High space-bandwidth product with high spatial phase sensitivity is indispensable for a single-shot quantitative phase microscopy (QPM) system. It opens avenue for widespread applications of QPM in the field of biomedical imaging. Temporally low coherence light sources are implemented to achieve high spatial phase sensitivity in QPM at the cost of either reduced temporal resolution or smaller field ...

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, ...

Terrain challenges the prediction of near-surface atmospheric conditions, even in kilometre-scale numerical weather prediction (NWP) models. In this study, the ALADIN-HIRLAM NWP system with 0.5 km horizontal grid spacing and an increased number of vertical levels is compared to the 2.5-km model system similar to the currently operational NWP system at the Norwegian Meteorological Institute. The ...

Acoustic target classification in multi-frequency echosounder data is a major interest for the marine ecosystem and fishery management since it can potentially estimate the abundance or biomass of the species. A key problem of current methods is the heavy dependence on the manual categorization of data samples. As a solution, we propose a novel semi-supervised deep learning method leveraging a few ...

The use of unmanned aerial vehicle (UAV)-mounted radar for obtaining snowpack parameters has seen considerable advances over recent years. However, a robust method of snow density estimation still needs further development. The objective of this work is to develop a method to reliably and remotely estimate snow water equivalent (SWE) using UAV-mounted radar and to perform initial field experiments. ...

Forecasting the dynamics of time-varying systems is essential to maintaining the sustainability of the systems. Recent studies have discovered that Recurrent Neural Networks(RNN) applied in the forecasting tasks outperform conventional models that include AutoRegressive Integrated Moving Average(ARIMA). However, due to the structural limitation of vanilla RNN which holds unit-length internal ...

This report documents a workshop organised by the COGMAR and CRIMAC projects. The objective of the workshop was twofold. The first objective was to give an overview of ongoing work using machine learning for Acoustic Target Classification (ATC). Machine learning methods, and in particular deep learning models, are currently being used across a range of different fields, including ATC. The objective ...

Comets hold the key to the understanding of our Solar System, its formation and its evolution, and to the fundamental plasma processes at work both in it and beyond it. A comet nucleus emits gas as it is heated by the sunlight. The gas forms the coma, where it is ionised, becomes a plasma, and eventually interacts with the solar wind. Besides these neutral and ionised gases, the coma also contains ...

Photonic chip-based total internal reflection fluorescence microscopy (c-TIRFM) is an emerging technology enabling a large TIRF excitation area decoupled from the detection objective. Additionally, due to the inherent multimodal nature of wide waveguides, it is a convenient platform for introducing temporal fluctuations in the illumination pattern. The fluorescence fluctuation-based nanoscopy technique ...

A polarimetric approach developed to discriminate oil slicks and look-alikes was used to study the polarimetric properties of newly-formed ice (NFI) observed near the Prirazlomnaya oil platform. This approach is based on the multipolarization parameter called Resonant to Non-resonant signal Damping (RND), which is related to the ratio between the ice damping and the short wind waves and wave breakings. ...