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IEEE Conference on Wireless Communications and Networking

作者： Caijuan Chen Chaofeng Zhang Gaolei Li Yuchen Liu The Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan Advanced Institute of Industrial Technology Tokyo Japan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science NC State University North Carolina US

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The escalating frequency of geological disasters, local conflicts, and public health crises necessitates robust emergency communication systems within Internet of Things (IoT) frameworks to ensure public safety. Employing Power-Domain NOMA (PD-NOMA) and Successive Interference Cancellation (SIC) enables effective multi-device communication that is crucial for emergency responses. However, the practical implementation of NOMA technologies in emergency scenarios is hindered by existing resource constraints and the requirement of prioritizing critical emergency information. To address these limitations, we propose a multi-objective optimization-based NOMA framework to maximize network throughput while ensuring quality of service (QoS) and energy efficiency. This framework incorporates a novel communication grouping algorithm based on queue penalty optimization and an Actor-Critic-based dynamic updating algorithm for multiple resource allocation. Our study proposes a practical approach to enhance emergency communication in disaster areas, ensuring efficient and reliable network performance under various constraints.

关键词： NOMA Interference cancellation Heuristic algorithms Disasters Quality of service Throughput Dynamic scheduling Internet of Things Resource management Optimization

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Deep Learning Model for Analyzing EEG Signal Analysis

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IEEE Access 2025年

作者： Gupta, Varun Kumar, Vivek Prince Singh, Saurabh Lee, Young-Seok Ra, In-Ho Department of Electronics and Communication National Institute of Technology Sikkim India National Institute of Technology Department of Electrical and Electronics Engineering Sikkim India Dongguk University Department of Industrial & Systems Engineering Seoul04620 Korea Republic of Woosong University Department of AI and Bigdata Daejeon34606 Korea Republic of Kunsan National University School of Computer & Software Gunsan Korea Republic of Kunsan National University School of Software Gunsan54150 Korea Republic of

To analyze the physiological information within the acquired EEG signal is very cumbersome due to the possibility of several factors, viz. noise and artifacts, complexity of brain dynamics, and inter-subject variability. To address these issues, this paper compares a U-shaped encoder-decoder network (UNET) and Bat-based UNET signal analysis (BUSA) techniques to classify depression rates in the Electroencephalogram (EEG) datasets. The main objective of including these two techniques is to reveal their effectiveness. It comprises pre-processing, feature extraction, feature selection, and classification stages. The framework excels at noise reduction during pre-processing, enhancing dataset integrity. Feature extraction leverages band power and correlation dimension to extract crucial features. Furthermore, feature selection optimizes classification accuracy by refining the fitness function of bats in the classification layer. The performance of UNET and BUSA are compared based on the following performance evaluating parameters viz. accuracy (Acc), Area Under the Curve (AUC), precision (P), and recall (R) (or sensitivity (Se)). The results indicated that the BUSA technique outperforms the UNET technique. © 2013 IEEE.

关键词： Electroencephalography

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Quantum Hamiltonian Descent for Non-smooth Optimization

arXiv

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arXiv 2025年

作者： Leng, Jiaqi Zheng, Yufan Jia, Zhiyuan Fan, Lei Zhao, Chaoyue Peng, Yuxiang Wu, Xiaodi Simons Institute for the Theory of Computing University of California Berkeley United States Department of Mathematics University of California Berkeley United States Department of Computer Science University of Maryland College Park United States Joint Center for Quantum Information and Computer Science University of Maryland United States Department of Applied Mathematics University of Washington United States Department of Engineering Technology University of Houston United States Department of Electrical and Computer Engineering University of Houston United States Department of Industrial and Systems Engineering University of Washington United States Artephi Computing Inc United States

Non-smooth optimization models play a fundamental role in various disciplines, including engineering, science, management, and finance. However, classical algorithms for solving such models often struggle with convergence speed, scalability, and parameter tuning, particularly in high-dimensional and non-convex settings. In this paper, we explore how quantum mechanics can be leveraged to overcome these limitations. Specifically, we investigate the theoretical properties of the Quantum Hamiltonian Descent (QHD) algorithm for non-smooth optimization in both continuous and discrete time. First, we propose continuous-time variants of the general QHD algorithm and establish their global convergence and convergence rate for non-smooth convex and strongly convex problems through a novel Lyapunov function design. Furthermore, we prove the finite-time global convergence of continuous-time QHD for non-smooth non-convex problems under mild conditions (i.e., locally Lipschitz). In addition, we propose discrete-time QHD, a fully digitized implementation of QHD via operator splitting (i.e., product formula). We find that discrete-time QHD exhibits similar convergence properties even with large time steps. Finally, numerical experiments validate our theoretical findings and demonstrate the computational advantages of QHD over classical non-smooth non-convex optimization algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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Cascading Failure Propagation and Perfect Storms in Interdependent Infrastructures

ASCE Open: Multidisciplinary Journal of Civil Engineering

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ASCE Open: Multidisciplinary Journal of Civil engineering 2025年第1期3卷

作者： Hoff, Ryan Sparks, Ryan Chester, Mikhail Mustafa, Ahmed Johnson, Nathan Birchfield, Adam McPhearson, Timon Li, Rui Ahmad, Nasir Searles, Ian School of Sustainable Engineering and the Built Environment Ira A. Fulton Schools of Engineering Arizona State Univ. 660 S College Ave. TempeAZ85287-3005 United States The Polytechnic School Ira A. Fulton Schools of Engineering Arizona State Univ. 7001 E Williams Field Rd. MesaAZ85212 United States Urban Systems Lab The New School 66 W 12th St. New YorkNY10011 United States Dept. of Computer and Electrical Engineering Texas A&M Univ. 3128 TAMU College StationTX77843-3128 United States Oak RidgeTN37830 United States

The increasingly complex conditions that are reshaping environments demand novel analysis of infrastructure weaknesses and behavior. Of critical concern are cascading failures and how small disruptions can spiral into large-scale outages. Significant evidence indicates infrastructures are increasingly stressed given a combination of disruptions including extreme climate events, disrepair, cyberattacks, and emerging and disruptive technology integration. Small disruptions appear increasingly likely to cascade to larger failures within and beyond infrastructures, and there is limited insight into how to protect systems that are increasingly integrated. As novel capabilities emerge to expedite the analysis of cascades (namely, synthetic infrastructure models and open-source network solvers), new opportunities exist to provide critical insights into cascading failures. Using the City of Phoenix as a case study, synthetic power and water networks are constructed and coupled, and disturbances are simulated to capture cascading failure behaviors within and across power and water distribution systems. Network solvers [PyPSA (version 0.24.0) for power and EPANET (version 2.2) for water] are used to capture network rebalancing. Failures are simulated starting with transmission line outages and 120,000 simulations used to capture stochasticity in the rebalancing of power and water systems and resulting differences in failure dynamics. In 89% of the simulations initial transmission line outages did not cause outages at substations or in water systems. Power failures did not lead to water outages in 96% of simulations. Despite significant variability in the networks, emergent failure patterns are observed when substation and resulting pump outages occur - a critical insight for resilience planning. Approximately 3.69% of the simulations lead to large cascading failures across both power and water systems. Furthermore, low-likelihood but high-consequence perfect storm outcomes we

关键词： Stochastic systems

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Comparison of the time-dependent discriminatory accuracy of femoral strength and bone mineral density for predicting future hip and major osteoporotic fractures: a 16-year follow-up of the AGES-Reykjavik cohort

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Osteoporosis International 2025年 1-10页

作者： Praveen, Anitha D. Jha, Dheeraj Baker, Alexander Fleps, Ingmar Björnsson, Páll Ellingsen, Lotta María Aspelund, Thor Sigurdsson, Sigurdur Gudnason, Vilmundur Pálsson, Halldór Matchar, David Johannesdottir, Fjola Ferguson, Stephen J. Helgason, Benedikt Future Health Technologies Programme Singapore-ETH Centre CREATE campus 1 Create Way CREATE Tower #06-01 138602 Singapore Singapore Institute for Biomechanics ETH Zürich Zurich Switzerland College of Mechanical Engineering Boston University Boston MA United States Faculty of Electrical and Computer Engineering University of Iceland Reykjavik Iceland Centre of Public Health Sciences Faculty of Medicine University of Iceland Reykjavik Iceland The Icelandic Heart Association Research Institute Kopavogur Iceland Faculty of Industrial Engineering Mechanical Engineering and Computer Science School of Engineering and Natural Sciences University of Iceland Reykjavik Iceland Department of Medicine Duke University Durham NC United States Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Center for Advanced Orthopaedic Studies Beth Israel Deaconess Medical Center and Department of Orthopaedic Surgery Harvard Medical School Boston MA United States

Summary: The discriminative accuracy of femoral strength was significantly higher than that of aBMD over 16 years of follow-up for classifying hip fractures and major osteoporotic fractures. The use of accurate thresholds, whether for aBMD or other imaging-based biomarkers, is crucial to improve sensitivity and identify high-risk older adults. Background: Areal bone mineral density (aBMD) is a surrogate for bone strength but has limited prognostic value. Finite element (FE)–derived femoral strength offers a biomechanical alternative to aBMD for fracture risk assessment, but its long-term predictive value remains unclear. This study compared the discriminatory accuracy of aBMD and femoral strength for hip (HFs) and major osteoporotic fractures (MOFs) over 16 years, accounting for mortality risk. Methods: In the prospective Age Gene/Environment Susceptibility‐Reykjavik (AGES‐Reykjavik) Study, elderly participants underwent CT scans at entry and automated algorithms were used to compute aBMD and femoral strength. Time-dependent area under the receiver operating characteristic curves (AUC) was used to compare the predictive abilities of aBMD and femoral strength. Optimal cutoffs at the Youden’s index were compared with the World Health Organization (WHO)–defined aBMD cutoffs at various time points. Results: The cohort comprised 4621 older adults (mean age 76 ± 5 years). Femoral strength had a significantly higher AUC than aBMD in identifying HFs (p < 0.05) from the 6th year in males and females, while their AUCs in predicting MOFs were similar. WHO-defined aBMD showed low sensitivity (17–52%) but high specificity (78–94%) for both HFs and MOFs. The sensitivity of optimal femoral strength was significantly higher than that of aBMD at comparable specificity by 5–19% for HFs and 2–10% for MOFs (p < 0.05). Conclusions: Both image-based markers predict long-term fracture risk and enable opportunistic screening with existing CT scans. However, femoral strength demonstrates be

关键词： Bone mineral density Competing risk Diagnostic accuracy Finite-element modelling Osteoporotic fractures Prediction Time-dependent AUC

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

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IEEE Transactions on Mobile Computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT

IEEE Networking Letters

引用

IEEE Networking Letters 2025年

作者： Li, Kai Liang, Yilei Yuan, Xin Ni, Wei Crowcroft, Jon Yuen, Chau Akan, Ozgur B. Group Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom Real-Time and Embedded Computing Systems Research Centre CISTER Porto4249-015 Portugal The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FA United Kingdom Commonwealth Scientific and Industrial Research Organization CSIRO SydneyNSW2122 Australia The School of Computing Science and Engineering University of New South Wales Australia Nanyang Technological University SG 639798 Singapore Group Division of Electrical Engineering Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom The Center for neXt-generation Communications CXC Koç University Istanbul34450 Turkey

This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT). In this framework, certain EdgeIoT devices train local models using the same data samples but analyze disparate data features, while the others focus on the same features using non-independent and identically distributed (non-IID) data samples. Thus, even though the data features are consistent, the data samples vary across devices. The proposed HoVeFL formulates the training of local and global models to minimize the global loss function. Performance evaluations on CIFAR-10 and SVHN datasets reveal that the testing loss of HoVeFL with 12 horizontal FL devices and six vertical FL devices is 5.5% and 25.2% higher, respectively, compared to a setup with six horizontal FL devices and 12 vertical FL devices. © 2019 IEEE.

关键词： Federated learning

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Learning-Based Position and Orientation Control of a Hybrid Rigid-Soft Arm Manipulator

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Journal of Mechanisms and Robotics 2025年第7期17卷 071010页

作者： Koe, Kendall Marri, Samhita Walt, Benjamin Kamtikar, Shivani Uppalapati, Naveen Kumar Krishnan, Girish Chowdhary, Girish https://***/047426m28University of Illinois Urbana-Champaign Computer Science University of Illinois 1210 Siebel Center 201 N. Goodwin Urbana IL 61801 https://***/047426m28University of Illinois Urbana-Champaign Electrical and Computer Engineering University of Illinois 306 N Wright St Urbana IL 61801 https://***/047426m28University of Illinois Urbana-Champaign Mechanical Science and Engineering University of Illinois 1206 W Green St Urbana IL 61801 National Center for Supercomputing Applications University of Illinois 1205 W Clark St Urbana IL 61801 Industrial and Systems Engineering University of Illinois 104 S Mathews Ave Urbana IL 61801 Computer Science University of Illinois 1210 Siebel Center 201 N. Goodwin Urbana IL 61801

We present a position and orientation controller for a hybrid rigid-soft manipulator arm where the soft arm is extruded from a two degrees-of-freedom rigid link. Our approach involves learning the dynamics of the hybrid arm operating at4Hzand leveraging it to generate optimal trajectories that serve as expert data to learn a control policy. We performed an extensive evaluation of the policy on a physical hybrid arm capable of jointly controlling rigid and soft actuation. We show that with a single policy, the arm is capable of reaching arbitrary poses in the workspace with3.73cm(⁠<6%overall arm length) and17.78deg error within12.5s⁠, operating at different control frequencies, and controlling the end effector with different loads. Our results showcase significant improvements in control speed while effectively controlling both the position and orientation of the end effector compared to previous quasistatic controllers for hybrid arms.

关键词： and control of mechanical systems Optimization dynamics Errors Stress Trajectories (Physics) Architecture Manipulators Control equipment End effectors Robotics kinematics Dynamics (Mechanics)

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A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures

arXiv

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arXiv 2025年

作者： Yan, Keqiang Bohde, Montgomery Kryvenko, Andrii Xiang, Ziyu Zhao, Kaiji Zhu, Siya Kolachina, Saagar Sarıtürk, Doğuhan Xie, Jianwen Arróyave, Raymundo Qian, Xiaoning Qian, Xiaofeng Ji, Shuiwang Department of Computer Science & Engineering Texas A&M University College StationTX United States Department of Electrical & Computer Engineering Texas A&M University College StationTX United States Department of Materials Science & Engineering Texas A&M University College StationTX United States Lambda Inc. United States J. Mike Walker’66 Department of Mechanical Engineering Texas A&M University College StationTX United States Wm Michael Barnes’64 Department of Industrial and Systems Engineering Texas A&M University College StationTX United States Brookhaven National Laboratory UptonNY United States Department of Physics & Astronomy Texas A&M University College StationTX United States

Materials foundation models can predict energy, force, and stress of materials and enable a wide range of downstream discovery tasks. A key design choice involves the trade-off between invariant and equivariant architectures. Invariant models offer computational efficiency but may not perform well when predicting high-order outputs. In contrast, equivariant models can capture high-order symmetries, but are computationally expensive. In this work, we propose HIENet, a hybrid invariant-equivariant foundation model that integrates both invariant and equivariant message passing layers. HIENet is designed to achieve superior performance with considerable computational speedups over prior models. Experimental results on both common benchmarks and downstream materials discovery tasks demonstrate the efficiency and effectiveness of HIENet. © 2025, CC BY-NC-SA.

关键词： Prediction models

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Model Order Reduction of Linear systems via (γ,δ)-Similarity

arXiv

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arXiv 2025年

作者： Bajaj, Shivam Beck, Carolyn L. Gupta, Vijay Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette United States Department of Industrial and Enterprise Systems Engineering University of Illinois Urbana-Champaign Champaign United States

Model order reduction aims to determine a low-order approximation of high-order models with least possible approximation errors. For application to physical systems, it is crucial that the reduced order model (ROM) is robust to any disturbance that acts on the full order model (FOM) – in the sense that the output of the ROM remains a good approximation of that of the FOM, even in the presence of such disturbances. In this work, we present a framework for model order reduction for a class of continuous-time linear systems that ensures this property for any L2 disturbance. Apart from robustness to disturbances in this sense, the proposed framework also displays other desirable properties for model order reduction: (1) a provable bound on the error defined as the L2 norm of the difference between the output of the ROM and FOM, (2) preservation of stability, (3) compositionality properties and a provable error bound for arbitrary interconnected systems, (4) a provable bound on the output of the FOM when the controller designed for the ROM is used with the FOM, and finally, (5) compatibility with existing approaches such as balanced truncation and moment matching. Property (4) does not require computation of any gap metric and property (5) is beneficial as existing approaches can also be equipped with some of the preceding properties. The theoretical results are corroborated on numerical case studies, including on a building model. © 2025, CC BY-NC-SA.

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