Viser treff 627-646 av 1261

    • M3D-VTON: A Monocular-to-3D Virtual Try-On Network 

      Zhao, Fuwei; Xie, Zhenyu; Kampffmeyer, Michael; Dong, Haoye; Han, Songfang; Zheng, Tianxiang; Zhang, Tao; Liang, Xiaodan (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-02-28)
      Virtual 3D try-on can provide an intuitive and realistic view for online shopping and has a huge potential commercial value. However, existing 3D virtual try-on methods mainly rely on annotated 3D human shapes and garment templates, which hinders their applications in practical scenarios. 2D virtual try-on approaches provide a faster alternative to manipulate clothed humans, but lack the rich and ...
    • Machine learning approach for identification and tracking of coherent structures in turbulent fluids and plasmas 

      Kirkeland, Leander (Master thesis; Mastergradsoppgave, 2022-12-15)
      In a fusion reactor, coherent structures of hot and dense plasma can drift radially outwards due to the conditions of the edge plasma and can cause erosion of the outer walls. This erosion can release impurities into the plasma and harm equipment at the walls. This thesis presents two methods of tracking blobs in the boundary region of fusion experiments. The first model is a simple Long Short-Term ...
    • Machine learning assisted multifrequency AFM: Force model prediction 

      Elsherbiny, Lamiaa; Santos Hernandez, Sergio; Gadelrab, Karim; Olukan, Tuza; Font, Josep; Barcons, Victor; Chiesa, Matteo (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-12-05)
      Multifrequency atomic force microscopy (AFM) enhances resolving power, provides extra contrast channels, and is equipped with a formalism to quantify material properties pixel by pixel. On the other hand, multifrequency AFM lacks the ability to extract and examine the profile to validate a given force model while scanning. We propose exploiting data-driven algorithms, i.e., machine learning packages, ...
    • Machine learning assisted quantification of graphitic surfaces exposure to defined environments 

      Lai, Chia-Yun; Santos, Sergio; Chiesa, Matteo (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-06-17)
      We show that it is possible to submit the data obtained from physical phenomena as complex as the tip-surface interaction in atomic force microscopy to a specific question of interest and obtain the answer irrespective of the complexity or unknown factors underlying the phenomena. We showcase the power of the method by asking “how many hours has this graphite surface been exposed to ambient conditions?” ...
    • Machine Learning Automatic Model Selection Algorithm for Oceanic Chlorophyll-a Content Retrieval 

      Blix, Katalin; Eltoft, Torbjørn (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-05-17)
      Ocean Color remote sensing has a great importance in monitoring of aquatic environments. The number of optical imaging sensors onboard satellites has been increasing in the past decades, allowing to retrieve information about various water quality parameters of the world’s oceans and inland waters. This is done by using various regression algorithms to retrieve water quality parameters from remotely ...
    • Machine learning derived input-function in a dynamic 18F-FDG PET study of mice 

      Kuttner, Samuel; Wickstrøm, Kristoffer Knutsen; Kalda, Gustav; Dorraji, Seyed Esmaeil; Martin-Armas, Montserrat; Oteiza, Ana; Jenssen, Robert; Fenton, Kristin Andreassen; Sundset, Rune; Axelsson, Jan (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-01-13)
      Tracer kinetic modelling, based on dynamic <sup>18</sup>F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is used to quantify glucose metabolism in humans and animals. Knowledge of the arterial input-function (AIF) is required for such measurements. Our aim was to explore two non-invasive machine learning-based models, for AIF prediction in a small-animal dynamic FDG PET study. 7 tissue ...
    • Machine Learning Detection of Dust Impact Signals Observed by The Solar Orbiter 

      Kvammen, Andreas; Wickstrøm, Kristoffer; Kociscak, Samuel; Vaverka, Jakub; Nouzak, Libor; Zaslavsky, Arnaud; Rackovic Babic, Kristina; Gjelsvik, Amalie; Pisa, David; Souček, Jan; Mann, Ingrid (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-08-11)
      This article present results from automatic detection of dust impact signals observed by the Solar Orbiter – Radio and Plasma Waves instrument.<p> <p>A sharp and characteristic electric field signal is observed by the Radio and Plasma Waves instrument when a dust particle impact the spacecraft at high velocity. In this way, ∼5–20 dust impacts are daily detected as the Solar Orbiter travels through ...
    • Machine Learning for Arctic Sea Ice Physical Properties Estimation Using Dual-Polarimetric SAR Data 

      Blix, Katalin; Espeseth, Martine; Eltoft, Torbjørn (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-09-22)
      This work introduces a novel method that combines machine learning (ML) techniques with dual-polarimetric (dual-pol) synthetic aperture radar (SAR) observations for estimating quad-polarimetric (quad-pol) parameters, which are presumed to contain geophysical sea ice information. In the training phase, the output parameters are generated from quad-pol observations obtained by Radarsat-2 (RS2), and ...
    • Machine Learning for Classifying Marine Vegetation from Hyperspectral Drone Data in the Norwegian coast 

      Grue, Silje B.S. (Master thesis; Mastergradsoppgave, 2022-05-30)
      Along the Norwegian coasts the presence of blue forests are the key marine habitats. Due to increased anthropogenic activity and climate change, the health and extent of the blue forests is threatened. However, no low-cost, reliable system for monitoring blue forests exists in Norway at this time. This thesis studied machine learning methods to classify marine vegetation from hyperspectral data ...
    • Machine Learning simulations of quad-polarimetric features from dual-polarimetric measurements over sea ice 

      Blix, Katalin; Espeseth, Martine; Eltoft, Torbjørn (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-06)
      In this paper, we investigated the capabilities of the Gaussian Process Regression (GPR) algorithm in predicting of two quad-polarimetric parameters (relevant for sea ice analysis) from 6-dimensional dual-polarimetric input vectors. The GRP is trained on few hundred samples selected randomly from an image subset, and tested on the entire image. The performance is assessed by visual comparisons, and ...
    • Magnetic field-aligned plasma currents in gravitational fields 

      Garcia, Odd Erik; Leer, Egil; Pecseli, Hans; Trulsen, Jan Karsten (Journal article; Tidsskriftartikkel; Peer reviewed, 2015-03-03)
      Analytical models are presented for currents along vertical magnetic field lines due to slow bulk electron motion in plasmas subject to a gravitational force. It is demonstrated that a general feature of this problem is a singularity in the plasma pressure force that develops at some finite altitude when a plasma that is initially in static equilibrium is set into slow motion. Classical fluid models ...
    • Magnetopause Compressibility at Saturn with Internal Drivers 

      Hardy, Flavien; Achilleos, Nicholas; Guio, Patrick (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-05-07)
      We use magnetopause crossings of the Cassini spacecraft to study the response of Saturn's magnetosphere to changes in external and internal drivers. We explain how solar wind pressure can be corrected to account for the local variability in internal plasma particle pressure. The physics‐based method is applied to perform the most robust estimation of magnetopause compressibility at Saturn to date, ...
    • Magnetotelluric signatures of the complex tertiary fold–thrust belt and extensional fault architecture beneath Brøggerhalvøya, Svalbard 

      Beka, Thomas Ibsa; Bergh, Steffen G; Smirnov, Maxim; Birkelund, Yngve (Journal article; Tidsskriftartikkel; Peer reviewed, 2017-12-18)
      Magnetotelluric (MT) data were recently collected on Brøggerhalvøya, Svalbard, in a 0.003–1000 s period range along a curved WNW–ESE profile. The collected data manifested strong three-dimensional (3D) effects. We modelled the full impedance tensor with tipper and bathymetry included in 3D, and benchmarked the result with determinant data two-dimensional (2D) inversion. The final inversion results ...
    • Magnitude of extreme heat waves in present climate and their projection in a warming world 

      Russo, S.; Dosio, A.; Graversen, Rune; Sillmann, Jana; Carrao, H.; Dunbar, M.B.; Singleton, Andrew B.; Montagna, P.; Barbosa, P.; Vogt, Jürgen V. (Journal article; Tidsskriftartikkel; Peer reviewed, 2014)
      An extreme heat wave occurred in Russia in the summer of 2010. It had serious impacts on humans and natural ecosystems, it was the strongest recorded globally in recent decades and exceeded in amplitude and spatial extent the previous hottest European summer in 2003. Earlier studies have not succeeded in comparing the magnitude of heat waves across continents and in time. This study introduces a new ...
    • Malin letar oljespill i ishavet 

      Johansson, Malin; Liljebäck, Lars-Erik (Chronicle; Kronikk, 2020-03-13)
    • Manx Arrays: Perfect Non-Redundant Interferometric Geometries 

      McKay, Derek; Grydeland, Tom; Gustavsson, Bjorn Johan (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-09-09)
      Interferometry applications (e.g., radio astronomy) often wish to optimize the placement of the interferometric elements. One such optimal criterion is a uniform distribution of non-redundant element spacings (in both distance and position angle). While large systems, with many elements, can rely on saturating the sample space, and disregard “wasted” sampling, small arrays with only a few elements ...
    • Mapping of solar energy potential on Tromsøya using solar analyst in ArcGIS 

      Falklev, Erlend Homme (Master thesis; Mastergradsoppgave, 2017-12-15)
      The price of solar energy is declining, and will continue to decline the coming years. This will make it easier for households and companies to utilize solar energy. Because of this, several solar map projects have been established in the recent years. The aim of this study is to create a solar map of Tromsøya, and thoroughly explain the process in doing so. The process chosen for making the map is ...
    • Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle 

      Lohse, Johannes; Doulgeris, Anthony Paul; Dierking, Wolfgang (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-06-23)
      Automated classification of sea-ice types in Synthetic Aperture Radar (SAR) imagery is complicated by the class-dependent decrease of backscatter intensity with Incidence Angle (IA). In the log-domain, this decrease is approximately linear over the typical range of space-borne SAR instruments. A global correction does not consider that different surface types show different rates of decrease in ...
    • Mapping the extent of giant Antarctic icebergs with deep learning 

      Braakmann-Folgmann, Anne Christina; Shepherd, Andrew; Hogg, David; Redmond, Ella (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-11-09)
      Icebergs release cold, fresh meltwater and terrigenous nutrients as they drift and melt, influencing the local ocean properties, encouraging sea ice formation and biological production. To locate and quantify the fresh water flux from Antarctic icebergs, changes in their area and thickness have to be monitored along their trajectories. While the locations of large icebergs are operationally tracked ...
    • Marine radar properties, analysis and applications 

      Kaspersen, Kai Magne (Master thesis; Mastergradsoppgave, 2017-08-01)
      In this thesis marine radars are compared with synthetic aperture radars (SAR) and the possibility of cross-over applications are investigated. A first cross-over has been demonstrated by using the TS-CFAR on marine radar images. The TS-CFAR was originally developed for SAR and is a constant false alarm rate (CFAR) detection algorithm based on truncated statistics. Detecting weak targets embedded ...