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Pervasive Indoor User Identification Leveraging Mobile Single Station Localization

IEEE Internet of Things Journal 2025年

作者： Nie, Wendi Liu, Zexing Wang, Xiaoyang Duan, Yaoxin Lam, Kam-Yiu Liu, Kai Ng, Joseph K. Y. Xue, Chun Jason Gui, Guan Ministry of Education Key Laboratory of Industrial Internet of Things and Networked Control China Chongqing University of Posts and Telecommunications School of Automation/School of Industrial Internet Chongqing40065 China Institute of Industrial Internet Chongqing401120 China City University of Hong Kong Computer Science Department Hong Kong Chongqing University College of Computer Science Chongqing400030 China Hong Kong Baptist University Computer Science Department Hong Kong Mohamed Bin Zayed University of Artificial Intelligence Department of Computer Science Abu Dhabi United Arab Emirates Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing210003 China

The utilization of Wi-Fi-based technology for pervasive indoor user identification has gained prominence due to its cost-effective nature and compatibility with user devices. Previous works proposed capturing the media access control (MAC) address emitted from a user's device and using Information Elements (IE)-based MAC de-randomization methods to mitigate the impairment caused by random MAC. However, IE types of different Wi-Fi devices are not consistently differentiated, leading to identification errors in IE-based methods. Additionally, typical Wi-Fi fingerprinting approaches require densely pre-deployed Wi-Fi stations, contradicting the principle of pervasive localization. To address these challenges, we propose the mobile single station-based user identification (***) technique, which leverages Wi-Fi mobile single stations for pervasive indoor user identification. *** includes mobile single station localization (MSL) and MAC de-randomization based on users' spatio-temporal location and IE information (***). MSL can be implemented on a standard mobile Wi-Fi station without extensive pre-deployment. *** performs MAC de-randomization using the LIC algorithm to identify users with random MAC addresses. Experimental results demonstrate that *** outperforms previous IE-based user identification methods and multi-station localization techniques. MSL achieves a localization error of 1.15 meters which is better than multi-station with 12 APs of 1.40 meters. *** demonstrates an identification accuracy of 95.24% which is better than AIMAC of 85.48%. © 2014 IEEE.

关键词： Medium access control

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2M3DF: Advancing 3D Industrial Defect Detection with Multi Perspective Multimodal Fusion Network

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Asad, Mujtaba Azeem, Waqar Jiang, He Mustafa, Hafiz Tayyab Yang, Jie Liu, Wei Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Department of Automation Shanghai200240 China Lahore Garrison University Department of Software Engineering Lahore54000 Pakistan China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China

In the context of Industrial Anomaly Detection (IAD), ensuring the quality of manufactured products is critical. Traditional 2D based methods often fail to capture anomalies present in complex 3D shapes. For effective anomaly detection in 3D shapes, it is essential to incorporate global semantic context, local geometric structure, and color information of the object. To fully leverage these features, we propose a network named 2M3DF, that leverages knowledge from multi-view RGB images and corresponding point cloud information for enhanced anomaly detection performance. Our model initially employs pre-trained feature extractors that generate local features from multi-view RGB images and corresponding point clouds. The novel inter-modality feature representation and fusion module first adapts these inter-modality features and then effectively aligns and aggregates these multimodality features on a pixel-to-point basis. To learn the normality from point-wise fused multimodal features, we fit a multivariate Gaussian distribution to model the normal feature distribution. Comprehensive experimental evaluations using the MVTec3D-AD and Eyecandies dataset validate the effectiveness of our propose model and demonstrate significant improvements in comparison to existing state-of-the-art methods. Our model achieves a 96.6% mean I-AUROC while delivering real-time results. © 1991-2012 IEEE.

关键词： Normal distribution

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A Robust Integrated Estimation of Unknown Disturbances, Parameters and States for Linear Time-Varying Systems

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IEEE Transactions on Industrial Electronics 2025年

作者： Xu, Xiaoyi Luo, Hao Huo, Mingyi Jiang, Yuchen Zhang, Xiao Lv, Xin Harbin Institute of Technology Control and Simulation Center Harbin 150001 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai 200093 China Beihang University Department of Automation Science and Electrical Engineering Beijing 100191 China Harbin Institute of Technology School of Electrical Engineering and Automation Harbin 150001 China

In the industrial fields, widespread disturbances often negatively impact system control performance. Accurately estimating the unknown system dynamics and disturbances is the key for improving the system control performance. Traditional estimationmethods typically require prior knowledge of model parameters and disturbance dynamics. However, due to practical constraints, accurate prior information is difficult to obtain. Facing such a practical demand, in this article we propose a novel approach formultiinput multioutput time-varying systems to estimate the system states, parameters, and disturbances simultaneously. More specifically, a robust integrated estimation approach is designed to achieve this goal by cooperatively regulating all estimation errors. The only requirement for the design of the proposed integrated estimation approach is the measurable data from system operations. Besides, the solid theoretical results, experiments on a numerical example and application to a quadrotor under payload disturbance are carried out to validate the effectiveness of the proposed approach. © 1982-2012 IEEE.

关键词： Adaptive observer data-driven integrated estimation time-varying system unknown disturbance

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Grid-distance-based selection for fine-grained object detection in aerial images

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Pattern Recognition Letters 2025年 194卷 13-19页

作者： Jiaqi Zhao Qingfeng Ou Yong Zhou Wenliang Du Rui Yao School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 Jiangsu China Mine Digitization Engineering Research Center of the Ministry of Education Xuzhou 221116 Jiangsu China Disaster Intelligent Prevention and Control and Emergency Rescue Innovation Research Center Xuzhou 221116 Jiangsu China

Fine-grained object detection is a novel research direction that has recently garnered significant attention in the remote sensing field. Unlike general datasets, fine-grained datasets typically present more minor inter-class variances. Specifically, we highlight the limitations of using Intersection over Union (IoU) as a metric for label assignment. In this letter, we propose a Grid-Distance-Based Selection (GDS) method, which utilizes a newly defined grid distance metric to assess the classification potential of anchors. This approach enables positive samples to capture more discriminative features. Furthermore, relationship modeling of key regions within an object is introduced into fine-grained object detection tasks. The classification branch is enhanced by incorporating a Swin Transformer block to aggregate the aligned features from different localizations of the object. The proposed method is effective and easily to be implemented. Notably, compared to the baseline S 2 A-Net, our method achieves 35.89% (+2.22%) on the Fair1m-1.0 and 36.95% (+1.92%) on the Fair1m-2.0.

关键词： Object detection

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Intermittent Observer for Chaotic Lur’e Systems With Multi-Nondifferentiable Delay and Partially Limited Output Channels and Its Application to Secure Communication

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IEEE Transactions on Cybernetics 2025年 PP卷 PP页

作者： Ding, Kui Zhu, Quanxin Huang, Tingwen Anhui Polytechnic University Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Wuhu241000 China Hunan Normal University MOE-LCSM CHP-LCOCS School of Mathematics and Statistics Changsha410081 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This study focuses on the state tracking problems of chaotic Lur’e systems with nondifferentiable delay and partial channel restriction via intermittent observer. Different from the results obtained by existing chaotic Lur’e systems, the time delay involved in the chaotic Lur’e system explored in this study does not require that the time delay must be differentiable and there is no limit that the time delay must be bounded, which is more in accordance with the operating situation in practical engineering. Subsequently, aiming at the inevitable cyber-attack in the communication network environment, an intermittent observer based on the loss of partial measurement information induced by cyber-attack is constructed. Especially, in the operation process of the designed intermittent estimator, it breaks through the strict requirement that the observation width must be greater than the upper limit of the delay in the existing work, which intuitively reflects the wider application range and has more exploratory significance. Moreover, a meaningful lemma is designed, which effectively overcomes the invalidity of existing Lyapunov function or Lyapunov–Krasovskii functional methods for nondifferentiable systems, and develops novel criteria for partial information loss and nondifferentiable systems, presenting an important theoretical guidance for related topics. Numerical simulations are provided to verify the effectiveness of the proposed intermittent observer mechanism and the stabilization criteria of the error system. Finally, an application of the proposed intermittent observation result for two identical chaotic systems to a secure communication approach is provided. © 2013 IEEE.

关键词： Lyapunov functions

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TSGN: Transaction Subgraph Networks Assisting Phishing Detection in Ethereum

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Jinhuan Chen, Pengtao Xu, Xinyao Wu, Jiajing Shen, Meng Xuan, Qi Yang, Xiaoniu Zhejiang University of Technology Institute of Cyberspace Security College of Information Engineering Hangzhou310023 China ZJUT Binjiang Institute of Artificial Intelligence Hangzhou310056 China Fudan University Adaptive Networks and Control Lab Department of Electronic Engineering School of Information Science and Technology Shanghai200433 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Shenzhen518066 China National Key Laboratory of Electromagnetic Space Security Jiaxing314033 China

Due to the decentralized and public nature of the blockchain ecosystem, malicious activities on the Ethereum platform impose immeasurable losses on users. At the same time, the transparency of cryptocurrency transactions provides a unique opportunity to analyze illegal activities, such as phishing scams, from a network perspective. Most existing phishing scam detection methods focus primarily on analyzing account interaction networks, which limits their ability to uncover transaction behavior patterns embedded within transaction interactions. To address this, we construct the Transaction SubGraph Network (TSGN) by using transaction subgraphs as basic elements and further propose a novel framework for Ethereum phishing account detection. Specifically, we rebuild the graph structures via three well-designed mapping mechanisms, yielding TSGN and its two variants, i.e., Directed-TSGN and Temporal-TSGN, to obtain direction-aware and time-aware transfer flow features. By further incorporating the mapping strategy into transaction multidigraphs, we develop the Multiple-TSGN, which could preserve more transaction flow features while concurrently reducing the time consumption of modeling large-scale networks. TSGN models based on transaction subgraph interactions can capture complex higher-order dependencies, which lay beyond the reach of models that exclusively capture pairwise account interactions. As a general framework, our model can incorporate various feature extraction methods to improve the performance of phishing detection. Extensive experimental results on Ethereum datasets show that our method achieves superior performance in phishing detection, yielding 3.27%∼6.71% relative improvement over previous state-of-the-art. © 2004-2012 IEEE.

关键词： Phishing

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Climate Downscaling Using Neural Operator: Spatiotemporal Multimodal Fusion Operator with State-Query Coupled Kernel

Climate Downscaling Using Neural Operator: Spatiotemporal Mu...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haodi Zhang Yichi Wang Yifan Jian Jiahui Jiang Zhaohai Bai Lin Ma College of Computer Science and Software Engineering Shenzhen University Center for Agricultural Resources Research Institute of Genetic and Developmental Biology Chinese Academy of Sciences State Key Laboratory of Pollution Control and Resource Reuse School of the Environment Nanjing University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Climate downscaling is crucial for detailed small- scale analysis and for acquiring climate data in regions without weather stations. Operator learning has proven potential for this task. However, several challenges remain in operator learning, such as multimodal fusion, spatiotemporal fusion and input state and query adaptation. To address these challenges, we propose a Spatiotemporal Multimodal Fusion Operator with a State- Query Coupled Kernel (SMCK). This framework includes a latent space fusion encoder that encodes climate variables using position-wise multihead attention for multimodal fusion and integrates historical information to generate robust and precise representation. Additionally, we introduce a state-query coupled kernel that combines radial basis functions and discrete fourier encoding to enhance query location representation, while also adapting to the state to obtain the coupled kernel. Extensive experiments demonstrate that our method achieves state-of-the-art performance and provides strong support for climate downscaling and the planning of climate-related strategies.

关键词： Couplings Signal processing Encoding Robustness Spatiotemporal phenomena Planning Kernel Speech processing Forecasting Meteorology

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Generalization and differentiation of affective associative memory circuit based on memristive neural network with emotion transfer

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Neural Networks 2025年 188卷 107502页

作者： Yao, Wei Gao, Kai Wang, You Lin, Hairong Wu, Hongwei Xu, Cong Zhang, Xin School of Computer and Communication Engineering Changsha University of Science & Technology Changsha410114 China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China School of Electronic Information Central South University Changsha410083 China School of Software Engineering Dalian University Dalian116622 China Co. Ltd. Dalian China College of Semiconductors Hunan University Changsha410082 China

Most existing research on affective associative memory neural network circuits has predominantly concentrated on reinforcement and extinction, with insufficient attention given to the integration of emotion transfer alongside the principles of generalization and differentiation. To enhance the functionality of associative memory, this paper introduces a memristor-based affective associative memory neural network that incorporates generalization and differentiation under emotion transfer. The circuit implementation is carried out using PSPICE. The capabilities of learning, forgetting, emotion transfer, as well as generalization and differentiation under emotion transfer, are effectively realized and validated through simulation results. By examining generalization and differentiation under emotion transfer, this study presents an innovative approach to the field of affective associative memory neural networks based on memristors, particularly in relation to complex biological behaviors. © 2025 Elsevier Ltd

关键词： SPICE

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Joint Power Allocation and Task Scheduling for Data Offloading in Non-Geostationary Orbit Satellite Networks

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IEEE Transactions on Network and Service Management 2025年

作者： He, Lijun Jia, Ziye Wang, Juncheng Lansard, Erick Han, Zhu Yuen, Chau China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xidian University State Key Laboratory of Integrated Services Networks Shaanxi Xi'an710071 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Hong Kong Baptist University Department of Computer Science China Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

In Non-Geostationary Orbit Satellite Networks (NGOSNs) with a large number of battery-carrying satellites, proper power allocation and task scheduling are crucial to improving data offloading efficiency. In this work, we jointly optimize power allocation and task scheduling to achieve energy-efficient data offloading in NGOSNs. Our goal is to properly balance the minimization of the total energy consumption and the maximization of the sum weights of tasks. Due to the tight coupling between power allocation and task scheduling, we first derive the optimal power allocation solution to the joint optimization problem with any given task scheduling policy. We then leverage the conflict graph model to transform the joint optimization problem into an Integer Linear Programming (ILP) problem with any given power allocation strategy. We explore the unique structure of the ILP problem to derive an efficient semidefinite relaxation-based solution. Finally, we utilize the genetic framework to combine the above special solutions as a two-layer solution for the original joint optimization problem. Simulation results demonstrate that our proposed solution can properly balance the reduction of total energy consumption and the improvement of the sum weights of tasks, thus achieving superior system performance over the current literature. © 2004-2012 IEEE.

关键词： Geostationary satellites

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Symmetry in linear physical systems

arXiv

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arXiv 2025年

作者： van der Schaft, Arjan Sepulchre, Rodolphe Chaffey, Thomas Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence Jan C. Willems Center for Systems and Control University of Groningen PO Box 407 9700 AK Netherlands KU Leuven Belgium University of Cambridge United Kingdom School of Electrical and Computer Engineering University of Sydney Australia

Physical systems with symmetry arise abundantly in applications, and are endowed with interesting mathematical structures. The present paper focusses on linear reciprocal and input-output Hamiltonian systems. Their characterization is studied from an input-output as well as from a state point of view. Geometrically, it turns out that they both define Lagrangian subspaces with corresponding generating functionals. Furthermore, the relations with time reversibility are analyzed. The system classes under consideration are expected to admit scalable control laws, and to be important building blocks in design. Copyright © 2025, The Authors. All rights reserved.

关键词： Hamiltonians

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