Blar i forfatter "Nordmo, Tor-Arne Schmidt"

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    • Arctic HARE. A Machine Learning-based System for Performance Analysis of Cross-country Skiers 

      Nordmo, Tor-Arne Schmidt (Master thesis; Mastergradsoppgave, 2018-06-01)
      The advances in sensor technology and big-data processing enable performance analysis of sport athletes. With the increase in data, both from on-body sensors and cameras, it is possible to quantify what makes a good athlete. However, typical approaches in sports performance analysis are not adequately equipped for automatically handling big data. This thesis presents Arctic Human Activity ...

    • Arctic HARE: A Machine Learning-Based System for Performance Analysis of Cross-Country Skiers 

      Nordmo, Tor-Arne Schmidt; Riegler, Michael; Dagenborg, Håvard Johansen; Johansen, Dag (Chapter; Bokkapittel, 2023-03-31)
      Advances in sensor technology and big data processing enable new and improved performance analysis of sport athletes. With the increase in data variety and volume, both from on-body sensors and cameras, it has become possible to quantify the specific movement patterns that make a good athlete. This paper describes Arctic Human Activity Recognition on the Edge (Arctic HARE): a skiing-technique training ...

    • Áika: A Distributed Edge System for AI Inference 

      Alslie, Joakim Aalstad; Ovesen, Aril Bernhard; Nordmo, Tor-Arne Schmidt; Johansen, Håvard D.; Halvorsen, Pål; Riegler, Michael; Johansen, Dag (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-06-17)
      Video monitoring and surveillance of commercial fisheries in world oceans has been proposed by the governing bodies of several nations as a response to crimes such as overfishing. Traditional video monitoring systems may not be suitable due to limitations in the offshore fishing environment, including low bandwidth, unstable satellite network connections and issues of preserving the privacy of crew ...

    • Dutkat: A Privacy-Preserving System for Automatic Catch Documentation and Illegal Activity Detection in the Fishing Industry 

      Nordmo, Tor-Arne Schmidt (Doctoral thesis; Doktorgradsavhandling, 2023-08-17)
      United Nations' Sustainable Development Goal 14 aims to conserve and sustainably use the oceans and their resources for the benefit of people and the planet. This includes protecting marine ecosystems, preventing pollution, and overfishing, and increasing scientific understanding of the oceans. Achieving this goal will help ensure the health and well-being of marine life and the millions of people ...

    • File System Support for Privacy-Preserving Analysis and Forensics in Low-Bandwidth Edge Environments 

      Ovesen, Aril Bernhard; Nordmo, Tor-Arne Schmidt; Johansen, Håvard D.; Riegler, Michael Alexander; Halvorsen, Pål; Johansen, Dag (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-10-18)
      In this paper, we present initial results from our distributed edge systems research in the domain of sustainable harvesting of common good resources in the Arctic Ocean. Specifically, we are developing a digital platform for real-time privacy-preserving sustainability management in the domain of commercial fishery surveillance operations. This is in response to potentially privacy-infringing mandates ...

    • Fish AI: Sustainable Commercial Fishing Challenge 

      Nordmo, Tor-Arne Schmidt; Kvalsvik, Ove; Kvalsund, Svein Ove; Hansen, Birte; Halvorsen, Pål; Hicks, Steven; Johansen, Dag; Johansen, Håvard D.; Riegler, Michael Alexander (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-06-02)
      FishAI: Sustainable Commercial Fishingis the second chal-lenge at theNordic AI Meetfollowing the successful MedAI,which had a focus on medical image segmentation and trans-parency in machine learning (ML)-based systems. FishAI fo-cuses on a new domain, namely, commercial fishing and howto make it more sustainable with the help of machine learning.A range of public available datasets is used to tackle ...

    • Fishing Trawler Event Detection: An Important Step Towards Digitization of Sustainable Fishing 

      Nordmo, Tor-Arne Schmidt; Ovesen, Aril Bernhard; Dagenborg, Håvard; Halvorsen, Pål; Riegler, Michael Alexander; Johansen, Dag (Chapter; Bokkapittel, 2023-08-02)
      Detection of anomalies within data streams is an important task that is useful for different important societal challenges such as in traffic control and fraud detection. To be able to perform anomaly detection, unsupervised analysis of data is an important key factor, especially in domains where obtaining labelled data is difficult or where the anomalies that should be detected are often ...

    • H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images 

      Pedersen, André; Smistad, Erik; Rise, Tor Vikan; Dale, Vibeke Grotnes; Pettersen, Henrik P Sahlin; Nordmo, Tor-Arne Schmidt; Bouget, David Nicolas Jean-Mar; Reinertsen, Ingerid; Valla, Marit (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-09-14)
      Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded ...

    • Njord: a fishing trawler dataset 

      Nordmo, Tor-Arne Schmidt; Ovesen, Aril Bernhard; Juliussen, Bjørn Aslak; Hicks, Steven; Thambawita, Vajira L B; Johansen, Håvard D.; Halvorsen, Pål; Riegler, Michael Alexander; Johansen, Dag (Chapter; Bokkapittel, 2022-08-05)
      Fish is one of the main sources of food worldwide. The commercial fishing industry has a lot of different aspects to consider, ranging from sustainability to reporting. The complexity of the domain also attracts a lot of research from different fields like marine biology, fishery sciences, cybernetics, and computer science. In computer science, detection of fishing vessels via for example remote ...

    • On Optimizing Transaction Fees in Bitcoin using AI: Investigation on Miners Inclusion Pattern 

      Tedeschi, Enrico; Nordmo, Tor-Arne Schmidt; Johansen, Dag; Johansen, Håvard D. (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-04-09)
      The transaction-rate bottleneck built into popular proof-of-work-based cryptocurrencies, like Bitcoin and Ethereum, leads to fee markets where transactions are included according to a first-price auction for block space. Many attempts have been made to adjust and predict the fee volatility, but even well-formed transactions sometimes experience unexpected delays and evictions unless a substantial ...

    • Predicting Transaction Latency with Deep Learning in Proof-of-Work Blockchains 

      Tedeschi, Enrico; Nordmo, Tor-Arne Schmidt; Johansen, Dag; Johansen, Håvard D. (Peer reviewed; Chapter, 2019)
      Proof-of-work based cryptocurrencies, like Bitcoin, have a fee market where transactions are included in the blockchain according to a first-price auction for block space. Many attempts have been made to adjust and predict the fee volatility, but even well-formed transactions sometimes experience delays and evictions unless an enormous fee is paid. % In this paper, we present a novel ...