Recent additions

  • Personalized Nudges with Edge Computing 

    Andersen, Elisabeth Sie (Mastergradsoppgave; Master thesis, 2022-05-02)
    This thesis aims to investigate the role of edge computing in a smart nudging system. A smart nudging system has requirements for efficient data processing of personal and context-aware data from heterogeneous sources. Furthermore, a smart nudging system needs to protect and preserve the privacy of data within the system. Edge computing has been proposed as a computing paradigm in a smart nudging ...
  • Investigating the latency cost of statistical learning of a Gaussian mixture simulating on a convolutional density network with adaptive batch size technique for background modeling 

    Phan, Hung Ngoc (Master thesis; Mastergradsoppgave, 2021-05-31)
    Background modeling is a promising field of study in video analysis, with a wide range of applications in video surveillance. Deep neural networks have proliferated in recent years as a result of effective learning-based approaches to motion analysis. However, these strategies only provide a partial description of the observed scenes' insufficient properties since they use a single-valued mapping ...
  • Polar Vantage and Oura Physical Activity and Sleep Trackers: Validation and Comparison Study 

    Henriksen, André; Grimsgaard, Sameline; Hartvigsen, Gunnar; Hopstock, Laila Arnesdatter (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-05-27)
    Background: Consumer-based activity trackers are increasingly used in research, as they have the potential to promote increased physical activity and can be used for estimating physical activity among participants. However, the accuracy of newer consumer-based devices is mostly unknown, and validation studies are needed.<p> <p>Objective: The objective of this study was to compare the Polar Vantage ...
  • Kvik: three-tier data exploration tools for flexible analysis of genomic data in epidemiological studies [version 1; peer review: 2 approved with reservations] 

    Fjukstad, Bjørn; Olsen, Karina Standahl; Jareid, Mie; Lund, Eiliv; Bongo, Lars Ailo (Journal article; Tidsskriftartikkel; Peer reviewed, 2015-03-30)
    Kvik is an open-source system that we developed for explorative analysis of functional genomics data from large epidemiological studies. Creating such studies requires a significant amount of time and resources. It is therefore usual to reuse the data from one study for several research projects. Often each project requires implementing new analysis code, integration with specific knowledge bases, ...
  • Information and communication technology-based interventions for chronic diseases consultation: Scoping review 

    Randine, Pietro; Sharma, Aakash; Hartvigsen, Gunnar; Johansen, Håvard D.; Årsand, Eirik (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-04-29)
    Background: Medical consultations are often critical meetings between patients and health personnel to provide treatment, health-management advice, and exchange of information, especially for people living with chronic diseases. The adoption of patient-operated Information and Communication Technologies (ICTs) allows the patients to actively participate in their consultation and treatment. The ...
  • 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 ...
  • Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge 

    Ross, Tobias; Reinke, Annika; M. Full, Peter; Wagner, Martin; Kenngott, Hannes; Apitz, Martin; Hempe, Hellena; Mindroc Filimon, Diana; Scholz, Patrick; Tran, Thuy Nuong; Bruno, Pierangela; Arbeláez, Pablo; Bian, Gui-Bin; Bodenstedt, Sebastian; Lindström Bolmgren, Jon; Bravo-Sánchez, Laura; Chen, Hua-Bin; González, Cristina; Guo, Dong; Halvorsen, Pål; Heng, Pheng-Ann; Hosgor, Enes; Hou, Zeng-Guang; Isensee, Fabian; Jha, Debesh; Jiang, Tingting; Jin, Yueming; Kirtac, Kadir; Kletz, Sabrina; Leger, Stefan; Li, Zhixuan; H. Maier-Hein, Klaus; Ni, Zhen-Liang; Riegler, Michael; Schoeffmann, Klaus; Shi, Ruohua; Speidel, Stefanie; Stenzel, Michael; Twick, Isabell; Wang, Gutai; Wang, Jiacheng; Wang, Liansheng; Wang, Lu; Zhang, Yujie; Zhou, Yan-Jie; Zhu, Lei; Wiesenfarth, Manuel; Kopp-Schneider, Annette; P. Müller-Stich, Beat; Maier-Hein, Lena (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-11-28)
    Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and roboticassisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods ...
  • A Health-Energy Nexus Perspective for Virtual Power Plants: Power Systems Resiliency and Pandemic Uncertainty Challenges 

    Mishra, Sambeet; Bordin, Chiara (Chapter; Bokkapittel, 2022-01-01)
    This chapter introduces and discusses a novel “health-energy nexus under pandemic uncertainty” concept that arises as a consequence of the current pandemic that we are experiencing worldwide. In light of the pandemic implications on the power and energy systems, we discuss how the global health conditions are tightly connected with the energy consumption needs and how the two areas closely interact ...
  • Meta-learning with implicit gradients in a few-shot setting for medical image segmentation 

    Khadka, Rabindra; Jha, Debesh; Riegler, Michael A.; Hicks, Steven; Thambawita, Vajira; Ali, Sharib; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-01-12)
    Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general application of any trained model is quite limited for medical imaging for clinical practice. Using separately trained models for each unique lesion category or a unique patient population will require sufficiently large curated ...
  • Aika: A Distributed Edge System For Machine Learning Inference. Detecting and defending against abnormal behavior in untrusted edge environments 

    Alslie, Joakim Aalstad (Master thesis; Mastergradsoppgave, 2021-12-14)
    The edge computing paradigm has recently started to gain a lot of momentum. The field of Artificial Intelligence (AI) has also grown in recent years, and there is currently ongoing research that investigates how AI can be applied to numerous of different fields. This includes the edge computing domain. In Norway, there is currently ongoing research being conducted that investigates how the confluence ...
  • Dataset of Consumer-Based Activity Trackers as a Tool for Physical Activity Monitoring in Epidemiological Studies During the COVID-19 Pandemic 

    Henriksen, André; Johannessen, Erlend; Hartvigsen, Gunnar; Grimsgaard, Sameline; Hopstock, Laila Arnesdatter (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-03-01)
    Physical activity (PA) data were downloaded from 113 participants who owned a Garmin or Fitbit activity tracker in 2019 and 2020. Upon participant authorization, data were automatically downloaded from the Garmin and Fitbit cloud storages. The mSpider tool, a solution for automatic and continuous data extraction from activity tracker providers, were used to download participant data. Available ...
  • The variability of physical match demands in elite women's football 

    Matias Do Vale Baptista, Ivan Andre; Winther, Andreas Kjæreng; Johansen, Dag; Bredsgaard Randers Thomsen, Morten; Pedersen, Sigurd; Pettersen, Svein Arne (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-01-21)
    Peak locomotor demands are considered as key metrics for conditioning drills prescription and training monitoring. However, research in female football has focused on absolute values when reporting match demands, leading to sparse information being provided regarding the degrees of variability of such metrics. Thus, the aims of this study were to investigate the sources of variability of match ...
  • Inverse and efficiency of heat transfer convex fin with multiple nonlinearities 

    Roy, Pranab Kanti; Mondal, Hiranmoy; Mallick, Ashis; Prasad, Dilip K. (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-07-22)
    In this article, we first propose the novel semi-analytical technique—modified Adomian decomposition method (MADM)—for a closed-form solution of the nonlinear heat transfer equation of convex profile with singularity where all thermal parameters are functions of temperature. The longitudinal convex fin is subjected to different boiling regimes, which are defined by particular values of n (power ...
  • Label-free non-invasive classification of rice seeds using optical coherence tomography assisted with deep neural network 

    Joshi, Deepa; Butola, Ankit; Kanade, Sheetal Raosaheb; Prasad, Dilip K.; Amitha Mithra, Mithra; Singh, N.K.; Bisht, Deepak Singh; Mehta, Dalip Singh (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-01-01)
    Identification of the seed varieties is essential in the quality control and high yield crop growth. The existing methods of varietal identification rely primarily on visual examination and DNA fingerprinting. Although the pattern of DNA fingerprinting allows precise classification of seed varieties but fraught with challenges such as low rate of polymorphism amongst closely related species, destructive ...
  • Employee-driven digital innovation: A systematic review and a research agenda 

    Opland, Leif Erik; Pappas, Ilias; Engesmo, Jostein; Jaccheri, Maria Letizia (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-02-01)
    As the digital shift in society affects both private and public organizations, the role of digital innovation is critical if digital transformations are to succeed. Research has developed models to explain how digital innovation affects organizations and societies. During the last ten years, employee-driven innovation has emerged as a new approach to explain innovation. Through this systematic ...
  • 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 (Journal article; Tidsskriftartikkel; Peer reviewed, 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 state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer vision methods that can be applied to polyp datasets. ...
  • Topic-based Video Analysis: A Survey 

    Pal, Ratnabali; Sekh, Arif Ahmed; Dogra, Debi Prosad; Kar, Samarjit; Roy, Partha Pratim; Prasad, Dilip K. (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-07-13)
    Manual processing of a large volume of video data captured through closed-circuit television is challenging due to various reasons. First, manual analysis is highly time-consuming. Moreover, as surveillance videos are recorded in dynamic conditions such as in the presence of camera motion, varying illumination, or occlusion, conventional supervised learning may not work always. Thus, computer ...
  • Augmenting SQLite for Local-First Software 

    Toft Tomter, Iver; Yu, Weihai (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-07-17)
    Local-first software aims at both the ability to work offline on local data and the ability to collaborate across multiple devices. CRDTs (conflict-free replicated data types) are abstractions for offline and collaborative work that guarantees strong eventual consistency. RDB (relational database) is a mature and successful computer industry for management of data, and SQLite is an ideal RDB candidate ...
  • Next frontiers in energy system modelling: A review on challenges and the state of the art 

    Fodstad, Marte; Crespo del Granado, Pedro; Hellemo, Lars; Knudsen, Brage Rugstad; Pisciella, Paolo; Silvast, Antti; Bordin, Chiara; Schmidt, Sarah; Straus, Julian (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-02-26)
    Energy Systems Modelling is growing in relevance on providing insights and strategies to plan a carbon-neutral future. The implementation of an effective energy transition plan faces multiple challenges, spanning from the integration of the operations of different energy carriers and sectors to the consideration of multiple spatial and temporal resolutions. In this review, we outline these challenges ...
  • Using 3D Convolutional Neural Networks for Real-time Detection of Soccer Events 

    Rongved, Olav Andre Nergård; Hicks, Steven; Thambawita, Vajira L B; Stensland, Håkon Kvale; Zouganeli, Evi; Johansen, Dag; Riegler, Michael Alexander; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-06)
    Developing systems for the automatic detection of events in video is a task which has gained attention in many areas including sports. More specifically, event detection for soccer videos has been studied widely in the literature. However, there are still a number of shortcomings in the state-of-the-art such as high latency, making it challenging to operate at the live edge. In this paper, we present ...

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