Search Results - Convolutional Denoising Autoencoder~

Conditioned fully convolutional denoising autoencoder for multi-target NILM

Authors: García, Diego Pérez, Daniel Papapetrou, Panagiotis et al.

Source: Neural Computing & Applications. 37(17):10491-10505

Subject Terms: Non-intrusive Load Monitoring, Energy Efficiency, Deep Convolutional Neural Networks, Interpretability, Multi-target NILM models, data- och systemvetenskap, Computer and Systems Sciences

File Description: print

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-237967
https://doi.org/10.1007/s00521-024-10552-0

Estimating Heart Rate Variability from Wrist-Worn Photoplethysmography Devices in Daily Activities: A Preliminary Convolutional Denoising Autoencoder Approach

Authors: Gianluca Rho Nicola Carbonaro Marco Laurino et al.

Source: 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). :207-212

Subject Terms: Wrist , Convolution , Time series analysis , Noise reduction , Autoencoders , Electrocardiography , Photoplethysmography , Reliability , Heart rate variability , Biomedical monitoring

File Description: application/pdf

Robust remaining useful life prediction of lithium-ion battery with convolutional denoising autoencoder

Authors: Asri Rizki Yuliani Hilman Ferdinandus Pardede Ade Ramdan et al.

Source: Journal of Mechatronics, Electrical Power, and Vehicular Technology, Vol 15, Iss 1, Pp 93-104 (2024)

Subject Terms: neural network, remaining useful life (rul), denoising autoencoder (dae), lithium-ion (li-ion) battery, TJ1-1570, system robustness, Electrical engineering. Electronics. Nuclear engineering, Mechanical engineering and machinery, 7. Clean energy, TK1-9971

Access URL: https://doaj.org/article/045065a2412848b69e82bedb7c7174af

Vector Hydrophone DOA Estimation Method Based on Complex⁃Valued Convolutional Denoising Autoencoder Denoising

Authors: Jing REN Xiuhui TAN Yanping BAI et al.

Source: journal of test and measurement technology. 39:475-482

Fault Diagnosis of Rolling Bearing Using Convolutional Denoising Autoencoder and Siamese Neural Network With Small Sample

Authors: Xufeng Zhao Ying Chen Mengshu Yang et al.

Source: IEEE Internet of Things Journal. 12:5233-5244

Overcomplete graph convolutional denoising autoencoder for noisy skeleton action recognition

Authors: Jiajun Guo Qingge Ji Guangwei Shan

Source: IET Image Processing, Vol 18, Iss 1, Pp 233-246 (2024)

Subject Terms: QA76.75-76.765, convolutional neural nets, signal denoising, graph theory, Photography, 0202 electrical engineering, electronic engineering, information engineering, Computer software, 02 engineering and technology, TR1-1050, computer vision

Access URL: https://doaj.org/article/00b9628a0619472cb07d01a0acb202f8

Elimination of Random Mixed Noise in ECG Using Convolutional Denoising Autoencoder With Transformer Encoder

Authors: Meng Chen Yongjian Li Liting Zhang et al.

Source: IEEE Journal of Biomedical and Health Informatics. 28:1993-2004

Subject Terms: Electrocardiography, Exercise Test, Humans, Signal Processing, Computer-Assisted, Signal-To-Noise Ratio, Artifacts, Algorithms

Access URL: https://pubmed.ncbi.nlm.nih.gov/38241105

Streamlined convolutional denoising autoencoder vibrothermography: Enhanced detection of bonding interface defects within CFRP-steel adhesive structures

Authors: Yu-Bin Zhang Chang-Hang Xu Peng-Qian Liu

Source: 2024 IEEE Far East NDT New Technology & Application Forum (FENDT). :214-218

Physical Layer Authentication for Industrial Control Based on Convolutional Denoising Autoencoder

Authors: Yanru Chen Haoyu He Shengjie Liu et al.

Source: IEEE Internet of Things Journal. 11:15633-15641

Noise Reduction in Photoplethysmography Signals Using a Convolutional Denoising Autoencoder With Unconventional Training Scheme

Authors: Fahimeh Mohagheghian Dong Han Om Ghetia et al.

Source: IEEE Transactions on Biomedical Engineering. 71:456-466

Subject Terms: Motion, Heart Rate, Atrial Fibrillation, Humans, Signal Processing, Computer-Assisted, Photoplethysmography, Artifacts, Algorithms, Monitoring, Physiologic

Access URL: https://pubmed.ncbi.nlm.nih.gov/37682653

Adaptive Genghis Khan Shark based convolutional denoising autoencoder for quality enhancement of satellite images

Authors: M. Rajalakshmi R. S. Sankara Subramanian S. Tamilselvan et al.

Source: International Journal of Information Technology.

A Convolutional Denoising Autoencoder Approach for Estimating Heart Rate Variability From Wrist Photoplethysmography Devices in Daily Activities

Authors: Gianluca Rho Nicola Carbonaro Marco Laurino et al.

Automatic Eyeblink Artifact Removal from Single Channel EEG Signals Using One-Dimensional Convolutional Denoising Autoencoder

Authors: Raktim Acharjee Shaik Rafi Ahamed

Source: 2024 International Conference on Computer, Electrical & Communication Engineering (ICCECE). :1-7

Automated computer-aided design of cranial implants using a deep volumetric convolutional denoising autoencoder

Authors: Morais, Ana Egger, Jan Alves, Victor et al.

Contributors: Morais, Ana Egger, Jan Alves, Victor et al.

Subject Terms: 3D shape completion, Computer-aided design (CAD), Cranial implant, Deep learning, Denoising autoencoder, Medical imaging

File Description: application/pdf

Relation: 978-3-030-16186-6; 2194-5357

Availability: https://hdl.handle.net/1822/71391

Galaxy Model Subtraction with a Convolutional Denoising Autoencoder

Authors: Liu, Rongrong Peng, Eric W. Wang, Kaixiang et al.

Subject Terms: Astrophysics of Galaxies, Instrumentation and Methods for Astrophysics

Access URL: http://arxiv.org/abs/2510.04957

Radar modulation recognition based on convolutional denoising autoencoder

Authors: Jie Yang Yaoxiang Hui Junpeng Zhu

Source: 2023 6th International Conference on Artificial Intelligence and Pattern Recognition (AIPR). :714-719

TumorAwareNet: Deep representation learning with attention based sparse convolutional denoising autoencoder for brain tumor recognition

Authors: Jyostna Devi Bodapati Bharadwaj Bagepalli Balaji

Source: Multimedia Tools and Applications. 83:22099-22117

Detection of Freezing of Gait Using Unsupervised Convolutional Denoising Autoencoder

Authors: Mohd Halim Mohd Noor Amril Nazir Mohd Nadhir Ab Wahab et al.

Source: IEEE Access, Vol 9, Pp 115700-115709 (2021)

Subject Terms: Artificial intelligence, Biomedical Engineering, Gait Recognition, Health Professions, Physical Therapy, Sports Therapy and Rehabilitation, 02 engineering and technology, unsupervised learning, FOS: Medical engineering, Pattern recognition (psychology), freezing of gait, Engineering, denoising autoencoder, Health Sciences, Machine learning, Image (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Embedded system, Gait, Sensory Feedback, Gait Analysis and Fall Prevention in Elderly, Deep learning, Wearable computer, Autoencoder, Computer science, Dimensionality reduction, TK1-9971, 3. Good health, Gait Recognition for Human Identification, Thresholding, Physical medicine and rehabilitation, Analysis of Electromyography Signal Processing, Physical Sciences, Parkinson's disease, Medicine, Electrical engineering. Electronics. Nuclear engineering, Gait Analysis

Access URL: https://ieeexplore.ieee.org/ielx7/6287639/6514899/09514558.pdf
https://doaj.org/article/34fba6708ae448288f041a3d55b928c7
https://zuscholars.zu.ac.ae/cgi/viewcontent.cgi?article=5464&context=works
https://zuscholars.zu.ac.ae/works/4465/
https://dblp.uni-trier.de/db/journals/access/access9.html#NoorNWO21

Multi-lingual Handwriting Recovery Framework based on Convolutional Denoising Autoencoder with Attention model

Authors: Besma Rabhi Abdelkarim Elbaati Houcine Boubaker et al.

Source: Multimedia Tools and Applications. 83:22295-22326

Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Denoising Autoencoder and Deep Convolutional Neural Network

Authors: Zhiyu Qu Wenyang Wang Changbo Hou et al.

Source: IEEE Access, Vol 7, Pp 112339-112347 (2019)

Subject Terms: Cohen class time frequency distribution, deep convolutional neural network, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, Radar signal recognition, convolutional denoising autoencoder, TK1-9971

Access URL: https://ieeexplore.ieee.org/ielx7/6287639/8600701/08798607.pdf
https://doaj.org/article/fa33e26e05ce42fbade007ceabff40d1
https://ieeexplore.ieee.org/document/8798607
https://dblp.uni-trier.de/db/journals/access/access7.html#QuWHH19
https://doaj.org/article/fa33e26e05ce42fbade007ceabff40d1
https://doi.org/10.1109/ACCESS.2019.2935247