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4th International Conference on Electronic Information engineering and computer Communication, EIECC 2024

作者： Di, Rui Hu, Hailing Hu, Meili Song, Baoan Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China Ningbo University Key Laboratory of Photoelectric Detection Materials and Devices of Zhejiang Province Ningbo315211 China Engineering Research Center for Advanced Infrared Photoelectric Materials and Devices of Zhejiang Province Ningbo315211 China

ISBN: (纸本)9798331534622

To enhance three-dimensional(3D) measurement precision with minimal fringes, the deep phase mapping 3D based on single-frame digital moiré fringe method (DDM) is proposed. By projecting just one high-frequency fringe and combining wrapped phase with fringe order to calculate continuous phase, it achieves 3D reconstruction through training on three process variables, thereby enhancing measurement precision. Experimental DDM measurements of 3D contours for fox and complex face models surpassed traditional single-frame methods in accuracy. Further evaluation on standard planes confirmed DDM's precision through lower RMSE (root mean square error) and MAE (mean absolute error) values, underlining its superior reconstruction accuracy. It holds great promise for its potential applications in real-time industrial measurements. © 2024 IEEE.

关键词： Mapping

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Optimal denial-of-service attack scheduling for remote state estimation via sequential observations 43

Optimal denial-of-service attack scheduling for remote state...

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43rd Chinese Control Conference, CCC 2024

作者： Yang, Xinxin Ni, Yuqing Chen, Xiaomeng Chen, Shan Liu, Fei Jiangnan University The Key Laboratory of Advanced Process Control for Light Industry Ministry of Education School of Internet of Things Engineering Wuxi214122 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong999077 Hong Kong

ISBN: (纸本)9789887581581

This study investigates the optimal allocation problem of attack energy in multi-system remote state estimation for Cyber-Physical Systems (CPSs). We consider a scenario where there are M independent systems, each equipped with a remote sensor monitoring system. The local estimates from these systems are sent to a remote estimator through a packet loss channel. The objective of the attacker is to degrade the communication channel between the sensors and the remote estimator. Due to capacity limitations, the attacker is restricted to attacking at most one out of M channels. We incorporate interference from other sensors or interference sources into the communication model based on the SINR (Signal-to-Interference-plus-Noise Ratio) model. It should be noted that the same attack power can lead to different packet loss rates in different network environments. From the attacker's perspective, the goal is to find the optimal attack strategy that maximizes the average estimation error of the remote estimator. Given the uncertainties of wireless network environments, we employ the sequentially-observed Markov decision process (SOMDP) framework to solve the problem. By observing the wireless channels, we gather additional information and incorporate it into the decision-making policy, resulting in better performance than using a standard Markov decision process (MDP). The simulation results support our theoretical findings. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Markov processes

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Medical image registration and its application in retinal images:a review

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 142-164页

作者： Qiushi Nie Xiaoqing Zhang Yan Hu Mingdao Gong Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Singapore Eye Research Institute Singapore 169856Singapore State Key Laboratory of Ophthalmology Optometry and Visual ScienceEye HospitalWenzhou Medical UniversityWenzhou 325027China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse informa-tion of images,which may be captured under different times,angles,or *** several surveys have reviewed the development of medical image registration,they have not systematically summarized the existing med-ical image registration *** this end,a comprehensive review of these methods is provided from traditional and deep-learning-based perspectives,aiming to help audiences quickly understand the development of medical image *** particular,we review recent advances in retinal image registration,which has not attracted much *** addition,current challenges in retinal image registration are discussed and insights and prospects for future research provided.

关键词： computer-aided diagnosis Medical image registration Deep learning Generative model Transformer Retina

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Real-time programmable metasurface for terahertz multifunctional wave front engineering

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Light(science & Applications) 2023年第9期12卷 1795-1806页

作者： Feng Lan Luyang Wang Hongxin Zeng Shixiong Liang Tianyang Song Wenxin Liu Pinaki Mazumder Ziqiang Yang Yaxin Zhang Daniel M.Mittleman Sichuan THz Communication Technology Engineering Research Center School of Electronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu 611731China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313000China Zhangjiang Laboratory Shanghai 201204China National Key Laboratory of Solid-State Microwave Devices and Circuits Hebei Semiconductor Research InstituteShijiazhuang 050051China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China University of Chinese Academy of Sciences School of ElectronicElectrical and Communication EngineeringBeijing 101408China Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMI 48109USA School of Engineering Brown UniversityProvidenceRI 02912USA

Terahertz(THz)technologies have become a focus of research in recent years due to their prominent role in envisioned future communication and sensing *** of the key challenges facing the field is the need for tools to enable agile engineering of THz wave ***,we describe a reconfigurable metasurface based on GaN technology with an array-of-subarrays *** subwavelength-spaced array,under the control of a 1-bit digital coding sequence,can switch between an enormous range of possible configurations,providing facile access to nearly arbitrary wave front control for signals near 0.34 *** demonstrate wide-angle beam scanning with 1°of angular precision over 70 GHz of bandwidth,as well as the generation of multi-beam and diffuse wave fronts,with a switching speed up to 100 *** device,offering the ability to rapidly reconfigure a propagating wave front for beam-forming or diffusively scattered wide-angle coverage of a scene,will open new realms of possibilities in sensing,imaging,and networking.

关键词： wave scattered arbitrary

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HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach

arXiv

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arXiv 2025年

作者： Hossain, Abrar Badawy, Abdel-Hameed A. Islam, Mohammad A. Patki, Tapasya Ahmed, Kishwar Department of Electrical Engineering and Computer Science The University of Toledo United States Department of Electrical and Computer Engineering New Mexico State University United States Department of Computer Science and Engineering The University of Texas Arlington United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory

The growing necessity for enhanced processing capabilities in edge devices with limited resources has led us to develop effective methods for improving high-performance computing (HPC) applications. In this paper, we introduce LASP (Lightweight Autotuning of Scientific Application Parameters), a novel strategy designed to address the parameter search space challenge in edge devices. Our strategy employs a multi-armed bandit (MAB) technique focused on online exploration and exploitation. Notably, LASP takes a dynamic approach, adapting seamlessly to changing environments. We tested LASP with four HPC applications: Lulesh, Kripke, Clomp, and Hypre. Its lightweight nature makes it particularly well-suited for resource-constrained edge devices. By employing the MAB framework to efficiently navigate the search space, we achieved significant performance improvements while adhering to the stringent computational limits of edge devices. Our experimental results demonstrate the effectiveness of LASP in optimizing parameter search on edge devices. © 2025, CC BY-NC-ND.

关键词： Edge computing

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Preface to Special Issue on advanced Optoelectronic and Electronic Devices toward Future Displays--Celebration of the 10th anniversary of the State Key laboratory of advanced Displays and Optoelectronics Technologies at HKUST

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Journal of Semiconductors 2023年第9期44卷 1-2页

作者： Hoi-Sing Kwok Zhiyong Fan Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong Kong SARChina State Key Laboratory of Advanced Displays and Optoelectronics Technologies HKUSTClear Water BayKowloonHong Kong SARChina

This year marks the tenth anniversary of the State Key laboratory of advanced Displays and Optoelectronics Technologies(SKLADOT)at the Hong Kong University of science and Technology(HKUST).The predecessor of SKLADOT w... 详细信息

关键词： ADO Key UST

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A Transactive Energy Framework for Hydrogen Production with Economically Viable Nuclear Power

A Transactive Energy Framework for Hydrogen Production with ...

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2022 IEEE/PES Transmission and Distribution Conference and Exposition, T and D 2022

作者： Mahmud, Sadab Sah, Suba Ponkiya, Binaka Katikaneni, Sravya Raker, David Heben, Michael Khanna, Raghav Javaid, Ahmad Yi, Zonggen Westover, Tyler Luo, Yusheng University of Toledo Electrical Engineering and Computer Science Department ToledoOH United States Energy Storage and Advanced Transportation Department Idaho National Laboratory Energy Storage and Advanced Transportation Department Idaho FallsID United States

ISBN: (纸本)9781665443296

This paper demonstrates the use of transactive energy (TE) to maximize the profitability and flexibility of a nuclear power plant. Reductions in the price of natural gas, and the concurrent influx of variable and distributed energy resources in the electricity market of the U.S. have impacted the economic viability of traditional nuclear power generation, which is currently untenable for adapting to dynamic pricing trends. This can be resolved by using TE concepts to control and coordinate an integrated nuclear energy system. This system can recuperate by using the excess nuclear thermal energy at times when the electricity prices are low to produce hydrogen and participate in the hydrogen market. A nuclear-renewable integrated energy system is demonstrated here with renewable sources, electrolyzers, a power conversion system, and storage systems along with the nuclear power plant. Deep reinforcement learning (DRL) methodology has been used to control, coordinate, and optimize the system based on TE concepts. The proposed framework demonstrates how future nuclear generation can flexibly participate in electric power markets. © 2022 IEEE.

关键词： Hydrogen production

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Leidenfrost droplet billiard balls

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Physical Review Fluids 2024年第11期9卷 L111601-L111601页

作者： Yukai Lin Canjun Zhao Fuqiang Chu Xiaomin Wu Nenad Miljkovic Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Department of Energy and Power Engineering Tsinghua University Beijing 100084 China School of Energy and Environmental Engineering University of Science and Technology Beijing Beijing 100083 China Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Materials Research Laboratory University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Institute for Sustainability Energy and Environment (iSEE) University of Illinois Urbana Illinois USA International Institute for Carbon Neutral Energy Research (WPI-I2CNER) Kyushu University 744 Moto-oka Nishi-ku Fukuoka 819-0395 Japan

We report the observation of two identical Leidenfrost droplets rebounding from each other after a head-on collision on a hot surface, resembling billiard ball collision. The observed phenomenon contradicts the established understanding that predicts the coalescence of two colliding Leidenfrost droplets. The collision dynamics of the Leidenfrost droplets is investigated using pressure and energy balance analysis at the macroscale, and a molecular interaction analysis at the nanoscale. The results suggest that droplet properties and the temperature of the hot surface determine the state of the gas film between the colliding droplets. Droplets with lower surface tension form thicker gas films, while lower surface temperatures mitigate the interaction of vapor molecules within the gas film, both facilitating droplet rebound after their collision. The discovered Leidenfrost droplet phenomenon is highly controllable, and has potential for a variety of applications, such as the selective chemical synthesis as explored in this work.

关键词： Drop & bubble phenomena Heat transfer Multiphase flows Surface & interfacial phenomena

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Gap minimization for knowledge sharing and transfer

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 1282-1338页

作者： Boyu Wang Jorge A. Mendez Changjian Shui Fan Zhou Di Wu Gezheng Xu Christian Gagné Eric Eaton Department of Computer Science University of Western Ontario Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology Institute Intelligence and Data Université Laval School of Transportation Science and Engineering Beihang University Department of Electrical and Computer Engineering McGill University Department of Computer and Information Science University of Pennsylvania

Learning from multiple related tasks by knowledge sharing and transfer has become increasingly relevant over the last two decades. In order to successfully transfer information from one task to another, it is critical to understand the similarities and differences between the domains. In this paper, we introduce the notion of performance gap, an intuitive and novel measure of the distance between learning tasks. Unlike existing measures which are used as tools to bound the difference of expected risks between tasks (e.g., H-divergence or discrepancy distance), we theoretically show that the performance gap can be viewed as a data- and algorithm-dependent regularizer, which controls the model complexity and leads to finer guarantees. More importantly, it also provides new insights and motivates a novel principle for designing strategies for knowledge sharing and transfer: gap minimization. We instantiate this principle with two algorithms: 1. gapBoost, a novel and principled boosting algorithm that explicitly minimizes the performance gap between source and target domains for transfer learning; and 2. gapMTNN, a representation learning algorithm that reformulates gap minimization as semantic conditional matching for multitask learning. Our extensive evaluation on both transfer learning and multitask learning benchmark data sets shows that our methods outperform existing baselines.

关键词： performance gap transfer learning multitask learning regularization algorithmic stability

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Mixture of Gradient: A Unified Enhancing Approach for Deep Learning-Based Wireless Network Optimization

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IEEE Internet of Things Journal 2025年第13期12卷 25487-25499页

作者： Cheng, Nan Ma, Longfei Dai, Yanpeng Wang, Xiucheng Li, Qihao Quan, Wei Liang, Hui Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan523808 China Xidian University State Key Laboratory of ISN and School of Telecommunications Engineering Xi’an710071 China Dalian Maritime University School of Information Science and Technology Dalian116026 China Jilin University College of Communication Engineering Jilin Changchun130021 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China Peng Cheng Laboratory Shenzhen518000 China University of Waterloo Department of Electrical and Computer Engineering WaterlooN2L 3G1 Canada

Deep learning plays increasingly important role in future wireless network management and optimization. Existing training methods such as label-based supervised learning and label-free learning have inherent limitations. The performance of supervised learning is limited by labels, while label-free training methods require extensive exploration. To address these limitations, this paper proposes a novel mixture of gradients (MoG) method, which integrates gradients from different sources within the training process in order to improve the convergence performance of neural networks (NNs). Particularly, MoG is a modular, plug-and-play solution requiring no structural modifications to existing NNs. Its implementation necessitates only minor modifications to the loss function, where the label-based supervised loss is combined with a label-free loss through weighted summation. The label-free loss can be either unsupervised loss or reinforcement learning loss. This flexibility allows seamless integration into nearly all NN-based methods, making it applicable to a wide range of wireless optimization problems with minimal implementation cost. Extensive simulations across multiple classic wireless scenarios demonstrate that MoG can significantly enhance the performance of NN decision-making, leading to higher transmission rates. © 2014 IEEE.

关键词： Self-supervised learning

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