Blar i TKD - Institutt for informasjonsteknologi på tidsskrift "IEEE Access"

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    • Affect Recognition in Muscular Response Signals 

      Boeker, Matthias; Jakobsen, Petter; Riegler, Michael; Stabell, Lena Antonsen; Fasmer, Ole Bernt; Halvorsen, Pål; Hammer, Hugo Lewi (Peer reviewed; Journal article, 2023)
      This study investigated the potential of recognising arousal in motor activity collected by wrist- worn accelerometers. We hypothesise that emotional arousal emerges from the generalised central nervous system which ...

    • An Area-Based Prior Value Method for Detectionof Micro Contamination in Hard Disk Drives 

      Sandnes, Frode Eika; Muneesawang, Paisarn; Ieamsaard, Jirarat (Journal article; Peer reviewed, 2017)
      This paper presents a new area-based prior value technique for the improvement of automatic visual inspection in hard disk drive manufacturing. Micro-contaminations are detected on the air-bearing surface of the head gimbal ...

    • Data-driven model of the power-grid frequency dynamics 

      Rydin Gorjao, Leonardo; Anvari, Mehrnaz; Kantz, Holger; Beck, Christian; Witthaut, Dirk; Timme, Marc; Schäfer, Benjamin (IEEE Access;Volume 8, 2020, Peer reviewed; Journal article, 2020-01-20)
      The energy system is rapidly changing to accommodate the increasing number of renewable generators and the general transition towards a more sustainable future. Simultaneously, business models and market designs evolve, ...

    • Edge information based image fusion metrics using fractional order differentiation and sigmoidal functions 

      Sengupta, Animesh; Seal, Ayan; Krejcar, Ondrej; Yazidi, Anis (IEEE Access;Volume: 8, Journal article; Peer reviewed, 2020)
      In recent years, the number of image fusion schemes presented by the research community has increased significantly. Measuring the performance of these schemes is an important issue. In this work, we introduce three ...

    • General TCP state inference model from passive measurements using machine learning techniques 

      Hagos, Desta Haileselassie; Engelstad, Paal E.; Yazidi, Anis; Kure, Øivind (IEEE Access;VOLUME 6, 2018, Journal article; Peer reviewed, 2018-05-04)
      Many applications in the Internet use the reliable end-to-end Transmission Control Protocol (TCP) as a transport protocol due to practical considerations. There are many different TCP variants widely in use, and each ...

    • A Learning Automaton-based Scheme for Scheduling Domestic Shiftable Loads in Smart Grids 

      Thapa, Rajan; Lei, Jiao; Oommen, John; Yazidi, Anis (Journal article; Peer reviewed, 2017)
      In this paper, we consider the problem of scheduling shiftable loads, over multiple users, in smart electrical grids. We approach the problem, which is becoming increasingly pertinent in our present energy-thirsty society, ...

    • Ontology-Based Fault Tree Analysis Algorithms in a Fuzzy Environment for Autonomous Ships 

      Sahin, Bekir; Yazidi, Anis; Roman, Dumitru; Soylu, Ahmet (IEEE Access;Volume 9, 2021, Peer reviewed; Journal article, 2021-02-24)
      This study deals with fault tree analysis algorithms based on an ontology-based approach in a fuzzy environment. We extend fuzzy fault tree analysis by embedding ontology-based fault tree structures. The ontology-based ...

    • Predicting High Delays in Mobile Broadband Networks 

      Mohamed Ahmed, Azza Hassan; Hicks, Steven; Riegler, Michael; Elmokashfi, Ahmed Mustafa Abdalla (IEEE Access;Volume 9: 2021, Peer reviewed; Journal article, 2021-12-24)
      The number of applications that run over mobile networks, expecting bounded end-to-end delay, is increasing steadily. However, the stochastic and shared nature of the wireless medium makes providing such guarantees ...

    • Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning 

      Jha, Debesh; Ali, Sharib; Tomar, Nikhil Kumar; Johansen, Håvard D.; Johansen, Dag; Rittscher, Jens; Riegler, Michael A.; Halvorsen, Pal (IEEE Access;Volume: 9, Peer reviewed; Journal article, 2021-03-04)
      Computer-aided detection, localization, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of ...

    • Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning 

      Jha, Debesh; Ali, Sharib; Tomar, Nikhil Kumar; Johansen, Håvard D.; Johansen, Dag; Rittscher, Jens; Riegler, Michael; Halvorsen, Pål (IEEE Access;Volume: 9, 2021, Peer reviewed; Journal article, 2021-03-04)
      Computer-aided detection, localization, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of ...

    • Semantic Modeling, Development and Evaluation for the Resistance Spot Welding Industry 

      Yahya, Muhammad; Zhou, Baifan; Breslin, John G.; Ali, Muhammad Intizar; Kharlamov, Evgeny (Peer reviewed; Journal article, 2023)
      The ongoing industrial revolution termed Industry 4.0 (I4.0) has borne witness to a series of profound changes towards increasing smart automation, particularly in the industrial sectors of automotive, aerospace, manufacturing, ...

    • A Survey on Multipath Transport Protocols Towards 5G Access Traffic Steering, Switching and Splitting 

      Wu, Hongjia; Ferlin, Simone; Caso, Giuseppe; Alay Erduran, Özgü; Brunstrom, Anna (IEEE Access;Volume: 9, Peer reviewed; Journal article, 2021-12-10)
      The fifth generation (5G) cellular network aims at providing very high data rates, ultra reliable low latency communications, and a vast increase of connection density. As one of the design trends towards these objectives, ...

    • Validation Methods for Energy Time Series Scenarios From Deep Generative Models 

      Cramer, Eike; Rydin Gorjao, Leonardo; Mitsos, Alexander; Schäfer, Benjamin; Witthaut, Dirk; Dahmen, Manuel (IEEE Access;Volume: 10, Peer reviewed; Journal article, 2022-01-11)
      The design and operation of modern energy systems are heavily influenced by time-dependent and uncertain parameters, e.g., renewable electricity generation, load-demand, and electricity prices. These are typically represented ...