (Oxford), 2012-01, Vol.88, p.95-103 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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