Search Results - sparse convolutional autoencoder (((sace OR sage) OR space))

A novel multichannel sparse convolutional autoencoder for electrocardiogram signal compression.

Authors: Bekiryazıcı T Damkacı M Aydemir G et al.

Source: Journal of electrocardiology [J Electrocardiol] 2025 Nov-Dec; Vol. 93, pp. 154125. Date of Electronic Publication: 2025 Oct 10.

Publication Type: Journal Article

Journal Info: Publisher: Churchill Livingstone Country of Publication: United States NLM ID: 0153605 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-8430 (Electronic) Linking ISSN: 00220736 NLM ISO Abbreviation: J Electrocardiol Subsets: MEDLINE

MeSH Terms: Electrocardiography*/methods , Data Compression*/methods , Signal Processing, Computer-Assisted* , Neural Networks, Computer*, Humans ; Algorithms ; Autoencoder

Leveraging variant of CAE with sparse convolutional embedding and two-stage application-driven data augmentation for image clustering.

Authors: Liu, Yanming Liu, Jinglei

Source: Soft Computing - A Fusion of Foundations, Methodologies & Applications. Feb2025, Vol. 29 Issue 3, p1419-1435. 17p.

Subject Terms: *DATA augmentation, *AUTOENCODERS

Reconstructing flow field from sparse sensor data: A deep learning framework combining autoencoder and cross-attention mechanism.

Authors: Kong, Xuan Fan, Qipei Li, Jinzhao et al.

Source: Physics of Fluids. Sep2025, Vol. 37 Issue 9, p1-12. 12p.

Subject Terms: *AUTOENCODERS, *MACHINE learning, *AERODYNAMIC measurements, *ATMOSPHERIC models, *DETECTORS

Deep-Learning-Based Dictionary Construction for MIMO Radar Detection in Complex Scenes

Authors: Hongyan Wang He Zhou Qinghua Guo et al.

Source: IEEE Internet of Things Journal. 11:12419-12434

Subject Terms: nonlinear correction, Multiple-input multiple-output (MIMO) radar, transceiving space-time resource allocation, sparse representation, complex-valued convolutional autoencoder

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

Application of Artificial Intelligence Technologies as an Intervention for Promoting Healthy Eating and Nutrition in Older Adults: A Systematic Literature Review.

Authors: Kalu, Kingsley Ataguba, Grace Onifade, Oyepeju et al.

Source: Nutrients. Oct2025, Vol. 17 Issue 20, p3223. 33p.

A novel intelligent model for high-precision reconstruction of dam-break free surface profiles from sparse monitoring data.

Authors: Deng, Yangyu Liu, Yakun Zhang, Di et al.

Source: Physics of Fluids. Feb2025, Vol. 37 Issue 2, p1-28. 28p.

Subject Terms: *PROPER orthogonal decomposition, *WATER levels, *AUTOENCODERS, *FREE surfaces, *FLOOD control

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

Real-time forecasting of chaotic dynamics from sparse data and autoencoders

Authors: Özalp, Elise Nóvoa, Andrea Magri, Luca

Subject Terms: 37N10, 65P20, Chaotic Dynamics, FOS: Physical sciences, Chaotic Dynamics (nlin.CD)

Access URL: http://arxiv.org/abs/2508.08729

3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network

Authors: Philippe Boutinaud Ami Tsuchida Alexandre Laurent et al.

Source: Frontiers in Neuroinformatics, Vol 15 (2021)

Subject Terms: perivascular space, deep learning, U-net, MRI, brain cohort, segmentation, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571

File Description: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fninf.2021.641600/full; https://doaj.org/toc/1662-5196

Access URL: https://doaj.org/article/ed3360c8420941b78108600cebb4efc3

Temperature scaling unmixing framework based on convolutional autoencoder

Authors: Jin Xu Mingming Xu Shanwei Liu et al.

Source: International Journal of Applied Earth Observations and Geoinformation, Vol 129, Iss , Pp 103864- (2024)

Subject Terms: Hyperspectral unmixing, Convolutional autoencoder, Sparsity constraint, Temperature scaling, Equal-frequency binning, Physical geography, GB3-5030, Environmental sciences, GE1-350

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S1569843224002188; https://doaj.org/toc/1569-8432

Access URL: https://doaj.org/article/aa7533b7fbf447e1a8b51942e4c8c5f4

Automated Video-EEG Analysis in Epilepsy Studies: A Narrative Review of Advances and Challenges.

Authors: Zuev VA Salmagambetova EG Djakov SN et al.

Source: Journal of medical systems [J Med Syst] 2025 Oct 20; Vol. 49 (1), pp. 142. Date of Electronic Publication: 2025 Oct 20.

Publication Type: Journal Article; Review

Journal Info: Publisher: Kluwer Academic/Plenum Publishers Country of Publication: United States NLM ID: 7806056 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-689X (Electronic) Linking ISSN: 01485598 NLM ISO Abbreviation: J Med Syst Subsets: MEDLINE

MeSH Terms: Electroencephalography*/methods , Epilepsy*/diagnosis , Epilepsy*/physiopathology , Video Recording*/methods, Humans ; Machine Learning

Application of a Convolutional Autoencoder to Half Space Radar Hrrp Recognition

Authors: Yong-Jun Xie Shi-Hao Yu

Source: 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). :48-53

Subject Terms: 2. Zero hunger, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

Access URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8521306
https://ieeexplore.ieee.org/document/8521306/

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

3D segmentation of perivascular spaces on T1-weighted 3 Tesla MR images with a convolutional autoencoder and a U-shaped neural network

Authors: Boutinaud, P. Tsuchida, A. Laurent, A. et al.

Source: Frontiers in Neuroinformatics

File Description: application/pdf

Access URL: http://hdl.handle.net/21.11116/0000-000B-12F0-1
http://hdl.handle.net/21.11116/0000-000B-12EE-5

Autoencoders for Semi-Supervised Water Level Modeling in Sewer Pipes with Sparse Labeled Data

Authors: Rius, Ferran Plana Philipsen, Mark P. Tur, Josep Maria Mirats et al.

Source: Rius, F P, Philipsen, M P, Tur, J M M, Moeslund, T B, Bahón, C A & Casas, M 2022, 'Autoencoders for Semi-Supervised Water Level Modeling in Sewer Pipes with Sparse Labeled Data', Water (Switzerland), vol. 14, no. 3, 333. https://doi.org/10.3390/w14030333

Subject Terms: Autoencoders and latent space, Data distribution, Self-supervised, Semi-supervised, Sparse data, Supervised, Water

File Description: application/pdf

Relation: info:eu-repo/semantics/altIdentifier/pissn/2073-4441

Availability: https://vbn.aau.dk/da/publications/65dbf42a-774e-45d2-906a-47e7bd52a17d
https://doi.org/10.3390/w14030333
https://vbn.aau.dk/ws/files/482740247/water_14_00333.pdf
https://www.scopus.com/pages/publications/85123710424

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

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

Source: IEEE Transactions on Neural Networks and Learning Systems, vol 32, iss 6

Subject Terms: FOS: Computer and information sciences, Artificial intelligence, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), cs.LG, Computer Science - Computer Vision and Pattern Recognition, ego motion, 02 engineering and technology, Computer Vision and Multimedia Computation, Optical imaging, Machine Learning (cs.LG), Information and Computing Sciences, 0202 electrical engineering, electronic engineering, information engineering, Training, Artificial Intelligence & Image Processing, object motion, sparse representation, cs.CV, overcomplete basis, Cameras, 16. Peace & justice, Dynamics, Convolutional autoencoder, Motion segmentation, Estimation, Adaptive optics

File Description: application/pdf

Access URL: https://escholarship.org/content/qt7p53p8zv/qt7p53p8zv.pdf?t=qgb7mw
https://pubmed.ncbi.nlm.nih.gov/32687472
http://arxiv.org/abs/1903.03731
https://europepmc.org/article/MED/32687472
https://arxiv.org/pdf/1903.03731.pdf
https://arxiv.org/abs/1903.03731
https://par.nsf.gov/biblio/10191836-sparse-representations-object-ego-motion-estimations-dynamic-scenes
https://pubmed.ncbi.nlm.nih.gov/32687472/
https://ui.adsabs.harvard.edu/abs/2019arXiv190303731K/abstract
https://escholarship.org/uc/item/7p53p8zv

3D segmentation of perivascular spaces on T1-weighted 3 Tesla MR images with a convolutional autoencoder and a U-shaped neural network

Authors: Philippe Boutinaud Ami Tsuchida Alexandre Laurent et al.

Source: Front Neuroinform
Frontiers in Neuroinformatics, Vol 15 (2021)

Subject Terms: 03 medical and health sciences, 0302 clinical medicine, segmentation, deep learning, Neurosciences. Biological psychiatry. Neuropsychiatry, perivascular space, U-net, brain cohort, MRI, RC321-571, Neuroscience

Access URL: https://oskar-bordeaux.fr/bitstream/20.500.12278/110227/1/BPH_FN_2021_Boutinaud.pdf
https://www.frontiersin.org/articles/10.3389/fninf.2021.641600/pdf
https://pubmed.ncbi.nlm.nih.gov/34262443
https://doaj.org/article/ed3360c8420941b78108600cebb4efc3
https://biorxiv.org/content/10.1101/2020.11.25.397364v1.full.pdf
https://www.biorxiv.org/content/10.1101/2020.11.25.397364v1

Enhanced heart sound anomaly detection via WCOS: a semi-supervised framework integrating wavelet, autoencoder and SVM.

Authors: Zeng, Peipei Kang, Shuimiao Fan, Fan et al.

Source: Frontiers in Neuroinformatics; 2025, p1-15, 15p

Subject Terms: ANOMALY detection (Computer security), HEART murmurs, SUPPORT vector machines, CONGENITAL heart disease, AUTOENCODERS

Unsupervised learning with sparse space-and-time autoencoders

Authors: Graham, Benjamin

Index Terms: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, text

URL: http://arxiv.org/abs/1811.10355