• Adaptive fluctuation imaging captures rapid subcellular dynamics 

      Opstad, Ida Sundvor; Ströhl, Florian; Birgisdottir, Åsa birna; Acuña Maldonado, Sebastian Andres; Kalstad, Trine; Myrmel, Truls; Agarwal, Krishna; Ahluwalia, Balpreet Singh (Journal article; Tidsskriftartikkel, 2019-07-22)
      In this work we have explored the live-cell friendly nanoscopy method Multiple Signal Classification Algorithm (MUSICAL) for multi-colour imaging of various organelles and sub-cellular structures in the cardiomyoblast cell line H2c9. We have tested MUSICAL for fast (up to 230Hz), multi-colour time-lapse sequences of various sub-cellular structures (mitochondria, endoplasmic reticulum, microtubules, ...
    • Adaptive fluctuation imaging captures rapid subcellular dynamics 

      Opstad, Ida Sundvor; Ströhl, Florian; Birgisdottir, Åsa birna; Maldonado, Sebastián Andrés Acuña; Kalstad, Trine; Myrmel, Truls; Agarwal, Krishna; Ahluwalia, Balpreet Singh (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-07-22)
      In this work we have explored the live-cell friendly nanoscopy method Multiple Signal Classification Algorithm (MUSICAL) for multi-colour imaging of various organelles and sub-cellular structures in the cardiomyoblast cell line H2c9. We have tested MUSICAL for fast (up to 230Hz), multi-colour time-lapse sequences of various sub-cellular structures (mitochondria, endoplasmic reticulum, microtubules, ...
    • Adaptive order polynomial algorithm in a multi-wavelet representation scheme 

      Durdek, Antoine Pacifique Romain; Jensen, Stig Rune; Juselius, Jonas; Wind, Peter; Flå, Tor; Frediani, Luca (Journal article; Tidsskriftartikkel; Peer reviewed, 2014)
    • Added value of multitemporal polarimetric UAVSAR data for permanent scatterers detection 

      Nikaein, Tina; Akbari, Vahid; Arefi, Hossein (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-11-05)
      In the last decades, differential synthetic aperture radar (SAR) interferometric (InSAR) (DInSAR) techniques have been used to estimate the Earth's surface deformation with high resolution. In this paper, we present an approach for increasing the quantity of permanent scattered pixels. These pixels are selected for DInSAR processing based on polarimetric information prepared by new sensors. The ...
    • Adding mobility to non-mobile web robots 

      Sudmann, Nils P.; Johansen, Dag (Research report; Forskningsrapport, 2000)
      In this paper we will show that it is possible to combine mobile agent technology with existing non-mobile data mining applications. The motivation for this is the advantage mobile agents offer in moving the computation closer to the data in a distributed system. This can save bandwidth and increase performance when the data is condensed as a result of data mining.
    • Adhesive free PVDF copolymer focused transducers for high frequency acoustic imaging 

      Habib, Anowarul; Wagle, Sanat; Melandsø, Frank (Conference object; Konferansebidrag, 2019)
      The present study has demonstrated to produce a reliable PVDF copolymer focused transducers from a layer-by-layer deposition method by engraving milled spherical cavies in a PEI polymer substrate. The proposed method which process P(VDF-TrFE) from the fluid phase, is adhesive-free in the sense that it does not require any additional adhesive layers for material binding. The transducer was acoustically ...
    • ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement 

      Hansen, Stine; Gautam, Srishti; Salahuddin, Suaiba Amina; Kampffmeyer, Michael Christian; Jenssen, Robert (Journal article; Tidsskriftartikkel, 2023-08-02)
      A major barrier to applying deep segmentation models in the medical domain is their typical data-hungry nature, requiring experts to collect and label large amounts of data for training. As a reaction, prototypical few-shot segmentation (FSS) models have recently gained traction as data-efficient alternatives. Nevertheless, despite the recent progress of these models, they still have some essential ...
    • Adsorption free energy of phenol onto coronene: Solvent and temperature effects 

      Malloum, Alhadji; Conradie, Jeanet (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-11-11)
      Molecular modeling can considerably speed up the discovery of materials with high adsorption capacity for wastewater treatment. Despite considerable efforts in computational studies, the molecular modeling of adsorption processes has several limitations in reproducing experimental conditions. Handling the environmental effects (solvent effects) and the temperature effects are part of the important ...
    • Adsorption of Organic Pollutants in Microplastic in the Arctic Ocean 

      Nordang, Unni Mette (Master thesis; Mastergradsoppgave, 2019-05-15)
      Oceans all over the world are housing large quantities of plastic pollution and persistent organic pollutants (POPs). Concerns regarding both of them having lipophilic characteristic that allows a successful partitioning of POPs to plastic if in contact in an aqueous medium, led to this study where the relationship between different types of plastic and POPs in the Arctic ocean are looked into. In ...
    • Advanced Data Analytics towards Energy Efficient and Emission Reduction Retrofit Technology Integration in Shipping 

      Perera, Lokukaluge Prasad; Ventikos, N P; Rolfsen, Sven; Öster, Anders (Chapter; Bokkapittel, 2021)
      An overview of integrating two energy efficient and emission reduction technologies to improve ship energy efficiency under advanced data analytics is presented in this study. The proposed technologies consist of developing engine and propulsion innovations that will be experimented under laboratory conditions and large-model-scale sea trials, respectively. These experiments will collect large ...
    • Advanced data cluster analyses in digital twin development for marine engines towards ship performance quantification 

      Taghavi, Mahmood; Perera, Lokukaluge Prasad Channa (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-02-24)
      Due to the growing rate of energy consumption, it is necessary to develop frameworks for enhancing ship energy efficiency. This paper proposes a solution for this issue by introducing a digital twin framework for quantifying ship performance. For this purpose, extensive low-level clustering is performed using Gaussian Mixture Models (GMM) with the Expectation Maximization algorithm on a dataset ...
    • An Advanced Non-Gaussian Feature Space Method for POL-SAR Image Segmentation 

      Doulgeris, Anthony Paul; Eltoft, Torbjørn (Conference object; Konferansebidrag, 2013)
      This work extends upon our simple feature-based multichannel SAR segmentation method to incorporate highly desirable statistical properties into a computationally simple approach. The desirable properties include Markov random field contextual smoothing and goodness-of-fit testing to automatically obtain the significant number of classes. To achieve this we need to find an explicit class model to ...
    • Advanced signal processing techniques with EISCAT3D 

      Stamm, Johann (Doctoral thesis; Doktorgradsavhandling, 2022-05-24)
      A new, modern ionospheric radar, called EISCAT3D, is under construction in northern Fennoscandia. In the first stage, the radar will have three sites, one combined transmit/receiver site shouth of Skibotn, and receiver sites in Kaaresuvanto and Kaiseniemi. The radar will consist of large groups of dipole antennas that are steered by shifting the phase of the transmitted or received signal. The beam ...
    • Advances in understanding subglacial meltwater drainage from past ice sheets 

      Simkins, Lauren M; Greenwood, Sarah L.; Winsborrow, Monica; Bjarnadóttir, Lilja Rún; Lepp, Allison (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-04-17)
      Meltwater drainage beneath ice sheets is a fundamental consideration for understanding ice–bed conditions and bed-modulated ice flow, with potential impacts on terminus behavior and iceshelf mass balance. While contemporary observations reveal the presence of basal water movement in the subglacial environment and inferred styles of drainage, the geological record of former ice sheets, including ...
    • Advancing Deep Learning for Automatic Autonomous Vision-based Power Line Inspection 

      Nguyen, Van Nhan (Doctoral thesis; Doktorgradsavhandling, 2019-12-03)
      Electricity is fundamental to the ability to function of almost all modern-day societies. To maintain the reliability, availability, and sustainability of electricity supply, electric utilities are usually required to perform visual inspections on their electrical grids regularly. These inspections have been typically carried out using a combination of airborne surveys via low-flying helicopters and ...
    • Advancing Deep Learning for Marine Environment Monitoring 

      Choi, Changkyu (Doctoral thesis; Doktorgradsavhandling, 2023-06-09)
      Marine environment monitoring has become increasingly significant due to the excessive exploitation of oceans, which detrimentally impacts ecosystems. Deep learning provides an effective monitoring approach by automating the analysis of vast amounts of observed image data, enabling stakeholders to make informed decisions regarding fishing quotas or conservation efforts. The success of deep learning ...
    • Advancing Deep Learning with Emphasis on Data-Driven Healthcare 

      Wickstrøm, Kristoffer Knutsen (Doctoral thesis; Doktorgradsavhandling, 2022-10-28)
      Retten til helse er en grunnleggende menneskerettighet, men mange utfordringer står overfor de som ønsker å etterleve denne retten. Mangel på utdannet helsepersonell, økte kostnader og en aldrende befolkning er bare noen få eksempler på nåværende hindringer i helsesektoren. Å takle slike problemer er avgjørende for å gi pålitelig helsehjelp med høy kvalitet til mennesker over hele verden. Mange ...
    • Advancing Land Cover Mapping in Remote Sensing with Deep Learning 

      Liu, Qinghui (Doctoral thesis; Doktorgradsavhandling, 2021-12-08)
      Automatic mapping of land cover in remote sensing data plays an increasingly significant role in several earth observation (EO) applications, such as sustainable development, autonomous agriculture, and urban planning. Due to the complexity of the real ground surface and environment, accurate classification of land cover types is facing many challenges. This thesis provides novel deep learning-based ...
    • Advancing relativistic electronic structure methods for solids and in the time domain 

      Kadek, Marius (Doctoral thesis; Doktorgradsavhandling, 2018-08-28)
      Effects arising from the special theory of relativity significantly influence the electronic structure and properties of molecules and solid-state materials containing heavy elements. At the same time, the inclusion of the relativistic effects in theoretical and computational models increases their methodological complexity and the computational cost. In the solid state, additional challenges ...
    • Advancing Segmentation and Unsupervised Learning Within the Field of Deep Learning 

      Kampffmeyer, Michael Christian (Doctoral thesis; Doktorgradsavhandling, 2018-10-19)
      Due to the large improvements that deep learning based models have brought to a variety of tasks, they have in recent years received large amounts of attention. However, these improvements are to a large extent achieved in supervised settings, where labels are available, and initially focused on traditional computer vision tasks such as visual object recognition. Specific application domains that ...