- Dynamiska Borel–Cantelli-lemmata Syftet med detta projekt är att studera så kallade dynamiska Borel–Cantelli-lemmata och deras tillämpningar.
- Reconstruction of 3D structure and situational awareness by fusing image sequences with lidar data ABB Crane systems today apply a great number of cameras that capture projections of the three dimensional surrounding to the container cranes under operation. LIDAR data is also being collected in several cases. Fusing these two measurement modalities would possibly yield an aggregate data that would benefit from both the strong point of the information rich camera image and the robust depth estimate of the LIDAR. One way of fusing such data would be to formulate a model representation that could be updated with the measurement of the image and lidar frame as they become available. The suggested theme for this master thesis work is to investigate the details of such reconstruction.
- Bildanalys för att tolka proteinkristallstrukturer Our understanding of the function of proteins, DNA, RNA and other biological macromolecules, as well as the design of new drug molecules, rely strongly on the possibility to obtain atomic-resolution structures by X-ray and neutron crystallography. Currently, almost 150 000 such structures are freely available in the protein databank. In Lund, data collection for such structures can be performed at the Max IV laboratory and when ESS is running, it will be possible to collect data for neutron structures at an unprecedented speed. To start with, we will restrict the project to identify water molecules in X-ray crystallographic maps. To train the model, we will employ a set of (~1000) curated maps where standard methods clearly identify the presence or absence of a water molecule. Additional data can easily be generated, both from existing crystal structures or from simulated molecular data.
- MODELLING NON-STATIONARY DISTURBANCES IN DCIP DATA Resistivity and time domain induced polarization (DCIP) measurements is a great tool for mapping properties of the subsurface. This master thesis project invloves analysis of existiving DCIP data to find suitable machine learning or signal processing apporaches to deal with non-stationary disturbances from trams and metro. Prerequisites: FMSF10/MASC04, (FMS051/MASM17), (FMSN35/MASM26)