Search Results - "Autoencoder network"

Mitigating the Concurrent Interference of Electrode Shift and Loosening in Myoelectric Pattern Recognition Using Siamese Autoencoder Network by Gao, Ge, Zhang, Xu, Chen, Xiang, Chen, Zhang

“…The objective of this work is to develop a novel myoelectric pattern recognition (MPR) method to mitigate the concurrent interference of electrode shift and…”
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Self-supervised autoencoder network for robust heart rate extraction from noisy photoplethysmogram: Applying blind source separation to biosignal analysis by Webster, Matthew B., Lee, Dongheon, Lee, Joonnyong

“… The trained network is then applied to a noisy PPG dataset collected during the daily activities of nine subjects and a surgical dataset comprising 4,681 patients…”
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Spatiotemporal Graph Autoencoder Network for Skeleton-Based Human Action Recognition by Abduljalil, Hosam, Elhayek, Ahmed, Marish Ali, Abdullah, Alsolami, Fawaz

“… In this study, we propose a novel, highly accurate spatiotemporal graph autoencoder network for HAR, designated as GA-GCN…”
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Brain fingerprinting and cognitive behavior predicting using functional connectome of high inter-subject variability by Lu, Jiayu, Yan, Tianyi, Yang, Lan, Zhang, Xi, Li, Jiaxin, Li, Dandan, Xiang, Jie, Wang, Bin

“…•High inter-subject variability for brain fingerprinting and cognitive behavior predicting…”
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Functional connectome fingerprinting: Identifying individuals and predicting cognitive functions via autoencoder by Cai, Biao, Zhang, Gemeng, Zhang, Aiying, Xiao, Li, Hu, Wenxing, Stephen, Julia M., Wilson, Tony W., Calhoun, Vince D., Wang, Yu‐Ping

“… In this paper, we propose to enhance the uniqueness of individual connectome based on an autoencoder network…”
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Facial Image Denoising Using Convolutional Autoencoder Network by Tun, Naing Min, Gavrilov, Alexander I., Tun, Nyan Linn

“… In this paper, we show that the solution of denoising process using the autoencoder networks based on the ORL face database…”
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Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-Ground Biomass by Shao, Zhenfeng, Zhang, Linjing, Wang, Lei

“… However, field data are limited in remote and unmanaged areas. In addition, optical reflectance usually saturates at high-density biomass level and is subject to cloud contaminations…”
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Industrial Process Fault Detection with Multi-View Feature Fusion Attention Mechanism Assisted Autoencoder Network by Tong, Yifan, Wang, Zhaojing, Yan, Xiaoyun, Hu, Xinrong, Li, Lijun

“… To address this gap, a novel fault detection framework is introduced in this study, leveraging a Multi-View Feature Fusion At-tention Mechanism Assisted Autoencoder Network (MFA-AE…”
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Multimodal Representation Learning for Alzheimer's Disease Diagnosis by Fan, Lian-xi, Liu, Yan-bei, Wang, Wen, Geng, Lei, Wu, Jun, Zhang, Fang, Xiao, Zhi-tao

“… no cure.Its early diagnosis and treatment have always been the focus of attention.The neuroimaging data of subjects has an important auxiliary role in the diagnosis…”
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Improving the repeatability of two-rate model parameter estimations by using autoencoder networks by Ozdemir, Murat C, Eggert, Thomas, Straube, Andreas

“… In this paper, we collected time-series data from an experimental paradigm involving repeated training and investigated the effect of various cleaning methods, including an autoencoder network (AE…”
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Early Diagnosis of Alzheimer's Disease Based on Resting-State Brain Networks and Deep Learning by Ju, Ronghui, Hu, Chenhui, Zhou, Pan, Li, Quanzheng

“… A targeted autoencoder network is built to distinguish normal aging from mild cognitive impairment, an early stage of AD…”
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Learning a Probabilistic Model for Diffeomorphic Registration by Krebs, Julian, Delingette, Herve, Mailhe, Boris, Ayache, Nicholas, Mansi, Tommaso

“… image. Our unsupervised method is based on the variational inference. In particular, we use a conditional variational autoencoder network and constrain transformations to be symmetric…”
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Bridging the Methodological Gap Between Inertial Sensors and Optical Motion Capture: Deep Learning as the Path to Accurate Joint Kinematic Modelling Using Inertial Sensors by Shah, Vaibhav R., Dixon, Philippe C.

“…), and an autoencoder network with a custom Biomech loss function was used to predict 16 marker positions from IMU data…”
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Topology-Guided Graph Masked Autoencoder Learning for Population-Based Neurodevelopmental Disorder Diagnosis by Li, Yueying, Zhang, Xiaotong, Guan, Shihan, Ma, Guolin, Kong, Youyong

“…Exploring the pathogenic mechanisms of brain disorders within population is an important research in the field of neuroscience. Existing methods either combine…”
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Online Adaptive Prediction of Human Motion Intention Based on sEMG by Ding, Zhen, Yang, Chifu, Wang, Zhipeng, Yin, Xunfeng, Jiang, Feng

“… The feature extraction utilizes the convolution autoencoder network combined with muscle synergy characteristics to get the high-compression sEMG feature to aid motion prediction…”
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BrainSTEAM: A Practical Pipeline for Connectome-based fMRI Analysis towards Subject Classification by Li, Alexis, Yang, Yi, Cui, Hejie, Yang, Carl

“… To address such challenge, this study proposes BrainSTEAM, an integrated framework featuring a spatio-temporal module that consists of an EdgeConv GNN model, an autoencoder network, and a Mixup strategy…”
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Coronary Artery Disease Detection Based on a Novel Multi-Modal Deep-Coding Method Using ECG and PCG Signals by Sun, Chengfa, Liu, Changchun, Wang, Xinpei, Liu, Yuanyuan, Zhao, Shilong

“…Coronary artery disease (CAD) is an irreversible and fatal disease. It necessitates timely and precise diagnosis to slow CAD progression. Electrocardiogram…”
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Leveraging deep feature learning for wearable sensors based handwritten character recognition by Singh, Shashank Kumar, Chaturvedi, Amrita

“…Despite rapid advancements in technology, handwritten characters still hold significant roles in various fields, including education, communication, biometric…”
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Transferring human emotions to robot motions using Neural Policy Style Transfer by Fernandez-Fernandez, Raul, Łukawski, Bartek, Victores, Juan G., Pacchierotti, Claudio

“…Neural Style Transfer (NST) was originally proposed to use feature extraction capabilities of Neural Networks as a way to perform Style Transfer with images…”
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Identification of autism spectrum disorder using multi-regional resting-state data through an attention learning approach by Liu, Yaya, Xu, Lingyu, Yu, Jie, Li, Jun, Yu, Xuan

“…•Identifying ASD with long-short term memory network and autoencoder network.•Heterogeneous data from ABIDE demonstrated classification accuracy of 74.7…”
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