6 citations · 34 across the 18 of their papers we have counts for
21 papers
Joint Beamforming Design for RIS-Empowered NOMA-ISAC Systems
Chunjie Wang, Xuhui Zhang, Jinke Ren +6
This paper investigates a reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system and proposes a joint communication and sensing beamfo…
PTSM: Physiology-aware and Task-invariant Spatio-temporal Modeling for Cross-Subject EEG Decoding
Changhong Jing, Yan Liu, Shuqiang Wang +5
Cross-subject electroencephalography (EEG) decoding remains a fundamental challenge in brain-computer interface (BCI) research due to substantial inter-subject variability and the…
Coordinated Beamforming for RIS-Empowered ISAC Systems over Secure Low-Altitude Networks
Chunjie Wang, Xuhui Zhang, Wenchao Liu +4
Emerging as a cornerstone for next-generation wireless networks, integrated sensing and communication (ISAC) systems demand innovative solutions to balance spectral efficiency and…
Full-Duplex Integrated Sensing, Communication, and Computation over Low-Altitude Wireless Networks
Yiyang Chen, Wenchao Liu, Xuhui Zhang +5
With low-altitude economies emerging as a pivotal sector, this study explores an integrated sensing, communication, and computation system over low-altitude wireless networks. A fu…
Hierarchical Learning for IRS-Assisted MEC Systems with Rate-Splitting Multiple Access
Yinyu Wu, Xuhui Zhang, Yingchao Jiao +5
Intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) systems have shown notable improvements in efficiency, such as reduced latency, higher data rates, and bet…
Energy-Efficient Multi-UAV-Enabled MEC Systems over Space-Air-Ground Integrated Networks
Wenchao Liu, Xuhui Zhang, Chunjie Wang +5
With the development of artificial intelligence integrated next-generation communication networks, mobile users (MUs) are increasingly demanding the efficient processing of computa…
Joint Association, Beamforming, and Resource Allocation for Multi-IRS Enabled MU-MISO Systems With RSMA
Chunjie Wang, Xuhui Zhang, Huijun Xing +5
Intelligent reflecting surface (IRS) and rate-splitting multiple access (RSMA) technologies are at the forefront of enhancing spectrum and energy efficiency in the next generation…
Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition
Yihang Dong, Xuhang Chen, Yanyan Shen +3
Emotion recognition based on Electroencephalography (EEG) has gained significant attention and diversified development in fields such as neural signal processing and affective comp…
Cross-Modal Transformer GAN: A Brain Structure-Function Deep Fusing Framework for Alzheimer's Disease
Junren Pan, Shuqiang Wang
Cross-modal fusion of different types of neuroimaging data has shown great promise for predicting the progression of Alzheimer's Disease(AD). However, most existing methods applied…
Feature-selected Graph Spatial Attention Network for Addictive Brain-Networks Identification
Changwei Gong, Changhong Jing, Junren Pan +1
Functional alterations in the relevant neural circuits occur from drug addiction over a certain period. And these significant alterations are also revealed by analyzing fMRI. Howev…
Dynamic Community Detection via Adversarial Temporal Graph Representation Learning
Changwei Gong, Changhong Jing, Yanyan Shen +1
Dynamic community detection has been prospered as a powerful tool for quantifying changes in dynamic brain network connectivity patterns by identifying strongly connected sets of n…
Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN
Wen Yu, Baiying Lei, Yanyan Shen +5
The diagnosis of early stages of Alzheimer's disease (AD) is essential for timely treatment to slow further deterioration. Visualizing the morphological features for the early stag…
A Prior Guided Adversarial Representation Learning and Hypergraph Perceptual Network for Predicting Abnormal Connections of Alzheimer's Disease
Qiankun Zuo, Baiying Lei, Shuqiang Wang +3
Alzheimer's disease is characterized by alterations of the brain's structural and functional connectivity during its progressive degenerative processes. Existing auxiliary diagnost…
DecGAN: Decoupling Generative Adversarial Network detecting abnormal neural circuits for Alzheimer's disease
Junren Pan, Baiying Lei, Shuqiang Wang +3
One of the main reasons for Alzheimer's disease (AD) is the disorder of some neural circuits. Existing methods for AD prediction have achieved great success, however, detecting abn…
Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis
Junren Pan, Baiying Lei, Yanyan Shen +3
Using multimodal neuroimaging data to characterize brain network is currently an advanced technique for Alzheimer's disease(AD) Analysis. Over recent years the neuroimaging communi…
Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction
Qiankun Zuo, Baiying Lei, Yanyan Shen +3
Multimodal neuroimage can provide complementary information about the dementia, but small size of complete multimodal data limits the ability in representation learning. Moreover,…
A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction
Bowen Hu, Baiying Lei, Yanyan Shen +2
Fusing medical images and the corresponding 3D shape representation can provide complementary information and microstructure details to improve the operational performance and accu…
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain
Senrong You, Yong Liu, Baiying Lei +1
Magnetic resonance imaging plays an important role in computer-aided diagnosis and brain exploration. However, limited by hardware, scanning time and cost, it's challenging to acqu…
Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis
Shengye Hu, Baiying Lei, Yong Wang +3
Fusing multi-modality medical images, such as MR and PET, can provide various anatomical or functional information about human body. But PET data is always unavailable due to diffe…
Tensorizing GAN with High-Order Pooling for Alzheimer's Disease Assessment
Wen Yu, Baiying Lei, Michael K. Ng +3
It is of great significance to apply deep learning for the early diagnosis of Alzheimer's Disease (AD). In this work, a novel tensorizing GAN with high-order pooling is proposed to…
Brain Stroke Lesion Segmentation Using Consistent Perception Generative Adversarial Network
Shuqiang Wang, Zhuo Chen, Wen Yu +1
The state-of-the-art deep learning methods have demonstrated impressive performance in segmentation tasks. However, the success of these methods depends on a large amount of manual…