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Intelligent integrated sensing and communication:a survey

Science China(information Sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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Dynamic Process Monitoring Based on Dot Product Feature Analysis for Thermal Power Plants

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 563-574页

作者： Xin Ma Tao Chen Youqing Wang the College of Information Science and Technology Beijing University of Chemical Technology the Department of Chemical and Process Engineering University of Surrey IEEE

Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems, such as thermal power plants being studied in this work. Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms. Mainstream dynamic algorithms rely on concatenating current measurement with past data. This work proposes a new, alternative dynamic process monitoring algorithm, using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples, thus naturally capturing the process dynamics through temporal correlation. At the same time, DPFA's online computational complexity is lower than not just existing dynamic algorithms, but also classical static algorithms(e.g., principal component analysis and slow feature analysis). The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias, process fault and gain change fault. Through experiments with a numerical example and real data from a thermal power plant, the DPFA algorithm is shown to be superior to the state-of-the-art methods, in terms of better monitoring performance(fault detection rate and false alarm rate) and lower computational complexity.

关键词： Computational complexity dot product feature analysis (DPFA) dynamic process multivariate statistics process monitoring

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Game-based computation offloading and resource allocation in stochastic geometry-modeling vehicular networks

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Science China(information Sciences) 2024年第12期67卷 329-330页

作者： Jianjie YANG Zhijian LIN Yingyang CHEN Xiaoqiang LU Yi FANG Department of Electronic and Information Engineering Fuzhou University College of Information Science and Technology Jinan University School of Information Engineering Guangdong University of Technology

The underlying vertical components represented by vehicleto-everything networks will largely accelerate the advance of the 6th generation wireless communications [1]. In this context, a plethora of Internet-of-Vehicles(IoV) applications have increasingly permeated our daily lives with the development of advocated intelligent connected vehicles [2].

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Enhancing the Goos-Hänchen shift based on quasi-bound states in the continuum through material asymmetric dielectric compound gratings

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Chinese Physics B 2024年第3期33卷 393-400页

作者：江孝伟方彬占春连 College of Optical and Electronic Technology China Jiliang UniversityHangzhou 310018China College of Information Engineering Quzhou College of TechnologyQuzhou 324000China

Quasi-bound state in the continuum(QBIC)resonance is gradually attracting attention and being applied in Goos-Hänchen(GH)shift enhancement due to its high quality(Q)factor and superior optical ***,symmetry-protected QBIC resonance is often achieved by breaking the geometric symmetry,but few cases are achieved by breaking the material *** paper proposes a dielectric compound grating to achieve a high Q factor and high-reflection symmetry-protectede QBIC resonance based on material *** calculations show that the symmetry-protected QBIC resonance achieved by material asymmetry can significantly increase the GH shift up to-980 times the resonance wavelength,and the maximum GH shift is located at the reflection peak with unity *** paper provides a theoretical basis for designing and fabricating high-performance GH shift tunable metasurfaces/dielectric gratings in the future.

关键词： bound state in the continuum Goos-Hänchen shift dielectric compound grating material asymmetry

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Traffic-Aware Fuzzy Classification Model to Perform IoT Data Traffic Sourcing with the Edge Computing

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Computers, Materials & Continua 2024年第2期78卷 2309-2335页

作者： Huixiang Xu College of Information Engineering Zhengzhou University of TechnologyZhengzhou450044China

The Internet of Things(IoT)has revolutionized how we interact with and gather data from our surrounding *** devices with various sensors and actuators generate vast amounts of data that can be harnessed to derive valuable *** rapid proliferation of Internet of Things(IoT)devices has ushered in an era of unprecedented data generation and *** IoT devices,equipped with many sensors and actuators,continuously produce vast volumes of ***,the conventional approach of transmitting all this data to centralized cloud infrastructures for processing and analysis poses significant ***,transmitting all this data to a centralized cloud infrastructure for processing and analysis can be inefficient and impractical due to bandwidth limitations,network latency,and scalability *** paper proposed a Self-Learning Internet Traffic Fuzzy Classifier(SLItFC)for traffic data *** proposed techniques effectively utilize clustering and classification procedures to improve classification accuracy in analyzing network traffic *** addresses the intricate task of efficiently managing and analyzing IoT data traffic at the *** employs a sophisticated combination of fuzzy clustering and self-learning techniques,allowing it to adapt and improve its classification accuracy over *** adaptability is a crucial feature,given the dynamic nature of IoT environments where data patterns and traffic characteristics can evolve *** the implementation of the fuzzy classifier,the accuracy of the clustering process is improvised with the reduction of the computational *** can reduce computational time while maintaining high classification *** efficiency is paramount in edge computing,where resource constraints demand streamlined data ***,SLItFC’s performance advantages make it a compelling choice for organizations seeking to harness the potential of IoT data for real-time insig

关键词： Internet of Things(IoT) edge computing traffic data self-learning fuzzy-learning

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PEAR: phrase-based hand-object interaction anticipation

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Science China(information Sciences) 2025年第5期68卷 133-152页

作者： Zichen ZHANG Hongchen LUO Wei ZHAI Yang CAO Yu KANG School of Information Science and Technology University of Science and Technology of China College of Information Science and Engineering Northeastern University Institute of Artificial Intelligence Hefei Comprehensive National Science Center

First-person hand-object interaction anticipation aims to predict the interaction process over a forthcoming period based on current scenes and prompts. This capability is crucial for embodied intelligence and human-robot *** complete interaction process involves both pre-contact interaction intention(i.e., hand motion trends and interaction hotspots) and post-contact interaction manipulation(i.e., manipulation trajectories and hand pose with contact). Existing research typically anticipates only interaction intention while neglecting manipulation, resulting in incomplete predictions and an increased likelihood of intention errors due to the lack of manipulation constraints. To address this issue, we propose a novel model, PEAR(phrase-based hand-object interaction anticipation), which jointly anticipates interaction intention and manipulation. To handle interaction uncertainty, we employ a twofold approach. Firstly, we perform cross-alignment of verbs, nouns, and images to reduce the diversity of hand movement patterns and object functional attributes, thereby mitigating intention uncertainty. Secondly, we establish bidirectional constraints between intention and manipulation using dynamic integration and residual connections, ensuring consistency among elements and thus overcoming manipulation uncertainty. To rigorously evaluate the performance of the proposed model, we collect a new task-relevant dataset, EGO-HOIP,with comprehensive annotations. Extensive experimental results demonstrate the superiority of our method.

关键词： hand-object interaction first-person perspective trajectory forecasting hand pose prediction embodied intelligence

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A lightweight two-stage physics-informed neural network for SOH estimation of lithium-ion batteries with different chemistries

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Journal of Energy Chemistry 2025年第6期105卷 261-279页

作者： Chunsong Lin Longxing Wu Xianguo Tuo Chunhui Liu Wei Zhang Zebo Huang Guiyu Zhang School of Mechanical Engineering Sichuan University of Science and Engineering School of Information Engineering Southwest University of Science and Technology College of Intelligent Manufacturing Anhui Science and Technology University School of Mechanical and Electrical Engineering Guilin University of Electronic Technology

Accurately estimating the battery state of health（SOH） is essential for ensuring the safe and reliable operation of battery systems of electric ***,due to the complex and variable operating conditions encountered in practical applications,achieving precise and physics-informed SOH estimation remains *** address these problems,this paper develops a lightweight two-stage physicsinformed neural network（TSPINN） method for SOH estimation of lithium-ion batteries with different ***,this paper utilizes firstly relaxation voltage data obtained after a full charge to determine the aging-related parameters of physical equivalent circuit model（ECM）.Additionally,incremental capacity（IC） feature is extracted by analyzing peak values of the IC curve during the charging phase,which thereby constitutes the first stage of the proposed TSPINN,termed as physics-informed data augmentation for SOH ***,the physical information can be further embedded by incorporating feature knowledge related to mechanisms into the loss function,and ultimately,the second stage of the proposed TSPINN is developed,which is named the physics-informed loss *** effectiveness of the TSPINN method was confirmed through the experimental data for LiNi0.86Co0.11Al0.03O2（NCA） and LiNi0.83Co0.11Mn0.07O2（NCM） battery materials under different temperature *** final experimental results indicate that the TSPINN method achieved SOH estimation with a mean absolute error（MAE） of 0.675%,showing improvements of approximately 29.3%,60.3%,and 8.1% compared to methods using only ECM,IC,and integrated features,*** results validate the effectiveness and adaptability of TSPINN,establishing it as a reliable solution for advanced battery management systems.

关键词： Lithium-ion battery Physics-information neural network State of health Voltage relaxation

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Association RuleMining Frequent-Pattern-Based Intrusion Detection in Network

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Computer Systems Science & engineering 2023年第2期44卷 1617-1631页

作者： S.Sivanantham V.Mohanraj Y.Suresh J.Senthilkumar Department of Information Technology Adhiyamaan College of EngineeringHosurTamilnaduIndia Department of Information Technology Sona College of TechnologySalemTamilnaduIndia

In the network security system,intrusion detection plays a signiﬁcant *** network security system detects the malicious actions in the network and also conforms the availability,integrity and conﬁdentiality of data informa-tion *** identiﬁcation system can easily detect the false positive *** large number of false positive alerts are created then it makes intrusion detection system as difﬁcult to differentiate the false positive alerts from genuine *** research works have been *** issues in the existing algo-rithms are more memory space and need more time to execute the transactions of *** paper proposes a novel framework of network security Intrusion Detection System(IDS)using Modiﬁed Frequent Pattern(MFP-Tree)via K-means *** accuracy rate of Modiﬁed Frequent Pattern Tree(MFPT)-K means method inﬁnding the various attacks are Normal 94.89%,for DoS based attack 98.34%,for User to Root(U2R)attacks got 96.73%,Remote to Local(R2L)got 95.89%and Probe attack got 92.67%and is optimal when it is compared with other existing algorithms of K-Means and APRIORI.

关键词： IDS K-means frequent pattern tree false alert mining L1-norm

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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

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Science China(information Sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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An Improved Practical Byzantine Fault-Tolerant Algorithm Based on XGBoost Grouping for Consortium Chains

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Computers, Materials & Continua 2025年第1期82卷 1295-1311页

作者： Xiaowei Wang Haiyang Zhang Jiasheng Zhang Yingkai Ge Kexin Cui Zifu Peng Zhengyi Li Lihua Wang College of Electronic and Information Engineering Shandong University of Science and TechnologyQingdao266590China

In response to the challenges presented by the unreliable identity of the master node,high communication overhead,and limited network support size within the Practical Byzantine Fault-Tolerant(PBFT)algorithm for consortium chains,we propose an improved PBFT algorithm based on XGBoost grouping called XG-PBFT in this ***-PBFT constructs a dataset by training important parameters that affect node performance,which are used as classification indexes for *** XGBoost algorithm then is employed to train the dataset,and nodes joining the system will be grouped according to the trained grouping *** them,the nodes with higher parameter indexes will be assigned to the consensus group to participate in the consensus,and the rest of the nodes will be assigned to the general group to receive the consensus *** order to reduce the resource waste of the system,XG-PBFT optimizes the consensus protocol for the problem of high complexity of PBFT ***,we evaluate the performance of *** experimental results show that XG-PBFT can significantly improve the performance of throughput,consensus delay and communication complexity compared to the original PBFT algorithm,and the performance enhancement is significant compared to other algorithms in the case of a larger number of *** results demonstrate that the XG-PBFT algorithm is more suitable for large-scale consortium chains.

关键词： Consortium chain PBFT grouping reputation XGBoost

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