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Worst-Case Integrity Attacks and Resilient State Estimation With Partially Secured Measurements

IEEE Transactions on Control of Network systems 2025年

作者： Zhou, Jing Shang, Jun Chen, Tongwen University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China

This paper examines the problem of optimal deception attacks against state estimation with partially secured measurements, where smart sensors transmit innovation sequences to the remote end for information fusion. Due to resource limitations or defensive countermeasures, the adversary can only modify data packets transmitted through unreliable channels. Meanwhile, the attack should be synthesized with sophistication to deceive an anomaly detector. To investigate the vulnerabilities of such estimation systems without feedback links and enhance security performance, the optimal attack policy is derived by formulating and explicitly solving a convex optimization problem, with the goal of maximizing the sum of estimation errors. Subsequently, a novel attack detection and resilient state estimation algorithm is proposed to ensure an acceptable level of estimation accuracy. The theoretical performance metrics, including false alarm rates for the proposed detector, are provided. Finally, the effectiveness of the results is confirmed through numerical examples. © 2014 IEEE.

关键词： Optimal detection

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Improved High-Speed Thermal Analysis Based on Two-Step Simulation for High-Frequency Transformers

Improved High-Speed Thermal Analysis Based on Two-Step Simul...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Zheyuan Yi Kai Sun Qiang Li Zengyang Liu Department of Electrical Engineering State Key Laboratory of Power System Tsinghua University Beijing China Delta Electronics Shanghai Co. Ltd. Shanghai China The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State Center for Power Electronics Systems University Blacksburg Virginia USA

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Thermal analysis is a crucial and time-consuming step in the optimization design process of high-frequency transformers (HFTs). This paper proposes an improved thermal analysis method based on heat dissipation-conduction two-step simulation, significantly reducing the computation time while ensuring the accuracy of thermal analysis. Simulation results validate the accuracy and of the proposed method and demonstrate that the proposed method saves 82% of computation time for a single thermal analysis case compared to direct CFD. Experimental results further verify the accuracy of the proposed method. An exemplary application of the proposed method in the optimization design of the HFT illustrates a 98% reduction in computation time for thermal analysis during the optimization process.

关键词： Heating systems Temperature distribution Accuracy Simulation Computational modeling Thermal analysis High-frequency transformers Optimization Surface treatment Heat transfer

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Dual-beam photothermal material trace detection method based on self-mixing interference

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Applied Optics 2025年第16期64卷 4549-4558页

作者： Yan Zhao Yunlong Zhi Hui Zhao Daming Dong Jigang Tong Chunling Tu School of Electrical Engineering and Automation Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin 300384 China National Engineering Research Center for Intelligent Agricultural Equipment Beijing Academy of Agriculture and Forestry Sciences Beijing 100097 China Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria 0188 South Africa

In this paper, a dual-beam photothermal self-mixing substance trace detection system is proposed. The crystal violet (CV) solution of the sample undergoes a thermal lens effect under the action of pump photoperiod excitation, and the refractive index changes cause the deviation of the probe beam, and the self-mixing interference (SMI) effect occurs when the modulated light returns to the cavity. By combining theoretical and experimental results, the power fluctuation of the SMI signal caused by the thermo-optic effect is linearly related to CV concentration and other parameters. At an excitation power of 24 mW, the detection limit can reach $8.0 \times {10^{- 8}}\;{\rm mol/L}$ . This method does not require labeling or complex CV pretreatment and has high sensitivity and flexibility, providing a guide for CV characterization in biological, environmental, and pharmaceutical research.

关键词： Gaussian beams Laser beams Optical fields Phase shift Refractive index Spatial resolution

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Optimal Sensor Placement for Full-Set TDOA Localization Accounting for Sensor Location Errors

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Zhang, Chengjie Han, Xinyang Kong, He Ho, K.C. Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Shenzhen518055 China University of Missouri Electrical Engineering and Computer Science Department ColumbiaMO65211 United States

The positioning accuracy of a signal source is influenced by where the sensors are deployed. Studies in the literature for the optimal sensor placement (OSP) of localization often ignore the presence of sensor position errors. In practice, however, it is impossible to place the sensors precisely at the desired locations. This work designs OSP by accounting for the presence of Gaussian sensor position errors that are independent and identically distributed (IID), with the full set of Time Difference of Arrival measurements corrupted by Gaussian IID noise. The criterion is the A-optimality equal to the trace of the source position Cramer-Rao Bound (CRB). We show that the presence of sensor position errors introduces a multiplication factor to the same criterion for the case without sensor position errors. The latter factor is dependent on the ratio of the sensor position error power to the range difference noise power but independent of the sensor positions, implying that the A-optimality OSP in the presence of sensor position errors is the same as in the absence. To our knowledge, this is the first time such results have been derived. Simulation studies validate the derived OSP, show the minimal OSP performance gap with sensor positioning error and without, and illustrate the better source localization accuracy by OSP. © 1965-2011 IEEE.

关键词： Cramer-Rao bounds

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit Kim, Dong In Jamalipour, Abbas Letaief, Khaled B. Tafazolli, Rahim University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of University of Sydney School of Electrical and Computer Engineering Sydney Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Surrey Institute for Communication Systems School of Computer Science and Electronic Engineering United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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User Grouping-Based Hierarchical Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Assisted Downlink Communication

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Yang, Zhixiang Feng, Lei Zhou, Fanqin Xie, Kunyi Qiu, Xuesong Xiong, Zehui Yuen, Chau Han, Zhu Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore487372 Singapore Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 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

The joint optimization of rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) offers a mutually beneficial solution. In this paper, we study a RIS-assisted downlink user grouping-based hierarchical RSMA communication network, where the base station transmits the common streams and private streams simultaneously to serve multiple users. The sum rate (SR) optimization problem is constructed to jointly optimize user grouping, beamforming, and rate splitting. To tackle the NP-hard problem, a user grouping algorithm is first provided to form the hierarchical RSMA model. Next, an equivalent weighted minimum mean-square error (WMMSE) problem is constructed and a WMMSE-based block coordinate descent (BCD) algorithm is designed to update the remaining variables in an iterated way. Specifically, the optimization of transmit beamforming and the rate splitting matrix is reduced to solving a typical convex quadratically constrained quadratic program problem. The solution for passive beamforming is recovered from the intermediate result obtained by the manifold optimization method. Finally, the process of the BCD algorithm is presented. The numerical results indicate that the proposed scheme evidently surpasses one-layer RSMA scheme, NOMA scheme, SDMA scheme, and no RIS scheme, especially in overloaded scenarios. The proposed scheme further increases the SR of common steams compared to the hierarchical RSMA scheme and significantly reduces computational complexity compared to the generalized RSMA scheme. © 2015 IEEE.

关键词： Mean square error

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Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks

arXiv

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Fu, Liqun Su, Yuhan Hosseinalipour, Seyyedali Wang, Xianbin Hong, Yiguang School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China School of Electronic Science and Engineering Xiamen University Xiamen China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering Western University ON Canada

Hierarchical Federated Learning (HFL) introduces intermediate aggregation layers, addressing the limitations of conventional Federated Learning (FL) in geographically dispersed environments with limited communication infrastructure. An application of HFL is in smart IoT systems, such as remote monitoring, disaster response, and battlefield operations, where cellular connectivity is often unreliable or unavailable. In these scenarios, UAVs serve as mobile aggregators, providing connectivity to the terrestrial IoT devices. This paper studies an HFL architecture for energy-constrained UAVs in smart IoT systems, pioneering a solution to minimize global training cost increased caused by UAV disconnection. In light of this, we formulate a joint optimization problem involving learning configuration, bandwidth allocation, and device-to-UAV association, and perform global aggregation in time before UAV drops disconnect and redeployment of UAVs. The problem explicitly accounts for the dynamic nature of IoT devices and their interruptible communications and is unveiled to be NP-hard. To address this, we decompose it into three subproblems. First, we optimize the learning configuration and bandwidth allocation using an augmented Lagrangian function to reduce training costs. Second, we propose a device fitness score, integrating data heterogeneity (via Kullback-Leibler divergence), device-to-UAV distances, and IoT device resources, and develop a twin-delayed deep deterministic policy gradient (TD3)-based algorithm for dynamic device-to-UAV assignment. Third, We introduce a low-complexity two-stage greedy strategy for finding the location of UAVs redeployment and selecting the appropriate global aggregator UAV. Experiments on real-world datasets demonstrate significant cost reductions and robust performance under communication interruptions. © 2025, CC BY-NC-ND.

关键词： Federated learning

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Generative AI-Driven Incentive Mechanism for Semantic Communications in RSMA Networks

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Liu, Dongqing Liu, Yongwen Zhang, Lei Hafid, Abdelhakim Khoukhi, Lyes Li, Mingyuan Liu, Yutao Dong, Yadong The First Affiliated Hospital of Zhengzhou University National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou China Zhengzhou University of Light Industry Software Engineering College Zhengzhou China Henan University of Technology College of Electrical Engineering Zhengzhou China University of Montreal Department of Computer Science and Operational Research Canada GREYC Laboratory ENSICAEN Normandie University France

This paper proposes a framework integrating Rate Splitting Multiple Access (RSMA), semantic communications, and generative AI for optimizing next-generation wireless networks. We present a unified model that combines RSMA with semantic communications to enhance both spectral efficiency and semantic fidelity. Our system model focuses on a downlink semantic communication system with a multi-antenna base station serving multiple single-antenna users. A dynamic contract-based incentive mechanism is developed to address user heterogeneity and information asymmetry in semantic RSMA scenarios. We introduce a diffusion model-based approach for joint optimization of resource allocation and contract design in RSMA systems. The semantic encoding process extracts key information, i.e., free-space detection, interest points, object attributes, and spatial relationships, from image data. A loss function is designed to train the semantic encoder and RSMA scheme, incorporating semantic extraction, partitioning, reconstruction, and task specific components. Our framework includes a multi-objective performance evaluation that considers both conventional metrics and semantic accuracy in a multi-user RSMA environment. We also define a multi-component semantic accuracy metric to assess the quality and utility of the extracted semantic information. Extensive simulation results demonstrate the superiority of our proposed framework over existing approaches in terms of system throughput, energy efficiency, and semantic fidelity across various network scenarios and user distributions. © 2015 IEEE.

关键词： Resource allocation

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Multibit Attention Fusion for Gaze Estimation Using 12-Bit RAW Data From CMOS Sensors

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Tian, Kaiwen Wang, Han Wu, Zhipeng Lyu, Yongxiang Vai, Mang I. Liu, Yu Tianjin University School of Microelectronics Tianjin300072 China University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology State Key Laboratory of Analog and Mixed-Signal VLSI Macau999078 China

In recent years, deep learning-based gaze estimation techniques using eye images have made significant progress. However, balancing prediction accuracy and computational complexity remains a challenge. In this paper, we propose using the RAW data from a CMOS image sensor as input, thereby avoiding the high computational demands of the traditional image signal processor (which converts RAW to RGB). We designed a special measuring scheme to collect and annotate a 12-bit RAW dataset. Additionally, we introduce the Multibit Attention Fusion Gaze Estimation Network (MAFN) to accurately perform gaze estimation using 12-bit RAW data. Our MAFN effectively extracts features of varying granularities through attention fusion techniques across different bit depths. We also employ Axial Sensitivity Spatial Attention, which is more sensitive to the horizontal and vertical positions of the pupil in gaze angle prediction. Furthermore, we propose Bit Rolling Channelization and Neural Network-Friendly Quantization for lossless input of 12-bit data and low-bit data generation, respectively. Experimental results validate the effectiveness of MAFN and demonstrate the superiority of our method, showing that it can accurately measure gaze direction using 12-bit RAW data. © 1963-2012 IEEE.

关键词： Image coding

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A frequency response, harmonic distortion, and intermodulation distortion test for BIST of a sigma-delta ADC

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IEEE TRANSACTIONS ON CIRCUITS AND systems II-ANALOG AND DIGITAL SIGNAL PROCESSING 1996年第8期43卷 608-613页

作者： Toner, MF Roberts, GW Microelectronics and Computer Systems Laboratory Department of Electrical Engineering McGill University Montreal Canada

Built-In-Self-Test (BIST) for VLSI systems is desirable for production-time testing and in-the-field diagnostics, This brief discusses a Mixed Analog-Digital BIST (MADBIST) for a frequency response test, a harmonic distortion test and an intermodulation distortion test of an analog-to-digital converter, The MADBIST strategy for the frequency response, harmonic distortion, and intermodulation distortion tests of the ADC is introduced, accuracy issues are discussed, and preliminary experimental results are presented.

关键词： Frequency response Harmonic distortion Intermodulation distortion Built-in self-test Cellular neural networks Neural networks Circuit testing Cellular networks Optical interconnections Optical receivers

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