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IET Blockchain 2022年第1期2卷 1-12页

作者： Iliakis, Konstantinos Koliogeorgi, Konstantina Litke, Antonios Varvarigou, Theodora Soudris, Dimitrios Microprocessors and Digital Systems Laboratory Department of Electrical and Computer Engineering National Technical University of Athens Zografou Campous 9 Iroon Polytechniou str Athens Greece Distributed Knowledge and Media Systems group Department of Electrical and Computer Engineering National Technical University of Athens Zografou Campous 9 Iroon Polytechniou str Athens Greece

Blockchain is a distributed ledger based on peer-to-peer networks, originally used for crypto-currency systems. Blockchains are being used as an enabling technology for decentralised applications in the areas of Internet-of-Things, finance, supply-chain and others. Consistency, data privacy, performance, and energy efficiency are of paramount importance in such applications. The full nodes of public, permissionless blockchains undertake the task of verifying the transactions generated by the network. Full nodes perform operations such as confirming balances, transactions, and history, i.e. mostly database search queries. Consequently, their throughput is crucial for the performance of blockchain systems. In this work, the benefits of accelerating the blockchain search and insert queries by leveraging GPU platforms are studied. An extensive comparison between the most dominant utilized database is provided, i.e. LevelDB, and MegaKV, a high-performance GPU-accelerated database. Realistic operations that take place in blockchain systems are emulated and evaluated over representative scenarios, showing three orders of magnitude gains in throughput and energy efficiency without compromising the security aspect. The extensive comparison between LevelDB and MegaKV indicates that GPU acceleration is an effective solution for runtime and energy efficiency enhancement of blockchain systems, and the integration of the two technologies is a promising field of research. © 2022 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Blockchain

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Feature Correlation Aggregation: on the Path to Better Graph Neural Networks

Feature Correlation Aggregation: on the Path to Better Graph...

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2023 International Conference on digital Image Computing: Techniques and Applications, DICTA 2023

作者： Zhou, Jieming Zhang, Tong Fang, Pengfei Petersson, Lars Harandi, Mehrtash Australian National University College of Engineering and Computer Science Canberra Australia Image and Visual Representation Laboratory Epfl Lausanne Switzerland Imaging and Computer Vision Group Csiro Canberra Australia Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9798350382204

Prior to the introduction of Graph Neural Networks (GNNs), modelling and analyzing irregular data, particularly graphs, was thought to be the Achilles' heel of deep learning. The core concept of GNNs is to find a representation by recursively aggregating the representations of a central node and those of its neighbor, and many GNNs' designs have demonstrated its success. However, most only focus on using the first-order information between a node and its neighbors. In this paper, we introduce a central node permutation variant function through a frustratingly simple and innocent-looking modification to the core operation of a GNN, namely the Feature cOrrelation aGgregation (FOG) module, which learns the second-order information from feature correlation between a node and its neighbors in the pipeline. By adding FOG into existing variants of GNNs, we empirically verify this second-order information complements the features generated by original GNNs across a broad set of benchmarks. A tangible boost in the model's performance is observed where the model surpasses previous state-of-the-art results by a significant margin while employing fewer parameters. e.g., 26.202% improvement on a real-world molecular dataset using graph convolutional networks. © 2023 IEEE.

关键词： Graph neural networks

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Quantitative comparison between reflection and transmission modes for the inspection of composite materials using pulsed thermography 45

Quantitative comparison between reflection and transmission ...

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Thermosense: Thermal Infrared Applications XLV 2023

作者： Alhammad, M. Avdelidis, N.P. Ibarra-Castanedo, C. Torbali, M.E. Zolotas, A. Maldague, X.P.V. School of Aerospace Transport and Manufacturing Cranfield University United Kingdom Universite Laval Computer Vision and Systems Laboratory Department of Electrical and Computer Engineering Quebec CityG1V 0A6 Canada

ISBN: (纸本)9781510661868

Composite materials have extensively been used in the aerospace industry. There are several inspection methods to ensure the safety of these composites;pulse thermography (PT) is one of the most promising ones. Both reflection and transmission modes of PT could be applied. However, few studies reveal the advantages and disadvantages of these two modes. This paper presents a quantitative comparison of pulsed thermal imaging in two modes of reflection and gradual heat transfer in carbon fibres. Experimental work was conducted on carbon fibre-reinforced plastic (CFRP) samples with different thicknesses and thermal images were recorded in both modes. Thermal images were further processed using statistical analysis and machine learning algorithms. Comparing the results from both modes, there is a marked improvement in the accuracy when the reflection mode is employed. © 2023 SPIE.

关键词： Nondestructive examination

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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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Implementing digital Twins for Enhanced Energy Management in Three Case Studies

Implementing Digital Twins for Enhanced Energy Management in...

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2024 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2024

作者： Testasecca, Tancredi Stamatopoulos, Stathis Natalini, Andrea Lazzaro, Marilena Capizzi, Chiara Maria Sarmas, Elissaios Arnone, Diego Università degli Studi di Palermo Dipartimento di Ingegneria Palermo Italy School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece Engineering Ingegneria Informatica S.p.A. Digital Twin and IOT Solutions Department Rome Italy Digital Hubs Engineering Ingegneria Informatica S.p.A. Palermo Italy

ISBN: (纸本)9798350385014

digitalization is recognized as a powerful enabler for decarbonizing the building sector. The International Energy Agency has highlighted its potential to improve safety, productivity, accessibility, and sustainability of energy systems. This work aims to present innovative applications of digital twins within the European Project DigiBUILD, focusing on optimizing energy systems for enhanced efficiency and reduced carbon footprint. The architecture of the developed digital twins w detailed, focusing on data collection, services description, and their implementation. Through three detailed case studies, the paper explores real applications of digital twins: the Heron pilot, whose aim is to manage a cluster of buildings and optimally charges electric vehicles to reduce carbon emissions;Veolia, where a district heating networks to optimally manage heat sources in the production plant to reduce gas consumption and emissions;and Emotion, focusing on a building equipped with photovoltaic panels and electric vehicles and implementing services for optimal vehicle charging to maximize self-consumption, in day-ahead scheduling but also in real time. A discussion finally highlights the challenges and limitations encountered in the deployment of digital twins, emphasizing the importance of user interaction in facilitating efficient energy management. This research contributes in underlining the significant potential of digital twins in advancing the digitalization of the energy sector, contributing to the European Union's goals for sustainability and reduced environmental impact. © 2024 IEEE.

关键词： Carbon footprint

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4631-4646页

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. Beijing Institute of Technology School of Information and Electronics Beijing100081 China Southern University of Science and Technology School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Shenzhen518055 China The University of Sydney School of Electrical and Information Engineering SydneyNSW2008 Australia Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines 487372 Singapore Kyung Hee University Department of Electronic Engineering Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2002-2012 IEEE.

关键词： Graph neural networks

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Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

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Intelligent Medicine 2024年第3期4卷 170-176页

作者： Bijan Roudini Boshra Khajehpiri Hamid Abrishami Moghaddam Mohamad Forouzanfar Department of Systems Engineering School of Advanced TechnologyUniversity of QuebecMontrealCanada Machine Vision and Medical Image Processing(MVMIP)Laboratory Faculty of Electrical and Computer EngineeringK.N.Toosi University of TechnologyTehranIran Research Center of the Montreal University Institute of Geriatrics MontrealCanada

Background Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new *** learning(ML)models have shown satisfactory performance in short-term mortality prediction in patients with heart disease,whereas their utility in long-term predictions is *** study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term *** This study used publicly available data from the Collaboration Center of Health Information Appli-cation at the Ministry of Health and Welfare,Taiwan,*** collected data were from patients admitted to the cardiac care unit for acute myocardial infarction(AMI)between November 2003 and September *** collected and analyzed mortality data up to December *** records were used to gather demo-graphic and clinical data,including age,gender,body mass index,percutaneous coronary intervention status,and comorbidities such as hypertension,dyslipidemia,ST-segment elevation myocardial infarction,and non-ST-segment elevation myocardial *** the data,collected from 139 patients with AMI,from medical and demographic records as well as two recently introduced biomarkers,brachial pre-ejection period(bPEP)and brachial ejection time(bET),we investigated the performance of advanced ensemble tree-based ML algorithms(random forest,AdaBoost,and XGBoost)to predict all-cause mortality within 14 years.A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression(LR)as the baseline *** The developed ML models achieved significantly better performance compared to the baseline LR(C-Statistic,0.80 for random forest,0.79 for AdaBoost,and 0.78 for XGBoost,vs.0.77 for LR)(PRF<0.001,PAdaBoost<0.001,a

关键词： Electrocardiogram Predication Mortality prediction Machine learning

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Self-Supervised Reflective Learning Through Self-Distillation and Online Clustering for Speaker Representation Learning

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1535-1550页

作者： Danwei Cai Zexin Cai Ze Li Ming Li Department of Electrical and Computer Engineering Duke University Durham NC USA Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Digital Innovation Research Center Duke Kunshan University Kunshan China School of Computer Science Wuhan University Wuhan China

Speaker representation learning is crucial for voice recognition systems, with recent advances in self-supervised approaches reducing dependency on labeled data. Current two-stage iterative frameworks, while effective, suffer from significant computational overhead due to repeated rounds of clustering and training. They also struggle with noisy pseudo labels that can impair model learning. This paper introduces self-supervised reflective learning (SSRL), an improved framework that addresses these limitations by enabling continuous refinement of pseudo labels during training. Through a teacher-student architecture and online clustering mechanism, SSRL eliminates the need for iterative training rounds. To handle label noise, we incorporate noisy label modeling and pseudo label queues that maintain temporal consistency. Experiments on VoxCeleb show SSRL's superiority over current two-stage iterative approaches, surpassing the performance of a 5-round method in just a single training round. Ablation studies validate the contributions of key components like noisy label modeling and pseudo label queues. Moreover, consistent improvements in pseudo labeling and the convergence of cluster counts demonstrate SSRL's effectiveness in deciphering unlabeled data. This work marks an important advancement in efficient and accurate self-supervised speaker representation learning through the novel reflective learning paradigm.

关键词： Training Representation learning Iterative methods Noise Adaptation models Noise measurement Predictive models Speech processing Robustness Convergence

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Analyzing Infrared Linescan Profiles of Steel Strips for Enhanced Cooling Pattern Prediction

Analyzing Infrared Linescan Profiles of Steel Strips for Enh...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Rubén Usamentiaga Adrian G. Gayo Francisco J. DelaCalle Dario G. Lema Stefano Sfarra Hai Zhang Department of Computer Science and Engineering University of Oviedo Gijón Asturias Spain Department of Industrial and Information Engineering and Economics (DIIIE) University of L'Aquila L'Aquila Italy Department of Electrical and Computer Engineering Computer Vision and Systems Laboratory (CVSL) Laval University Quebec Canada

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

Real-time temperature information is crucial for optimizing cooling processes during steel strip rolling, ensuring the attainment of desired microstructural properties and surface quality at an optimal cooling rate. Infrared line scanners emerge as the preferred choice for temperature measurement in highspeed rolling operations, delivering temperature readings with high resolution and enabling the capture of detailed temperature profiles. By analyzing these profiles, cooling systems can be finely adjusted and precisely controlled to optimize the rolling operation. However, developing effective cooling strategies becomes challenging when dealing with temperature profiles comprising numerous discrete data points, often numbering in the thousands per profile. This study presents an innovative approach that integrates the detection of steel strip boundaries within temperature profiles and subsequent temperature pattern characterization using polynomial fitting. A significant advantage is demonstrated by leveraging the coefficients of Legendre polynomials, which provide a concise description of temperature profile shapes, facilitating straightforward approaches to cooling strategies. By integrating boundary detection with temperature characterization, the system enhances its ability to predict tailored cooling patterns, optimizing cooling efficiency, and enhancing product quality in the manufacturing process. Rigorous testing using both synthetic data and real-world applications in cold and hot rolling validates the proposed system's practical utility and reliability. These results underscore its potential to enhance efficiency and quality in industrial steel manufacturing operations.

关键词： Temperature measurement Temperature sensors Strips Cooling Shape Polynomials Real-time systems Steel Monitoring Testing

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