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Spatio-temporal fusion for remote sensing data:an overview and new benchmark

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Science China(Information Sciences) 2020年第4期63卷 7-23页

作者： Jun LI Yunfei LI Lin HE Jin CHEN Antonio PLAZA Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and Planning Sun Yat-sen University School of Automation Science and Engineering South China University of Technology State Key Laboratory of Earth Surface Processes and Resource Ecology Institute of Remote Sensing Science and Engineering Faculty of Geographical ScienceBeijing Normal University Hyperspectral Computing Laboratory Department of Technology of Computers and CommunicationsEscuela Politécnica University of Extremadura

Spatio-temporal fusion(STF) aims at fusing(temporally dense) coarse resolution images and(temporally sparse) fine resolution images to generate image series with adequate temporal and spatial resolution. In the last decade, STF has drawn a lot of attention and many STF methods have been ***, to date the STF domain still lacks benchmark datasets, which is a pressing issue that needs to be addressed in order to foster the development of this field. In this review, we provide(for the first time in the literature) a robust benchmark STF dataset that includes three important characteristics:(1) diversity of regions,(2) long timespan, and(3) challenging scenarios. We also provide a survey of highly representative STF techniques, along with a detailed quantitative and qualitative comparison of their performance with our newly presented benchmark dataset. The proposed dataset is public and available online.

关键词： spatio-temporal fusion(STF) multi-temporal remote sensing data benchmark data experimental validation

A preliminary study about gender gap perception in informatics studies in Spain 12

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A preliminary study about gender gap perception in informati...

12th International Conference on Virtual Campus, JICV 2022

作者： Garcia-Holgado, Alicia Gonzalez-Gonzalez, Carina S. Fernandez, Inmaculada Garcia Lopez, Lourdes Moreno Barrenechea, Edurne Rueda, Silvia Navarro, Angeles G. Martinez, Paloma Masia, Belen Navarro, Elena Fornes, Alicia Grial Research Group Universidad DeSalamanca Computer Science and Automation Department Salamanca Spain University of la Laguna Computer Engineering Department San Cristobal de La Laguna Spain Universidad de Málaga Computer Architecture Department Málaga Spain Universidad Carlos Iii de Madrid Computer Science and Engineering Department Madrid Spain Universidad Pública de Navarra Departamento de Estadística Informática y Matemáticas Pamplona Spain Universitat de València Departament of Computer Science Valencia Spain Universidad de Zaragoza Computer Science Department Zaragoza Spain Universidad de Castilla la Mancha Departament of Computing Systems Albacete Spain Computer Vision Center Universitat Autónoma de Barcelona Barcelona Spain

ISBN: (数字)9781665451062

ISBN: (纸本)9781665451062

The gender gap in STEM is an issue that affects regions and countries worldwide. Furthermore, the percentage of women in these areas depends on a range of different factors. In particular, the gender gap is critical in technology in general and, more specifically, in informatics. This problem affects not only the business sector but also society. Informatics is part of our lives;however, 50% of the world population is not represented in the teams that are developing solutions for solving society's problems. In Spain, the number of women with informatics degrees is around 15%. This work describes a case study developed by the committee of Women in Informatics at the Scientific Society of Spanish Informatics (SCIE) to analyse the perception of informatics scientists on the gender gap in informatics. © 2022 IEEE.

关键词： Computers

Generative AI for Game Theory-based Mobile Networking

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arXiv 2024年

作者： He, Long Sun, Geng Niyato, Dusit Du, Hongyang Mei, Fang Kang, Jiawen Debbah, Mérouane Han, Zhu College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong School of Automation Guangdong University of Technology Guangzhou China Khalifa University of Science and Technology P O Box 127788 Abu Dhabi United Arab Emirates University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446701 Korea Republic of

With the continuous advancement of network technology, various emerging complex networking optimization problems have created a wide range of applications utilizing game theory. However, since game theory is a mathematical framework, game theory-based solutions often rely heavily on the experience and knowledge of human experts. Recently, the remarkable advantages exhibited by generative artificial intelligence (GAI) have gained widespread attention. In this work, we propose a novel GAI-enabled game theory solution that combines the powerful reasoning and generation capabilities of GAI to the design and optimization of mobile networking. Specifically, we first outline the game theory and key technologies of GAI, and explore the advantages of combining GAI with game theory. Then, we review the contributions and limitations of existing research and demonstrate the potential application values of GAI applied to game theory in mobile networking. Subsequently, we develop a large language model (LLM)-enabled game theory framework to realize this combination, and demonstrate the effectiveness of the proposed framework through a case study in secured UAV networks. Finally, we provide several directions for future extensions. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation

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arXiv 2024年

作者： Feng, Yifan Huang, Jiangang Du, Shaoyi Ying, Shihui Yong, Jun-Hai Li, Yipeng Ding, Guiguang Ji, Rongrong Gao, Yue School of Software BNRist THUIBCS BLBCI Tsinghua University Beijing100084 China Institute of Artificial Intelligence and Robotics College of Artificial Intelligence Xi’an Jiaotong University Xi’an710049 China The Department of Mathematics School of Science Shanghai University Shanghai200444 China The Department of Automation Tsinghua University Beijing100084 China The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Institute of Artificial Intelligence Fujian Engineering Research Center of Trusted Artificial Intelligence Analysis and Application Xiamen University 361005 China

We introduce Hyper-YOLO, a new object detection method that integrates hypergraph computations to capture the complex high-order correlations among visual features. Traditional YOLO models, while powerful, have limitations in their neck designs that restrict the integration of cross-level features and the exploitation of high-order feature interrelationships. To address these challenges, we propose the Hypergraph Computation Empowered Semantic Collecting and Scattering (HGC-SCS) framework, which transposes visual feature maps into a semantic space and constructs a hypergraph for high-order message propagation. This enables the model to acquire both semantic and structural information, advancing beyond conventional feature-focused learning. Hyper-YOLO incorporates the proposed Mixed Aggregation Network (MANet) in its backbone for enhanced feature extraction and introduces the Hypergraph-Based Cross-Level and Cross-Position Representation Network (HyperC2Net) in its neck. HyperC2Net operates across five scales and breaks free from traditional grid structures, allowing for sophisticated high-order interactions across levels and positions. This synergy of components positions Hyper-YOLO as a state-of-the-art architecture in various scale models, as evidenced by its superior performance on the COCO dataset. Specifically, HyperYOLO-N significantly outperforms the advanced YOLOv8-N and YOLOv9-T with 12% APval and 9% APval improvements. The source codes are at https://***/iMoonLab/Hyper-YOLO. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

Chaotic dynamics, topological analysis and flat analog electronic control by physiological signals of a neurally-inspired system

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Chinese Journal of Physics 2025年 96卷 20-52页

作者： Tabekoueng Njitacke, Zeric Ngamsa Tegnitsap, Joakim Vianney Zhao, Manyu Barà, Chiara Fozin, Théophile Fonzin Awrejcewicz, Jan Yoshimura, Natsue Valdes-Sosa, Pedro A. Letellier, Christophe Minati, Ludovico School of Life Science and Technology University of Electronic Science and Technology of China Chengdu 611731 China Department of Electrical and Electronic Engineering College of Technology (COT) University of Buea P.O. Box 63 Buea Cameroon Fotso-Victor University Institute of Technology University of Dschang P.O. Box 134 Dschang Cameroon Research Unit of Condensed Matter Electronics and Signal Processing Department of Physics University of Dschang Cameroon Department of Engineering University of Palermo Palermo 90128 Italy Department of Electrical and Electronic Engineering Faculty of Engineering and Technology (FET) University of Buea P.O. Box 63 Buea Cameroon Department of Automation Biomechanics and Mechatronics Łódź University of Technology Stefanowskiego Str. 1/15 Lodz 90-924 Poland School of Computing Institute of Science Tokyo (formerly Tokyo Institute of Technology) Yokohama Japan ATR Brain Information Communication Research Laboratory Group Kyoto Japan Department of Neuroinformatics Cuban Neuroscience Center Havana 11600 Cuba CORIA Rouen Normandie University Saint-Etienne du Rouvray 76800 France Center for Mind/Brain Sciences (CIMeC) University of Trento Trento 38123 Italy

A novel abstract bicompartmental neural model based on a cubic nonlinearity, consisting of introducing a Josephson junction as a coupling element, is presented. Through numerical simulations and experimental measurements involving an analog electronic circuit, it is found that the system can generate a double-scroll chaotic attractor endowed with a remarkably complex topology. This is accompanied by diversification of the spectral signature across its four variables and multistability. An analysis of the global observability and controllability is conducted, leading to the synthesis of a flat controller that can steer the dynamics towards those of structurally different entities, as exemplified by a modified Lorenz system. The first analog realization of flat control is then introduced. The possibility of low-latency and high-accuracy manipulation of the dynamics is demonstrated by considering the challenging case of tracking extraneous external signals, namely, physiological recordings indexing autonomic and central nervous system activity. Besides showing the flexibility and adaptability of the approach, the controller enables the concomitant generation of multiple additional signals with spectrogram features compared to the individual inputs, offering a promising substrate for future applications in physical time series data augmentation. © 2025 The Physical Society of the Republic of China (Taiwan)

关键词： Chaotic circuits Flat control Josephson junction Multistability Neural models Physiological recordings Topological analysis

A Quantum Convolutional Neural Network for Image Classification

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A Quantum Convolutional Neural Network for Image Classificat...

第40届中国控制会议

作者： Yanxuan Lü Qing Gao Jinhu Lü Maciej Ogorza?ek Jin Zheng School of Automation Science and Electrical Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Department of Information Technologies Jagiellonian University

Artificial neural networks have achieved great success in many fields ranging from image recognition to video understanding. However, its high requirements for computing and memory resources have limited further development on processing big data with high dimensions. In recent years, advances in quantum computing show that building neural networks on quantum processors is a potential solution to this problem. In this paper, we propose a novel neural network model named Quantum Convolutional Neural Network(QCNN), aiming at utilizing the computing power of quantum systems to accelerate classical machine learning tasks. The designed QCNN is based on implementable quantum circuits and has a similar structure as classical convolutional neural networks. Numerical simulation results on the MNIST dataset demonstrate the effectiveness of our model.

关键词： Quantum computing Machine Learning Neural Network Quantum Convolutional Neural Network

Decentralized Nonconvex Robust Optimization over Unsafe Multiagent Systems: System Modeling, Utility, Resilience, and Privacy Analysis

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arXiv 2024年

作者： Hu, Jinhui Chen, Guo Li, Huaqing Cheng, Huqiang Guo, Xiaoyu Huang, Tingwen School of Automation Central South University Changsha410083 China Department of Biomedical Engineering City University of Hong Kong Kowloon Hong Kong School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Key Laboratory of Dependable Services Computing in Cyber Physical Society Ministry of Education College of Computer Science Chongqing University Chongqing400044 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

Privacy leakage and Byzantine failures are two adverse factors to the intelligent decision-making process of multiagent systems (MASs). Considering an unsafe MAS with these two issues, this paper targets the resolution of a class of nonconvex optimization problems under the Polyak-Lojasiewicz (P-L) condition. To address this problem, we first identify and construct the unsafe MAS model. Under this kind of unfavorable MASs, we mask the local gradients with Gaussian noises and adopt a resilient aggregation method self-centered clipping (SCC) to design a differentially private (DP) decentralized Byzantine-resilient algorithm, namely DP-SCC-PL, which simultaneously achieves differential privacy and Byzantine resilience. The convergence analysis of DP-SCC-PL is challenging since the convergence error can be contributed jointly by privacy-preserving and Byzantine-resilient mechanisms, as well as the nonconvex relaxation, which is addressed via seeking the contraction relationships among the disagreement measure of reliable agents before and after SCC aggregation, together with the optimal gap. Theoretical results reveal that DP-SCC-PL achieves consensus among all reliable agents and sublinear (inexact) convergence with well-designed step-sizes. It has also been proved that if there are no privacy issues and Byzantine agents, then the asymptotic exact convergence can be recovered. Numerical experiments verify the utility, resilience, and differential privacy of DP-SCC-PL by tackling a nonconvex optimization problem satisfying the P-L condition under various Byzantine attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

Federated Digital Twin Construction via Distributed Sensing: A Game-Theoretic Online Optimization with Overlapping Coalitions

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arXiv 2025年

作者： Chen, Ruoyang Yi, Changyan Zhou, Fuhui Kang, Jiawen Wu, Yuan Niyato, Dusit College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China College of Electronic and Information Engineering Nanjing University of Astronautics and Aeronautics Nanjing210016 China School of Automation Guangdong University of Technology Guangzhou510006 China State Key Laboratory of Internet of Things for Smart City University of Macau China Department of Computer and Information Science University of Macau China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

In this paper, we propose a novel federated framework for constructing the digital twin (DT) model, referring to a living and self-evolving visualization model empowered by artificial intelligence, enabled by distributed sensing under edge-cloud collaboration. In this framework, the DT model to be built at the cloud is regarded as a global one being split into and integrating from multiple functional components, i.e., partial-DTs, created at various edge servers (ESs) using feature data collected by associated sensors. Considering time-varying DT evolutions and heterogeneities among partial-DTs, we formulate an online problem that jointly and dynamically optimizes partial-DT assignments from the cloud to ESs, ES-sensor associations for partial-DT creation, and as well as computation and communication resource allocations for global-DT integration. The problem aims to maximize the constructed DT’s model quality while minimizing all induced costs, including energy consumption and configuration costs, in long runs. To this end, we first transform the original problem into an equivalent hierarchical game with an upper-layer two-sided matching game and a lower-layer overlapping coalition formation game. After analyzing these games in detail, we apply the Gale-Shapley algorithm and particularly develop a switch rules-based overlapping coalition formation algorithm to obtain short-term equilibria of upper-layer and lower-layer subgames, respectively. Then, we design a deep reinforcement learning-based solution, called DMO, to extend the result into a long-term equilibrium of the hierarchical game, thereby producing the solution to the original problem. Simulations show the effectiveness of the introduced framework, and demonstrate the superiority of the proposed solution over counterparts. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

Magnetic Properties and Giant Magnetoimpedance Effect of Carbon and Fecoga Nanolayer Coated Co-Based Amorphous Composite Ribbons

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SSRN

SSRN 2023年

作者： Yang, Zhen Liu, Mengyu Sun, Xuecheng Wang, Zhenbao Shen, Yuanwei Zhu, Mengjiao Lei, Chong Guo, Yongbin School of Electronic and Information Engineering School of Integrated Circuits Guangxi Normal University Guangxi Guilin541004 China Education Department of Guangxi Zhuang Autonomous Region Guangxi Guilin541004 China Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin541004 China Microelectronic Research & Development Center School of Mechatronics Engineering and Automation Shanghai University Shanghai200444 China Guangxi Normal University China Jinan250014 China

In this paper, the influence of carbon and FeCoGa nano-coating layer on the roughness, magnetic properties and giant magneto-impedance (GMI) effect of amorphous composite ribbons were systematically investigated. The FeCoGa layer is able to close the magnetic flux path and reduce the coercivity. The carbon nanolayer effectively tune the characteristic frequency and GMI ratio of composite ribbons. When the thickness of carbon is 60 nm, amorphous composite ribbons exhibit small roughness of 9.78 nm, dipole interaction of 3.4 Oe, a maximum value of GMI ratio of 41% at 2MHz, which superior to the bare as-cast ribbon and FeCoGa coated composite ribbon. The results show that a reasonable combination of carbon layer and FeCoGa soft magnetic nanolayer can improve the GMI effect and magnetic field sensitivity of the ribbons, demonstrating the potential of the composite for future bio-detection and energy sensing applications. © 2023, The Authors. All rights reserved.

关键词： Cobalt alloys