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RIS Enabled SCLAM: An Approach for Simultaneous Radio Communication, Localization, and Mapping

IEEE Internet of Things Journal 2025年第10期12卷 14663-14676页

作者： Zhao, Jinqiu Bai, Zhiquan Guo, Shuaishuai Ma, Dejie Li, Na Kwak, Kyung Sup Shandong University Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration School of Information Science and Engineering Qingdao266237 China Shandong University Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration School of Control Science and Engineering Jinan250061 China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Jinan250200 China INHA University Graduate School of Information Technology and Telecommunications Incheon22212 Korea Republic of

Radio-based simultaneous localization and mapping (SLAM) facilitates unmanned systems to fulfill self-localization and navigation in complex environments. However, the existing studies overlooked communication between devices, which may lead to low efficiency and high costs when performing SLAM tasks. Reconfigurable intelligent surface (RIS) can satisfy the growing demands of users and improve SLAM accuracy in dynamic and complex environments. This paper proposes a RIS enabled simultaneous radio communication, localization, and mapping (SCLAM) system, where an unmanned aerial vehicle (UAV) can perform concurrent communication and SLAM with the help of RIS by utilizing communication signals from base station (BS). The proposed method enables the UAV to simultaneously accomplish SLAM and ensure communication with the BS, while also enhancing the spectrum utilization efficiency. We derive the Bayesian Fisher information matrix (BFIM) for joint SLAM and symbol detection of the proposed system, in which the trade-off between the BFIM of SLAM and the upper bound of the ergodic mutual information is illustrated. Then, a weighted factor based BFIM is presented to further achieve a performance trade-off between SLAM and communication. We formulate an optimization problem of joint BS active beamforming and RIS passive beamforming to maximize the log determinant of weighted BFIM. Numerical results verify the superiority of the proposed SCLAM system on position error bound (PEB), mapping error bound (MEB), and spectral efficiency (SE). The performance trade-off between communication and SLAM is also discussed and explored. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Complementary Learning Subnetworks Towards Parameter-Efficient Class-Incremental Learning

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IEEE Transactions on Knowledge and Data engineering 2025年第6期37卷 3240-3252页

作者： Li, Depeng Zeng, Zhigang Dai, Wei Suganthan, Ponnuthurai Nagaratnam Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Qatar University Kindi Center for Computing Research College of Engineering Doha2713 Qatar

In the scenario of class-incremental learning (CIL), deep neural networks have to adapt their model parameters to non-stationary data distributions, e.g., the emergence of new classes over time. To mitigate the catastrophic forgetting phenomenon, typical CIL methods either cumulatively store exemplars of old classes for retraining model parameters from scratch or progressively expand model size as new classes arrive, which, however, compromises their practical value due to little attention paid to parameter efficiency. In this paper, we contribute a novel solution, effective control of the parameters of a well-trained model, by the synergy between two complementary learning subnetworks. Specifically, we integrate one plastic feature extractor and one analytical feed-forward classifier into a unified framework amenable to streaming data. In each CIL session, it achieves non-overwritten parameter updates in a cost-effective manner, neither revisiting old task data nor extending previously learned networks;Instead, it accommodates new tasks by attaching a tiny set of declarative parameters to its backbone, in which only one matrix per task or one vector per class is kept for knowledge retention. Experimental results on a variety of task sequences demonstrate that our method achieves competitive results against state-of-the-art CIL approaches, especially in accuracy gain, knowledge transfer, training efficiency, and task-order robustness. Furthermore, a graceful forgetting implementation on previously learned trivial tasks is empirically investigated to make its non-growing backbone (i.e., a model with limited network capacity) suffice to train on more incoming tasks. © 2025 IEEE.

关键词： Contrastive Learning

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Improving graph collaborative filtering with network motifs

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Neural computing and Applications 2025年 1-20页

作者： Zhang, Yuqi Yu, Jian Liu, Zhizhong Wang, Guiling Nguyen, Minh Sheng, Quan Z. Wang, Nancy Department of Computer and Information Sciences Auckland University of Technology Auckland Central1010 New Zealand School of Computer and Control Engineering Yantai University Shandong Yantai264005 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Computing Macquarie University SydneyNSW2109 Australia

Deep learning on graphs, specifically graph convolutional networks (GCNs), has exhibited exceptional efficacy in the domain of recommender systems. Most GCNs have a message-passing architecture that enables nodes to aggregate information from neighbours iteratively through multiple layers. This enables GCNs to learn from higher-order information, but the model does not allow for direct captions of the local structural patterns. Our rationale is to investigate the effectiveness of capturing such local patterns for graph-based collaborative filtering to enhance model’s learning ability per layer. This technique combines lower-order and higher-order interactions during layer-wise propagation. In this paper, we propose MotifGCN to aggregate both lower-order and higher-order information in each graph convolution layer. Specifically, we develop dedicated algorithms of generating motif adjacency matrices. The matrices are then used for motif-enhanced neighbourhood aggregation in each layer. As this paper focuses on recommender systems, MotifGCN is built on the basis of bipartite graphs. Our experiments on four real-world datasets show that MotifGCN has a superior performance compared to various state-of-the-art methods. © The Author(s) 2025.

关键词： Collaborative filtering

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MACLSTM: A Weather Attributes Enabled Recurrent Approach to Appliance-Level Energy Consumption Forecasting

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Computers, Materials & Continua 2025年第2期82卷 2969-2984页

作者： Ruoxin Li Shaoxiong Wu Fengping Deng Zhongli Tian Hua Cai Xiang Li Xu Xu Qi Liu NARI-TECH Nanjing Control System Co. Ltd.Nanjing211106China School of Software Nanjing University of Information Science&TechnologyNanjing210044China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044China

Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and environmental variables is presented. Then, a resampling process is applied to the initial data set to generate three other subsets of data. All the subsets were evaluated to deduce the adequate granularity for the prediction of the energy demand. Then, a cloud-assisted deep neural network model is designed to forecast short-term energy consumption in a residential area while preserving user privacy. The solution is applied to the consumption data of four appliances elected from a set of real household power data. The experiment results show that the proposed framework is effective for estimating consumption with convincing accuracy.

关键词： Electrical load forecasting cloud computing smart grid weather attributes energy consumption time-series analysis

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Obtaining Electromagnetic Properties of Multi-Type Media in Realistic Environments:State-of-the-Art and Prospects

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China Communications 2025年第1期22卷 25-40页

作者： Guo Lantu Guan Ke Liu Ting He Danping Zhang Haixia Zhu Qiuming Lu Jun Zhang Minggao China Research Institute of Radiowave Propagation Qingdao 266107China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong UniversityBeijing 100044China School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Control Science and Engineering Shandong UniversityJinan 250061China Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Jinan 250061China College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China Jiaxing University Jiaxing 314001China

To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions that can break through the bottlenecks of current ***,based on the survey of three mainstream approaches for acquiring EM properties of media,we identify the difficulties when implementing them in realistic *** a focus on addressing these problems and challenges,we propose a novel paradigm for obtaining the EM properties of multi-type media in realistic ***,within this paradigm,we describe the implementation approach of the key technology,namely“multipath extraction using heterogeneous wave propagation data in multi-spectrum cases”.Finally,the latest measurement and simulation results show that the EM properties of multi-type media in realistic environments can be precisely and efficiently acquired by the methodology proposed in this study.

关键词： electromagnetic properties of media multi-type media parameter inversion ray tracing realistic environment

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Energy-based control approaches for weakly coupled electromechanical systems

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Automatica 2025年 177卷

作者： Javanmardi, Najmeh Borja, Pablo Yazdanpanah, Mohammad Javad Scherpen, Jacquelien M.A. Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen Netherlands School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

This paper addresses the stabilization and trajectory-tracking problems for two classes of weakly coupled electromechanical systems. To this end, we formulate an energy-based model for these systems within the port-Hamiltonian framework. Then, we employ Lyapunov theory and the notion of contractive systems to develop control approaches in the port-Hamiltonian framework. Remarkably, these control methods eliminate the need to solve partial differential equations or implement any change of coordinates and are endowed with a physical interpretation. We also investigate the effect of coupled damping on the transient performance and convergence rate of the closed-loop system. Finally, the applicability of the proposed approaches is illustrated in two applications of electromechanical systems through simulations. © 2025 The Author(s)

关键词： Damping

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A Two-Stage Solution to Quantum Process Tomography: Error Analysis and Optimal Design

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IEEE Transactions on Information Theory 2025年第3期71卷 1803-1823页

作者： Xiao, Shuixin Wang, Yuanlong Zhang, Jun Dong, Daoyi Mooney, Gary J. Petersen, Ian R. Yonezawa, Hidehiro University of New South Wales School of Engineering and Technology CanberraACT2600 Australia University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China The Australian National University School of Engineering CanberraACT2601 Australia Chinese Academy of Sciences Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China The Australian National University CIICADA Laboratory School of Engineering CanberraACT2601 Australia The University of Melbourne School of Physics ParkvilleVIC3010 Australia RIKEN Center for Quantum Computing Saitama351-0198 Japan Centre for Quantum Computation and Communication Technology Australian Research Council CanberraACT2600 Australia

Quantum process tomography is a critical task for characterizing the dynamics of quantum systems and achieving precise quantum control. In this paper, we propose a two-stage solution for both trace-preserving and non-trace-preserving quantum process tomography. Utilizing a tensor structure, our algorithm exhibits a computational complexity of O(MLd2) where d is the dimension of the quantum system and M, L(M≥ d2, L≥ d2) represent the numbers of different input states and measurement operators, respectively. We establish an analytical error upper bound and then design the optimal input states and the optimal measurement operators, which are both based on minimizing the error upper bound and maximizing the robustness characterized by the condition number. Numerical examples and testing on IBM quantum devices are presented to demonstrate the performance and efficiency of our algorithm. © 1963-2012 IEEE.

关键词： Error analysis

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Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Automation Science and engineering 2025年

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

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