The time series cluster kernel (TCK) provides a powerful tool for analysing multivariate time series subject to missing data. TCK is designed using an ensemble learning approach in which Bayesian mixture models form the base models. Because of the Bayesian approach, TCK can naturally deal with missing values without resorting to imputation and the ensemble strategy ensures robustness to hyperparameters, ...

This study presents the weighted least squares method based on Interferometric Synthetic Aperture Radar (InSAR) images to retrieve spatial-temporal evolution of land subsidence in Mashhad Valley, northeast Iran. Using the analysis of a few interferograms covering the 2003-2005 period, Motagh et al (GJI 2006) presented a preliminary analysis of the subsidence in this area. Here we extend this study ...

A time-dependent multi-stream electron transport model, AURORA, has been developed for studies of auroral emission-rates during precipitation with large variations on sub-second time-scales. The transport-code accurately takes time-of-flight, energy degradation, scattering and production of secondary electrons into account. AURORA produces ionospheric electron-flux as a function of energy, altitude, ...

One of the methods to cope with the increase of emitted greenhouse gases has been to capture CO2 gas from a point source and storing it within the Earths subsurface; “Carbon Capture and Storage” (CCS). Since 1996, Statoil and its partners have injected CO2 into a saline aquifer called the Utsira formation located in the North Sea. About 0.9Mt of CO2 is injected into the formation yearly, with a ...

This master project was inspired by challenges faced by commercial fisheries in the north of Norway of controlling food quality and food safety. In this thesis, four different ML models’ ability to do object and keypoint detection on specific anatomy parts of fish, has been studied. With the aim of recommending a suitable model to be part of a CV system for an industrial fish gutting machine that ...

In marine science, there is a need for tools for population counting of species. Through this thesis we aim to achieve the follow three objectives: first, briefly discuss the state-of-the-art object detectors that can be used for the detection of porpoises in drone images/videos. Second, test and compare a few stateof-the-art object detectors in both quantitative and qualitative manner. Third, based ...

Image-based virtual try-on is one of the most promising applications of human-centric image generation due to its tremendous real-world potential. Yet, as most try-on approaches fit in-shop garments onto a target person, they require the laborious and restrictive construction of a paired training dataset, severely limiting their scalability. While a few recent works attempt to transfer garments ...

In this paper, we introduce a new framework to train a class of recurrent neural network, called Echo State Network, to predict real valued time-series and to provide a visualization of the modeled system dynamics. The method consists in projecting the output of the internal layer of the network on a lower dimensional space, before training the output layer to learn the target task. Notably, we ...

We present experimental demonstration of tilt-mirror assisted transmission structured illumination microscopy (tSIM) that offers a large field of view super resolution imaging. An assembly of custom-designed tilt-mirrors are employed as the illumination module where the sample is excited with the interference of two beams reflected from the opposite pair of mirror facets. Tunable frequency structured ...

The spatial and temporal characterization of trapped charged particle trajectories in magnetospheres has been extensively studied in dipole magnetic field structures. Such studies have allowed the calculation of spatial quantities, such as equatorial loss cone size as a function of radial distance, the location of the mirror points along particular field lines (<i>L</i>‐shells) as a function of the ...