Blar i tittel Artikler, rapporter og annet (informatikk)

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    • A Mobile Health Intervention for Self-Management and Lifestyle Change for Persons With Type 2 Diabetes, Part 2: One-Year Results From the Norwegian Randomized Controlled Trial RENEWING HEALTH 

      Holmen, Heidi; Torbjørnsen, Astrid; Wahl, Astrid Klopstad; Jenum, Anne Karen; Småstuen, Milada Cvancarova; Årsand, Eirik; Ribu, Lis (Journal article; Tidsskriftartikkel; Peer reviewed, 2014)

    • Mobile Phone-Based Pattern Recognition and Data Analysis for Patients with Type 1 Diabetes 

      Skrøvseth, Stein Olav; Årsand, Eirik; Godtliebsen, F.; Hartvigsen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2012)
      Persons with type 1 diabetes who use electronic self-help tools, most commonly blood glucose meters, record a large amount of data about their personal condition. Mobile phones are powerful and ubiquitous computers that have a potential for data analysis, and the purpose of this study is to explore how self-gathered data can help users improve their blood glucose management. Thirty patients with ...

    • Mobile software on mobile hardware. Experiences with TACOMA on PDAs. 

      Jacobsen, Kjetil; Johansen, Dag (Research report; Forskningsrapport, 1997)
      In this paper, we present experiences from adding software mobility to mobile, hand-held computers. In particular, we have built TACOMA Lite, a mobile code system, for this environment. With TACOMA Lite installed, hand-held computers can host and execute mobile code. TACOMA Lite has been used as platform for several mobile code applications. Through experience with these applications, we have derived ...

    • Model-driven diabetes care: study protocol for a randomized controlled trial 

      Skrøvseth, Stein Olav; Årsand, Eirik; Godtliebsen, Fred; Joakimsen, Ragnar Martin (Journal article; Tidsskriftartikkel; Peer reviewed, 2013)
      Background: People with type 1 diabetes who use electronic self-help tools register a large amount of information about their disease on their participating devices; however, this information is rarely utilized beyond the immediate investigation. We have developed a diabetes diary for mobile phones and a statistics-based feedback module, which we have named Diastat, to give data-driven feedback ...

    • Modeling Prognostic Factors in Resectable Pancreatic Adenocarcinomas 

      Botsis, Taxiarchis; Anagnostou, Valsamo K.; Hripcsak, George; Hartvigsen, Gunnar; Weng, Chunhua (Journal article; Tidsskriftartikkel; Peer reviewed, 2009)

    • Models for energy consumption of data structures and algorithms 

      Ha, Hoai Phuong; Tran, Ngoc Nha Vi; Umar, Ibrahim; Tsigas, Philippas; Gidenstam, Anders; Renaud-Goud, Paul; Walulya, Ivan; Atalar, Aras (Research report; Forskningsrapport, 2014)
      This deliverable reports our early energy models for data structures and algorithms based on both micro-benchmarks and concurrent algorithms. It reports the early results of Task 2.1 on investigating and modeling the trade-off between energy and performance in concurrent data structures and algorithms, which forms the basis for the whole work package 2 (WP2). The work has been ...

    • A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images 

      Balasubramaniam, Sathiyabhama; Velmurugan, Yuvarajan; Jaganathan, Dhayanithi; Dhanasekaran, Seshathiri (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-08-24)
      Convolutional neural networks (CNNs) have been extensively utilized in medical image processing to automatically extract meaningful features and classify various medical conditions, enabling faster and more accurate diagnoses. In this paper, LeNet, a classic CNN architecture, has been successfully applied to breast cancer data analysis. It demonstrates its ability to extract discriminative ...

    • Motivation detection using EEG signal analysis by residual-in-residual convolutional neural network 

      Chattopadhyay, Soham; Zary, Laila; Quek, Chai; Prasad, Dilip K. (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-07-05)
      While we know that motivated students learn better than non-motivated students but detecting motivation is challenging. Here we present a game-based motivation detection approach from the EEG signals. We take an original approach of using EEG-based brain computer interface to assess if motivation state is manifest in physiological EEG signals as well, and what are suitable conditions in order to ...

    • The Movitz development platform 

      Fjeld, Frode V. (Research report; Forskningsrapport, 2003-12-19)
      We present a technical description of the Movitz development platform for stand-alone applications on the x86 PC architecture.

    • Mr. Clean: A Tool for Tracking and Comparing the Lineage of Scientific Visualization Code 

      Tartari, Giacomo; Tiede, Lars; Holsbø, Einar; Knudsen, Kenneth; Raknes, Inge Alexander; Fjukstad, Bjørn; Mode, Nicolle; Bjørndalen, John Markus; Lund, Eiliv; Bongo, Lars Ailo (Conference object; Konferansebidrag, 2014)

    • MRNG: Accessing Cosmic Radiation as an Entropy Source for a Non-Deterministic Random Number Generator 

      Kutschera, Stefan; Slany, Wolfgang; Ratschiller, Patrick; Gursch, Sarina; Dagenborg, Håvard (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-05-26)
      Privacy and security require not only strong algorithms but also reliable and readily available sources of randomness. To tackle this problem, one of the causes of single-event upsets is the utilization of a non-deterministic entropy source, specifically ultra-high energy cosmic rays. An adapted prototype based on existing muon detection technology was used as the methodology during the experiment ...

    • MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation 

      Srivastava, Abhishek; Jha, Debesh; Chanda, Sukalpa; Pal, Umapada; Johansen, Håvard D.; Johansen, Dag; Riegler, Michael; Ali, Sharib; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-12-23)
      Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of variable sizes and train on small and biased datasets, which are common for biomedical use cases. While methods exist that incorporate multi-scale fusion approaches to address the challenges arising with variable ...

    • Muithu: Smaller Footprint, Potentially Larger Imprint 

      Johansen, Dag; Stenhaug, Magnus; Hansen, Roger Bruun Asp; Christensen, Agnar; Høgmo, Per-Mathias (Research report; Forskningsrapport, 2012)
      We describe our experience with the Muithu sports notational analysis system, a novel digital information system in the popular sports domain. The system integrates real-time coach notations with related video sequences, and is configured with small, off-the shelf and cheap components. Muithu requires little or no human post-processing, which is in strong contrast to state-of-the art resource-intensive ...

    • Multi-Agent Collision Avoidance Method Using Fuzzy Risk Estimation and Information Sharing in Unknown Environments 

      Håkansson, Anne Eva Margareta; Karlsen, Randi; Bremdal, Bernt Arild; Dundar, Yigit Can (Chapter; Bokkapittel, 2023-09-22)
      Automated vehicles within Industry 4.0 are used as logistics units where they can move resources from one place to another safely and efficiently. The automated vehicles can be tasked to work in unknown environments where collision-free navigation is challenging due to uncertainty and lack of environmental information. Collisions can damage equipment and may even cause harm to human workers sharing ...

    • A multi-centre polyp detection and segmentation dataset for generalisability assessment 

      Ali, Sharib; Jha, Debesh; Ghatwary, Noha; Realdon, Stefano; Cannizzaro, Renato; Salem, Osama E.; Lamarque, Dominique; Daul, Christian; Riegler, Michael Alexander; Ånonsen, Kim Vidar; Petlund, Andreas; Halvorsen, Pål; Rittscher, Jens; de Lange, Thomas; East, James E (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-02-06)
      Polyps in the colon are widely known cancer precursors identifed by colonoscopy. Whilst most polyps are benign, the polyp’s number, size and surface structure are linked to the risk of colon cancer. Several methods have been developed to automate polyp detection and segmentation. However, the main issue is that they are not tested rigorously on a large multicentre purpose-built dataset, one reason ...

    • Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer 

      Rajagopalan, Arul; Nagarajan, Karthik; Montoya, Oscar Danilo; Dhanasekaran, Seshathiri; Abdul Kareem, Inayathullah; Sendraya Perumal, Angalaeswari; Lakshmaiya, Natrayan; Paramasivam, Prabhu (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-11-29)
      Optimal energy management has become a challenging task to accomplish in today’s advanced energy systems. If energy is managed in the most optimal manner, tremendous societal benefits can be achieved such as improved economy and less environmental pollution. It is possible to operate the microgrids under grid-connected, as well as isolated modes. The authors presented a new optimization algorithm, ...

    • A Multi-pronged Self-adaptive Controller for Analyzing Misconfigurations for Kubernetes Clusters and IoT Edge Devices 

      Elgazazz, Areeg Samir Ahmed; Al-Wosabi, Abdo; Khan, Mohsin; Dagenborg, Håvard Johansen (Conference object; Konferansebidrag, 2023-10-12)
      Kubernetes default configurations do not always provide optimal security and performance for all clusters and IoT edge devices deployed, making them vulnerable to security breaches and information leakage if misconfigured. Misconfiguration leads to a compromised system that disrupts the workload, allows access to system resources, and degrades the system’s performance. To provide optimal security ...

    • A multiagent architecture for a distributed artificial intelligence application 

      Hartvigsen, Gunnar; Johansen, Dag (Research report; Forskningsrapport, 1990)
      Artificial intelligence applications are normally complex, demanding much computing power. This paper presents the multiagent architecture of StormCast, a distributed artificial intelligence application for severe storm forecasting. Important objectives as scaleability, robustness and reusability can be met by partitioning an ove rall task in subtasks, distribution of the subtasks on different nodes, ...

    • A multihorizon approach for the reliability oriented network restructuring problem, considering learning effects, construction time, and cables maintenance costs 

      Bordin, Chiara; Mishra, Sambeet; Palu, Ivo (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-12-29)
      This paper presents a techno-economic optimisation tool to study how the power system expansion decisions can be taken in a more economical and efficient way, by minimising the consequent costs of network reinforcement and reconfiguration. Analyses are performed to investigate how the network reinforcement and reconfiguration should be planned, within a time horizon of several years, by continuously ...

    • MultiStage: Acting across Distance 

      Su, Fei; Tartari, Giacomo; Bjørndalen, John Markus; Ha, Hoai Phuong; Anshus, Otto (Conference object; Konferansebidrag, 2013)
      We report on a prototype system helping actors on a stage to interact and perform with actors on other stages as if they were on the same stage. At each stage four 3D cameras tiled back to back for an almost 360 degree view, continuously record actors. The system processes the recorded data on-the-fly to discover actions by actors that it should react to, and it streams data about actors and their ...