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arXiv 2024年

作者： Gill, Sukhpal Singh Wu, Huaming Patros, Panos Ottaviani, Carlo Arora, Priyansh Pujol, Victor Casamayor Haunschild, David Parlikad, Ajith Kumar Cetinkaya, Oktay Lutfiyya, Hanan Stankovski, Vlado Li, Ruidong Ding, Yuemin Qadir, Junaid Abraham, Ajith Ghosh, Soumya K. Song, Houbing Herbert Sakellariou, Rizos Rana, Omer Rodrigues, Joel J.P.C. Kanhere, Salil S. Dustdar, Schahram Uhlig, Steve Ramamohanarao, Kotagiri Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Center for Applied Mathematics Tianjin University Tianjin China Raygun Performance Monitoring Wellington New Zealand Department of Computer Science York Centre for Quantum Technologies University of York York United Kingdom Microsoft Schiphol Netherlands Distributed Systems Group Vienna University of Technology Vienna Austria Detecon International GmbH Munich Germany Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computer Science University of Western Ontario London Canada Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Institute of Science and Engineering Kanazawa University Japan Tecnun School of Engineering University of Navarra Spain Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Bennett University Greater Noida India Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Baltimore United States Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia COPELABS Lusófona University Lisbon Portugal Sydney Australia The University of Melbourne Victoria Australia Laboratory School of Computing and Information Systems The University of Melbourne Australia

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress. © 2024, CC BY.

关键词： Obsolescence

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Multiangle QAOA Does Not Always Need All Its Angles

Multiangle QAOA Does Not Always Need All Its Angles

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IEEE/ACM Symposium on Edge Computing (SEC)

作者： Kaiyan Shi Rebekah Herrman Ruslan Shaydulin Shouvanik Chakrabarti Marco Pistoia Jeffrey Larson Mathematics and Computer Science Division Argonne National Laboratory Lemont IL USA Dept. of Computer Science University of Maryland College Park MD USA Industrial and Systems Engineering University of Tennessee at Knoxville Knoxville TN USA Global Technology Applied Research JPMorgan Chase New York NY USA

ISBN: (数字)9781665486118

ISBN: (纸本)9781665486125

Introducing additional tunable parameters to quantum circuits is a powerful way of improving per-formance without increasing hardware requirements. A recently introduced multiangle extension of the quantum approximate optimization algorithm (ma-QAOA) signifi-cantly improves the solution quality compared with QAOA by allowing the parameters for each term in the Hamilto-nian to vary independently. Prior results suggest, however, considerable redundancy in parameters, the removal of which would reduce the cost of parameter optimization. In this work we show numerically the connection between the problem symmetries and the parameter redundancy by demonstrating that symmetries can be used to reduce the number of parameters used by ma-QAOA without decreasing the solution quality. We study Max-Cut on all 7,565 connected, non-isomorphic 8-node graphs with a nontrivial symmetry group and show numerically that in 67.4% of these graphs, symmetry can be used to reduce the number of parameters with no decrease in the objective, with the average ratio of parameters reduced by 28.1%. Moreover, we show that in 35.9% of the graphs this reduction can be achieved by simply using the largest symmetry. For the graphs where reducing the number of parameters leads to a decrease in the objective, the largest symmetry can be used to reduce the parameter count by 37.1% at the cost of only a 6.1% decrease in the objective. We demonstrate the central role of symmetries by showing that a random parameter reduction strategy leads to much worse performance.

关键词： Costs Redundancy Approximation algorithms Hardware Quantum circuit Optimization Edge computing

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Metal–organic frameworks modified electrode for H₂S detections in biological and pharmaceutical agents

MedComm - Biomaterials and Applications

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MedComm - Biomaterials and Applications 2023年第3期2卷

作者： Durmus, Ceren Arul, Ponnusamy Alhaji, A. Shekhah, Osama Mani, Veerappan Eddaoudi, Mohamed Salama, Khaled Nabil Sensors Lab Advanced Membranes and Porous Materials Center Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Department of Chemical Engineering and Biotechnology Institute of Biochemical and Biomedical Engineering National Taipei University of Technology Taipei Taiwan Physical Sciences and Engineering Division Functional Materials Design Discovery and Development Research Group (FMD3) Advanced Membranes and Porous Materials Centre (AMPMC) King Abdullah University of Science and Technology (KAUST) Jeddah Saudi Arabia

The development of hydrogen sulfide (H2S) sensors is essential to address H2S-related pharmacology since slow-releasing H2S medications have been identified to be prospective options for cancer treatments. Here, we de... 详细信息

The development of hydrogen sulfide (H₂S) sensors is essential to address H₂S-related pharmacology since slow-releasing H₂S medications have been identified to be prospective options for cancer treatments. Here, we described an electrochemical sensor for highly selective and sensitive detection of aqueous H₂S, using a thin film of fumarate-based face-centered cubic (fcu)-based metal–organic frameworks (fum-fcu-MOF) modified on laser-scribed graphene (LSGE). The fum-fcu-MOF has shown a strong affinity and chemical stability to H₂S analysis. The electrochemical and H₂S catalytic properties were studied for fum-fcu-MOF/LSGE. An amperometry and differential pulse voltammetry techniques were demonstrated to validate the sensor. The resulting sensor delivered acceptable analytical parameters in terms of;detection limit (3.0 µM), dynamic range (10–500 µM), reproducibility, and stability (94.7%). The sensor's practical validity was demonstrated in bacterial cells and H₂S-releasing drug, where the sensor was able to monitor the continuous release of in-situ H₂S. The pharmacokinetics of a slow releasing H₂S donor is accessed at different time intervals and different concentration levels. Our research indicate that this fum-fcu-MOF based H₂S sensor holds potential in understanding pharmacokinetics of H₂S releasing drugs. © 2023 The Authors. MedComm – Biomaterials and Applications published by John Wiley & Sons Australia, Ltd on behalf of Sichuan International Medical Exchange & Promotion Association (SCIMEA).

关键词： H2S pharmacology hydrogen sulfide metal organic frameworks

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Using Susceptible-Exposed-Infectious-Recovered Model to Forecast Coronavirus Outbreak

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computers, Materials & Continua 2021年第5期67卷 1595-1612页

作者： Debabrata Dansana Raghvendra Kumar Arupa Parida Rohit Sharma Janmejoy Das Adhikari Hiep Van Le Binh Thai Pham Krishna Kant Singh Biswajeet Pradhan Department of Computer Science and Engineering GIET UniversityGunupurOdishaIndia Department of Electronics&Communication Engineering Faculty of Engineering and TechnologySRM Institute of Science and TechnologyNCR CampusDelhi-NCR CampusModinagarGhaziabadIndia Institute of Research and Development Duy Tan UniversityDa Nang550000Viet Nam University of Transport Technology Hanoi100000Viet Nam Department of ECE KIET Group of InstitutionsGhaziabadIndia Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) University of Technology SydneySydneyNSW2007Australia Department of Energy and Mineral Resources Engineering Sejong UniversitySeoul05006Korea Center of Excellence for Climate Change Research King Abdulaziz UniversityJeddah21589Saudi Arabia Earth Observation Center Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600SelangorMalaysia

The Coronavirus disease 2019(COVID-19)outbreak was rst discovered in Wuhan,China,and it has since spread to more than 200 *** World Health Organization proclaimed COVID-19 a public health emergency of international concern on January 30,***,a quickly spreading infection that could jeopardize the well-being of countless individuals requires prompt action to forestall the malady in a timely ***19 is a major threat worldwide due to its ability to rapidly *** vaccines are yet available for *** objective of this paper is to examine the worldwide COVID-19 pandemic,specically studying Hubei Province,China;Taiwan;South Korea;Japan;and Italy,in terms of exposed,infected,recovered/deceased,original conrmed cases,and predict conrmed cases in specic countries by using the susceptible-exposed-infectious-recovered model to predict the future outbreak of *** applied four differential equations to calculate the number of conrmed cases in each country,plotted them on a graph,and then applied polynomial regression with the logic of multiple linear regression to predict the further spread of the *** also compared the calculated and predicted cases of conrmed population and plotted them in the graph,where we could see that the lines of calculated and predicted cases do intersect with each other to give the perfect true results for the future spread of the *** study considered the cases from 22 January 2020 to 25 April 2020.

关键词： COVID-19 SEIR forecasting global pandemic predict conrmed case

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Access Control Based on Self-Sovereign Identity

Access Control Based on Self-Sovereign Identity

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Stefania Loredana Nita Marius Iulian Mihailescu Valentina Marascu Marius Rogobete Department of Computers and Cybersecurity Faculty of Information Systems and Cyber Security Military Technical Academy “Ferdinand I” Bucharest Romania Scientific Research Center in Mathematics and Informatics Faculty of Engineering and Computer Science “Spiru Haret” University Bucharest Romania Low Temperature Plasma Department National Institute for Laser Plasma and Radiation Physics Bucharest Romania Telematics Harman International Bucharest Romania

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

In the rapidly growing interest in digital identity management, there is an important shift toward adopting verifiable credentials, often based on blockchain technology. This transition highlights a growing interest in the concept of Self-Sovereign Identity (SSI), a paradigm that empowers individuals with control over their own digital identities through the use of verifiable credentials. This paper introduces a novel access control framework that leverages the principles of SelfSovereign Identity to significantly enhance the security of system access based on the users’ credentials. By integrating the decentralized and user-centric nature of SSI, our proposed framework aims to address existing vulnerabilities in traditional access control systems. It provides a robust, scalable, and controlled mechanism that ensures higher levels of trust and security in digital interactions. This approach aligns with the broader digital identity trends favoring transparency, user autonomy, and trust.

关键词： Access control computers Privacy Computational modeling computer architecture Market research Blockchains

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Non-convex potential games for finding global solutions to sensor network localization

arXiv

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arXiv 2023年

作者： Xu, Gehui Chen, Guanpu Hong, Yiguang Fidan, Baris Parisini, Thomas Johansson, Karl H. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-anchor and anchor nodes. Attributed to the intrinsic non-convexity, obtaining a globally optimal solution to SNL is challenging, as well as implementing corresponding algorithms. In this paper, we formulate a non-convex multi-player potential game for a generic SNL problem to investigate the identification condition of the global Nash equilibrium (NE) therein, where the global NE represents the global solution of SNL. We employ canonical duality theory to transform the non-convex game into a complementary dual problem. Then we develop a conjugation-based algorithm to compute the stationary points of the complementary dual problem. On this basis, we show an identification condition of the global NE: the stationary point of the proposed algorithm satisfies a duality relation. Finally, simulation results are provided to validate the effectiveness of the theoretical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Sensor networks

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Double-free-layer stochastic magnetic tunnel junctions with synthetic antiferromagnets

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Physical Review Applied 2024年第5期21卷 054002-054002页

作者： Kemal Selcuk Shun Kanai Rikuto Ota Hideo Ohno Shunsuke Fukami Kerem Y. Camsari Department of Electrical and Computer Engineering University of California Santa Barbara California 93106 USA Research Institute of Electrical Communication Tohoku University 2-1-1 Katahira Aoba-ku Sendai 980-8577 Japan Graduate School of Engineering Tohoku University 6-6 Aramaki Aza Aoba Aoba-ku Sendai 980-8579 Japan WPI Advanced Institute for Materials Research (WPI-AIMR) Tohoku University 2-1-1 Katahira Aoba-ku Sendai 980-8577 Japan Center for Science and Innovation in Spintronics (CSIS) Tohoku University 2-1-1 Katahira Aoba-ku Sendai 980-8577 Japan PRESTO Japan Science and Technology Agency (JST) Kawaguchi 332-0012 Japan Division for the Establishment of Frontier Sciences of Organization for Advanced Studies at Tohoku University Tohoku University Sendai 980-8577 Japan National Institutes for Quantum Science and Technology Takasaki 370-1207 Japan Center for Innovative Integrated Electronic Systems (CIES) Tohoku University 468-1 Aramaki Aza Aoba Aoba-ku Sendai 980-0845 Japan Inamori Research Institute of Science (InaRIS) Kyoto 600-8411 Japan

Stochastic magnetic tunnel junctions (SMTJs) using low-barrier nanomagnets have shown promise as fast, energy-efficient, and scalable building blocks for probabilistic computing. Despite recent experimental and theoretical progress, SMTJs exhibiting the ideal characteristics necessary for probabilistic bits (p-bits) are still lacking. Ideally, the SMTJs should have (a) voltage bias independence, preventing read disturbance; (b) uniform randomness in the magnetization angle between the two magnetic layers; and (c) fast fluctuations without requiring external magnetic fields, while being robust to magnetic field perturbations. Here, we propose a design that satisfies all of these requirements, using double-free-layer SMTJs with synthetic antiferromagnets (SAFs). We evaluate the proposed SMTJ design with experimentally benchmarked spin-circuit models, accounting for transport physics, coupled with the stochastic Landau-Lifshitz-Gilbert equation for magnetization dynamics. We find that the use of low-barrier SAF layers reduces dipolar coupling, achieving uncorrelated fluctuations at zero-magnetic field, surviving up to diameters exceeding D≈100nm if the nanomagnets can be made thin enough (≈1–2nm). The double-free-layer structure retains bias independence and the circular nature of the nanomagnets provides near-uniform randomness with fast fluctuations. Combining our full SMTJ model with advanced transistor models, we estimate the energy to generate a random bit to be about 3.6fJ, with fluctuation rates of about 3.3GHz per p-bit. Our results will guide the experimental development of superior stochastic magnetic tunnel junctions for large-scale and energy-efficient probabilistic computation for problems relevant to machine learning and artificial intelligence.

关键词： Machine learning Neuromorphic computing Probabilistic computing Spintronics Stochastic processes Devices for digital logic, storage & processing Magnetic tunnel junctions Synthetic antiferromagnetic multilayers

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A data-driven rutting depth short-time prediction model with metaheuristic optimization for asphalt pavements based on RIOHTrack

arXiv

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arXiv 2023年

作者： Li, Zhuoxuan Korovin, Iakov Shi, Xinli Gorbachev, Sergey Gorbacheva, Nadezhda Huang, Wei Cao, Jinde The School of Mathematics Southeast University Nanjing210096 China The Scientific Research Institute of Multiprocessor Computer Systems Southern Federal University Taganrog347928 Russia The School of Cyber Science & Engineering Southeast University Nanjing210096 China The Russian Academy of Engineering Moscow125009 Russia The Intelligent Transportation System Research Center Southeast University Nanjing210096 China The School of Mathematics Southeast University Nanjing210096 China Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements’ rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from 19 asphalt pavements with different crude oil sources on a 2.038 km long full-scale field accelerated pavement test track (RIOHTrack, Road Track Institute) in Tongzhou, Beijing. In addition, this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction (RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of Average Root Mean Squared Error, Average Mean Absolute Error, and Average Mean Absolute Percentage Error for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry. © 2023, CC BY.

关键词： Complex networks

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Fault Analysis of Microgrids with Inverter Interfaced Resources in Grid-Connected and Islanded Modes

Fault Analysis of Microgrids with Inverter Interfaced Resour...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Jigyesh Sharma Kushan Tharuka Lulbadda Anindita Golder Tarlochan Sidhu Sheldon S. Williamson Department of Electrical and Computer Engineering Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech-Automotive Center of Excellence (ACE) Faculty of Engineering and Applied Science Smart Transportation Electrification and Energy Research (STEER) Group Ontario Tech University Oshawa ON Canada

The increasing penetration of inverter-based generation such as solar, wind and battery energy storage systems (BESS) has an impact on the fault currents in a microgrid. Also, if the microgrid is grid-connected or islanded affects the fault current. As such, it is important to conduct fault analysis of microgrids in grid-connected and islanded modes with different penetration levels of inverter-based generation sources to understand the impact of increasing levels of renewable energy on the fault currents. Different types of faults in a microgrid with inverter-based resources are simulated and the results are discussed and analyzed in this paper.

关键词： Wind energy generation Industrial electronics Renewable energy sources Wind energy Microgrids Inverters Batteries

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Support Vector Machine reconfigurable hardware implementation on FPGA

Franklin Open

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Franklin Open 2024年 7卷

作者： Yacoub, Mohammed H. Ismail, Samar M. Said, Lobna A. Madian, Ahmed H. Radwan, Ahmed G. Nanoelectronics Integrated Systems Center (NISC) Nile University Giza 12588 Egypt School of Engineering and Computer Science University of Hertfordshire Egypt Engineering Mathematics and Physics Department Faculty of Engineering Cairo University Giza 12613 Egypt Radiation Engineering Department National Centre for Radiation Research and Technology Egyptian Atomic Energy Authority Cairo 11787 Egypt School of Engineering and Applied Sciences Nile University Giza 12588 Egypt

Support Vector Machine (SVM) is a robust Machine Learning (ML) algorithm used extensively in classification tasks. This work proposes a reconfigurable hardware implementation of the SVM classification algorithm for the linear and three kernel cases on FPGA. Efficient implementations of two generalization techniques, One-versus-All (OvA) and One-versus-One (OvO), to deal with multi-class problems are also realized on FPGA to overcome the binary nature of the SVM algorithm. The presented model is fully reconfigurable and can easily be adapted to any dataset with any number of classes or features. The results show that the proposed model excels in power efficiency, requires low area utilization, and reaches high performance up to 250.7 MHz. The two realized generalization methods, OvO and OvA, offer a trade-off between accuracy and hardware cost. OvA provides lower accuracy than OvO and is more affected by the data imbalance problem, which becomes more dominant as the number of classes increases;however, it is more resource-efficient than OvO. The proposed hardware design is realized and experimentally tested on FPGA for three different datasets, employing different linear and kernel SVM cases. The classification accuracy achieved by the reconfigurable hardware design matches the software simulation results. © 2024 The Authors

关键词： Artificial intelligence Classification Floating point FPGA Support Vector Machine

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