Výsledky vyhľadávania - "Convolutional denoising autoencoder"

Noise Reduction in Photoplethysmography Signals using a Convolutional Denoising Autoencoder with Unconventional Training Scheme Autor Mohagheghian, Fahimeh, Han, Dong, Ghetia, Om, Peitzsch, Andrew, Nishita, Nishat, Nejad, Mahdi Pirayesh Shirazi, Ding, Eric Y., Noorishirazi, Kamran, Hamel, Alexander, Otabil, Edith Mensah, DiMezza, Danielle, Dickson, Emily L., Tran, Khanh-Van, McManus, David D., Chon, Ki H.

“…Objective : We propose an efficient approach based on a convolutional denoising autoencoder (CDA…”
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Automatic Eyeblink Artifact Removal from Single Channel EEG Signals Using One-Dimensional Convolutional Denoising Autoencoder Autor Acharjee, Raktim, Ahamed, Shaik Rafi

“… In this work, we proposed a one-dimensional Convolutional Denoising Autoencoder (CDAE) architecture to efficiently remove the eyeblink artifacts from the single channel EEG signals…”
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Atrial fibrillation detection on reconstructed photoplethysmography signals collected from a smartwatch using a denoising autoencoder Autor Mohagheghian, Fahimeh, Han, Dong, Ghetia, Om, Chen, Darren, Peitzsch, Andrew, Nishita, Nishat, Ding, Eric Y., Mensah Otabil, Edith, Noorishirazi, Kamran, Hamel, Alexander, Dickson, Emily L., DiMezza, Danielle, Tran, Khanh-Van, McManus, David D., Chon, Ki H.

“…; however, the subjects were mostly in clinics or controlled settings with data collection lasting several minutes to at most several hours with minimal MNA…”
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Classification of massive noisy image using auto-encoders and convolutional neural network Autor Roy, Sudipta Singha, Ahmed, Mahtab, Akhand, M. A. H.

“… Most of the research works are conducted over pre-possessed image data in the lab. But, these methods fail in the real world scenario as most of the time the image required to classify is subject to noise and other disfigurement…”
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DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices Autor Jessica Torres Soto, Ashley, Euan

“…Wearable devices enable theoretically continuous, longitudinal monitoring of physiological measurements like step count, energy expenditure, and heart rate…”
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