205 citations · 464 across the 19 of their papers we have counts for
19 papers
Development and Evaluation of Ultrasound Image Learning Pipelines for MASLD Risk Stratification
Guangyi Zhang, Xiaohong Wang, Eugene Cheah +4
Metabolic dysfunction-associated steatotic liver disease (MASLD) affects approximately 30% of the general population. Ultrasound-based imaging, including B-mode imaging and shear w…
Alleviating Distortion Accumulation in Multi-Hop Semantic Communication
Guangyi Zhang, Qiyu Hu, Yunlong Cai +1
Recently, semantic communication has been investigated to boost the performance of end-to-end image transmission systems. However, existing semantic approaches are generally based…
SCAN: Semantic Communication with Adaptive Channel Feedback
Guangyi Zhang, Qiyu Hu, Yunlong Cai +1
In existing semantic communication systems for image transmission, some images are generally reconstructed with considerably low quality. As a result, the reliable transmission of…
Deep-Unfolding for Next-Generation Transceivers
Qiyu Hu, Yunlong Cai, Guangyi Zhang +2
The stringent performance requirements of future wireless networks, such as ultra-high data rates, extremely high reliability and low latency, are spurring worldwide studies on def…
FAST: Feature Arrangement for Semantic Transmission
Kequan Zhou, Guangyi Zhang, Yunlong Cai +2
Although existing semantic communication systems have achieved great success, they have not considered that the channel is time-varying wherein deep fading occurs occasionally. Mor…
Adaptive CSI Feedback for Deep Learning-Enabled Image Transmission
Guangyi Zhang, Qiyu Hu, Yunlong Cai +1
Recently, deep learning-enabled joint-source channel coding (JSCC) has received increasing attention due to its great success in image transmission. However, most existing JSCC stu…
Partial Label Learning for Emotion Recognition from EEG
Guangyi Zhang, Ali Etemad
Fully supervised learning has recently achieved promising performance in various electroencephalography (EEG) learning tasks by training on large datasets with ground truth labels.…
A Unified Multi-Task Semantic Communication System for Multimodal Data
Guangyi Zhang, Qiyu Hu, Zhijin Qin +3
Task-oriented semantic communications have achieved significant performance gains. However, the employed deep neural networks in semantic communications have to be updated when the…
Robust Semantic Communications with Masked VQ-VAE Enabled Codebook
Qiyu Hu, Guangyi Zhang, Zhijin Qin +3
Although semantic communications have exhibited satisfactory performance for a large number of tasks, the impact of semantic noise and the robustness of the systems have not been w…
A Unified Multi-Task Semantic Communication System with Domain Adaptation
Guangyi Zhang, Qiyu Hu, Zhijin Qin +2
The task-oriented semantic communication systems have achieved significant performance gain, however, the paradigm that employs a model for a specific task might be limited, since…
PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition
Guangyi Zhang, Vandad Davoodnia, Ali Etemad
We propose PARSE, a novel semi-supervised architecture for learning strong EEG representations for emotion recognition. To reduce the potential distribution mismatch between the la…
Robust Semantic Communications Against Semantic Noise
Qiyu Hu, Guangyi Zhang, Zhijin Qin +3
Although the semantic communications have exhibited satisfactory performance in a large number of tasks, the impact of semantic noise and the robustness of the systems have not bee…
Holistic Semi-Supervised Approaches for EEG Representation Learning
Guangyi Zhang, Ali Etemad
Recently, supervised methods, which often require substantial amounts of class labels, have achieved promising results for EEG representation learning. However, labeling EEG data i…
Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition
Guangyi Zhang, Ali Etemad
EEG-based emotion recognition often requires sufficient labeled training samples to build an effective computational model. Labeling EEG data, on the other hand, is often expensive…
Distilling EEG Representations via Capsules for Affective Computing
Guangyi Zhang, Ali Etemad
Affective computing with Electroencephalogram (EEG) is a challenging task that requires cumbersome models to effectively learn the information contained in large-scale EEG signals,…
Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean Space
Guangyi Zhang, Ali Etemad
We present a novel deep neural architecture for learning electroencephalogram (EEG). To learn the spatial information, our model first obtains the Riemannian mean and distance from…
Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation
Guangyi Zhang, Ali Etemad
Driver vigilance estimation is an important task for transportation safety. Wearable and portable brain-computer interface devices provide a powerful means for real-time monitoring…
Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network
Guangyi Zhang, Vandad Davoodnia, Alireza Sepas-Moghaddam +2
Classifying limb movements using brain activity is an important task in Brain-computer Interfaces (BCI) that has been successfully used in multiple application domains, ranging fro…
Errors-in-variables Modeling of Personalized Treatment-Response Trajectories
Guangyi Zhang, Reza Ashrafi, Anne Juuti +2
Estimating the effect of a treatment on a given outcome, conditioned on a vector of covariates, is central in many applications. However, learning the impact of a treatment on a co…