Lithium-ion power batteries have been widely used in transportation due to their advantages
of long life, high specific power, and energy. However, the safety problems caused by the …

Autonomous ships are expected to improve the level of safety and efficiency in future
maritime navigation. Such vessels need perception for two purposes: to perform …

While there is currently a lot of enthusiasm about “big data”, useful data is usually “small”
and expensive to acquire. In this paper, we present a new paradigm of learning partial …

A number of problems in probability and statistics can be addressed using the multivariate
normal (Gaussian) distribution. In the one-dimensional case, computing the probability for a …

We introduce the concept of numerical Gaussian processes, which we define as Gaussian
processes with covariance functions resulting from temporal discretization of time …

The field of prognostics has attracted significant interest from the research community in
recent times. Prognostics enables the prediction of failures in machines resulting in benefits …

Hundreds of millions of people lack access to electricity. Decentralized solar-battery systems
are key for addressing this while avoiding carbon emissions and air pollution but are …

In this paper, we propose using Gaussian processes to track an extended object or group of
objects, that generates multiple measurements at each scan. The shape and the kinematics …

Spatial linear mixed-models, consisting of a linear covariate effect and a Gaussian process
(GP) distributed spatial random effect, are widely used for analyses of geospatial data. We …

This thesis contributes to the field of Bayesian machine learning. Familiarity with most of the
material in Bishop [2007], MacKay [2003] and Hastie et al.[2009] would thus be convenient …