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17th International Conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Singh, Kirti Ribeiro, Vinay J. Mandal, Susmita Indian Institute of Technology Bombay Department of Computer Science and Engineering Mumbai India Institute for Development and Research in Banking Technology Hyderabad India

ISBN: (纸本)9798331531195

Decentralized exchanges (DEXs) have emerged as a promising solution to enhance trustlessness in blockchain ecosystems and mitigate security threats associated with centralized exchanges. While platforms like Uniswap offer certain advantages, their limitation to exchanging tokens within a single blockchain restricts their utility. Moreover, exchanges relying on atomic swaps, such as AtomicDEX, are susceptible to grief attacks and exhibit low success ratios. In response to these challenges, we introduce FlexiSwap, an auction-based decentralized exchange protocol that leverages atomic swaps for exchanging native tokens between smart contract-compatible blockchains. FlexiSwap stands out by combining multiple bids to facilitate one-to-many exchanges, resulting in improved exchange values, successful execution of large-volume exchanges, and an improved success ratio. The protocol incorporates a penalization mechanism to deter grief attacks, ensuring that participants cannot withdraw from an exchange without incurring a loss once the counterparty has committed its assets. We also prove the protocol's safety, liveness, and grief prevention. Our simulation results demonstrate that combining multiple bids helps achieve better exchange values and order-matching success rates. Additionally, we implemented our protocol on Ethereum-based blockchains. The experimental results show that combining bids increases overall gas usage, but the increase in the average gas user per bid is marginal. © 2025 IEEE.

关键词： Kyoto Protocol

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Blur-Deblur Algorithm for Pressure-Sensitive Paint Image Based on Variable Attention Convolution

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computers, Materials & Continua 2025年第3期82卷 5239-5256页

作者： Ruizhe Yu Tingrui Yue Lei Liang Zhisheng Gao School of Computer and Software Engineering Xihua UniversityChengdu610039China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing210016China Low Speed Aerodynamics Institute China Aerodynamics Research and Development CenterMianyang621000China

In the PSP(Pressure-Sensitive Paint),image deblurring is essential due to factors such as prolonged camera exposure times and highmodel velocities,which can lead to significant image *** deblurring methods applied to PSP images often suffer from limited accuracy and require extensive computational *** address these issues,this study proposes a deep learning-based approach tailored for PSP image *** that PSP applications primarily involve the accurate pressure measurements of complex geometries,the images captured under such conditions exhibit distinctive non-uniform motion blur,presenting challenges for standard deep learning models utilizing convolutional or attention-based *** this paper,we introduce a novel deblurring architecture featuring multiple DAAM(Deformable Ack Attention Module).These modules provide enhanced flexibility for end-to-end deblurring,leveraging irregular convolution operations for efficient feature extraction while employing attention mechanisms interpreted as multiple 1×1 convolutions,subsequently reassembled to enhance ***,we incorporate a RSC(Residual Shortcut Convolution)module for initial feature processing,aimed at reducing redundant computations and improving the learning capacity for representative shallow *** preserve critical spatial information during upsampling and downsampling,we replace conventional convolutions with wt(Haar wavelet downsampling)and dysample(Upsampling by Dynamic Sampling).This modification significantly enhances high-precision image *** integrating these advanced modules within an encoder-decoder framework,we present the DFDNet(Deformable Fusion Deblurring Network)for image blur removal,providing robust technical support for subsequent PSP data *** evaluations on the FY dataset demonstrate the superior performance of our model,achieving competitive results on the GOPRO and HIDE datasets.

关键词： Pressure-sensitive paint deep learning image deblurring typeset variable attention convolution

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Voltage and power control for distributed generation units in islanded microgrids 6th

Voltage and power control for distributed generation units i...

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6th International Conference on Renewable Energies Offshore, RENEW 2024

作者： Rouch, T. Bagheri M’zoughi, F. Aboutalebi, P. Ahmad, I. Garrido, A.J. Garrido, I. Institute of Research and Development of Processes—IIDP Department of Automatic Control and Systems Engineering Faculty of Engineering of Bilbao University of the Basque Country Bilbao Spain

ISBN: (纸本)9781032905570

This article presents an optimized control strategy tailored for DC islanded microgrids, featuring a voltage controller based on mixed H2/H∞ state feedback using linear matrix inequalities (LMIs) and a power controller guided by droop characteristics. A polytopic modeling approach addresses system uncertainties and nonlinearities. The controller aims to stabilize the system, reject disturbances, equitably distribute power among distributed generation units (DGUs), and minimize energy consumption. Regional pole placement ensures welldamped transient responses. Simulation results demonstrate effective management of load perturbations and DGU disconnections, with a comparative analysis against an H∞ controller offering additional insights into performance. © 2025 The Author(s).

关键词： Microgrids

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Machine Learning Assessment of Heart Rate Confidence from Video Magnification 23

Machine Learning Assessment of Heart Rate Confidence from Vi...

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23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： Elhajjar, Diane Wallace, Bruce Law, Andrew Goubran, Rafik Knoefel, Frank Systems and Computer Engineering Carleton University Ottawa Canada Systems and Computer Engineering Carleton University Bruyère Research Institute SAM3 Innovation Hub Ottawa Canada Flight Research Laboratory National Research Council CanadaSystems and Computer Engineering Carleton University Ottawa Canada Systems and Computer Engineering Carleton University Bruyère Research Institute Ottawa Canada Bruyère Research Institute Elizabeth Bruyère HospitalSystems and Computer Engineering Carleton University Ottawa Canada

ISBN: (纸本)9798350393118

Video magnification (VM) provides an alternative health monitoring solution by enabling contactless and remote measurement of vital signs such as heart rate (HR). HR is a crucial biomarker for assessing the overall health of individuals, and the increased need to replace traditional wearable devices and avoid complex applications of sensors makes VM essential for in-home health assessment. VM can measure HR by detecting subtle changes of skin color due to blood flow, however, external factors such as subject or camera motion, variable lighting and skin tone can greatly affect performance. Previous work implemented methods that improved the overall accuracy of VM-based HR predictions but did not indicate if a given HR prediction was likely to be correct or incorrect. This work assesses the confidence of the predicted HR using a set of features derived during the VM process. Several machine learning models were evaluated to analyze the confidence parameters from three different VM processing methods. The results show an accuracy of 92.1% for an SVM model that classifies if VM-based HR is correct or incorrect. The high classification accuracy indicates the effectiveness of the confidence metrics for discriminating between correct and incorrect HR predictions using VM. © 2023 IEEE.

关键词： Forecasting

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Cybersecurity Landscape on Remote State Estimation:A Comprehensive Review

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 851-865页

作者： Jing Zhou Jun Shang Tongwen Chen the Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 1H9Canada the Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control *** state estimation(RSE)is an indispensable functional module of ***,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance *** paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against ***,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of ***,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from ***,some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers'and defenders'***,several challenges and open problems are presented to inspire further exploration and future research in this field.

关键词： Cyber-attacks Kalman filtering remote state estimation unreliable transmission channels

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A Lightweight Intrusion Detection System Using Convolutional Neural Network and Long Short-Term Memory in Fog Computing

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computers, Materials & Continua 2024年第9期80卷 4703-4728页

作者： Hawazen Alzahrani Tarek Sheltami Abdulaziz Barnawi Muhammad Imam Ansar Yaser Computer Engineering Department Interdisciplinary Research Center of Smart Mobility and LogisticsKing Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Computer Engineering Department Interdisciplinary Research Center for Intelligent Secure SystemsKing Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Transportation Research Institute(IMOB) Hasselt UniversityHasselt3500Belgium

The Internet of Things(IoT)links various devices to digital services and significantly improves the quality of our ***,as IoT connectivity is growing rapidly,so do the risks of network vulnerabilities and *** interesting Intrusion Detection systems(IDSs)are presented based on machine learning(ML)techniques to overcome this *** the resource limitations of fog computing environments,a lightweight IDS is *** paper introduces a hybrid deep learning(DL)method that combines convolutional neural networks(CNN)and long short-term memory(LSTM)to build an energy-aware,anomaly-based *** test this system on a recent dataset,focusing on reducing overhead while maintaining high accuracy and a low false alarm *** compare CICIoT2023,KDD-99 and NSL-KDD datasets to evaluate the performance of the proposed IDS model based on key metrics,including latency,energy consumption,false alarm rate and detection rate *** findings show an accuracy rate over 92%and a false alarm rate below 0.38%.These results demonstrate that our system provides strong security without excessive resource *** practicality of deploying IDS with limited resources is demonstrated by the successful implementation of IDS functionality on a Raspberry Pi acting as a Fog *** proposed lightweight model,with a maximum power consumption of 6.12 W,demonstrates its potential to operate effectively on energy-limited devices such as low-power fog nodes or edge *** prioritize energy efficiency whilemaintaining high accuracy,distinguishing our scheme fromexisting *** experiments demonstrate a significant reduction in false positives,ensuring accurate identification of genuine security threats while minimizing unnecessary alerts.

关键词： Intrusion detection fog computing CNN LSTM energy consumption

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Optimisation of sparse deep autoencoders for dynamic network embedding

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1361-1376页

作者： Huimei Tang Yutao Zhang Lijia Ma Qiuzhen Lin Liping Huang Jianqiang Li Maoguo Gong College of Computer Science and Software Engineering Shenzhen UniversityShenzhenChina Institute for Infocomm Research Agency for Science Technology and ResearchSingaporeSingapore Key Laboratory of Collaborative Intelligence Systems Ministry of EducationXidian UniversityXi'anChina

Network embedding(NE)tries to learn the potential properties of complex networks represented in a low-dimensional feature ***,the existing deep learningbased NE methods are time-consuming as they need to train a dense architecture for deep neural networks with extensive unknown weight parameters.A sparse deep autoencoder(called SPDNE)for dynamic NE is proposed,aiming to learn the network structures while preserving the node evolution with a low computational *** tries to use an optimal sparse architecture to replace the fully connected architecture in the deep autoencoder while maintaining the performance of these models in the dynamic ***,an adaptive simulated algorithm to find the optimal sparse architecture for the deep autoencoder is *** performance of SPDNE over three dynamical NE models(*** architecture-based deep autoencoder method,DynGEM,and ElvDNE)is evaluated on three well-known benchmark networks and five real-world *** experimental results demonstrate that SPDNE can reduce about 70%of weight parameters of the architecture for the deep autoencoder during the training process while preserving the performance of these dynamical NE *** results also show that SPDNE achieves the highest accuracy on 72 out of 96 edge prediction and network reconstruction tasks compared with the state-of-the-art dynamical NE algorithms.

关键词： deep autoencoder dynamic networks low-dimensional feature space network embedding sparse structure

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Nanoimprint lithography-assisted block copolymer self-assembly for hyperfine fabrication of magnetic patterns based on L10-FePt nanoparticles

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Science China Chemistry 2025年第5期68卷 2027-2034页

作者： Jie Zhang Yi Chen Guijun Li Zelin Sun Zhengong Meng Wai-Yeung Wong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute Division of Integrative Systems and Design and Department of Electronic and Computer EngineeringThe Hong Kong University of Science and Technology School of Flexible Electronics (Future Technology) and Institute of Advanced Materials (IAM) Nanjing Tech University

L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands is still challenging, especially with the target for miniaturization and scalable production simultaneously. Herein, Fe,Pt-containing block copolymers were utilized as single-source precursors for solution-processable patterning and subsequent generation of the demanding magnetic FePt dots by in situ pyrolysis. High-throughput nanoimprint lithography was initially employed to fabricate the predefined bit cells precisely,and then the intrinsic self-assembly of phase-separated block copolymers further drove the formation of accurate bit *** from the synergistic effect of top-down lithographic approach and bottom-up self-assembly, the customizable patterns could be achieved for large-scale mass production in targeted areas, but high-density isolated dots could also be accurately aligned along the patterned features after subsequent self-assembly. This reliable strategy would provide a good avenue to precisely construct ultrahigh-density magnetic data storage devices.

关键词： organometallic precursor magnetic nanoparticles block copolymer self-assembly nanoimprint lithography

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Digital Educational Resources in Industrial Radiography 7

Digital Educational Resources in Industrial Radiography

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7th International Conference on Information Technologies in engineering Education, Inforino 2024

作者： Kuvshinnikov, Vladimir Kovshov, Evgeny Korchagin, Valery JSC 'Research and Development Institute of Construction Technology - Atomstroy' Research Laboratory of Digital Computer Systems and Automation Moscow Russia Dmitry Mendeleev University of Chemical Technology of Russia Disruptive Engineering School of Chemical Engineering and Machinery Moscow Russia

ISBN: (纸本)9798350371369

Nowadays, the technological level of production is an important factor for all areas of modern economic development and life quality improvement. Neither in the informational, nor in the industrial sphere it is possible to produce in-demand, modern, and therefore high-tech and complex products without the establishment of an appropriate system of quality examination at multiple stages of production. This article deals with the problem of forming a comprehensive educational environment with the use of heterogeneous educational resources in the training of specialists in the radiation type of nondestructive testing. The purpose of the research and development is to address the shortcomings of classroom, remote digital learning, and laboratory practice listed in the article by developing a solution based on virtual reality technologies. The purpose stated lies in the pursuit of intensive solutions to the existing problems. Extensive development paths are not effective in improving the efficiency of training and checking students' knowledge in the context of the rapidly growing demand for nondestructive testing specialists. The concept of a digital twin as an educational resource is considered. The structural approach to the formation of complex educational resources both at the level of organization and at the level of digital product is presented. The structure of interrelationships between data in the formation of a digital twin of a radiographic image is presented. The results of the radiographic image simulation and its quality verification are shown. © 2024 IEEE.

关键词： Nondestructive examination

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3D Modeling for a Flexible Detector in VR Radiography Simulator 7

3D Modeling for a Flexible Detector in VR Radiography Simula...

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7th International Conference on Information Technologies in engineering Education, Inforino 2024

作者： Kuvshinnikov, Vladimir Kovshov, Evgeny Korchagin, Valery Institute of Construction Technology - Atomstroy Research Laboratory of Digital Computer Systems and Automation JSC 'Research and Development' Moscow Russia Dmitry Mendeleev University of Chemical Technology of Russia Disruptive Engineering School of Chemical Engineering and Machinery Moscow Russia

ISBN: (纸本)9798350371369

Digital simulators based on virtual reality expand the range of educational opportunities and improve the quality of learning in various areas of knowledge through deeper "immersion"in the learning process. Attention to the details of the modeled process plays an important role. One of the tasks of interactive simulators is to provide an opportunity to see the relationship between the user's actions and the obtained qualitative and quantitative parameters and quality attributes of the simulated process result. Considered the issue of curved shape detectors application relevance in the practice of penetrating radiation evaluation in general and in industrial radiography in particular. The prerequisites for the creation of flexible (bendable) form of digital radiographic detectors and the features of two generations of practically implemented devices are analyzed. development of a flexible detector digital twin is considered, models and algorithms for its realization in a virtual reality environment are proposed, and the main results of computational and laboratory experiments as well as the obtained radiographic images are presented. The main task of virtual reality technologies within the qualified technical specialists training programs is the development of their professional skills and the formation of stable behavior patterns that ensure strict compliance with the rules and requirements of industrial safety, thereby ensuring the high quality of the end product. © 2024 IEEE.

关键词： Virtual reality

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