Search Results - convolutional denoising autoencoder-convolutional neural network*

Within‐project and cross‐project just‐in‐time defect prediction based on denoising autoencoder and convolutional neural network.

Authors: Zhu, Kun Zhang, Nana Ying, Shi et al.

Source: IET Software (Wiley-Blackwell). Jun2020, Vol. 14 Issue 3, p185-195. 11p.

Denoising and Baseline Correction Methods for Raman Spectroscopy Based on Convolutional Autoencoder: A Unified Solution.

Authors: Han, Ming Dang, Yu Han, Jianda

Source: Sensors (14248220); May2024, Vol. 24 Issue 10, p3161, 15p

Subject Terms: RAMAN spectroscopy, CONVOLUTIONAL neural networks, IMAGE denoising, NOISE control, SIGNAL processing

A denoising semi-supervised deep learning model for remaining useful life prediction of turbofan engine degradation.

Authors: Wang, Youming Wang, Yue

Source: Applied Intelligence; Oct2023, Vol. 53 Issue 19, p22682-22699, 18p

Subject Terms: REMAINING useful life, DEEP learning, CONVOLUTIONAL neural networks, TURBOFAN engines, SUPERVISED learning, STANDARD deviations, FEATURE extraction

Seismic Random Noise Attenuation Using a Tied-Weights Autoencoder Neural Network

Authors: Huailai Zhou Yangqin Guo Ke Guo

Source: Minerals, Vol 11, Iss 10, p 1089 (2021)

Subject Terms: autoencoder convolutional neural network, noise suppression, seismic data, tied weights, self-supervised learning, Mineralogy, QE351-399.2

Relation: https://www.mdpi.com/2075-163X/11/10/1089; https://doaj.org/toc/2075-163X; https://doaj.org/article/cbdef19ae3d04a5c823f633e54853862

Availability: https://doi.org/10.3390/min11101089
https://doaj.org/article/cbdef19ae3d04a5c823f633e54853862

Effective Denoising Architecture for Handling Multiple Noises.

Authors: Na Hyoun Kim Namgyu Kim

Source: Computer Systems Science & Engineering; 2023, Vol. 44 Issue 3, p2667-2682, 16p

Subject Terms: OBJECT tracking (Computer vision), IMAGE quality analysis, SIGNAL denoising, COMPUTER architecture, CONVOLUTIONAL neural networks

Classification of massive noisy image using auto-encoders and convolutional neural network

Authors: Mahtab Ahmed Sudipta Singha Roy M. A. H. Akhand

Source: 2017 8th International Conference on Information Technology (ICIT). :971-979

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://ieeexplore.ieee.org/document/8079976/
http://ieeexplore.ieee.org/document/8079976

Signal speech reconstruction and noise removal using convolutional denoising audioencoders with neural deep learning

Authors: Houda Abouzid Otman Chakkor Oscar Gabriel Reyes et al.

Source: Analog Integrated Circuits and Signal Processing. 100:501-512

Subject Terms: 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0305 other medical science

Access URL: https://link.springer.com/article/10.1007/s10470-019-01446-6
https://jglobal.jst.go.jp/detail?JGLOBAL_ID=201902267377708548

Dual Autoencoder Network with Separable Convolutional Layers for Denoising and Deblurring Images.

Authors: Solovyeva, Elena Abdullah, Ali

Source: Journal of Imaging; Sep2022, Vol. 8 Issue 9, pN.PAG-N.PAG, 22p

Hybrid deep autoencoder network based adaptive cross guided bilateral filter for motion artifacts correction and denoising from MRI.

Authors: Samuel, Shiju Ochawar, Rohini S. Rukmini, M.S.S.

Source: Imaging Science Journal. Feb2024, Vol. 72 Issue 1, p76-91. 16p.

Subject Terms: *HYBRID systems, *MOTION, *OPTIMIZATION algorithms, *MAGNETIC resonance imaging, *MEDICAL artifacts, *SIGNAL-to-noise ratio

Signal speech reconstruction and noise removal using convolutional denoising audioencoders with neural deep learning.

Authors: Abouzid, Houda Chakkor, Otman Reyes, Oscar Gabriel et al.

Source: Analog Integrated Circuits & Signal Processing; Sep2019, Vol. 100 Issue 3, p501-512, 12p

Subject Terms: DEEP learning, AUTOMATIC speech recognition, SIGNAL reconstruction

Deep stacked denoising autoencoder for unsupervised anomaly detection in video surveillance.

Authors: Roka, Sanjay Diwakar, Manoj

Source: Journal of Electronic Imaging; May/Jun2023, Vol. 32 Issue 3, p33015-033015-23, 1p

Subject Terms: VIDEO surveillance, ANOMALY detection (Computer security), IMAGE denoising, OPTICAL flow, CONVOLUTIONAL neural networks, DEEP learning

About the Application of Autoencoders For Visual Defect Detection

Authors: Rádli, Richárd Czúni, László Skala, Václav

Contributors: Rádli, Richárd Czúni, László Skala, Václav

Subject Terms: neuronová síť autoencoderu, konvoluční neuronová síť, detekce defektů, detekce anomálií bez dozoru, autoencoder neural network, convolutional neural network, defect detection, unsupervised anomaly detection

File Description: 8 s.; application/pdf

Relation: 2464–4625(CD/DVD); http://hdl.handle.net/11025/45023; https://doi.org/10.24132/CSRN.2021.3101.20

Availability: http://hdl.handle.net/11025/45023
https://doi.org/10.24132/CSRN.2021.3101.20

A Comprehensive Survey of ECG Signal Denoising Techniques, Challenges and Novel Trends.

Authors: Saad, Maryam El-Rabaie, El-Sayed Mahmoud Saber, Mohamed et al.

Source: Delta University Scientific Journal; 2024 Special Issue, Vol. 7, p291-308, 18p

Subject Terms: HILBERT-Huang transform, SIGNAL denoising, WAVELET transforms, DEEP learning, ELECTRIC lines

A Review of Deep Learning Approaches Using ECG Signal Analysis

Authors: Gupta, Sandeep

Contributors: Gupta, Sandeep

Subject Terms: Electrocardiogram (ECG), Signal Processing, Transformer, Autoencoder, ECG Denoising, Arrhythmia Detection, Wearable Health Devices, Cardiovascular Disease (CVD), Feature Extraction, Biomedical Signal Analysis

Relation: https://zenodo.org/records/16784368; oai:zenodo.org:16784368; https://doi.org/10.5281/zenodo.16784368

Availability: https://doi.org/10.5281/zenodo.16784368
https://zenodo.org/records/16784368

Commonality Autoencoder: Learning Common Features for Change Detection From Heterogeneous Images.

Authors: Wu, Yue Li, Jiaheng Yuan, Yongzhe et al.

Source: IEEE Transactions on Neural Networks & Learning Systems; Sep2022, Vol. 33 Issue 9, p4257-4270, 14p

Subject Terms: SYNTHETIC apertures, ARTIFICIAL neural networks, SYNTHETIC aperture radar, FEATURE extraction, IMAGE representation

Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models.

Authors: Bond-Taylor, Sam Leach, Adam Long, Yang et al.

Source: IEEE Transactions on Pattern Analysis & Machine Intelligence; Nov2022, Vol. 44 Issue 11, p7327-7347, 21p

Subject Terms: ARTIFICIAL neural networks, AUTOREGRESSIVE models, PROBABILISTIC generative models, GENERATIVE adversarial networks

CCpos: WiFi Fingerprint Indoor Positioning System Based on CDAE-CNN.

Authors: Qin, Feng Zuo, Tao Wang, Xing et al.

Source: Sensors (14248220); 2/15/2021, Vol. 21 Issue 4, p1114-1114, 1p

Subject Terms: INDOOR positioning systems, CONVOLUTIONAL neural networks, K-means clustering, NANOPOSITIONING systems

Signal‐to‐noise ratio enhancement for Raman spectra based on optimized Raman spectrometer and convolutional denoising autoencoder.

Authors: Fan, Xian‐guang Zeng, Yingjie Zhi, Yu‐Liang et al.

Source: Journal of Raman Spectroscopy; Apr2021, Vol. 52 Issue 4, p890-900, 11p

Subject Terms: RAMAN spectroscopy, SIGNAL-to-noise ratio, IMAGE denoising, RAMAN lasers, SPECTROMETERS

Practical Convolutional Neural Networks : Implement Advanced Deep Learning Models Using Python

Authors: Mohit Sewak Md. Rezaul Karim Pradeep Pujari

Resource Type: eBook.

Subjects: Computer vision, Neural networks (Computer science), Machine learning, Python (Computer program language)

Categories: COMPUTERS / Image Processing, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Information Technology

A Deep Learning Approach for Automatic Seizure Detection in Children With Epilepsy.

Authors: Abdelhameed, Ahmed Bayoumi, Magdy

Source: Frontiers in Computational Neuroscience; 4/8/2021, Vol. 15, pN.PAG-N.PAG, 15p

Subject Terms: CHILDHOOD epilepsy, DEEP learning, SUPERVISED learning, EPILEPSY, ELECTROENCEPHALOGRAPHY, BIOMEDICAL signal processing, CHILDREN'S hospitals, SHORT-term memory, DIAGNOSIS of epilepsy

Geographic Terms: BOSTON (Mass.)

Company/Entity: MASSACHUSETTS Institute of Technology