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Guest Editorial: Digital Twins and Pervasive Intelligence Synergy in 6G and beyond

IEEE Wireless Communications 2025年第3期32卷 88-89页

作者： Peng, Haixia Suto, Katsuya Wang, Ping Zhang, Kuan Puligheddu, Corrado School of Information and Communications Engineering Xi'an Jiaotong University China Graduate School of Informatics and Engineering University of Electro-Communications Tokyo Japan Department of Electrical Engineering and Computer Science York University Canada Department of Electrical and Computer Engineering University of Nebraska-Lincoln United States Politecnico di Torino Italy

Digital twin (DT) technology, which creates virtual representations of physical entities and systems, has emerged as a transformative enabler across diverse domains, including manufacturing, healthcare, urban planning, and environmental management. When integrated with pervasive intelligence — the embedding of artificial intelligence (AI) and machine learning (ML) algorithms into physical and digital environments — this synergy significantly amplifies innovation potential. In the context of sixth-generation (6G) wireless networks, DTs serve as real-time simulators of network behaviors, allowing AI to continuously learn from real-world data, optimize network performance, and proactively identify potential issues.

关键词： Special issues and sections Digital twins 6G mobile communication Urban planning Artificial intelligence Real-time systems Pervasive computing Environmental factors Manufacturing Medical services

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Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1539-1556页

作者： Weihao Song Zidong Wang Zhongkui Li Jianan Wang Qing-Long Han IEEE the State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of Engineering Peking University the Department of Computer Science Brunel University London the School of Aerospace Engineering Beijing Institute of Technology the School of Science Computing and Engineering Technologies Swinburne University of Technology

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

关键词： Communication constraints maximum correntropy filter networked nonlinear filtering particle filter sample-based approximation unscented Kalman filter

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ediction Model for a Good Learning Environment Using an Ensemble Approach

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computer Systems science & engineering 2023年第3期44卷 2081-2093页

作者： S.Subha S.Baghavathi Priya Department of Computer Science and Engineering Rajalakshmi Institute of TechnologyChennaiTamil NaduIndia Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennaiTamil NaduIndia

This paper presents an efficient prediction model for a good learning environment using Random Forest(RF)*** consists of a series of modules;data preprocessing,data normalization,data split andfinally classification or prediction by the RF *** preprocessed data is normalized using minmax normalization often used before *** the input data or variables are measured at different scales,it is necessary to normalize them to contribute equally to the ***,the RF classifier is employed for course selection which is an ensemble learning method and k-fold cross-validation(k=10)is used to validate the *** proposed Prediction Model for Course Selection(PMCS)system is considered a multi-class problem that predicts the course for a particular learner with three complexity levels,namely low,medium and *** is operated under two modes;locally and *** former considers the gender of the learner and the later does not consider the gender of the *** database comprises the learner opinions from 75 males and 75 females per category(low,medium and high).Thus the system uses a total of 450 samples to evaluate the performance of the PMCS *** show that the system’s performance,while using locally i.e.,gender-wise has slightly higher performance than the global *** RF classifier with 75 decision trees in the global system provides an average accuracy of 97.6%,whereas in the local system it is 97%(male)and 97.6%(female).The overall performance of the RF classifier with 75 trees is better than 25,50 and 100 decision trees in both local and global systems.

关键词： Machine learning ensemble learning random forest data mining prediction system

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Analyzing the Impact of Demographic and Operational Variables on 1-to-Many Face ID Search

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2024年第2期5卷 217-230页

作者： Pangelinan, Gabriella Bhatta, Aman Wu, Haiyu King, Michael C. Bowyer, Kevin W. Florida Institute of Technology Department of Electrical Engineering and Computer Science MelbourneFL32901 United States University of Notre Dame Department of Computer Science and Engineering Notre DameIN46556 United States

Concerns related to the proliferation of automated face recognition technology with the intent of solving or preventing crimes continue to mount. The technology being implicated in wrongful arrest following 1-to-many face identification has heightened scrutiny in the media and spawned lawsuits. We analyze the accuracy of 1-to-many face identification across African-American and Caucasian demographic groups and in the presence of blur and reduced resolution in the probe image, as might occur in "surveillance camera quality"images. Because of the high accuracy of modern face recognition algorithms with "government ID quality"images and the size of available datasets, we use the following indirect metrics to assess the relative propensity for false positive identifications: (1) the traditional d-prime statistic between mated and non-mated score distributions, (2) absolute score difference between thresholds in the high-similarity tail of the non-mated distribution and the low-similarity tail of the mated distribution, and (3) distribution of (mated - non-mated rank-one scores) across the set of probe images. We find that demographic variation patterns in 1-to-many accuracy largely, but not perfectly, follow that observed in 1-to-1 accuracy. We show that, different from the case with 1-to-1 matching accuracy, demographic comparison of 1-to-many accuracy can be affected by different numbers of identities and images across demographics. We also show that increased blur or reduced resolution of the face in the probe image can significantly increase the false positive identification rate. This point is important because the reputation of modern face recognition algorithms for high accuracy in 1-to-many face identification comes from statistics based on analyzing "government ID quality"rather than "surveillance camera quality"images. © 2024 IEEE.

关键词： Face recognition

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Research on temperature distribution characteristics of oilimmersed power transformers based on fluid network decoupling

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High Voltage 2024年第5期9卷 1136-1148页

作者： Yongming Xu Ziyi Xu Congrui Ren Yaodong Wang School of Electrical and Information Engineering Changzhou Institute of TechnologyChangzhouChina School of Electrical and Electronic Engineering Harbin University of Science and TechnologyHarbinChina Department of Engineering Durham UniversityDurhamUK

Due to the complex structure and large size of large-capacity oil-immersed power transformers,it is difficult to predict the winding temperature distribution directly by numerical analysis.A 180 MVA,220 kV oil-immersed self-cooling power transformer is used as the research *** authors decouple the internal fluid domain of the power transformer into four regions:high voltage windings,medium voltage windings,low voltage windings,and radiators through fluid networks and establish the 3D fluidtemperature field numerical analysis model of the four regions,*** results of the fluid network model are used as the inlet boundary conditions for the 3D fluidtemperature numerical analysis *** turn,the fluid resistance of the fluid network model is corrected according to the results of the 3D fluid-temperature field numerical analysis *** prediction of the temperature distribution of windings is realised by the coupling calculation between the fluid network model and the 3D fluid-temperature field numerical analysis *** on this,the effect of the loading method of the heat source is also investigated using the proposed *** hotspot temperatures of the high-voltage,medium-voltage,and low-voltage windings are 89.43,86.33,and 80.96°C,***,an experimental platform is built to verify the *** maximum relative error between calculated and measured values is 4.42%,which meets the engineering accuracy requirement.

关键词： winding network distribution

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Cross-modal heterogeneous graph reasoning network for visual question answering

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Neural Computing and Applications 2025年 1-21页

作者： Zhang, Jing Teng, Jiong Ding, Weichao Wang, Zhe Department of Computer Science and Engineering East China University of Science and Technology Shanghai China

Most current Visual Question Answering (VQA) methods struggle to achieve effective cross-modal interaction between visual and semantic information, resulting in difficulties in accurately combining visual content with contextual semantics for answer prediction. To address this problem, a Cross-modal Heterogeneous Graph Reasoning Network (CHGRN) is proposed for VQA, incorporating a novel Cross-modal Reasoning Module (CRM) to enhance the interactive analysis between images and questions, enabling more profound joint reasoning of visual and semantic features. The CRM improves cross-modal information reasoning by effectively analyzing and understanding the visual semantic information in the target regions of images. Additionally, an answer type prediction module is introduced, employing multi-task learning with answer type annotations to filter out irrelevant semantic information, thereby improving reasoning accuracy. Moreover, the semantic-assisted attention-aligned decoder ensures precise alignment between visual and semantic data. Extensive experiments demonstrate that the proposed CHGRN achieves excellent performance in visual question answering and outperforms most state-of-the-art methods on widely used public datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Multi-task learning

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Joint Optimization of AAV Trajectory, Task Offloading, and Resource Allocation in AAV-Aided Emergency Response Operations

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IEEE Internet of Things Journal 2025年第12期12卷 21944-21959页

作者： Akter, Shathee Van Anh Duong, Dat Yoon, Seokhoon University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan44610 Korea Republic of

In emergency response operations, the role of autonomous aerial vehicles (AAVs) has become vital for performing tasks such as human detection. However, these tasks are computationally intensive, and AAVs have limited resources. Therefore, fixed-wing AAV mother-ships with edge servers (UMECs), which usually have more speed and payload capacity than rotary-wing AAVs, can be deployed near AAV scouts (generate the tasks) to provide processing services. However, the design of fixed-wing AAVs requires adherence to a minimum turning radius, which limits maneuverability and impacts both energy consumption and task execution time, thus necessitating careful trajectory planning. Furthermore, UMECs may lack the capacity to process all types of tasks due to memory restrictions and require both central processing units and graphics processing units for faster execution of tasks. Therefore, this article aims to minimize the energy consumption in the network and task execution time by jointly optimizing the trajectory of UMECs, task offloading, and resource allocation (TTR) problem while taking into account UMECs movement constraints, task performance limitations, and computational resource budgets. In this regard, we first propose a particle swarm optimization (PSO)-based TTR scheme (P-TTR), where a PSO algorithm finds the trajectory solution, and a block coordinate descent-based method is developed to solve the task offloading and resource allocation problem. Then, for real-time decision-making, a deep-reinforcement-learning-based algorithm, namely soft-actor–critic-based TTR (S-TTR), is introduced to address the problem, which can adapt to dynamic environments and have lower computational overhead. Simulation results confirm that the proposed approaches can outperform the baseline method. © 2014 IEEE.

关键词： Fixed wings

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Fog-Enabled Multimodal Chest-Worn Device for Systolic Blood Pressure Monitoring

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IEEE Access 2025年 13卷 90345-90357页

作者： Salas, Pamela Mejia-Munoz, Jose-Manuel Gonzalez-Landaeta, Rafael BIOCIM Research Group Department of Electrical and Computer Engineering Juarez Chihuahua32310 Mexico

This paper presents a Fog-enabled chest-worn device for estimating systolic blood pressure (SBP). The device integrates two sensors that simultaneously detect the phonocardiogram (PCG), ballistocardiogram (BCG), and seismocardiogram (SCG) from a single location on the chest. These signals are good indicators of the early stages of cardiovascular diseases (CVD). Here, a new fog-based deep learning architecture that extracts features directly from raw signals to estimate the SBP is proposed. Three signals were transmitted to a fog computing system via a socket. Four models were implemented for analysis: Random Forest (RF), AdaBoost, Gradient Boosting (GB), and a novel deep learning architecture. The RF, AdaBoost, and GB models focused on the time delays between the main waves of the PCG, BCG, and SCG, while the deep learning architecture extracted features directly from the signals using convolutional layers. The tests involved 36 healthy volunteers under both resting and physically active conditions. The device was built using surface-mount components and powered by a 3.7 V, 250 mAh battery. The dimensions were 65 mm × 54 mm, and the weight was 56 g. The three cardiac signals were simultaneously detected, with a signal-to-noise ratio higher than 41 dB. Among the four implemented models for SBP estimation in fog, the proposed deep learning architecture demonstrated the best performance, achieving an MAE of 3.5 mmHg without estimating the timing between the three detected signals. Integrating the methodology discussed here, the proposed device offers a novel method for estimating blood pressure through raw biosignals detected by a wearable device. © 2013 IEEE.

关键词： Surface mount technology

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Content-aware malicious webpage detection using convolutional neural network

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Multimedia Tools and Applications 2024年第3期83卷 8145-8163页

作者： Chang, Yen-Jen Tsai, Kun-Lin Jiang, Wei-Cheng Liu, Meng-Kun Department of Computer Science and Engineering National Chung Hsing University Taichung City Taiwan Department of Electrical Engineering Tunghai University Taichung City Taiwan

Malicious websites often install malware on user devices to gather user information or to disrupt device operations, violate user privacy, or adversely affect company interests. Many commercial tools are available to prevent malicious webpages from accessing devices;however, current versions of these tools may become useless as soon as a new generation of malware is released. In this study, a content-aware malicious webpage detection (CAMD) method was developed;this CAMD method can verify whether a webpage is malicious by applying a novel webpage contextual visualization process, which retrieves the critical codes of webpages, transforms those codes into one-dimensional grayscale images, and applies convolutional neural networks to detect any malicious webpages. To verify the feasibility of proposed CAMD, 50000 normal and 50000 malicious webpages from the VirusTotal website were used. The results indicated that the proposed CAMD achieved an accuracy of > 98%. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Websites

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Secure signal and image transmissions using chaotic synchronization scheme under cyber-attack in the communication channel

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Soft Computing 2025年第1期29卷 339-353页

作者： Nobakht, Shaghayegh Ahmadi, Ali-Akbar Industrial Automation Laboratory Electrical and Computer Engineering Department Kharazmi University Tehran Iran

In this paper, problem of secure message (signal and image) transmission is studied. The message is encrypted by masking it with a chaotic system state and then transmitted to receiver-side via a communication channel. It is assumed that the communication channel may be prone to a cyber-attack. The transmission system dynamics in presence of the cyber-attack in the communication channel is modeled by a singular dynamical system. Then, at the receiver-side, a novel observer is designed to recover the message together with the cyber-attack signal. Estimation of the cyber-attack is crucial for monitoring the communication channel and enable a network supervisor to mitigate the adverse consequences and make an appropriate strategy. It is assumed neither restrictions on the message nor on the cyber-attack signals. This makes the proposed scheme, the best solution in the real applications. Moreover, comparing to the existing methods which two public and private channels are required, only one communication channel is used in this paper for transmission the encrypted signal which results in a cheaper solution. Solving the optimization problem can be easily performed with the help of the existing toolboxes such as YALMIP and CVX software in MATLAB. Finally, numerical simulations for signal and image transmission are presented to justify the applicability and superior performance of the proposed scheme. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Cyber attacks

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