Search Results - deep convolutional autoencoder

DOA estimation method for sparse arrays based on deep convolutional autoencoder and deep convolutional neural network by Guo, Shuhan, Zhang, Qin, Fu, Xiaolong, Zheng, Guimei, Zhou, Hao

“…This paper proposes a Direction-of-Arrival (DOA) estimation method based on Deep Convolutional Autoencoder (DCAE…”
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MR‐DCAE: Manifold regularization‐based deep convolutional autoencoder for unauthorized broadcasting identification by Zheng, Qinghe, Zhao, Penghui, Zhang, Deliang, Wang, Hongjun

“…‐based deep convolutional autoencoder (MR‐DCAE) model for unauthorized broadcasting identification…”
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Classification of Atypical White Blood Cells in Acute Myeloid Leukemia Using a Two-Stage Hybrid Model Based on Deep Convolutional Autoencoder and Deep Convolutional Neural Network by Elhassan, Tusneem A., Mohd Rahim, Mohd Shafry, Siti Zaiton, Mohd Hashim, Swee, Tan Tian, Alhaj, Taqwa Ahmed, Ali, Abdulalem, Aljurf, Mahmoud

“…Recent advancements in artificial intelligence (AI) have led to numerous medical discoveries. The field of computer vision (CV) for medical diagnosis has…”
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A Deep Convolutional Autoencoder–Enabled Channel Estimation Method in Intelligent Wireless Communication Systems by Tian, Xinyu

“… In this paper, we propose a deep convolutional autoencoder–based channel estimation method in intelligent wireless communication systems…”
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VECTOR: Very deep convolutional autoencoders for non‐resonant background removal in broadband coherent anti‐Stokes Raman scattering by Wang, Zhengwei, O' Dwyer, Kevin, Muddiman, Ryan, Ward, Tomas, Camp, Charles H., Hennelly, Bryan M.

“… In this paper, we propose a deep neural network architecture called Very…”
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Deep convolutional autoencoders for the time–space reconstruction of liquid rocket engine flames by Zapata Usandivaras, José F., Bauerheim, Michael, Cuenot, Bénédicte, Urbano, Annafederica

“…). In recent years, significant progress has been made in the ROM literature through the adoption of non-linear methods, coming from the realm of deep learning…”
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Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial–temporal features extraction by D’Angelo, Gianni, Palmieri, Francesco

“… For this purpose, a novel autoencoder-based deep neural network architecture…”
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SDCA: a novel stack deep convolutional autoencoder – an application on retinal image denoising by Ghosh, Swarup Kr, Biswas, Biswajit, Ghosh, Anupam

“… This study represents a deep learning based approach to denoising images and restoring features using stack denoising convolutional autoencoder…”
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Transfer learning for denoising the echolocation clicks of finless porpoise (Neophocaena phocaenoides sunameri) using deep convolutional autoencoders by Yang, Wuyi, Chang, Wenlei, Song, Zhongchang, Zhang, Yu, Wang, Xianyan

“… In this study, deep convolutional autoencoders (DCAEs) are presented to denoise the echolocation clicks of the finless porpoise…”
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Segmentation of digital rock images using deep convolutional autoencoder networks by Karimpouli, Sadegh, Tahmasebi, Pejman

“… Recently, deep learning and machine learning algorithms have proposed several algorithms working with images, including Convolutional Neural Networks (CNN…”
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Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: Comparison with linear subspace techniques by Kadeethum, T., Ballarin, F., Choi, Y., O’Malley, D., Yoon, H., Bouklas, N.

“…, the process of CO2 sequestration). Here, we extend and present a non-intrusive reduced order model of natural convection in porous media employing deep convolutional…”
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Modeling Task fMRI Data Via Deep Convolutional Autoencoder by Huang, Heng, Hu, Xintao, Zhao, Yu, Makkie, Milad, Dong, Qinglin, Zhao, Shijie, Guo, Lei, Liu, Tianming

“…Task-based functional magnetic resonance imaging (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is…”
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Exploiting probability density function of deep convolutional autoencoders’ latent space for reliable COVID-19 detection on CT scans by Sarv Ahrabi, Sima, Piazzo, Lorenzo, Momenzadeh, Alireza, Scarpiniti, Michele, Baccarelli, Enzo

“… To this end, we design and train, in an unsupervised manner, a deep convolutional autoencoder (DCAE…”
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Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Denoising Autoencoder and Deep Convolutional Neural Network by Qu, Zhiyu, Wang, Wenyang, Hou, Changbo, Hou, Chenfan

“… A radar signal intra-pulse modulation recognition method based on convolutional denoising autoencoder (CDAE…”
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Petrophysical characterization of deep saline aquifers for CO2 storage using ensemble smoother and deep convolutional autoencoder by Liu, Mingliang, Grana, Dario

“…•The deep convolutional autoencoder is used to reduce the seismic data with big size to avoid ensemble collapse…”
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Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities by Seyfioglu, Mehmet Saygin, Ozbayoglu, Ahmet Murat, Gurbuz, Sevgi Zubeyde

“… Moreover, a three-layer, deep convolutional autoencoder (CAE) is proposed, which utilizes unsupervised pretraining to initialize the weights in the subsequent convolutional layers…”
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Hyperspectral unmixing using deep convolutional autoencoder by Elkholy, Menna M., Mostafa, Marwa, Ebied, Hala M., Tolba, Mohamed F.

“… In this paper, we address the linear unmixing problem with an unsupervised Deep Convolutional Autoencoder network (DCAE…”
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Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders by Lee, Kookjin, Carlberg, Kevin T.

“…•Analysis including conditions under which the two methods are equivalent.•A novel convolutional autoencoder architecture to construct the nonlinear manifold…”
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Deep Convolutional Asymmetric Autoencoder-Based Spatial-Spectral Clustering Network for Hyperspectral Image by Liu, Baisen, Kong, Weili, Wang, Yan

“… In this paper, we propose a novel deep convolutional asymmetric autoencoder-based spatial-spectral clustering network (DCAAES2C-Net…”
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Fast Full Wave Electromagnetic Forward Solver Based on Deep Conditional Convolutional Autoencoders by Zhang, Huan Huan, Yao, He Ming, Jiang, Lijun, Ng, Michael

“… This proposed fast full-wave solver for EMF process is designed based on the deep conditional convolutional autoencoder (DCCAE…”
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