Suchergebnisse - Deep convolutional encoder–decoder

NL-CoWNet: A Deep Convolutional Encoder–Decoder Architecture for OCT Speckle Elimination Using Nonlocal and Subband Modulated DT-CWT Blocks

Autoren: P. S. Arun Bibin Francis Varun P. Gopi

Quelle: IEEE Transactions on Artificial Intelligence. 6:700-709

Physics-informed deep convolutional hierarchical encoder-decoder neural network for flow field prediction in wind farms

Autoren: Saeede Hasanpoor David A. Romero Joaquin E. Moran et al.

Quelle: Energy and AI, Vol 21, Iss , Pp 100553- (2025)

Schlagwörter: Surrogate models, Physics-informed convolutional encoder-decoder networks, Wind farm layout optimization, Annual energy production, Wind turbines, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Computer software, QA76.75-76.765

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2666546825000850; https://doaj.org/toc/2666-5468

Zugangs-URL: https://doaj.org/article/7f1fc5809be842d381b9768a56a52dbd

Unsupervised Image Hashing Using a Deep Convolutional Encoder-Decoder Model for Fast Image Retrieval

Autoren: Enver AKBACAK

Quelle: Volume: 23, Issue: 6 1458-1465
Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi

Schlagwörter: EncoderDecoder, Denetimsiz Öğrenme, Deep Learning, Hash Codes, Yapay Zeka, Artificial Intelligence, Hash Kodlar, Derin Öğrenme, Denetimsiz öğrenme, Derin öğrenme, Kodlayıcı ve kod çözücü, Hash kodları, Unsupervised Learning, KodlayıcıKod Çözücü, Unsupervised learning, Deep learning, Encoder-decoder, Hash codes

Dateibeschreibung: application/pdf

Zugangs-URL: https://hdl.handle.net/11630/12660
https://dergipark.org.tr/tr/pub/akufemubid/issue/81488/1236064

Optimized encoder-decoder cascaded deep convolutional network for leaf disease image segmentation

Autoren: David, Femi Manapakkam Anandan, Mukunthan

Quelle: Network: Computation in Neural Systems. 36:480-506

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38775271

High-to-low flow dynamics learning with deep convolutional encoder–decoder networks for randomly packed pebble-bed geometry

Autoren: David Lanade Yang Liu Yassin Hassan

Quelle: Physics of Fluids. 37

A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection

Autoren: Noppadol Maneerat Athasart Narkthewan Kazuhiko Hamamoto

Quelle: Applied Sciences, Vol 15, Iss 13, p 7300 (2025)

Schlagwörter: Technology, Bayesian Optimization, QH301-705.5, Physics, QC1-999, Engineering (General). Civil engineering (General), chest radiographs, atrous convolution, Chemistry, tuberculosis detection, DeepLabv3+, TA1-2040, Biology (General), deep convolutional neural networks, QD1-999

Zugangs-URL: https://doaj.org/article/6193c9b2392e4794bc4596afaddeb06e

Wsp-Extractor: A Generic Extraction Framework of Weld Seam Profiles Based on Deep Convolutional Encoder-Decoder Architecture for Automated Robotic Welding

Autoren: Yinshui He Keyi Song Run Cai

Deep convolutional encoder–decoder networks based on ensemble learning for semantic segmentation of high-resolution aerial imagery

Autoren: Huming Zhu Chendi Liu Qiuming Li et al.

Quelle: CCF Transactions on High Performance Computing. 6:408-424

DSLSTM: a deep convolutional encoder–decoder architecture for abnormality detection in video surveillance

Autoren: Sanjay Roka Manoj Diwakar

Quelle: Cluster Computing. 27:4925-4940

Semantic Segmentation of Anaemic RBCs Using Multilevel Deep Convolutional Encoder-Decoder Network

Autoren: Muhammad Shahzad Arif Iqbal Umar Syed Hamad Shirazi et al.

Quelle: IEEE Access, Vol 9, Pp 161326-161341 (2021)

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, deep learning, segmentation analysis, 02 engineering and technology, Electrical Engineering and Systems Science - Image and Video Processing, semantic segmentation, TK1-9971, Machine Learning (cs.LG), Artificial Intelligence (cs.AI), multi-level deep convolutional encoder-decoder network, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, blood cells, CNN

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/6287639/6514899/09631221.pdf
http://arxiv.org/abs/2202.04650
https://doaj.org/article/94ee1e64c9af481099e455c44b79f319
http://xplorestaging.ieee.org/ielx7/6287639/6514899/09631221.pdf?arnumber=9631221

A new real-time image semantic segmentation framework based on a lightweight deep convolutional encoder-decoder architecture for robotic environment sensing

Autoren: Yuxia Yuan Yachao Zhang

Quelle: Journal of Intelligent & Fuzzy Systems. 45:2331-2345

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

IMG2HEIGHT: height estimation from single remote sensing image using a deep convolutional encoder-decoder network

Autoren: Shouhang Du Jianghe Xing Shihong Du et al.

Quelle: International Journal of Remote Sensing. 44:5686-5712

A novel deep convolutional encoder–decoder network: application to moving object detection in videos

Autoren: Avatharam Ganivada Srinivas Yara

Quelle: Neural Computing and Applications. 35:22027-22041

Architecture of Deep Convolutional Encoder-Decoder Networks for Building Footprint Semantic Segmentation

Autoren: Abderrahim Norelyaqine Rida Azmi Abderrahim Saadane

Quelle: Scientific Programming. 2023:1-15

Schlagwörter: 11. Sustainability, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, 0105 earth and related environmental sciences

Dateibeschreibung: text/xhtml

Solving Electromagnetic Inverse Scattering Problems in Inhomogeneous Media by Deep Convolutional Encoder–Decoder Structure

Autoren: Huan Huan Zhang He Ming Yao Lijun Jiang et al.

Quelle: IEEE Transactions on Antennas and Propagation. 71:2867-2872

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Elastic Full-Waveform Inversion Using a Physics-Guided Deep Convolutional Encoder–Decoder

Autoren: Arnab Dhara Mrinal K. Sen

Quelle: IEEE Transactions on Geoscience and Remote Sensing. 61:1-18

Implementing the Fast Full-Wave Electromagnetic Forward Solver Using the Deep Convolutional Encoder-Decoder Architecture

Autoren: He Ming Yao Lijun Jiang Michael Ng

Quelle: IEEE Transactions on Antennas and Propagation. 71:1152-1157

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Deep Convolutional Encoder–Decoder Models for Road Extraction from Aerial Imagery

Autoren: Ashish Kumar M. Izharul Hasan Ansari Amit Garg

Quelle: Lecture Notes in Networks and Systems ISBN: 9789819994854

DeepCEDNet: An Efficient Deep Convolutional Encoder-Decoder Networks for ECG Signal Enhancement

Autoren: Pingping Bing Wei Liu Zhihua Zhang

Quelle: IEEE Access, Vol 9, Pp 56699-56708 (2021)

Schlagwörter: noise reduction, deep neural network, 0202 electrical engineering, electronic engineering, information engineering, Electrocardiogram signal, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, sparse representation, time-frequency domain, TK1-9971

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/6287639/9312710/09400836.pdf
https://doaj.org/article/ba77d596a0074e77b5b0e7dcc6c529a6
https://ieeexplore.ieee.org/document/9400836/
https://dblp.uni-trier.de/db/journals/access/access9.html#BingLZ21

Improving the Accuracy of R-Peak Detection in a Wearable Armband Device for Daily Life Electrocardiogram Monitoring Using a Deep Convolutional Denoising Encoder-Decoder Network

Autoren: Shirin, Hajeb-Mohammadalipour Md-Billal, Hossain Ki H, Chon

Quelle: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). :4291-4294

Schlagwörter: Electrocardiography, Wearable Electronic Devices, 0206 medical engineering, Electrocardiography, Ambulatory, Humans, Neural Networks, Computer, 02 engineering and technology, Artifacts

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/36085851