Now showing items 201-220 of 415

    • Prediction of cloud fractional cover using machine learning 

      Svennevik, Hanna; Riegler, Michael A.; Hicks, Steven; Storelvmo, Trude; Hammer, Hugo L. (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-11-03)
      Climate change is stated as one of the largest issues of our time, resulting in many unwanted effects on life on earth. Cloud fractional cover (CFC), the portion of the sky covered by clouds, might affect global warming and different other aspects of human society such as agriculture and solar energy production. It is therefore important to improve the projection of future CFC, which is usually ...
    • Artificial intelligence in dry eye disease 

      Storås, Andrea Marheim; Strumke, Inga; Riegler, Michael Alexander; Grauslund, Jakob; Hammer, Hugo Lewi; Yazidi, Anis; Halvorsen, Pål; Gundersen, Kjell Gunnar; Utheim, Tor Paaske; Jackson, Catherine Joan (Journal article; Tidsskriftartikkel, 2021-11-27)
      Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. ...
    • A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging 

      Jha, Debesh; Ali, Sharib; Hicks, Steven; Thambawita, Vajira L B; Borgli, Hanna; Smedsrud, Pia H.; de Lange, Thomas; Pogorelov, Konstantin; Wang, Xiaowei; Harzig, Philipp; Tran, Minh-Triet; Meng, Wenhua; Hoang, Trung-Hieu; Dias, Danielle; Ko, Tobey H.; Agrawal, Taruna; Ostroukhova, Olga; Khan, Zeshan; Tahir, Muhammed Atif; Liu, Yang; Chang, Yuan; Kirkerød, Mathias; Johansen, Dag; Lux, Mathias; Johansen, Håvard D.; Riegler, Michael; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-02-19)
      Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. ...
    • 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 (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. Benchmarking ...
    • PyPSA meets Africa: Developing an open source electricity network model of the African continent 

      Kirli, Desen; Hampp, Johannes; van Greevenbroek, Koen; Grant, Rebecca; Mahmood, Matin; Parzen, Maximilian; Kiprakis, Aristides (Chapter; Bokkapittel, 2021-10-25)
      Electricity network modelling and grid simulations form a key enabling element for the integration of newer and cleaner technologies such as renewable energy generation and electric vehicles into the existing grid and energy system infrastructure. This paper reviews the models of the African electricity systems and highlights the gaps in the open model landscape. Using PyPSA (an open Power System ...
    • 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 ...
    • Unraveling the Impact of Land Cover Changes on Climate Using Machine Learning and Explainable Artificial Intelligence 

      Kolevatova, Anastasiia; Riegler, Michael; Cherubini, Francesco; Hu, Xiangping; Hammer, Hugo Lewi (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-10-15)
      A general issue in climate science is the handling of big data and running complex and computationally heavy simulations. In this paper, we explore the potential of using machine learning (ML) to spare computational time and optimize data usage. The paper analyzes the effects of changes in land cover (LC), such as deforestation or urbanization, on local climate. Along with green house gas emission, ...
    • Smart contract formation enabling energy-as-a-service in a virtual power plant 

      Mishra, Sambeet; Crasta, Cletus John; Bordin, Chiara; Mateo-Fornés, Jordi (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-10-22)
      Energy as a service (EaaS) is an emerging business model that enables the otherwise passive energy consumers to play an active role and participate in the energy utility services. This platform is formed through smart contracts registering peer-to-peer (P2P) transactions of energy through price and quantity. Many industries, including finance, have already leveraged smart contracts to introduce ...
    • IRON-MAN: An Approach to Perform Temporal Motionless Analysis of Video Using CNN in MPSoC 

      Dey, Somdip; Singh, Amit Kumar; Prasad, Dilip K.; McDonald-Maier, Klaus Dieter (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-07-20)
      This paper proposes a novel human-inspired methodology called IRON-MAN (Integrated RatiONal prediction and Motionless ANalysis) for mobile multi-processor systems-on-chips (MPSoCs). The methodology integrates analysis of the previous image frames of the video to represent the analysis of the current frame in order to perform Temporal Motionless Analysis of the Video (TMAV). This is the first work ...
    • Experiences Building and Deploying Wireless Sensor Nodes for the Arctic Tundra 

      Murphy, Michael J.; Tveito, Øystein; Kleiven, Eivind Flittie; Rais, Issam; Soininen, Eeva M; Bjørndalen, John Markus; Anshus, Otto (Journal article; Tidsskriftartikkel, 2021-08-02)
      The arctic tundra is most sensitive to climate change. The change can be quantified from observations of the fauna, flora and weather conditions. To do observations at sufficient spatial and temporal resolution, ground-based observation nodes with sensors are needed. However, the arctic tundra is resource-limited with regards to energy, data networks, and humans. There are also regulatory and practical ...
    • Opportunities for thermal energy storage in Longyearbyen 

      van Greevenbroek, Koen; Klein, Lars-Stephan (Research report; Forskningsrapport, 2021-07-02)
      Energy storage is needed in Longyearbyen to enable a transition to local renewable energy sources. As heating accounts for more than half the energy use in Longyearbyen, affordable large-scale thermal storage is a good option. We investigate the opportunities for hot water, molten salt and hot rocks storage systems using a techno-economic optimisation model for the Longyearbyen energy system.
    • Accountable Human Subject Research Data Processing using Lohpi 

      Sharma, Aakash; Bye Nilsen, Thomas; Brenna, Lars; Johansen, Dag; Johansen, Håvard D. (Conference object; Konferansebidrag, 2021-06)
      In human subject research, various data about the studied individuals are collected. Through re-identification and statistical inferences, this data can be exploited for interests other than the ones the subjects initially consented to. Such exploitation must be avoided to maintain trust with the researched population. We argue that keeping data-access policies up-to-date and building accountability ...
    • Engaging Social Media Users with Health Education and Physical Activity Promotion 

      Gabarron, Elia; Larbi, Dillys; Årsand, Eirik; Wynn, Rolf (Journal article; Tidsskriftartikkel; Peer reviewed, 2021)
      Health-dedicated groups on social media provide different contents and social support to their peers. Our objective is to analyze users’ engagement with health education and physical activity promotion posts according to the expressed social support and social media. All health education and physical activity promotion posts on Facebook, Twitter, and Instagram during 2017–2019 by a diabetes association ...
    • Highly efficient and scalable framework for high-speed super-resolution microscopy 

      Do, Quan; Acuña Maldonado, Sebastian Andres; Kristiansen, Jon Ivar; Agarwal, Krishna; Ha, Hoai Phuong (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-07-05)
      The multiple signal classification algorithm (MUSICAL) is a statistical super-resolution technique for wide-field fluorescence microscopy. Although MUSICAL has several advantages, such as its high resolution, its low computational performance has limited its exploitation. This paper aims to analyze the performance and scalability of MUSICAL for improving its low computational performance. We first ...
    • Behavioural change in green transportation: Micro-economics perspectives and optimization strategies 

      Bordin, Chiara; Tomasgard, Asgeir (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-06-22)
      The increasing demand for Electric Vehicle (EV) charging is putting pressure on the power grids and capacities of charging stations. This work focuses on how to use indirect control through price signals to level out the load curve in order to avoid the power consumption from exceeding these capacities. We propose mathematical programming models for the indirect control of EV charging that aim at ...
    • Preliminary Evaluation of a mHealth Coaching Conversational Artificial Intelligence for the Self-Care Management of People with Sickle-Cell Disease 

      Issom, David-Zacharie; Rochat, Jessica; Hartvigsen, Gunnar; Lovis, Christian (Journal article; Tidsskriftartikkel; Peer reviewed, 2020)
      Adherence to the complex set of recommended self-care practices among people with Sickle-Cell Disease (SCD) positively impacts health outcomes. However, few patients possess the required skills (i.e. disease-specific knowledge, adequate levels of self-efficacy). Consequently, adherence rates remain low and only 1% of patients are empowered enough to master the self-care practices. Health coaching ...
    • Up-to-the-Minute Privacy Policies via Gossips in Participatory Epidemiological Studies 

      Sharma, Aakash; Nilsen, Thomas Bye; Czerwinska, Katja P; Onitiu, Daria; Brenna, Lars; Johansen, Dag; Johansen, Håvard D. (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-05-13)
      Researchers and researched populations are actively involved in participatory epidemiology. Such studies collect many details about an individual. Recent developments in statistical inferences can lead to sensitive information leaks from seemingly insensitive data about individuals. Typical safeguarding mechanisms are vetted by ethics committees; however, the attack models are constantly evolving. ...
    • Toward a Conversational Agent to Support the Self-Management of Adults and Young Adults With Sickle Cell Disease: Usability and Usefulness Study 

      Issom, David-Zacharie; Hardy-Dessources, Marie-Dominique; Romana, Marc; Hartvigsen, Gunnar; Lovis, Christian (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-01-29)
      Sickle cell disease (SCD) is the most common genetic blood disorder in the world and affects millions of people. With aging, patients encounter an increasing number of comorbidities that can be acute, chronic, and potentially lethal (e.g., pain, multiple organ damages, lung disease). Comprehensive and preventive care for adults with SCD faces disparities (e.g., shortage of well-trained providers). ...
    • Photoperiod-dependent developmental reprogramming of the transcriptional response to seawater entry in Atlantic salmon (Salmo salar) 

      Iversen, Marianne; Mulugeta, Teshome Dagne; West, Alexander Christopher; Jørgensen, Even Hjalmar; Martin, Samuel A. M.; Sandve, Simen Rød; Hazlerigg, David (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-03-12)
      The developmental transition of juvenile salmon from a freshwater resident morph (parr) to a seawater (SW) migratory morph (smolt), known as smoltification, entails a reorganization of gill function to cope with the altered water environment. Recently, we used RNAseq to characterize the breadth of transcriptional change which takes place in the gill in the FW phase of smoltification. This highlighted ...
    • Educating the energy informatics specialist: opportunities and challenges in light of research and industrial trends 

      Bordin, Chiara; Mishra, Sambeet; Safari, Amir; Eliassen, Frank (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-05-30)
      Contemporary energy research is becoming more interdisciplinary through the involvement of technical, economic, and social aspects that must be addressed simultaneously. Within such interdisciplinary energy research, the novel domain of energy informatics plays an important role, as it involves different disciplines addressing the socio-techno-economic challenges of sustainable energy and power ...