Search Results - sparse deep convolutional autoencoder~

Sparse deep encoded features with enhanced sinogramic red deer optimization for fault inspection in wafer maps.

Authors: Altantawy, Doaa A. Yakout, Mohamed A.

Source: Journal of Intelligent Manufacturing. Jun2025, Vol. 36 Issue 5, p3359-3397. 39p.

Subject Terms: *MANUFACTURING processes, *INFORMATION processing, CONVOLUTIONAL neural networks, OPTIMIZATION algorithms, AUTOENCODERS

SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography

Authors: Bardia Yousefi Hamed Akbari Michelle Hershman et al.

Source: Applied Sciences, Vol 11, Iss 7, p 3248 (2021)

Subject Terms: breast cancer, thermography, sparse deep convolutional autoencoder, matrix factorization, dimensionality reduction, thermomics, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/11/7/3248; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/3c70757effa743f3b9bef5e4d264eb2f

DOA estimation method for sparse arrays based on deep convolutional autoencoder and deep convolutional neural network.

Authors: Guo, Shuhan Zhang, Qin Fu, Xiaolong et al.

Source: Digital Signal Processing. Jan2026:Part D, Vol. 168, pN.PAG-N.PAG. 1p.

Subject Terms: *DIRECTION of arrival estimation, *CONVOLUTIONAL neural networks, *SPARSE matrices, *MULTIPLE Signal Classification, *FEATURE extraction, *COVARIANCE matrices, *ESTIMATION theory

A novel multichannel sparse convolutional autoencoder for electrocardiogram signal compression.

Authors: Bekiryazıcı, Tahir Damkacı, Mehmet Aydemir, Gürkan et al.

Source: Journal of Electrocardiology. Nov2025, Vol. 93, pN.PAG-N.PAG. 1p.

SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography.

Authors: Yousefi, Bardia Akbari, Hamed Hershman, Michelle et al.

Source: Applied Sciences (2076-3417); Apr2021, Vol. 11 Issue 7, p3248, 15p

Subject Terms: EARLY detection of cancer, THERMOGRAPHY, MATRIX decomposition, DEEP learning, INFRARED imaging, NONNEGATIVE matrices

Automatic Baseline Correction of 1D Signals Using a Parameter-Free Deep Convolutional Autoencoder Algorithm.

Authors: Górski, Łukasz Jakubowska, Małgorzata

Source: Applied Sciences (2076-3417); Nov2025, Vol. 15 Issue 22, p12069, 18p

Subject Terms: AUTOENCODERS, ANALYTICAL chemistry, DEEP learning, ALGORITHMS, SIGNAL processing

Integrating multi-source monitoring data and deep convolutional autoencoder technology for slope failure pattern recognition.

Authors: Han, Nana Miao, Wending Li, Mingzhi et al.

Source: Frontiers in Earth Science; 2025, p1-11, 11p

Subject Terms: PATTERN recognition systems, SLOPES (Soil mechanics), AUTOENCODERS, INDUSTRIAL safety, FAILURE mode & effects analysis, LANDSLIDES

A Deep Convolutional Autoencoder–Enabled Channel Estimation Method in Intelligent Wireless Communication Systems.

Authors: Tian, Xinyu Jiang, Weiwei

Source: International Journal of Intelligent Systems; 10/30/2024, Vol. 2024, p1-12, 12p

Subject Terms: WIRELESS communications, CHANNEL estimation, SIGNAL detection, DEEP learning, DEMODULATION, WIRELESS channels, TRANSMITTERS (Communication)

Unsupervised Learning for Machinery Adaptive Fault Detection Using Wide-Deep Convolutional Autoencoder with Kernelized Attention Mechanism.

Authors: Yan, Hao Si, Xiangfeng Liang, Jianqiang et al.

Source: Sensors (14248220); Dec2024, Vol. 24 Issue 24, p8053, 19p

Subject Terms: FAULT diagnosis, AUTOENCODERS, INDUSTRIAL applications, SCREWS, MACHINERY, DEEP learning

Fast Dual Deep Convolutional Autoencoder Network for Denoising Monte Carlo Rendering.

Authors: Alzbier, Ahmed Mustafa Taha Chunyi Chen Zhongye Shen et al.

Source: Journal of Imaging Science & Technology; May/Jun2023, Vol. 67 Issue 3, p1-12, 12p

Subject Terms: DEEP learning, CONVOLUTIONAL neural networks, PIXELS, IMAGE reconstruction, SIGNAL-to-noise ratio

Modeling Task fMRI Data via Deep Convolutional Autoencoder

Authors: Heng Huang Xintao Hu Yu Zhao et al.

Source: Lecture Notes in Computer Science ISBN: 9783319590493

Subject Terms: 2. Zero hunger, 03 medical and health sciences, Deep Learning, 0302 clinical medicine, Connectome, Brain, Humans, Magnetic Resonance Imaging, Unsupervised Machine Learning

Access URL: https://pubmed.ncbi.nlm.nih.gov/28641247
https://link.springer.com/chapter/10.1007%2F978-3-319-59050-9_33
https://pubmed.ncbi.nlm.nih.gov/28641247/
http://ieeexplore.ieee.org/document/7949140/
https://xplqa30.ieee.org/document/7949140/
https://www.ncbi.nlm.nih.gov/pubmed/28641247
https://ieeexplore.ieee.org/document/7949140
https://dblp.uni-trier.de/db/conf/ipmi/ipmi2017.html#HuangHMDZH0L17
https://link.springer.com/10.1007/978-3-319-59050-9_33
https://doi.org/10.1007/978-3-319-59050-9_33
http://doi.org/10.1007/978-3-319-59050-9_33

Fast and scalable distributed deep convolutional autoencoder for fMRI big data analytics

Authors: Milad Makkie Heng Huang Yu Zhao et al.

Source: Neurocomputing. 325:20-30

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Machine Learning (stat.ML), Machine Learning (cs.LG), 03 medical and health sciences, 0302 clinical medicine, Computer Science - Distributed, Parallel, and Cluster Computing, Statistics - Machine Learning, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, Neurons and Cognition (q-bio.NC), Distributed, Parallel, and Cluster Computing (cs.DC), Neural and Evolutionary Computing (cs.NE)

Access URL: https://europepmc.org/articles/pmc6660166?pdf=render
https://pubmed.ncbi.nlm.nih.gov/31354187
http://arxiv.org/abs/1710.08961
http://arxiv.org/pdf/1710.08961.pdf
https://dblp.uni-trier.de/db/journals/corr/corr1710.html#abs-1710-08961
https://arxiv.org/abs/1710.08961
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6660166
https://pubmed.ncbi.nlm.nih.gov/31354187/
http://www.diva-portal.org/smash/record.jsf?pid=diva2:1255524

Sensor-Driven Surrogate Modeling and Control of Nonlinear Dynamical Systems Using FAE-CAE-LSTM and Deep Reinforcement Learning

Authors: Kherad, M. Moayyedi, M. K. Fotouhi-Ghazvini, F. et al.

Source: Sensors. 25(16)

Subject Terms: Autoencoders, Control Of Dynamical Systems, Cyber-physical Systems, Deep Reinforcement Learning, Long Short-term Memory, Sensor-driven Modeling, Circular Cylinders, Computer Control Systems, Convolution, Dynamical Systems, Dynamics, Embedded Systems, Flow Control, Fuel Additives, Nonlinear Dynamical Systems, Real Time Control, Robust Control, Auto Encoders, Control Of Dynamical System, Cybe-physical Systems, High-dimensional, Higher-dimensional, Reinforcement Learning Agent, Reinforcement Learnings, Short Term Memory

File Description: print

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-73172
https://doi.org/10.3390/s25165149

Fast and scalable distributed deep convolutional autoencoder for fMRI big data analytics

Authors: Makkie, M Huang, H Zhao, Y et al.

Index Terms: Journal Article

URL: http://hdl.handle.net/10453/139547
Neurocomputing
10.1016/j.neucom.2018.09.066

High-Resolution SAR Image Classification via Deep Convolutional Autoencoders

Authors: Jianchao Fan Baoming Li Fuliang Chen et al.

Source: IEEE Geoscience and Remote Sensing Letters. 12:2351-2355

Subject Terms: 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 10. No inequality, 16. Peace & justice

TumorEncode - Deep Convolutional Autoencoder for Computed Tomography Tumor Treatment Assessment

Authors: Dominik Nörenberg Michael Sühling and Horst-Michael Gros et al.

Source: 2018 International Joint Conference on Neural Networks (IJCNN). :1-8

Subject Terms: 03 medical and health sciences, 0302 clinical medicine, 3. Good health

Sparse Representations for Object- and Ego-Motion Estimations in Dynamic Scenes

Authors: Kashyap, Hirak J Fowlkes, Charless C Krichmar, Jeffrey L

Source: IEEE Transactions on Neural Networks and Learning Systems. 32(6)

Subject Terms: Information and Computing Sciences, Computer Vision and Multimedia Computation, Estimation, Dynamics, Optical imaging, Cameras, Training, Adaptive optics, Motion segmentation, Convolutional autoencoder, ego motion, object motion, overcomplete basis, sparse representation, cs.CV, cs.LG, Artificial Intelligence & Image Processing, Artificial intelligence

File Description: application/pdf

Access URL: https://escholarship.org/uc/item/7p53p8zv
https://escholarship.org/content/qt7p53p8zv/qt7p53p8zv.pdf

Mine Microseismic Signal Denoising Based on a Deep Convolutional Autoencoder.

Authors: Hu, Ting Xu, Bin Wang, Yongfa et al.

Source: Shock & Vibration; 8/30/2023, p1-15, 15p

Subject Terms: DEEP learning, SIGNAL denoising, HILBERT-Huang transform, SIGNAL-to-noise ratio, FOURIER transforms, AUDITORY masking, MINE safety

DLSIA: Deep Learning for Scientific Image Analysis

Authors: Roberts, Eric J Chavez, Tanny Hexemer, Alexander et al.

Source: Journal of Applied Crystallography. 57(2)

Subject Terms: 3402 Inorganic Chemistry (for-2020), 34 Chemical Sciences (for-2020), 3406 Physical Chemistry (for-2020), 51 Physical Sciences (for-2020), 5104 Condensed Matter Physics (for-2020), Bioengineering (rcdc), Biomedical Imaging (rcdc), Machine Learning and Artificial Intelligence (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), deep learning, convolutional neural networks, X-ray scattering, tomography, data compression, X-ray scattering, convolutional neural networks, data compression, deep learning, tomography, 01 Mathematical Sciences (for), 02 Physical Sciences (for), 09 Engineering (for), Inorganic & Nuclear Chemistry (science-metrix), 3402 Inorganic chemistry (for-2020), 3406 Physical chemistry (for-2020), 5104 Condensed matter physics (for-2020)

File Description: application/pdf

Access URL: https://escholarship.org/uc/item/966220w4
https://escholarship.org/content/qt966220w4/qt966220w4.pdf

Deep convolutional autoencoder thermography for artwork defect detection.

Authors: Liu, Yi Wang, Fumin Liu, Kaixin et al.

Source: Quantitative InfraRed Thermography Journal; Nov2024, Vol. 21 Issue 6, p367-383, 17p