Images are commonly used on a daily basis for research, information and entertainment. The introduction of digital cameras and especially the incorporation of cameras in mobile phones makes people able to snap photos almost everywhere at any time since their mobile phone is almost always brought with them. The fast evolution in hardware enables users to store large image collection without high ...

The deep waters surrounding the coastline of the northern parts of Norway represent an exciting biotope for marine exploration. Dark and cold Arctic water generates a hostile environment where the ability to adapt is crucial to survival. These waters are nonetheless bountiful and a diverse plethora of marine organisms thrive in these extreme conditions, many with the help of specialised ...

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 ...

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?” ...

Tracer kinetic modelling, based on dynamic ¹⁸F-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 ...

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 ...

In this thesis, methods to support high level situation awareness in ship navigators through appropriate automation are investigated. Situation awareness relates to the perception of the environment (level 1), comprehension of the situation (level 2), and projection of future dynamics (level 3). Ship navigators likely conduct mental simulations of future ship traffic (level 3 projections), that ...

The quality of wind data from the numerical weather prediction significantly influences the accuracy of wind power forecasting systems for wind parks. Therefore, an in-depth investigation of these wind data themselves is essential to improve wind power generation efficiency and maintain grid reliability. This paper proposes a novel framework based on machine learning for concurrently analyzing and ...

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 ...

This work utilizes Machine Learning (ML) regression and feature ranking techniques for water quality monitoring from remotely sensed data. The investigated regression methods include the Gaussian Process Regression (GPR), Suport Vector Regression (SVR) and Partial Least Squares Regression (PLSR). Feature relevance in the GPR model is as- sessed by the probabilistic Sensitivity Analysis (SA) approach.This ...