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2022 IEEE Delhi Section Conference, DELCON 2022

作者： Barhoumi, El Manaa Zghaibeh, Manaf Okonkwo, Paul C. Hasan, Najam Ul Farhani, Slah Bacha, Faouzi College of Engineering Dhofar University Electrical and Computer Engineering Department Salalah Oman College of Engineering Dhofar University Mechanical and Mechatronic Engineering Department Salalah Oman Insat University of Carthage Laboratory of Computer and Industrial Systems Tunis Tunisia Ensit University of Tunis Department of Electrical Engineering Tunis Tunisia

ISBN: (纸本)9781665458832

This paper presents the design and implementation of an energy management system (EMS) for a renewable energy system using Matlab Simulink and Arduino Mega. The proposed renewable energy system is composed of an electrolyzer, a battery, photovoltaic modules, and fuel cells. The developed energy management is implemented on the Arduino board to control the connection of the different components to the load based on the load power consumption, the battery's charge state, and the energy produced by the photovoltaic modules. The energy management system inputs include the information on the amount of energy produced by the photovoltaic modules, the power used by the load, and the battery's charge state. When the produced energy exceeds the energy used by the load and the batteries are fully charged, the system allows the electrolyzer to connect to the photovoltaic modules to produce the hydrogen. Our proposed system is based on low cost technology that intends to extend the life of the batteries and enhance the efficiency of the renewable energy system by the production and using the hydrogen. The obtained experimental results show the effectiveness of the EMS in controlling the connection of the different components. © 2022 IEEE.

关键词： Energy management systems

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Measurement-Device-Independent Deterministic Quantum Key Distribution 9

Measurement-Device-Independent Deterministic Quantum Key Dis...

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9th Annual International Conference on Network and Information systems for computers, ICNISC 2023

作者： Zhang, Ting Liu, Jie Kang, Guodong Jishou University School of Physics and Mechanical and Electrical Engineering Jishou China Jishou University School of Computer Science and Engineering School of Physics and Mechanical and Electrical Engineering Jishou China Hunan Normal University Key Laboratory of Low Dimensional Quantum Structures and Quantum Control Ministry of Education College of Physics and Electronic Science Changsha China

ISBN: (纸本)9798350306682

Improve the secure key rate and the limited communication distance is the main goal in the field of quantum key distribution. Measurement-device-independent quantum key distribution, not only removes all detector side channel attacks, but also doubles the secure transmission distance. However, most of measurement-device -independent quantum key distribution protocols are non-deterministic. In this paper, we propose a measurement-device-independent deterministic quantum key distribution protocol that doubles the secure distance and amends the non-deterministic factor of current measurement-device -independent quantum key distribution. And with imperfect devices model assumption, we derived the formula of the performances for it and find that the limited distance can be extended over 200km with relatively high secure key rate by employing parameters of current technologies. © 2023 IEEE.

关键词： Quantum cryptography

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Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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PD-Refiner: An Underlying Surface Inheritance Refiner with Adaptive Edge-Aware Supervision for Point Cloud Denoising 24

PD-Refiner: An Underlying Surface Inheritance Refiner with A...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Chengwei Zhang, Xueyi Yue, Xianghu Lao, Mingrui Jiang, Tao Wang, Jiawei Zhang, Fubo Chen, Longyong National Key Laboratory of Microwave Imaging Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Laboratory for Big Data and Decision National University of Defense Technology Hunan Changsha China Department of Electrical and Computer and Engineering National University of Singapore Singapore Singapore National Key Laboratory of Information Systems Engineering National University of Defense Technology Hunan Changsha China School of Software Engineering Tongji University Shanghai China

ISBN: (纸本)9798400706868

Point clouds from real-world scenarios inevitably contain complex noise, significantly impairing the accuracy of downstream tasks. To tackle this challenge, cascading encoder-decoder architecture has become a conventional technical route to iterative denoise. However, circularly feeding the output of denoiser as its input again involves the re-extraction of underlying surface, leading to unstable denoising process and over-smoothed geometric details. To address these issues, we propose a novel denoising paradigm dubbed PD-Refiner that employs a single encoder to model the underlying surface. Then, we leverage several lightweight hierarchical Underlying Surface Inheritance Refiners (USIRs) to inherit and strengthen it, thereby avoiding the re-extraction from the intermediate point cloud. Furthermore, we design adaptive edge-aware supervision to improve the edge awareness of the USIRs, allowing for the adjustment of the denoising preferences from global structure to local details. The results demonstrate that our method not only achieves state-of-the-art performance in terms of denoising stability and efficacy, but also enhances edge clarity and point cloud uniformity. © 2024 ACM.

关键词： edge-aware supervision

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Simultaneous Beamforming and Anti-Jamming With Intelligent Omni-Surfaces

arXiv

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arXiv 2025年

作者： Wang, Yuhan Zeng, Shuhao Liu, Qingyu Di, Boya Zhang, Hongliang School of Electronic and Computer Engineering Peking University Shenzhen Graduate School China Pengcheng Laboratory China School of Electronics Peking University China Department of Electrical and Computer Engineering Princeton University United States

Wireless transmission is vulnerable to malicious jamming attacks due to the openness of wireless channels, posing a severe threat to wireless communications. Current anti-jamming studies primarily focus on either enhancing desired signals or mitigating jamming, resulting in limited performance. To address this issue, intelligent omni-surface (IOS) is a promising solution. By jointly designing its reflective and refractive properties, the IOS can simultaneously nullify jamming and enhance desired signals. In this paper, we consider an IOS-aided multi-user anti-jamming communication system, aiming to improve desired signals and nullify jamming by optimizing IOS phase shifts and transmit beamforming. However, this is challenging due to the coupled and discrete IOS reflection and refraction phase shifts, the unknown jammer’s beamformer, and imperfect jammer-related channel state information. To tackle this, we relax IOS phase shifts to continuous states and optimize with a coupling-aware algorithm using the Cauchy-Schwarz inequality and S-procedure, followed by a local search to recover discrete states. Simulation results show that the proposed scheme significantly improves the sum rate amid jamming attacks. Copyright © 2025, The Authors. All rights reserved.

关键词： Beamforming

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Extinction Chains Reveal Intermediate Phases Between the Safety and Collapse in Mutualistic Ecosystems

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engineering 2024年第12期43卷 89-98页

作者： Guangwei Wang Xueming Liu Ying Xiao Ye Yuan Linqiang Pan Xiaohong Guan Jianxi Gao Hai-Tao Zhang The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and TechnologyDongguan 523808China Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels Dongguan 523808China MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi’an Jiaotong UniversityXi’an 710049China Tsinghua National Laboratory of Information Science and Technology Department of AutomationTsinghua UniversityBeijing 100084China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are *** recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked *** this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and *** discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed *** validated our approach using a 20-year dataset from an eelgrass restoration ***,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase *** computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词： Complex system Network science Overexploitation Regime shift Metastability

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Design of awideband symmetric large back-off range Doherty power amplifier based on impedance and phase hybrid optimization

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Frontiers of Information Technology & Electronic engineering 2025年第1期26卷 146-156页

作者： Zhongpeng NI Jing XIA Xinyu ZHOU Wa KONG Wence ZHANG Xiaowei ZHU School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic UniversityHong Kong 999077China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China

The present paper proposes an optimization design method for the Doherty output matching network(OMN)using impedance–phase hybrid objective function constraints,which possesses the capability of enhancing the efficiency consistency of the Doherty power amplifier(DPA)using integrated enhancing reactance(IER)during the back-off power(BOP)*** calculating the desired reactance for an extended BOP range and combining it with the two-impedance matching method,the S-parameters of the OMN are ***,the impedance and phase constraints of the OMN are proposed to narrow the distribution range of the ***,a fragmenttype structure is employed in the OMN optimization so as to enhance the flexibility of the circuit optimization *** validate the proposed method,a 1.7–2.5 GHz symmetric DPA with a large BOP range was designed and *** results demonstrate that across the entire operating frequency band,the saturated output power is>44 dBm,and the efficiency ranges from 45%to 55%at a 9-dB BOP.

关键词： Back-off power range Doherty power amplifier Fragment-type structure Impedance-phase hybrid function

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Tri-Level Linear Programming Model for Automatic Load Shedding Using Spectral Clustering

Tri-Level Linear Programming Model for Automatic Load Sheddi...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Baquedano-Aguilar, Mario D. Bretas, Arturo Meyn, Sean Aljohani, Nader University of Florida Department of Electrical and Computer Engineering GainesvilleFL United States Pacific Northwest National Laboratory Distributed Systems Group RichlandWA United States Universite Grenoble Alpes G2Elab Grenoble INP CNRS Grenoble38000 France Taibah University College of Engineering Department of Electrical Engineering Medina Saudi Arabia

ISBN: (纸本)9798350313604

Traditional load shedding schemes can be inadequate in grids with high renewable penetration, leading to unstable events and unnecessary grid islanding. Although for both manual and automatic operating modes load shedding areas have been predefined by grid operators, they have remained fixed, and may be sub-optimal due to dynamic operating conditions. In this work, a distributed tri-level linear programming model for automatic load shedding to avoid system islanding is presented. Preventing islanding is preferred because it reduces the need for additional load shedding besides the disconnection of transmission lines between islands. This is crucial as maintaining the local generation-demand balance is necessary to preserve frequency stability. Furthermore, uneven distribution of generation resources among islands can lead to increased load shedding, causing economic and reliability challenges. This issue is further compounded in modern power systems heavily dependent on non-dispatchable resources like wind and solar. The upper-level model uses complex power flow measurements to determine the system areas to shed load depending on actual operating conditions using a spectral clustering approach. The mid-level model estimates the area system state, while the lower-level model determines the locations and load values to be shed. The solution is practical and promising for real-world applications. © 2024 IEEE.

关键词： Linear programming

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ML-Assisted Resource Allocation Outage Probability: Simple, Closed-Form Approximations 17

ML-Assisted Resource Allocation Outage Probability: Simple, ...

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17th IEEE International Conference on Advanced Networks and Telecommunications systems, ANTS 2023

作者： Raina, Rashika Simmons, Nidhi Simmons, David E. Yacoub, Michel Daoud Queen's University Centre for Wireless Innovation School of Electronics Electrical Engineering and Computer Science BelfastBT3 9DT United Kingdom Dhali Holdings Ltd. BelfastBT5 7HW United Kingdom University of Campinas Wireless Technology Laboratory School of Electrical and Computer Engineering Campinas13083-970 Brazil

ISBN: (纸本)9798350307672

In this paper, we establish simple and efficient approximations for the outage probability of a single-user multi-resource allocation system that consists of a machine learning (ML) based outage predictor whose task is to assign resources to the user while minimizing outages. We begin by presenting the outage probability expressions for this system. We then propose the approximations to this system's outage probability using both the sinc function and the zeroth-order Bessel function of the first kind. These approximations are based on naive upper and lower bounds and stem from understanding how the channel samples de-correlate over time. Our results demonstrate that the outage probability indeed lies within the range defined by the bounds. Moreover, the effectiveness of the proposed outage probability approximations are evident as they exhibit a strong alignment with the trend of the outage probability curve. Finally, because of the simplicity of our approximations, they can be calculated in a computationally efficient manner. © 2023 IEEE.

关键词： Resource allocation

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Research on Deep Learning Model of Feature Extraction Based on Convolutional Neural Network 2

Research on Deep Learning Model of Feature Extraction Based ...

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2nd IEEE International Conference on Image Processing and computer Applications, ICIPCA 2024

作者： Liu, Houze Li, Iris Liang, Yaxin Sun, Dan Yang, Yining Yang, Haowei New York University Department Computer Science and Engineering New York United States Graduate School of Art & Science New York University New York United States Viterbi School of Engineering University of Southern California Los Angeles United States Washington University Department of Electrical and Systems Engineering St. Louis United States School of Computer Science Carnegie Mellon University Pittsburgh United States University of Houston Cullen College of Engineering Houston United States

ISBN: (纸本)9798350360240

Neural networks with relatively shallow layers and simple structures may have limited ability in accurately identifying pneumonia. In addition, deep neural networks also have a large demand for computing resources, which may cause convolutional neural networks to be unable to be implemented on terminals. Therefore, this paper will carry out the optimal classification of convolutional neural networks. Firstly, according to the characteristics of pneumonia images, AlexNet and InceptionV3 were selected to obtain better image recognition results. Combining the features of medical images, the forward neural network with deeper and more complex structure is learned. Finally, knowledge extraction technology is used to extract the obtained data into the AlexNet model to achieve the purpose of improving computing efficiency and reducing computing costs. The results showed that the prediction accuracy, specificity, and sensitivity of the trained AlexNet model increased by 4.25 percentage points, 7.85 percentage points, and 2.32 percentage points respectively. The graphics processing usage has decreased by 51% compared to the InceptionV3 mode. © 2024 IEEE.

关键词： Convolutional neural networks

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