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Precision-Adaptive Task Offloading and Resource Allocation for Efficient Positioning and Sensing in Near-Field IoV systems

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IEEE Internet of Things Journal 2025年第13期12卷 22635-22646页

作者： Dai, Cheng Bao, Song Chen, Songlin Garg, Sahil Kaddoum, Georges Sichuan University College of Computer Science Sichuan Chengdu China École de technologie supérieure Electrical Engineering Department MontréalQCH3C 1K3 Canada Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India École de technologie supérieure MontréalQCH3C1K3 Canada King Saud University College of Computer and Information Sciences Department of Information Systems Riyadh11543 Saudi Arabia

With the rapid advancement of sixth-generation (6G) network communication technology, improvements in data transmission rates, latency, and reliability have driven substantial growth in Internet of Vehicles (IoV) applications. Among these, the integration of 6G-enabled extremely large-scale antenna arrays (ELAAs) has extended the range of near-field (NF) communication, enabling their application in IoV to facilitate efficient and accurate environmental sensing. Through NF communication, vehicles can achieve high-accuracy localization and perception by analyzing the signal phase, channel state information, and beamforming calculations. However, positioning and sensing tasks place substantial computational and energy demands on edge devices, often exceeding traditional capacity limits. To address this challenge, task offloading has emerged as a solution, with mobile edge computing (MEC) offering a lower-latency alternative to centralized cloud computing by processing tasks at the network edge. Despite these advantages, MEC’s limited resources present challenges as the number of connected vehicles increases. Existing approaches to resource allocation often overlook the varied accuracy requirements of IoV tasks, where high-accuracy tasks like indoor navigation require stringent performance standards, while lower-accuracy tasks may tolerate reduced precision to save resources. Motivated by this, we propose an accuracy-based classification scheme for IoV positioning and sensing tasks, which dynamically adjusts accuracy requirements to reduce delay and energy consumption. Our approach maps total energy, accuracy loss, and delay to an overall quality of service (QoS) metric, and employs an optimization algorithm that leverages gradient descent and greedy strategies to balance resource allocation and accuracy selection. Extensive simulations demonstrate the effectiveness of the proposed scheme in reducing delay and energy consumption while maintaining high accuracy, signific

关键词： Resource allocation

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ML-Based Power System Stability Assessment Considering Network Topology Changes: WECC 20,000+ Bus System Case Study

ML-Based Power System Stability Assessment Considering Netwo...

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2023 IEEE PES/IAS PowerAfrica, PowerAfrica 2023

作者： Jia, Xinlan Dong, Jiaojiao Zhao, Yinfeng Tan, Jin Liu, Yilu National Renewable Energy Laboratory GoldenCO United States University of Tennessee Department of Electrical Engineering & Computer Science KnoxvilleTN United States Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798350337556

Modern power grids are fast-changing and thus require real-time monitoring and online stability assessment. With the rapid development of machine learning (ML) techniques, using data-driven models to provide fast and accurate estimations of power system stability marginal information, such as frequency nadir for frequency stability and critical clearing time (CCT) for transient stability, have become possible. However, despite the numerous research on ML-based methods for frequency nadir and CCT prediction, there is limited work on the impact of different network topology changes. Furthermore, most previous studies only focused on small or synthetic systems, and there is a lack of research on actual large power system models. In this paper, the above issues are addressed by studying the actual U.S. Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. Massive simulations are conducted in PowerWorld Simulator to study the impact of various topology change scenarios on both frequency stability and transient stability. System operating information is extracted from the success dispatch cases of various network topologies to generate a comprehensive dataset for ML-based models. Two ML methods, random forest (RF) and multilayer perceptron (MLP) neural network, are trained and tested for both frequency nadir prediction and CCT prediction. Test results have proven the models are capable of online stability assessment for large power networks such as the WECC system with sufficient accuracy. © 2023 IEEE.

关键词： Network topology

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Topological Nonlinear Optics in Twisted h-BN Interface

Topological Nonlinear Optics in Twisted h-BN Interface

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Ni, Xueqi Zhang, Mingjie Lou, Beicheng Fan, Shanhui Mazur, Eric Cao, Yuan Tang, Haoning School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Applied Physics Ginzton Laboratory Stanford University StanfordCA94305 United States Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States

Our research demonstrates tunable second harmonic generation in quantum material heterostructures, revealing nontrivial topological properties in their nonlinear optical responses with potential applications in quantu... 详细信息

ISBN: (纸本)9781957171395

关键词： Nonlinear optics

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Non-Invasive Stress Monitoring From Video

Non-Invasive Stress Monitoring From Video

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IEEE International Symposium on Biomedical Imaging

作者： Akshata Tiwari Brian Matejek Daniel Haehn Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts USA Computer Science Laboratory SRI International Arlington Virginia USA Department of Computer Science University of Massachusetts Boston Boston Massachusetts USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Identifying stress is crucial for maintaining a healthy lifestyle. Current stress-detection methods are relatively slow and subjective and often take place through invasive measurements via medical devices. We instead propose end-to-end, noninvasive detection of stress through video. We incorporate several modalities to perform holistic detection of a user’s stress level. Our framework employs an emotion recognition model to detect expressions through facial recordings. Then, we evaluate a user’s heart rate by amplifying changes in skin coloration through Eulerian Video Magnification. Finally, we analyze differentials in eyebrow and lip movements. We combine these three measurements to output a final stress score per unit of time. Our chosen emotion recognition model achieves an accuracy of 96.46%, and our remote heart rate detection module has a mean absolute error of 5.79 BPM. We provide a web-based application that allows for rapid, contactless stress detection through a webcam. We achieve over 84% accuracy on a dataset of video clips with individuals labeled as undergoing low, moderate, or high-stress levels. All code and data are openly available.

关键词： Emotion recognition Accuracy Current measurement Anxiety disorders Streaming media Biomedical measurement Heart rate detection

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A provably effective method for pruning experts in fine-tuned sparse mixture-of-experts 24

A provably effective method for pruning experts in fine-tune...

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Proceedings of the 41st International Conference on Machine Learning

作者： Mohammed Nowaz Rabbani Chowdhury Meng Wang Kaoutar El Maghraoui Naigang Wang Pin-Yu Chen Christopher Carothers Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute NY IBM Research Yorktown Heights NY Department of Computer Science Rensselaer Polytechnic Institute NY

The sparsely gated mixture of experts (MoE) architecture sends different inputs to different subnetworks (experts), through trainable routers. MoE reduces the training computation significantly for large models, but its deployment can be still memory/computation expensive for some downstream tasks. Model pruning is a popular approach to reduce inference computation, but its application in MoE architecture is largely unexplored. To the best of our knowledge, this paper provides the first provably efficient technique for pruning experts in fine-tuned MoE models. We theoretically prove that prioritizing the pruning of the experts with a smaller change of the router's l2 norm from the pre-trained model guarantees the preservation of test accuracy, while significantly reducing the model size and the computational requirements. Although our theoretical analysis is centered on binary classification tasks on simplified MoE architecture, our expert pruning method is verified on large vision MoE models such as V-MoE and E3-MoE fine-tuned on benchmark datasets such as CIFAR-10, CIFAR-100, and ImageNet.

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Investigation on Structures of Axial Gap Type Magnetic Multiple Spur Gear for In-Wheel Motor System of Electric Vehicle

Investigation on Structures of Axial Gap Type Magnetic Multi...

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2023 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2023

作者： Kamijo, Taiga Aiso, Kohei Akatsu, Kan Aoyama, Yasuaki Shibaura Institute of Technology Electrical Engineering and Computer Science Tokyo Japan Shibaura Institute of Technology Department of Electrical Engineering Tokyo Japan Yokohama National University Division of Intelligent Systems Engineering Department of Faculty of Engineering Yokohama Japan Center for Technology Innovation - Materials Hitachi Ltd. Research & Development Group Ibaraki Japan

ISBN: (纸本)9798350314274

A novel drive system using Magnetic Multiple Spur Gear (MMSG) and multiple high-speed motors is characterized by small size, lightweight, and high efficiency even at high-speed region, it is expected to apply to in-wheel motor systems. However, there is a risk of contact between the rotors when applied to in-wheel motor system. In this study, a novel axial-gap MMSG is proposed to solve this problem, stabilize the air gap between the high-speed and low-speed rotors, and effectively utilize the magnets, and its performance is evaluated by finite element analysis. © 2023 IEEE.

关键词： Electric vehicles

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Automatic Scene Generation: State-of-the-Art Techniques, Models, Datasets, Challenges, and Future Prospects

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IEEE Access 2025年 13卷 95753-95796页

作者： Ahmed Fime, Awal Mahmud, Saifuddin Das, Arpita Islam, Md. Sunzidul Kim, Jong-Hoon Southern Illinois University School of Computing CarbondaleIL62901 United States Bradley University Department of Computer Science and Information Systems PeoriaIL61625 United States The University of Texas at Arlington Department of Computer Science and Engineering ArlingtonTX76019 United States Khulna University of Engineering and Technology Department of Computer Science and Engineering Khulna9203 Bangladesh Kent State University Advanced Telerobotics Research Laboratory Department of Computer Science KentOH44240 United States

Automatic scene generation is an essential area of research with applications in robotics, recreation, visual representation, training and simulation, education, and more. This survey provides a comprehensive review of the current state-of-the-arts in automatic scene generation, focusing on techniques that leverage machine learning, deep learning, embedded systems, and natural language processing (NLP). We categorize the models into four main types: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers, and Diffusion Models. Each category is explored in detail, discussing various sub-models and their contributions to the field. We also review the most commonly used datasets, such as COCO-Stuff, Visual Genome, and MS-COCO, which are critical for training and evaluating these models. Methodologies for scene generation are examined, including image-to-3D conversion, text-to-3D generation, graph-based methods, and interactive scene generation. Evaluation metrics such as Fréchet Inception Distance (FID), Kullback-Leibler (KL) Divergence, Inception Score (IS), Intersection over Union (IoU), and Mean Average Precision (mAP) are discussed in the context of their use in assessing model performance. The survey identifies key challenges and limitations in the field, such as maintaining realism, handling complex scenes with multiple objects, and ensuring consistency in object relationships and spatial arrangements. By summarizing recent advances and pinpointing areas for improvement, this survey aims to provide a valuable resource for researchers and practitioners working on automatic scene generation. © 2013 IEEE.

关键词： Deep learning

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Programmable lattices for non-Abelian topological photonics and braiding

arXiv

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arXiv 2024年

作者： Kim, Gyunghun Li, Jensen Piao, Xianji Park, Namkyoo Yu, Sunkyu Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Department of Engineering University of Exeter EX4 4QF United Kingdom Wave Engineering Laboratory School of Electrical and Computer Engineering University of Seoul Seoul02504 Korea Republic of Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

Non-Abelian physics, originating from noncommutative sequences of operations, unveils novel topological degrees of freedom for advancing band theory and quantum computation. In photonics, significant efforts have been devoted to developing reconfigurable non-Abelian platforms, serving both as classical testbeds for non-Abelian quantum phenomena and as programmable systems that harness topological complexities. Here we establish topological spinor lattices for non-Abelian programmable photonics. We design a building block for reconfigurable unitary coupling between pseudospin resonances, achieving a universal set of rotation gates through coupling along the unit cell boundary. The lattice assembly of our building blocks enables the emulation of the extended quantum Hall family across various eigenspinor bases. Particularly, we reveal the emergence of a non-Abelian interface even when the bulks are Abelian, which allows the topologically trivial engineering of topologically protected edge states. We also define the braid group for pseudospin observables, demonstrating non-Abelian braiding operations and the Yang–Baxter relations. Our results pave the way for realizing a reconfigurable testbed for a wide class of Abelian and non-Abelian topological phenomena and braiding operations. © 2024, CC BY.

关键词： Topology

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Hyperspectral Tensor Completion Using Low-Rank Modeling and Convex Functional Analysis

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IEEE Transactions on Neural Networks and Learning systems 2024年第8期35卷 10736-10750页

作者： Lin, Chia-Hsiang Liu, Yangrui Chi, Chong-Yung Hsu, Chih-Chung Ren, Hsuan Quek, Tony Q. S. National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan National Tsing Hua University Department of Electrical Engineering Hsinchu300044 Taiwan National Cheng Kung University Department of Statistics Tainan70101 Taiwan National Central University Center for Space and Remote Sensing Research Taoyuan320317 Taiwan Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan

Hyperspectral tensor completion (HTC) for remote sensing, critical for advancing space exploration and other satellite imaging technologies, has drawn considerable attention from recent machine learning community. Hyperspectral image (HSI) contains a wide range of narrowly spaced spectral bands hence forming unique electrical magnetic signatures for distinct materials, and thus plays an irreplaceable role in remote material identification. Nevertheless, remotely acquired HSIs are of low data purity and quite often incompletely observed or corrupted during transmission. Therefore, completing the 3-D hyperspectral tensor, involving two spatial dimensions and one spectral dimension, is a crucial signal processing task for facilitating the subsequent applications. Benchmark HTC methods rely on either supervised learning or nonconvex optimization. As reported in recent machine learning literature, John ellipsoid (JE) in functional analysis is a fundamental topology for effective hyperspectral analysis. We therefore attempt to adopt this key topology in this work, but this induces a dilemma that the computation of JE requires the complete information of the entire HSI tensor that is, however, unavailable under the HTC problem setting. We resolve the dilemma, decouple HTC into convex subproblems ensuring computational efficiency, and show state-of-the-art HTC performances of our algorithm. We also demonstrate that our method has improved the subsequent land cover classification accuracy on the recovered hyperspectral tensor. © 2012 IEEE.

关键词： Convex optimization

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A Discrete-Time Networked Competitive Bivirus SIS Model

A Discrete-Time Networked Competitive Bivirus SIS Model

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European Control Conference (ECC)

作者： Sebin Gracy Ji Liu Tamer Başar César A. Uribe Department of Electrical Engineering and Computer Science South Dakota School of Mines and Technology Rapid City SD USA Department of Electrical and Computer Engineering Stony Brook University Stony Brook NY USA Coordinated Science Laboratory University of Illinois Urbana-Champaign Department of Electrical and Computer Engineering Rice University Houston TX USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

The paper deals with the analysis of a discrete-time networked competitive bivirus susceptible-infected-susceptible (SIS) model. More specifically, we suppose that virus 1 and virus 2 are circulating in the population and are in competition with each other. We show that the model is strongly monotone, and that, under certain assumptions, it does not admit any periodic orbit. We identify a sufficient condition for exponential convergence to the disease-free equilibrium (DFE). Assuming only virus 1 (resp. virus 2) is alive, we establish a condition for global asymptotic convergence to the single-virus endemic equilibrium of virus 1 (resp. virus 2) - our proof does not rely on the construction of a Lyapunov function. Assuming both virus 1 and virus 2 are alive, we establish a condition which ensures local exponential convergence to the single-virus equilibrium of virus 1 (resp. virus 2). Finally, we provide a sufficient (resp. necessary) condition for the existence of a coexistence equilibrium.

关键词： Sufficient conditions Analytical models Sociology Europe Orbits Statistics Viruses (medical)

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