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arXiv 2025年

作者： Sun, Geng Ma, Weilong Li, Jiahui Sun, Zemin Wang, Jiacheng Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Software Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas. The computation task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV. In this paper, we consider a UAV-assisted MEC system where the UAV carries the edge servers to facilitate task offloading for ground devices (GDs), and formulate a calculation delay and energy consumption multi-objective optimization problem (CDECMOP) to simultaneously improve the performance and reduce the cost of the system. Then, by modeling the formulated problem as a multi-objective Markov decision process (MOMDP), we propose a multi-objective deep reinforcement learning (DRL) algorithm within an evolutionary framework to dynamically adjust the weights and obtain non-dominated policies. Moreover, to ensure stable convergence and improve performance, we incorporate a target distribution learning (TDL) algorithm. Simulation results demonstrate that the proposed algorithm can better balance multiple optimization objectives and obtain superior non-dominated solutions compared to other methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Cloud-Based 3D Virtual Interactive Lab Design

Cloud-Based 3D Virtual Interactive Lab Design

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Cyber Resilience (ICCR), International Conference on

作者： Suhad Ibraheem Kadhem Yitong Niu Jamal F. Tawfeq Ahmed Dheyaa Radhi Karamath Ateeq Saif. E. A. Alnawayseh Computer Science Ministry of Finance School of Aeronautical Engineering AnYang University Anyang China Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq College of Pharmacy University of Al-Ameed Karbala Iraq School of Computing Skyline University College University City Sharjah Sharjah UAE Department of Electrical Engineering College of Engineering Mutah University Al-Karak Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

With the continuous development of cloud computing technology, cloud technology has become an optional solution for constructing college laboratories. In this paper, we introduce the design and implementation of a cloud computing technology-based laboratory from physical facilities and the virtualization of a cloud computing technology laboratory. The virtual laboratory can make students learn in the virtual environment, which is really outside the classroom boundary. The data used for teaching is stored in the cloud space, which can make teaching data storage more convenient and low-cost. Furthermore, 3d labs show learners 3D video images of teaching resources, which makes the presentation look realistic and improves the absorption of key knowledge in the course. Compared with the traditional non-cloud technology college laboratory platform, this platform can effectively improve management efficiency and reduce configuration costs. It can incorporate the relevant technologies in the cloud computing framework into the existing computer-related professional courses.

关键词： computers Industries Cloud computing Three-dimensional displays Costs Laboratories Education

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Intracardiac Electrogram Signals Key Feature Points Recognition Using Dense Convolutional Network

Intracardiac Electrogram Signals Key Feature Points Recognit...

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Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Jiang Yihang Gu Kaihao Wu Xiaomei Department of Biomedical Engineering School of information Science and Technology Fudan University Shanghai China Department of Biomedical Engineering Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai School of information Science and Technology Fudan University Academy for Engineering and Technology Shanghai Engineering Research Center of Assistive Devices Shanghai China Yiwu Research Institute of Fudan University Zhejiang China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

During radiofrequency ablation procedures for atrial fibrillation patients, physicians can assist in locating ablation targets by analyzing the conduction pathways and low-voltage areas of the intracardiac electrogram signals. The feature points of intracardiac electrogram signals serve as criteria for assessing excitation patterns and low-voltage areas. Therefore, extracting feature points from intracardiac signals is of significant value. To identify key points in intracardiac electrogram signals through deep learning, this experiment collected intracardiac electrogram signals from 21 clinical patients, which were then filtered using wavelet transformation. Subsequently, each excitation was treated as a signal group, indicating the key feature points within. Finally, a total of 36,183 groups of intracardiac electrogram signals annotated with key points were obtained. The dataset consisted of data from 17 patients allocated for training, data from 2 patients reserved for testing, and data from the remaining 2 patients designated for validation purposes. Training was conducted using a DenseNet network incorporating both convolutional attention and feature pyramid modules. The final network successfully identifies the feature points of intracardiac electrogram signals. It outputs six feature points, including the starting point, first rising point, first falling point, peak point, valley point, and termination point, with average absolute errors of only 0.34, 0.27, 0.31, 0.01, 0.27, and 0.42 ms, respectively. The final detection results are highly satisfactory, indicating the potential utility of the findings in assisting the analysis of intracardiac electrogram signals. The research outcomes presented in this paper lay a foundation for the analysis of intracardiac electrogram signals.

关键词： Training Wavelet transforms Radio frequency Low voltage Training data medical services Myocardium Feature extraction Pattern recognition Testing

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H-GCN: A Graph Convolutional Network Accelerator on Versal ACAP Architecture

H-GCN: A Graph Convolutional Network Accelerator on Versal A...

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International Conference on Field Programmable Logic and Applications

作者： Chengming Zhang Tong Geng Anqi Guo Jiannan Tian Martin Herbordt Ang Lit Dingwen Tao School of Electrical Engineering and Computer Science Washington State University Pullman WA USA School of Informatics Computing and Engineering Indiana University Bloomington IN USA Mathematics and Computer Science Division Pacific Northwest National Laboratory Richland WA USA Department of Electrical and Computer Engineering University of Rochester Rochester NY USA Department of Electrical and Computer Engineering Boston University Boston MA USA

Graph Neural Networks (GNNs) have drawn tremendous attention due to their unique capability to extend Machine Learning (ML) approaches to applications broadly-defined as having unstructured data, especially graphs. Compared with other Machine Learning (ML) modalities, the acceleration of Graph Neural Networks (GNNs) is more challenging due to the irregularity and heterogeneity derived from graph typologies. Existing efforts, however, have focused mainly on handling graphs' irregularity and have not studied their heterogeneity. To this end we propose H-GCN, a PL (Programmable Logic) and AIE (AI Engine) based hybrid accelerator that leverages the emerging heterogeneity of Xilinx Versal Adaptive Compute Acceleration Platforms (ACAPs) to achieve high-performance GNN inference. In particular, H-GCN partitions each graph into three subgraphs based on its inherent heterogeneity, and processes them using PL and AIE, respectively. To further improve performance, we explore the sparsity support of AIE and develop an efficient density-aware method to automatically map tiles of sparse matrix-matrix multiplication (SpMM) onto the systolic tensor array. Compared with state-of-the-art GCN accelerators, H-GCN achieves, on average, speedups of 1.1~2.3x.

关键词： Tensors Adaptive systems Machine learning computer architecture Systolic arrays Graph neural networks Sparse matrices

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GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph

arXiv

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arXiv 2024年

作者： Liu, Shaonan Chen, Wenting Liu, Jie Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China Department of Electrical Engineering City University of Hong Kong Hong Kong AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians’ ocular movements during image interpretation, thereby elucidating their visual attention patterns and information-processing strategies. In this paper, we initially define the context-aware gaze estimation problem in medical radiology report settings. To understand the attention allocation and cognitive behavior of radiologists during the medical image interpretation process, we propose a context-aware Gaze EstiMation (GEM) network that utilizes eye gaze data collected from radiologists to simulate their visual search behavior patterns throughout the image interpretation process. It consists of a context-awareness module, visual behavior graph construction, and visual behavior matching. Within the context-awareness module, we achieve intricate multimodal registration by establishing connections between medical reports and images. Subsequently, for a more accurate simulation of genuine visual search behavior patterns, we introduce a visual behavior graph structure, capturing such behavior through high-order relationships (edges) between gaze points (nodes). To maintain the authenticity of visual behavior, we devise a visual behavior-matching approach, adjusting the high-order relationships between them by matching the graph constructed from real and estimated gaze points. Extensive experiments on four publicly available datasets demonstrate the superiority of GEM over existing methods and its strong generalizability, which also provides a new direction for the effective utilization of diverse modalities in medical image interpretation and enhances the interpretability of models in the field of medical imaging. https://***/Tiger-SN/GEM. Copyright © 2024, The Authors. All rights reserved.

关键词： Eye movements

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Polyhedra Encoding Transformers: Enhancing Diffusion MRI Analysis Beyond Voxel and Volumetric Embedding

arXiv

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arXiv 2025年

作者： Yao, Tianyuan Li, Zhiyuan Kanakaraj, Praitayini Archer, Derek B. Schilling, Kurt Beason-Held, Lori Resnick, Susan Landman, Bennett A. Huo, Yuankai Department of Computer Science Vanderbilt University NashvilleTN United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Department of Neurology Vanderbilt University Medical Center NashvilleTN United States Department of Biomedical Engineering Vanderbilt University NashvilleTN United States Laboratory of Behavioral Neuroscience National Institute on Aging BaltimoreMD United States

Diffusion-weighted Magnetic Resonance Imaging (dMRI) is an essential tool in neuroimaging. It is arguably the sole noninvasive technique for examining the microstructural properties and structural connectivity of the brain. Recent years have seen the emergence of machine learning and data-driven approaches that enhance the speed, accuracy, and consistency of dMRI data analysis. However, traditional deep learning models often fell short, as they typically utilize pixel-level or volumetric patch-level embeddings similar to those used in structural MRI, and do not account for the unique distribution of various gradient encodings. In this paper, we propose a novel method called Polyhedra Encoding Transformer (PE-Transformer) for dMRI, designed specifically to handle spherical signals. Our approach involves projecting an icosahedral polygon onto a unit sphere to resample signals from predetermined directions. These resampled signals are then transformed into embeddings, which are processed by a transformer encoder that incorporates orientational information reflective of the icosahedral structure. Through experimental validation with various gradient encoding protocols, our method demonstrates superior accuracy in estimating multi-compartment models and Fiber Orientation Distributions (FOD), outperforming both conventional CNN architectures and standard transformers. © 2025, CC BY.

关键词： Magnetic resonance imaging

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Energy Efficient Collaborative Federated Learning Design: A Graph Neural Network based Approach

Energy Efficient Collaborative Federated Learning Design: A ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

In this paper, we consider the design of an energy efficient collaborative federated learning (CFL) methodology where devices exchange their local FL parameters with a subset of their neighbors without reliance on a parameter server. In the considered model, mobile devices implement the designed CFL to train their local FL models using their own datasets over a realistic wireless network. Due to the limited wireless resources and user movements, each device may not be able to transmit its FL parameters with all neighboring devices. Therefore, each device must select a subset of devices to share its FL parameters and optimize the transmit power. This problem is formulated as an optimization problem, whose goal is to minimize CFL training energy consumption while satisfying the delay and CFL training loss requirements. To solve this problem, a two-stage solution is proposed. At the first stage, a graph neural network (GNN) based algorithm is proposed, which enables each device to individually determine the subset of devices to transmit FL parameters using its neighboring devices' location and connection information. Compared to standard iterative algorithms that need to iteratively optimize device connections and transmit power, the proposed GNN based method can directly obtain the optimal device connections without iterative optimization. Given the optimal device connections, at the second stage, each device can directly obtain the optimal transmit power. Simulation results show that the proposed algorithm can decrease energy consumption by up to 46% compared to the algorithm where each device will directly connect to its first and second nearest neighbors.

关键词：

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Dual UAV Cluster-Assisted Maritime Physical Layer Secure Communications via Collaborative Beamforming

arXiv

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arXiv 2024年

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Hong Kong University 999077 Hong Kong

Unmanned aerial vehicles (UAVs) can be utilized as relay platforms to assist maritime wireless communications. However, complex channels and multipath effects at sea can adversely affect the quality of UAV transmitted signals. Collaborative beamforming (CB) can enhance the signal strength and range to assist the UAV relay for remote maritime communications. However, due to the open nature of UAV channels, security issue requires special consideration. This paper proposes a dual UAV cluster-assisted system via CB to achieve physical layer security in maritime wireless communications. Specifically, one UAV cluster forms a maritime UAV-enabled virtual antenna array (MUVAA) relay to forward data signals to the remote legitimate vessel, and the other UAV cluster forms an MUVAA jammer to send jamming signals to the remote eavesdropper. In this system, we formulate a secure and energy-efficient maritime communication multi-objective optimization problem (SEMCMOP) to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the total flight energy consumption of UAVs. Since the SEMCMOP is an NP-hard and large-scale optimization problem, we propose an improved swarm intelligence optimization algorithm with chaotic solution initialization and hybrid solution update strategies to solve the problem. Simulation results indicate that the proposed algorithm outperforms other comparison algorithms, and it can achieve more efficient signal transmission by using the CB-based method. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Data-Driven with IoT Sensing and Deep Learning Model for Dynamic Skin Cancer Diagnosis

Data-Driven with IoT Sensing and Deep Learning Model for Dyn...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： S. Mohan Kumar R. Thenmozhi T. S. Balaji Damodhar P. Epsiba N. Malathi B. Meenakshi Director Research and Innovations CMR University Bangalore Karnataka India Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu India Department of Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Electronics and Communication Engineering Sri Indu College of Engineering and Technology Hyderabad India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

This research introduces an innovative approach to continuously assess the condition of the skin barrier by combining advanced deep learning algorithms with Internet of Things (IoT) sensor technologies. For the system to function, it constantly uses IoT sensors to collect real-time data from various skin metrics, including moisture levels, pH balance, and environmental conditions. A complex analysis, made possible by deep learning, may be built using this vast amount of information. A type of deep learning technique that works well with data, recurrent neural networks (RNNs) are at the heart of this system. RNNs capture subtle changes over time in skin health by capitalizing on the temporal dependsequentialencies in skin data. Training RNNs iteratively on historical data covering a wide range of skin types and conditions allows them to detect patterns indicating healthy and damaged skin barriers. The proposed system has several benefits, such as individualized skincare suggestions, real-time information to guide adaptive skincare regimens, and continuous monitoring to identify skin concerns early. The system can discover complex patterns and correlations in the skin data, which results in better skincare solutions.

关键词： Deep learning Training Measurement Technological innovation Recurrent neural networks Accuracy Skin Real-time systems Internet of Things Skin cancer

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Dynamic Cardiovascular Rehabilitation for Personalized Exercise Plans Guided by Recurrent Neural Networks in the Cloud

Dynamic Cardiovascular Rehabilitation for Personalized Exerc...

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Smart Systems for applications in electrical sciences (ICSSES), International Conference on

作者： Lk Shoba B. Maruthukannan N.L. Venkataraman D. Chandrakala N. Mohankumar N. Malathi Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chenagalpattu Chennai Tamil Nadu India Department of Computer Science and Engineering NPR College of Engineering & Technology Dindigul Tamil Nadu India Department of Electronics and Communication Engineering J.J.College of Engineering and Technology Tiruchirappalli Tamil Nadu India Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Maharashtra India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

Cardiovascular rehabilitation is essential for illness prevention and management. A unique method for cardiovascular rehabilitation uses cloud computing and recurrent neural networks (RNNs) to dynamically create personalized workout regimens. Traditional rehabilitation plans lack flexibility and are less successful. The suggested system analyses real-time physiological data and adapts workout regimens in the cloud using RNNs. Heart rate, blood pressure, and physical activity are monitored using wearable sensors. These gadgets smoothly send data to the cloud, where a powerful RNN model processes it. The RNN adapts workout suggestions to the user’s physiological reactions, historical data, and health objectives. The cloud-based system, which effectively processes sizable datasets and accommodates expanding user bases, ensures scalability. Remote access to rehabilitation services allows healthcare practitioners to monitor patients and alter workout routines remotely. The personalized approach tailors therapies to each person’s health profile to promote adherence and rehabilitation results. The suggested dynamic cardiovascular rehabilitation system uses cloud computing and powerful machine learning to build adaptive, personalized workout regimens, revolutionizing rehabilitation. This technique may improve cardiovascular rehabilitation plans, improving long-term health outcomes for at-risk or recovering cardiovascular disease patients.

关键词： Training Cloud computing Recurrent neural networks Scalability Heuristic algorithms medical treatment Real-time systems

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