In an earlier paper (Manson et al., 1999a) tidal data (1990–1997) from six Medium Frequency Radars (MFR) were compared with the Global Scale Wave Model (GSWM, original 1995 version). The radars are located between the equator and high northern latitudes: Christmas Island (2° N), Hawaii (22° N), Urbana (40° N), London (43° N), Saskatoon (52° N) and Tromsø (70° N). Common harmonic analysis was applied, ...

Segmenting and labelling tumors in multimodal medical imaging are often vital parts of diagnostics and can in many cases be very labor intensive for clinicians. The effort in advancing time-saving methods in the medical health sector might be of great help for busy clinicians and can maybe even save lives. Furthermore, creating methods that generically, accurately and successfully process unlabelled ...

EISCAT VHF radar data are used for observing, monitoring, and understanding Earth’s upper atmosphere. This paper presents an approach to segment Polar Mesospheric Summer Echoes (PMSE) from datasets obtained from EISCAT VHF radar data. The data consist of 30 observations days, corresponding to 56,250 data samples. We manually labeled the data into three different categories: PMSE, Ionospheric ...

Multivariate time series often contain missing values for reasons such as failures in data collection mechanisms. Since these missing values can complicate the analysis of time series data, imputation techniques are typically used to deal with this issue. However, the quality of the imputation directly affects the performance of downstream tasks. In this paper, we propose a selective imputation ...

Capturing global contextual representations in remote sensing images by exploiting long-range pixel-pixel dependencies has been shown to improve segmentation performance. However, how to do this efficiently is an open question as current approaches of utilising attention schemes, or very deep models to increase the field of view, increases complexity and memory consumption. Inspired by recent work ...

We present the results of a numerical investigation of charged-particle transport across a synthesized magnetic configuration composed of a constant homogeneous background field and a multiscale perturbation component simulating an effect of turbulence on the microscopic particle dynamics. Our main goal is to analyze the dispersion of ideal test particles faced to diverse conditions in the turbulent ...

In this thesis we present a new semi-supervised classification technique based on the Kernel Entropy Component Analysis (KECA) transformation and the least absolute shrinkage selection operator (LASSO). The latter is a constrained version of the least squares classifier. Traditional supervised classification techniques only use a limited set of labeled data to train the classifier, thus leaving ...

There is a growing interest in automatic crafting of neural network architectures as opposed to expert tuning to fnd the best architecture. On the other hand, the problem of stock trading is considered one of the most dynamic systems that heavily depends on complex trends of the individual company. This paper proposes a novel self-evolving neural network system called self-evolving Multi-Layer ...

The machine learning method, Gaussian Process Regression (GPR), has lately been introduced for chlorophyll content mapping from remotely sensed data. It has been shown that GPR has outperformed other machine learning and empirical methods in accuracy, speed and stability. Moreover, GPR not only estimates the chlorophyll content, it also provides the certainty level of the prediction, allowing the ...

The principle focus of this thesis is the characterization of an on-chip methane sensor based on a waveguide interferometer. It incorporates cryptophane-A molecules in the waveguide cladding to enhance sensitivity and selectivity towards methane. First, the sensor was characterized for sensitivities to ambient conditions, in particular its temperature and pressure sensitivity. The measurement ...