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Optical Flow-Enhanced Mamba U-Net for Cardiac Phase Detection in Ultrasound Videos
Yuhuan Lu;Guanghua Tan;Bin Pu;Pak-Hei Yeung;Hang Wang;Shengli Li;Jagath C. Rajapakse;Kenli Li
IEEE Transactions on Medical Imaging
Year: 2025 | Early Access Article |
The detection of cardiac phase in ultrasound videos, identifying end-systolic (ES) and end-diastolic (ED) frames, is a critical step in assessing cardiac function, monitoring structural changes, and diagnosing congenital heart disease. Current popular methods use recurrent neu ral networks to track dependencies over long sequences for cardiac phase detection, but often overlook the short-term moti...Show More
TextHair3D: Text-driven 3D Hair Editing with Generative Priors
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Text-driven hair editing on 3D heads is a challenging problem in computer vision and graphics. In this paper, we propose TextHair3D, a NeRF-based text-driven 3D hair editing method that uses 3D perception to generate priors, edit hair attributes from user-provided text, and preserve facial features. TextHair3D uses the Contrastive Language-Image Pre-training (CLIP) model to encode textual conditio...Show More
AP-Net: Semi-Supervised Ultrasound Cardiac Segmentation Using Enhanced Anatomical Prior
Yuhuan Lu;Jintang Li;Jianxin Lin;Ying Yuan;Jagath C. Rajapakse;Ningbo Zhu;Chunlian Wang;Kenli Li
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Semi-supervised segmentation is gaining popularity in medical image analysis due to challenges in data acquisition and annotation. However, most methods focus on generating additional training pairs from unlabeled data through augmentation or perturbation for contrastive learning, often overlooking the unique characteristics and inherent priors of medical images. We identified two key anatomical p...Show More
AP-Net: Semi-Supervised Ultrasound Cardiac Segmentation Using Enhanced Anatomical Prior
Yuhuan Lu;Jintang Li;Jianxin Lin;Ying Yuan;Jagath C. Rajapakse;Ningbo Zhu;Chunlian Wang;Kenli Li
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Stackelberg Game-Based Pricing and Offloading for the DVFS-Enabled MEC Systems
IEEE Transactions on Network and Service Management
Year: 2025 | Early Access Article |
Due to the limited computing resources of both mobile devices (MDs) and the mobile edge computing (MEC) server, devising reasonable strategies for MD task offloading, MEC server resource pricing, and resource allocation is crucial. In this paper, a scenario is considered, comprising multiple MDs and a single MEC server. Each MD has a divisible task in each time slot, allowing for partial offloadin...Show More
Robust Hashing with Bilinear Drift for Image-Text Retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Year: 2025 | Early Access Article |
Supervised hashing models for image-text retrieval are fundamental and versatile in social media analysis and cross-lingual web search. Among them, supervised bilinear drift hashing is one of the most popular approaches. However, it still faces several challenges. For instance, how to leverage the power of bilinear drift hashing to distinguish similar and dissimilar data samples effectively; how t...Show More
Power Management Optimization for Data Centers: A Power Supply Perspective
IEEE Transactions on Sustainable Computing
Year: 2025 | Early Access Article |
With the escalating demand for cloud computing services, data centers (DCs) encounter formidable challenges extending beyond capital investment needs to accommodate increasing computational demands and routine infrastructure maintenance. These challenges include substantial electricity costs due to high energy consumption and the environmental issues caused by high carbon emissions. To reduce cost...Show More
A Low-rate DoS Attack Mitigation Scheme Based on Port and Traffic State in SDN
Dan Tang;Rui Dai;Chenguang Zuo;Jingwen Chen;Keqin Li;Zheng Qin
IEEE Transactions on Computers
Year: 2025 | Early Access Article |
Low-rate Denial of Service (DoS) attacks can significantly compromise network availability and are difficult to detect and mitigate due to their stealthy exploitation of flaws in congestion control mechanisms. Software-Defined Networking (SDN) is a revolutionary architecture that decouples network control from packet forwarding, emerging as a promising solution for defending against low-rate DoS a...Show More
FastCRL: A Fast Network With Adaptive Fourier Transform and Offset Prediction for Fetal Crown-Rump Length Measurement and Position Estimation in Ultrasound Images
Jiatao Liu;Ying Tan;Chunlian Wang;Kenli Li;Guanghua Tan;Chubo Liu
IEEE Journal of Biomedical and Health Informatics
Year: 2025 | Early Access Article |
Fetal crown-rump length (CRL) is one of the most accurate method for estimating gestational age in early pregnancy. Typically, the process of manual CRL measurement by physicians is cumbersome, prone to errors due to fetal position, and susceptible to inter-observer variability. To provide an accurate, real-time, and reliable fetal CRL measurement solution, we propose FastCRL that utilizes key lan...Show More
A Cost-Aware Operator Migration Approach for Distributed Stream Processing System
Jiawei Tan;Zhuo Tang;Wentong Cai;Wen Jun Tan;Xiong Xiao;Jiapeng Zhang;Yi Gao;Kenli Li
Stream processing is integral to edge computing due to its low-latency attributes. Nevertheless, variability in user group sizes and disparate computing capabilities of edge devices necessitate frequent operator migrations within the stream. Moreover, intricate dependencies among stream operators often obscure the detection of potential bottleneck operators until an identified bottleneck is migrat...Show More
Joint Computation Offloading and Resource Allocation in Mobile-Edge Cloud Computing: A Two-Layer Game Approach
Zhenli He;Ying Guo;Xiaolong Zhai;Mingxiong Zhao;Wei Zhou;Keqin Li
Mobile-Edge Cloud Computing (MECC) plays a crucial role in balancing low-latency services at the edge with the computational capabilities of cloud data centers (DCs). However, many existing studies focus on single-provider settings or limit their analysis to interactions between mobile devices (MDs) and edge servers (ESs), often overlooking the competition that occurs among ESs from different prov...Show More
MSCNet: Multi-Scale Network with Convolutions for Long-term Cloud Workload Prediction
IEEE Transactions on Services Computing
Year: 2025 | Early Access Article |
Accurate workload prediction is crucial for resource allocation and management in large-scale cloud data centers. While many approaches have been proposed, most existing methods are based on Recurrent Neural Networks (RNNs) or their variants, focusing on short-term cloud workload prediction without considering or identifying the long-term changes and different periodic patterns of cloud workloads....Show More
Towards Accurate Truth Discovery With Privacy-Preserving Over Crowdsourced Data Streams
Zhimao Gong;Zhibang Yang;Shenghong Yang;Siyang Yu;Kenli Li;Mingxing Duan
Truth discovery endeavors to extract valuable information from multi-source data through weighted aggregation. Some studies have integrated differential privacy techniques into traditional truth discovery algorithms to protect data privacy. However, due to the neglect of outliers and limitations in budget allocation, these schemes still need improvement in the accuracy of discovery results. To sol...Show More
Zram Instance Pool Framework for Adaptive Memory Compression in Resource-Sensitive Embedded Operating Systems
Yin Deng;Guoqi Xie;Chenglai Xiong;Sirong Zhao;Wei Ren;Kenli Li
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Year: 2025 | Early Access Article |
Memory compression can reduce the size of the inactive data in the random access memory (RAM), thereby freeing up unused space and allowing more programs to run; however, current mainstream memory compression frameworks (e.g., Zram and Zswap) and algorithms (e.g., Zstd and Lz4) do not effectively solve the problem of increased CPU utilization, causing they cannot be directly applied to the resourc...Show More
Reputation-Based Model Aggregation and Resource Optimization in Wireless Federated Learning Systems
IEEE Transactions on Wireless Communications
Year: 2025 | Early Access Article |
Federated learning (FL) has received widespread attention from academia and industry because it overcomes traditional security limitations associated with model training data. However, the FL process is vulnerable to manipulation by locally malicious users, who can alter their local data, thus impacting the accuracy of the model’s training outcomes. Meanwhile, optimizing delay in FL needs to take ...Show More
A Novel Completion Method for Sparse Traffic Data Imputation
Renqiu Ouyang;Yikun Hu;Haotian Wang;Rong Hu;Wangdong Yang;Kenli Li
IEEE Intelligent Transportation Systems Magazine
Year: 2025 | Early Access Article |
Traffic data imputation is essential in smart cities and the Internet of Things (IoT). Tensor completion is an efficient method for traffic data imputation. However, these methods overlook the integration of contextual and spatial information, which are important for traffic data imputation. Hence, this study proposes a novel tensor completion method leveraging contextual and spatial information f...Show More
Machine Unlearning Through Fine-Grained Model Parameters Perturbation
Machine unlearning involves retracting data records and reducing their influence on trained models, aiding user privacy protection, at a significant computational cost potentially. Weight perturbation-based unlearning is common but typically modifies parameters globally. We propose fine-grained Top-K and Random-k parameters perturbed inexact machine unlearning that address the privacy needs while ...Show More
A Novel Dynamic Hill Cipher and Its Applications on Medical IoT
IEEE Internet of Things Journal
Year: 2025 | Early Access Article |
Cryptography is one of the most important areas in information security. Cryptography ensures secure communication and data privacy, and it is increasingly being applied in healthcare and related fields. As an important classical cryptographic method, the Hill cipher has always been closely studied by experts and scholars. In order to enhance the security of the conventional Hill cipher (CHC), a n...Show More
AVL Function Table for LeafHooks Insertion With Obfuscated Control Flow Integrity
Sirong Zhao;Guoqi Xie;Chenglai Xiong;Kenli Li;Xuejun Yu;Bo Wan;Yiwen Jiang
Control flow is the execution order of individual statements, instructions, or function calls within an imperative program. Malicious operation of control flow (e.g., tampering with normal function addresses) leads to severe consequences such as data leakage and system crash. Control Flow Integrity (CFI) is a defense restricting the execution order of program within Control Flow Graph (CFG). Index...Show More
Prediction of Heterogeneous Device Task Runtime Based on Edge Server-Oriented Deep Neuro-Fuzzy System
Haijie Wu;Weiwei Lin;Wangbo Shen;Xiumin Wang;C. L. Philip Chen;Keqin Li
Predicting the runtime of tasks is of great significance as it can help users better understand the future runtime consumption of the tasks and make decisions for their heterogeneous devices, or be applied to task scheduling. Learning features from user task history data for predicting task runtime is a mainstream method. However, this method faces many challenges when applied to edge intelligence...Show More
GroupFace: Imbalanced Age Estimation Based on Multi-Hop Attention Graph Convolutional Network and Group-Aware Margin Optimization
With the recent advances in computer vision, age estimation has significantly improved in overall accuracy. However, owing to the most common methods do not take into account the class imbalance problem in age estimation datasets, they suffer from a large bias in recognizing long-tailed groups. To achieve high-quality imbalanced learning in long-tailed groups, the dominant solution lies in that th...Show More
A Context-Awareness and Hardware-Friendly Sparse Matrix Multiplication Kernel for CNN Inference Acceleration
Haotian Wang;Yan Ding;Yumeng Liu;Weichen Liu;Chubo Liu;Wangdong Yang;Kenli Li
Sparsification technology is crucial for deploying convolutional neural networks in resource-constrained environments. However, the efficiency of sparse models is hampered by irregular memory access patterns in sparse matrix multiplication kernels. Hardware-level support for 2:4 granularity in sparse tensor cores presents an opportunity for designing efficient sparse matrix multiplication kernels....Show More
Distributed Edge Intelligence for Rapid In-Vehicle Medical Emergency Response in Internet of Vehicles
The unparalleled possibilities of Internet of Vehicles (IoV) development prompt the enhancement of in-vehicle medical emergency response. Nevertheless, the IoV environment is still affected by data privacy, latency, and network instability, which hamper effective and reliable emergency medical systems. In this regard, this article suggests the emergency-aware distributed edge intelligence (DEI) fo...Show More
TFEGRU: Time-Frequency Enhanced Gated Recurrent Unit With Attention for Cloud Workload Prediction
Feiyu Zhao;Weiwei Lin;Shengsheng Lin;Haocheng Zhong;Keqin Li
Accurate prediction of cloud workload is crucial for effective resource allocation in cloud computing. However, due to the complexity and high dimensionality of workloads in the cloud environment, achieving precise workload prediction is a complex and challenging problem. Current approaches to cloud workload prediction mainly rely on deep learning methods based on the Recurrent Neural Network (RNN...Show More
PetTC: Pairwise Joint Embedding Based Contrastive Tensor Completion for Network Traffic Monitoring Services
Haoxuan Wang;Kun Xie;Jigang Wen;Guangxing Zhang;Wei Liang;Gaogang Xie;Kenli Li
Network traffic matrices often suffer from incompleteness and sparsity due to various factors, including network device policies and system limitations. The incompleteness can undermine the reliability and accuracy of network traffic monitoring services, negatively impacting downstream tasks such as network planning and fault diagnosis. Our focus is on network traffic data recovery, intending to i...Show More
Collaborative Recommendation of National Image Resources for Targeted International Communication via Multidimensional Features and E-CARGO Modeling
With the acceleration of globalization, the targeted international communication of national images contributes to enhancing a nation’s soft power and international recognition. It is challenging to select appropriate resources from the mass candidates for creating promotional works of national image. Existing research only focuses on the methodologies and lacks the systematic modeling and solving...Show More
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