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IEEE Transactions on Artificial Intelligence 2025年第06期6卷 1513-1528页

作者： Zhang, Tiehua Liu, Yuze Shen, Zhishu Ma, Xingjun Qi, Peng Ding, Zhijun Jin, Jiong Tongji University School of Computer Science and Technology Shanghai China Ant Group Shanghai China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan China Fudan University School of Computer Science Shanghai China Tongji University Department of Control Science and Engineering Shanghai China Swinburne University of Technology School of Science Computing and Engineering Technologies Melbourne Australia

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named Learning from Heterogeneity (LFH) that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterwards, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with twelve state-of-the- art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experiment results demonstrate a significant performance gain (an average of 12.9% in node classification and 12.8% in link prediction) compared with recent state-of-the-art methods. © 2020 IEEE.

关键词： Contrastive Learning

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Exploring FMCW Radars and Feature Maps for Activity Recognition: A Benchmark Study

arXiv

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arXiv 2025年

作者： Fard, Ali Samimi Mashhadigholamali, Mohammadreza Zolfaghari, Samaneh Abedi, Hajar Chakraborty, Mainak Borzì, Luigi Daneshtalab, Masoud Shaker, George Department of Control and Computer Engineering Polytechnic University of Turin Turin Italy School of Innovation Design and Engineering Division of Intelligent Future Technologies Mälardalen University Västerås Sweden Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada

Human Activity Recognition has gained significant attention due to its diverse applications, including ambient assisted living and remote sensing. Wearable sensor-based solutions often suffer from user discomfort and reliability issues, while video-based methods raise privacy concerns and perform poorly in low-light conditions or long ranges. This study introduces a Frequency-Modulated Continuous Wave radar-based framework for human activity recognition, leveraging a 60 GHz radar and multi-dimensional feature maps. Unlike conventional approaches that process feature maps as images, this study feeds multi-dimensional feature maps—Range-Doppler, Range-Azimuth, and Range-Elevation —as data vectors directly into the machine learning (SVM, MLP) and deep learning (CNN, LSTM, ConvLSTM) models, preserving the spatial and temporal structures of the data. These features were extracted from a novel dataset with seven activity classes and validated using two different validation approaches. The ConvLSTM model outperformed conventional machine learning and deep learning models, achieving an accuracy of 90.51% and an F1-score of 87.31% on cross-scene validation, and an accuracy of 89.56% and an F1-score of 87.15% on leave-one-person-out cross-validation. The results highlight the approach’s potential for scalable, non-intrusive, and privacy-preserving activity monitoring in real-world scenarios. © 2025, CC BY-NC-ND.

关键词： Deep learning

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Design of Nanorobots for an Early Detection of Alzhimer's Disease

Design of Nanorobots for an Early Detection of Alzhimer's Di...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Yosra Ben Fadhel Khawla Kachroudi Achraf Derbel Hedi Trabelsi Nabil Derbel University of Tunis El Manar Higher Institute of Medical Technologies Tunisia Departement of Computer Systems Eng. Mediterranean Institute of Technology South Mediterranean University Tunisia University of Sfax National Engineering School of Sfax Control & Energy Management Laboratory Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

In this study, we propose a novel nanorobot model for the early detection of Alzheimer's disease. As a first step, we explored the polymorphism of the TREM2 gene's exons to assess its association with Alzheimer's. Our case-control study involved 124 Tunisian participants-64 Alzheimer's patients and 60 age-matched controls, averaging 67 years. Each participant underwent neurological evaluation, neuropsychological testing, brain imaging, and molecular analysis. Using Polymerase Chain Reaction (PCR) and sequencing techniques, we identified the rs2234253 variant in exon 2 and a novel intronic variant in intron 4, both of which were significantly correlated with Alzheimer's risk. Based on these genetic findings, we designed a nanorobot capable of detecting early biomarkers of Alzheimer's disease, such as amyloid plaques and tau tangles. The nanorobots are approximately 100 nm in size, enabling them to navigate the blood-brain barrier and specifically target regions of the brain affected by Alzheimer's pathology. They are constructed from biocompatible materials, such as gold nanoparticles and silica, and functionalized with surface receptors that selectively bind to Alzheimer's-related biomarkers. Equipped with nanosensors, the nanorobots are designed to detect binding events and transmit data to an external monitoring system in real time, facilitating early diagnosis and continuous monitoring of disease progression. These technological advancements offer a promising approach to enhancing Alzheimer's detection and improving patient out-comes.

关键词： Pathology Sequential analysis Biomarkers Real-time systems Safety Polymers Alzheimer's disease Monitoring Silicon dioxide Testing

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Seamless Graph Task Scheduling over Dynamic Vehicular Clouds: A Hybrid Methodology for Integrating Pilot and Instantaneous Decisions

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IEEE Transactions on Services Computing 2025年

作者： Guo, Bingshuo Liwang, Minghui Xia, Xiaoyu Li, Li Jiao, Zhenzhen Hosseinalipour, Seyyedali Wang, Xianbin Xiamen University School of Informatics Fujian China Rmit University School of Computing Technologies MelbourneVIC Australia Tongji University Department of Control Science and Engineering Shanghai China Beijing Teleinfo Technology Co. Ltd. Caict IF-Labs China University at Buffalo-SUNY Department of Electrical Engineering NY United States Western University Department of Electrical and Computer Engineering ON Canada

Vehicular clouds (VCs) play a crucial role in the Internet-of-Vehicles (IoV) ecosystem by securing essential computing resources for a wide range of tasks. This paPertackles the intricacies of resource provisioning in dynamic VCs for computation-intensive tasks, represented by undirected graphs for parallel processing over multiple vehicles. We model the dynamics of VCs by considering multiple factors, including varying communication quality among vehicles, fluctuating computing capabilities of vehicles, uncertain contact duration among vehicles, and dynamic data exchange costs between vehicles. Our primary goal is to obtain feasible assignments between task components and nearby vehicles, called templates, in a timely manner with minimized task completion time and data exchange overhead. To achieve this, we propose a hybrid graph task scheduling (P-HTS) methodology that combines offline and online decision-making modes. For the offline mode, we introduce an approach called risk-aware pilot isomorphic subgraph searching (RA-PilotISS), which predicts feasible solutions for task scheduling in advance based on historical information. Then, for the online mode, we propose time-efficient instantaneous isomorphic subgraph searching (TE-InstaISS), serving as a backup approach for quickly identifying new optimal scheduling template when the one identified by RA-PilotISS becomes invalid due to changing conditions. Through comprehensive experiments, we demonstrate the superiority of our proposed hybrid mechanism compared to state-of-the-art methods in terms of various evaluative metrics, e.g., time efficiency such as the delay caused by seeking for possible templates and task completion time, as well as cost function, upon considering different VC scales and graph task topologies. © 2025 IEEE.

关键词： Undirected graphs

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LDNet-RUL: Lightweight Deformable Neural Network for Remaining Useful Life Prognostics of Lithium-ion Batteries

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IEEE Transactions on Power Electronics 2025年

作者： Wu, Xiankui Li, Penghua Deng, Zhongwei Liu, Zhitao Kurboniyon, Mekhrdod S. Xiang, Sheng Yin, Gang Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications School of Automation Chongqing400065 China Chongqing University of Science and Technology School of Computer Science and Engineering Chongqing401331 China University of Electronic Science and Technology School of Mechanical and Electrical Engineering Chengdu611731 China Zhejiang University Institute of Cyber-Systems and Control Zhejiang 310058 China National Academy of Sciences of Tajikistan Center of Innovative Development of Science and New Technologies Dushanbe734025 Tajikistan Chongqing University College of Resource and Safety Engineering Chongqing400044 China

Deep neural networks (DNNs)-based battery remaining useful life (RUL) prognostics models suffer from high computational costs, long inference times, and a tendency to overfit, posing significant challenges for deployment on memory-constrained edge devices. To address this, LDNet-RUL, a lightweight yet high-performance model, is proposed for lithiumion battery lifetime prognostics. Specifically, a deformable depthwise convolution unit is designed that efficiently adapts to variations in input features with relatively low computational cost, which leverages fewer parameters and an adaptive receptive field to capture battery degradation dynamics. A multi-scale feature extraction method is introduced to extract degradation features from both short-term and long-term data during the battery charging process, which makes the method applicable to various battery charging protocols. To address overfitting and accelerate model inference, an adaptive channel selector, an adaptive residual connector, and a progressive dimensionalitygrouped bottleneck filter are incorporated to refine feature selection and reduce the model's complexity, boosting the model's overall performance. Experiments on vehicle lithium-ion battery datasets demonstrate that LDNet-RUL achieves state-of-the-art (SOTA) performance, with average RMSE of 0.23 and Score of 0.49. The model is lightweight, with only 7.92K parameters, achieves inference times of 3.94ms on GPU and CPU, and 5.39ms on the NVIDIA Jetson TX2. © 1986-2012 IEEE.

关键词： Convolution

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Blockage-Resilient Integrated Sensing and Communication in mmWave Networks: Multi-View Collaboration and Efficient Task Allocation

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IEEE Transactions on Mobile Computing 2025年

作者： Cui, Yue Ding, Haichuan Ma, Ying Li, Xuanheng Zhang, Haixia Fang, Yuguang Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China Dalian University of Technology School of Information and Communication Engineering Dalian116023 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Shandong University Shandong Provincial Key Laboratory of Wireless Communication Technologies School of Control Science and Engineering Jinan250061 China City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

Integrated sensing and communication (ISAC) has emerged as a promising technology for future millimeter wave (mmWave) networks. However, the susceptibility of mmWave signals to blockages poses considerable challenges for ISAC as it can result in unreliable links and disrupted sensing. As a result, this paper investigates the blockage-resilient ISAC design that leverages the robustness offered by multi-base station (BS) collaboration. Given the dynamic blockages and the fluctuation in the targets' radar cross section (RCS), the blockage-resilient multiBS collaborative ISAC design is cast as a chance constrained integer programming (CCIP) by jointly considering the diverse deadlines of different sensing tasks and the spatial/temporal user-target pairing for dual-functional radar and communication (DFRC) waveform scheduling. To facilitate efficient solution finding, we develop a group concatenating assisted reinforcement learning (GCRL) algorithm, where we linearize the chance constraints via variable grouping and concatenation, enabling the RL agent to understand the problem structure with bipartite graphs so as to develop an efficient branching policy. Extensive experiments demonstrate the resilience of the obtained ISAC scheme to dynamic blockages. © 2025 IEEE. All rights reserved.

关键词： Integer programming

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Seamless Graph Task Scheduling over Dynamic Vehicular Clouds: A Hybrid Methodology for Integrating Pilot and Instantaneous Decisions

arXiv

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arXiv 2025年

作者： Guo, Bingshuo Liwang, Minghui Xia, Xiaoyu Li, Li Jiao, Zhenzhen Hosseinalipour, Seyyedali Wang, Xianbin School of Informatics Xiamen University Fujian China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering Tongji University Shanghai China iF-Labs Beijing Teleinfo Technology Co. Ltd. CAICT China Department of Electrical Engineering University at Buffalo-SUNY NY United States Department of Electrical and Computer Engineering Western University ON Canada

关键词： Cost functions

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Privacy-Aware Joint DNN Model Deployment and Partition Optimization for Delay-Efficient Collaborative Edge Inference

arXiv

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arXiv 2025年

作者： Cheng, Zhipeng Xia, Xiaoyu Wang, Hong Liwang, Minghui Chen, Ning Fan, Xuwei Wang, Xianbin School of Future Science and Engineering Soochow University Suzhou215006 China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai201804 China Qingdao China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Department of Electrical and Computer Engineering Western University ON Canada

Edge inference (EI) is a key solution to address the growing challenges of delayed response times, limited scalability, and privacy concerns in cloud-based Deep Neural Network (DNN) inference. However, deploying DNN models on resource-constrained edge devices faces more severe challenges, such as model storage limitations, dynamic service requests, and privacy risks. This paper proposes a novel framework for privacy-aware joint DNN model deployment and partition optimization to minimize long-term average inference delay under resource and privacy constraints. Specifically, the problem is formulated as a complex optimization problem considering model deployment, user-server association, and model partition strategies. To handle the NP-hardness and future uncertainties, a Lyapunov-based approach is introduced to transform the long-term optimization into a single-time-slot problem, ensuring system performance. Additionally, a coalition formation game model is proposed for edge server association, and a greedy-based algorithm is developed for model deployment within each coalition to efficiently solve the problem. Extensive simulations show that the proposed algorithms effectively reduce inference delay while satisfying privacy constraints, outperforming baseline approaches in various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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Development of DNA computing and information processing based on DNA-strand displacement

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Science China Chemistry 2015年第10期58卷 1515-1523页

作者： Yafei Dong Chen Dong Fei Wan Jing Yang Cheng Zhang College of Life Science Shaanxi Normal University School of Control and Computer Engineering North China Electric Power University Key Laboratory of High Confidence Software Technologies of Ministry of Education Software Engineering Institute School of Electronics Engineering and Computer Science Peking University

DNA computing, currently a hot research field in information processing, has the advantages of parallelism, low energy consumption, and high storability, therefore, it has been applied to a variety of complicated computational problems. The emerging field of DNA nanotechnology has also developed quickly; within it, the method of DNA strand displacement has drawn great attention because it is self-induced, sensitive, accurate, and operationally simple. This article summarizes five aspects of the recent developments of DNA-strand displacement in DNA computing:(1) cascading circuits;(2) catalyzed reaction;(3) logic computation;(4) DNA computing on surfaces; and(5) logic computing based on nanoparticles guided by strand displacement. The applications and mechanisms of strand displacement in DNA computing are discussed and possible future developments are presented.

关键词： DNA computing information processing strand displacement self-assembly nanomaterial

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On Transfer Function Representation and Frequency Response of Linear Active Disturbance Rejection control

On Transfer Function Representation and Frequency Response o...

引用

第三十二届中国控制会议

作者： HUANG Congzhi GAO Zhiqiang School of Control and Computer Engineering North China Electric Power University Center for Advanced Control Technologies Cleveland State UniversityClevelandOH 44145USA

ISBN: (纸本)9781479900305

The algebraic relationship between the transfer function and the state space representations of Linear Active Disturbance Rejection control(LADRC) is derived,upon which the unique frequency response characteristics of LADRC can be seen and *** transfer function form of LADRC for arbitrary order plant is derived from its state space representation and this allows that the open loop frequency characteristics of the LADRC based control system to be conveniently obtained and *** frequency response of LADRC not only shows excellent loop gain shape but also validates the original ADRC design concept:that the internal and external disturbances can be estimated in real time and *** time,the evidence comes not from simulation,or test,or time-domain analysis,but in the language of practicing engineers:frequency response.

关键词： Linear Active Disturbance Rejection control Transfer Function Frequency Analysis Magnitude Margin Phase Margin

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