Shuaiqi Liu - IEEE Xplore Author Profile

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Despite the high spectral resolution and abundant information of hyperspectral images (HSIs), their spatial resolution is relatively low due to limitations in sensor technology. Sensors often need to sacrifice some spatial resolution to ensure accurate light energy measurement when pursuing high spectral resolution. This tradeoff results in HSI’s inability to capture fine spatial details, thereby ...Show More
Data centers have become the digital backbone of modern society, and the deployment of data center networks has evolved toward massive scale and multipath. However, existing designs still face challenges in terms of ease of deployment and plug-and-play. To address these issues, this paper proposes a hybrid data center network SHCN that supports visible light communication (VLC). First, all racks a...Show More
Hyperspectral images (HSIs) are extensively utilized in several fields due to their abundant spectral band, particularly for tasks like ground object classification and environmental monitoring. However, as a result of equipment and imaging condition constraints, HSI frequently demonstrates a restricted spatial resolution. The fusion of a low-resolution HSI and a high-resolution multispectral imag...Show More
Electroencephalogram (EEG) is widely utilized in emotion recognition owing to its unique advantages. To achieve more optimal cross-subject emotion recognition, a cross subject emotion recognition method based on interconnection dynamic domain adaptation (IDDA) is proposed. In IDDA, dynamic graph convolution (DGC) is employed to dynamically learn the intrinsic relationships between different EEG ch...Show More
The early diagnosis of focal liver lesions (FLLs) plays a key role in the successful treatment of liver cancer. To effectively diagnose focal liver lesions, we used contrast-enhanced ultrasound (CEUS) to diagnose FLLs. A hybrid CNN and Transformer network is used to extract local and global spatio-temporal features of CEUS. Firstly, the R(2+1)D with pre-trained weights is used to extract local mul...Show More
To reduce the dependence on tagged data, we proposed a Contrastive Functional Connectivity Graph Learning Network (CFCG-Net) for the diagnosis of autism spectrum disorder. CFCG-Net is mainly composed of three parts: construction of contrastive Functional Connection (FC) graphs, learning of contrastive FC graphs, and dynamic graph classification based on population graph. Firstly, we constructed co...Show More
Synthetic aperture radar (SAR) tends to be seriously affected by speckle noise due to its inherent imaging characteristics, which brings great challenges to the high-level visualization task of SAR images. Speckle suppression, therefore, plays a crucial role in remote sensing image processing. Attention-based SAR image denoising algorithms frequently struggle to capture rich feature information an...Show More
Abstractive summarization aims to generate a concise summary covering the input document's salient information. Within a report document, the salient information can be scattered in the textual and non-textual content. However, existing document summarization datasets and methods usually focus on the text and filter out the non-textual content. Missing tabular data can limit produced summaries’ in...Show More
Goal: The purpose of this paper is to recognize autism spectrum disorders (ASD) using graph attention network. Methods: we propose a node features graph attention network (NF-GAT) for learning functional connectivity (FC) features to achieve ASD diagnosis. Firstly, node features are modelled based on functional magnetic resonance imaging (fMRI) data, with each subject modelled as a graph. Next, we...Show More
Aspect-based meeting transcript summarization aims to produce multiple summaries, each focusing on one aspect of content in a meeting transcript. It is challenging as sentences related to different aspects can mingle together, and those relevant to a specific aspect can be scattered throughout the long transcript of a meeting. The traditional summarization methods produce one summary mixing inform...Show More
Product attribute value extraction is an important task in e-Commerce which can help several downstream applications such as product search and recommendation. Most previous models handle this task using sequence labeling or question answering method which rely on the sequential position information of values in the product text and are vulnerable to data discrepancy between training and testing. ...Show More
The existence of speckles in synthetic aperture radar (SAR) images affects its subsequent application in computer vision tasks, so the research of speckle suppression plays a very important role. Convolutional neural networks based speckle suppression algorithms cannot reach a good balance between despeckling effect and structure detail preservation. Considering these issues, a multiscale feature ...Show More
Cell-free multiple-input multiple-output (MIMO) and reconfigurable intelligent surface (RIS) have been envisioned as two promising techniques to enhance the data transmission rate of high-speed railway (HSR) networks. This letter considers the HSR cell-free MIMO system empowered by RIS with finite discrete phase shifters to pursue performance improvement. Particularly, the RIS phase shift optimiza...Show More
The psychiatric condition known as autism spectrum disorder (ASD) affects children and adults alike. As a medical imaging technology, functional magnetic resonance imaging (fMRI) is widely used to study the brains of persons with ASD. This study introduces a novel technique: a pseudo 4D ResNet (P4D ResNet) to simultaneously extract and classify the brain activity of ASD patients. A P4D ResNet can ...Show More
Hyperspectral image (HSI) and multispectral image (MSI) fusion has the potential to significantly improve the quality and usefulness of data, leading to better decision-making and a more complete understanding of the observed scene. For HSI and MSI fusion, capturing matched pairs of HSI and MSI images is challenging. This hampers the pretraining of neural-network-based HSI–MSI fusion methods and y...Show More
Deep learning-based detection methods have achieved great success in ship target detection in synthetic aperture radar (SAR) images. However, due to the interference of imaging mechanism, speckle noise, and sea and land clutter, ship detection in SAR images still suffers from difficult interpretation. It is found that most ship detection algorithms focus on object-level detection while ignoring pi...Show More
Compared with the traditional remote sensing image, there is a large amount of spectral information in the hyperspectral image (HSI), which makes HSI better reflect the actual condition of surface features. However, due to the limitations of imaging conditions, HSI tends to have a lower spatial resolution. In order to overcome this issue, we propose a spectral–spatial attention-based U-Net named S...Show More
In this paper, an active-user cooperative scheme for overlay cognitive radio (OCR) vehicle-to-vehicle (V2V) communication system based on three-dimensional (3D) channel model is proposed. Based on the proposed cooperative scheme, the achievable rate regions of the primary users (PU) and secondary users (SU) with outdated channel state information (CSI) are analyzed. According to the tradeoff betwe...Show More
As a kind of non-invasive, low-cost, and readily available brain examination, EEG has attached significance to the means of clinical diagnosis of epilepsy. However, the reading of long-term EEG records has brought a heavy burden to neurologists and experts. Therefore, automatic EEG classification for epileptic patients plays an essential role in epilepsy diagnosis and treatment. This paper propose...Show More
Recently, methods based on deep learning have been successfully applied to ship detection for synthetic aperture radar (SAR) images. However, most current ship detection networks rely too much on the anchor mechanism. These methods have low accuracy and poor generalization ability for multiscale ship detection. To solve the aforementioned problems, an anchor-free framework for multiscale ship dete...Show More
Synthetic aperture radar (SAR), due to its inherent characteristics, will produce speckle noise, which results in the deterioration of image quality, so the removal of speckle in SAR image is very important for the subsequent high-level image processing. In order to balance the relationship between denoising and texture preservation, we propose a multiscale residual dense dual attention network (M...Show More
Since electroencephalogram (EEG) signals can truly reflect human emotional state, emotion recognition based on EEG has turned into a critical branch in the field of artificial intelligence. Aiming at the disparity of EEG signals in various emotional states, we propose a new deep learning model named three-dimension convolution attention neural network (3DCANN) for EEG emotion recognition in this p...Show More
To reconstruct images with high spatial resolution and high spectral resolution, one of the most common methods is to fuse a low-resolution hyperspectral image (HSI) with a high-resolution (HR) multispectral image (MSI) of the same scene. Deep learning has been widely applied in the field of HSI-MSI fusion, which is limited with hardware. In order to break the limits, we construct an unsupervised ...Show More
Face anti-spoofing is essential to the face recognition system. Although previous work has made great progress in spatial auxiliary supervision and temporal feature extraction, there are still great challenges in exploring the characteristics of facial depth as auxiliary information and modeling lightweight temporal networks. To address the above problems, we design a two-stream spatial-temporal n...Show More
Attention deficit/Hyperactivity disorder (ADHD) is a complex, universal and heterogeneous neurodevelopmental disease. The traditional diagnosis of ADHD relies on the long-term analysis of complex information such as clinical data (electroencephalogram, etc.), patients' behavior and psychological tests by professional doctors. In recent years, functional magnetic resonance imaging (fMRI) has been d...Show More