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Quantum Annealing-Based Sum Rate Maximization for Multi-UAV-Aided Wireless Networks

IEEE Internet of Things Journal 2025年

作者： Jeong, Seon-Geun Duc, Pham Dang Anh Do, Quang Vinh Noh, Dae-Il Tung, Nguyen Xuan Chien, Trinh Van Pham, Quoc-Viet Hasegawa, Mikio Sekiya, Hiroo Hwang, Won-Joo Department of Information Convergence Engineering Pusan National University Pusan National University Busan46241 Korea Republic of Wireless Communications Research Group Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Graduate School of Engineering Chiba University Chiba263-8522 Japan Hanoi100000 Viet Nam School of Computer Science and Statistics Trinity College Dublin DublinD02PN40 Ireland Department of Electrical Engineering Tokyo University of Science Tokyo162-8601 Japan School of Computer Science and Engineering Center for Artificial Intelligence Research Pusan National University Busan46241 Korea Republic of

In wireless communication networks, it is difficult to solve many NP-hard problems owing to computational complexity and high cost. Recently, quantum annealing (QA) based on quantum physics was introduced as a key enabler for solving optimization problems quickly. However, only some studies consider quantum-based approaches in wireless communications. Therefore, we investigate the performance of a QA solution to an optimization problem in wireless networks. Specifically, we aim to maximize the sum rate by jointly optimizing clustering, sub-channel assignment, and power allocation in a multi-unmanned aerial vehicle-aided wireless network. We formulate the sum rate maximization problem as a combinatorial optimization problem. Then, we divide it into two sub-problems: 1) a QA-based clustering and 2) sub-channel assignment and power allocation for a given clustering configuration. Subsequently, we obtain an optimized solution for the joint optimization problem by solving these two sub-problems. For the first sub-problem, we convert the problem into a simplified quadratic unconstrained binary optimization (QUBO) model. As for the second sub-problem, we introduce a novel QA algorithm with optimal scaling parameters to address it. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of the sum rate and running time. © 2025 IEEE.

关键词： Combinatorial optimization

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VLC-enabled UAV Network for IoT with Co-channel Interference: Joint Spatial Deployment and Resource Allocation

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IEEE Internet of Things Journal 2025年

作者： Liu, Jiaji Yang, Fang An, Nan Song, Jian Han, Zhu State Key Laboratory of Widegap Semiconductor Optoelectronic Materials and Technologies Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Research Institute of Tsinghua University Shenzhen518057 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China 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

Unmanned aerial vehicles (UAVs) have gained significant attention due to their low cost and efficient deployment. Specifically, UAV-based communications hold promise to supplement the coverage limitations of terrestrial networks and achieve superior connectivity for the Internet of Things (IoT). Moreover, UAVs with visible light communication (VLC) capability do not interfere with existing radio frequency (RF) communication infrastructure, thereby providing flexible, high-throughput on-demand wireless communications. In this paper, considering the limited battery capacity of UAVs, we aim to maximize the energy efficiency (EE) of a multi-UAV downlink VLC system for IoT. To this end, the system model with co-channel interference is first established, followed by the formulation of the joint spatial deployment and resource allocation problem. Besides, the original problem is decomposed into three subproblems, focusing on spatial deployment, subchannel assignment, and power allocation. By employing the block coordinate descent method, the three subproblems are solved alternately. Moreover, simulation results demonstrate that the proposed method achieves superior improvements in EE compared to other baselines. In addition, the convergence and computational complexity of the proposed algorithm, as well as the impacts of key parameters on the overall system performance are also investigated. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images

arXiv

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arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

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A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges

arXiv

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arXiv 2025年

作者： Ding, Lei Hong, Danfeng Zhao, Maofan Chen, Hongruixuan Li, Chenyu Deng, Jie Yokoya, Naoto Bruzzone, Lorenzo Chanussot, Jocelyn Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Information Engineering University Zhengzhou China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Mathematics and Statistics Southeast University Nanjing211189 China Graduate School of Frontier Sciences The University of Tokyo Chiba277-8561 Japan Department of Complexity Science and Engineering Graduate School of Frontier Sciences The University of Tokyo Chiba277-8561 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Department of Information Engineering and Computer Science University of Trento Trento38123 Italy Univ. Grenoble Alpes Inria CNRS Grenoble INP LJK Grenoble38000 France

In the last decade, the rapid development of deep learning (DL) has made it possible to perform automatic, accurate, and robust Change Detection (CD) on large volumes of Remote Sensing Images (RSIs). However, despite advances in CD methods, their practical application in real-world contexts remains limited due to the diverse input data and the applicational context. For example, the collected RSIs can be time-series observations, and more informative results are required to indicate the time of change or the specific change category. Moreover, training a Deep Neural Network (DNN) requires a massive amount of training samples, whereas in many cases these samples are difficult to collect. To address these challenges, various specific CD methods have been developed considering different application scenarios and training resources. Additionally, recent advancements in image generation, self-supervision, and visual foundation models (VFMs) have opened up new approaches to address the’data-hungry’ issue of DL-based CD. The development of these methods in broader application scenarios requires further investigation and discussion. Therefore, this article summarizes the literature methods for different CD tasks and the available strategies and techniques to train and deploy DL-based CD methods in sample-limited scenarios. We expect that this survey can provide new insights and inspiration for researchers in this field to develop more effective CD methods that can be applied in a wider range of contexts. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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A Benchmark and Frequency Compression Method for Infrared Few-Shot Object Detection

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Ruiheng Yang, Biwen Xu, Lixin Huang, Yan Xu, Xiaofeng Zhang, Qi Jiang, Zhizhuo Liu, Yu Beijing Institute of Technology State Key Laboratory of Electromechanical Dynamic Control Beijing100081 China University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia Anhui Polytechnic University School of Computer and Information Wuhan241000 China Technical University of Munich Data Science in Earth Observation Munich85521 Germany Tsinghua University Shenzhen International Graduate School Shenzhen518055 China Tsinghua University Department of Electronic Engineering Beijing100084 China

Infrared few-shot object detection (IFSOD) aims to detect infrared objects with limited labeled examples. Current infrared datasets, however, suffer from limited diversity in object types and classes, hindering robust evaluation of model generalization on novel classes. To systematically assess dataset quality, we propose metrics for class diversity, instance variability, and object density. By integrating three widely used infrared datasets, we construct the first dataset specifically tailored for IFSOD, increasing instance density to 4.8 (a 1.1 improvement) and expanding the number of classes to 18 (a 5-class increase) compared to the source datasets. Furthermore, frequency analysis of spatial features reveals that sparse annotations introduce spectral bias in the frequency domain. Directly transforming spatial features to the frequency domain, however, mixes background noise with object features, causing spectral leakage and impairing the learning of discriminative features for novel classes. To address these issues, we propose the frequency compression few-shot detection (FC-fsd) method, which incorporates a frequency compression (FC) module. The FC module leverages Discrete Cosine Transform (DCT) within localized windows to reduce spectral leakage and enhance feature clarity. With minimal additional computational overhead, FC-fsd significantly outperforms state-of-the-art methods, achieving nAP50 scores of 28.57 (+13.37) and 35.63 (+2.59) in 1-shot and 2-shot settings, respectively. Our dataset is published at https://***/RuihengZhang/IFSOD-dataset. © 2025 IEEE.

关键词： Zero-shot learning

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Fast ringing artifact reduction in diffraction calculations using Fresnel integrals

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Journal of Optics (United Kingdom) 2025年第5期27卷

作者： Shimobaba, Tomoyoshi Huang, Chung-Hsuan Tahara, Tatsuki Nagahama, Yuki Wang, Fan Udjaja, Yogi Cheng, Chau-Jern Ito, Tomoyoshi Graduate School of Engineering Chiba University 1-33 Yayoi-cho Inage-ku Chiba263-8522 Japan Institute of Electro-Optical Engineering National Taiwan Normal University Taipei11677 Taiwan Applied Electromagnetic Research Center Radio Research Institute National Institute of Information and Communications Technology 4-2-1 Nukuikitamachi Koganei Tokyo184-8795 Japan Institute of Engineering Tokyo University of Agriculture and Technology 2-24-16 Naka-cho Koganei Tokyo184-8588 Japan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia

Diffraction calculations, which are fundamental in wave optics, can be significantly accelerated using Fourier transforms. However, they often suffer from ringing artifacts, also known as the Gibbs phenomenon, owing to the discontinuous borders of the calculation windows. Various techniques to suppress ringing artifacts have been proposed, but are typically time-consuming and require additional memory resources. This study proposes a fast method for reducing ringing artifacts using Fresnel integrals without necessitating additional memory. A comparison between the proposed and conventional techniques demonstrates that the proposed method achieves comparable image quality to conventional methods. Moreover, it offers the advantages of fast time and low memory usage. This paper demonstrates that the proposed method can be used to efficiently reduce ringing artifacts in reproduced images of two inline holograms optically acquired with laser and light-emitting diode sources. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.

关键词： Fourier optics

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Unleashing Collaborative Potentials: Multifaceted Collaboration among Agents in Multitask Internet of Things Networks

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IEEE Internet of Things Journal 2025年

作者： Liu, Jiadi Li, Ting Wang, Quyuan Wang, Ying Guo, Zhiwei Yu, Keping Chongqing Technology and Business University Chongqing Key Laboratory of Intelligent Perception and Blockchain Technology Chongqing400067 China Southwest University College of Computer and Information Science College of Software Chongqing400710 China Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan

The rapid advancement of Internet of Things (IoT) and multi-agent systems has transformed how complex IoT tasks are managed across domains. While individual edge agents demonstrate proficiency in specialized tasks such as data collection and edge learning, they encounter substantial challenges when confronting complex IoT scenarios that demand diverse skill sets. This paper introduces a novel group formation framework facilitating effective IoT agent collaboration in complex task environments, including smart manufacturing, intelligent transportation, and smart cities. We propose a hybrid competition mechanism that optimizes initial multi-agent cooperation strategies by integrating task requirements, agent capabilities, and system-wide performance metrics. Our approach combines intra-task and inter-task competition to achieve optimal agent-task matching and resource allocation in large-scale IoT networks. Through comprehensive simulations across various IoT scenarios, we demonstrate that our framework substantially enhances task completion efficiency and system performance compared to existing methods. The results confirm our approach’s effectiveness in resource-constrained environments, achieving minimal agent grouping time costs while increasing total task revenue by 30%-42% and resource utilization by 38% compared to baseline heuristic methods. © 2014 IEEE.

关键词： Multi agent systems

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The economics of global personality diversity

arXiv

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arXiv 2025年

作者： McCarthy, Paul X. Gong, Xian Coetzer, Marieth Rizoiu, Marian-Andrei Kern, Margaret L. Johnson, John A. Holden, Richard Braesemann, Fabian The Data Science Institute University of Technology SydneyNSW Australia School of Computer Science and Engineering UNSW Sydney NSW Australia Faculty of Engineering and Information Technology University of Technology Sydney Australia Oxford Internet Institute University of Oxford Oxford United Kingdom Einstein Center Digital Future Berlin Germany Melbourne Graduate School of Education The University of Melbourne ParkvilleVIC Australia Penn State University Pennsylvania United States

This study explores the relationship between personality diversity and national economic performance, introducing the Global Personality Diversity Index (Ψ-GPDI) as a novel metric. Leveraging a dataset of 760,242 individuals across 135 countries, we quantify within-country diversity based on the Big Five personality traits. Our findings reveal that personality diversity accounts for 19.9% of the variance in GDP per capita and provides an additional 2.8% explanatory power beyond institutional quality and immigration, underscoring its unique contribution to economic vitality. Through multi-factor analysis, we demonstrate how personality diversity complements existing economic frameworks, offering actionable insights for policymakers seeking to enhance innovation, productivity, and resilience. This research positions psychological diversity as a critical yet under explored factor in driving economic growth, bridging the fields of psychology and economics. © 2025, CC BY.

关键词： Inflation

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A semi-empirical analytic model for threshold voltage instability in MOSFETs with high-k gate stacks

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Journal of Semiconductors 2009年第8期30卷 63-66页

作者：何进马晨月张立宁张健张兴 School of Computer & Information Engineering Shenzhen Graduate SchoolPeking University EECS School of Electronic Engineering and Computer SciencePeking University

A semi-empirical analytic model for the threshold voltage instability of a MOSFET is derived from Shockley-Read-Hall （SRH） statistics to account for the transient charging effects in a MOSFET high-k gate stack. Starting from the single energy level and single trap assumption, an analytical expression for the filled trap density in terms of dynamic time is derived from SRH statistics. The semi-empirical analytic model for the threshold voltage instability is developed based on MOSFET device physics between the threshold voltage and the induced trap density. The obtained model is also verified by extensive experimental data of trapping and de-trapping stress from different high-k gate configurations.

关键词： high-k gate stack nanoscale MOSFETs interface trap and charges trapping and detrapping threshold voltage dynamic behavior compact modeling

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Mobility-aware Decentralized Federated Learning for Autonomous Underwater Vehicles

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IEEE Transactions on Wireless Communications 2025年

作者： He, Hongyi Du, Jun Jiang, Chunxiao Wang, Jintao Song, Jian Han, Zhu Tsinghua University Department of Electronic Engineering Beijing100084 China Tsinghua University Department of Electronic Engineering State Key laboratory of Space Network and Communication Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology and State Key laboratory of Space Network and Communication Beijing100084 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China 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 underwater Internet of Things (UIoT) is crucial in developing marine resources. However, due to the low data rate of underwater channels, it is difficult to have a central server to process data from numerous devices as using terrestrial communications. Therefore, decentralized federated learning (DFL) with communication-efficient modifications is a promising alternative to empower UIoT with artificial intelligence and collaborative training. However, existing DFL strategies rely on a carefully designed small aggregation weight when aggregating parameters from neighbor nodes to mitigate the compression error, resulting in a slow convergence rate. In addition, the effect of data compression under time-varying topologies is not considered in current DFL algorithms. In response to these problems, this work studies a DFL framework with underwater acoustic channel and time-varying topology. Firstly, considering the low data rate and dynamics of the acoustic channel, we propose a practical scheme for adaptive compression and device connectivity. Moreover, we combine data compression and the error-compensation technique with time-varying topology and propose a DFL algorithm with aggregation weights decaying over time to achieve fast convergence under non-independent and identically distributed (non-IID) data. We derive a convergence bound for the proposed algorithm with respect to compression and time-varying topology and demonstrate that it achieves the same asymptotic convergence rate as centralized FL with perfect communication. Simulation results show that, compared with DFL algorithms without decaying aggregation weights and centralized FL schemes, the proposed algorithm exhibits higher accuracy and faster convergence rate in underwater environments. © 2002-2012 IEEE.

关键词： Autonomous underwater vehicles

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