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.
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.
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