Probabilistic graphical models for semi-supervised traffic classification

Traffic classification using machine learning continues to be an active research area. The majority of work in this area uses off-the-shelf machine learning tools and treats them as black-box classifiers. This approach turns all the modelling complexity into a feature selection problem. In this paper, we build a problem-specific solution to the traffic classification problem by designing a custom probabilistic graphical model. Graphical models are a modular framework to design classifiers which incorporate domain-specific knowledge. More specifically, our solution introduces semi-supervised learning which means we learn from both labelled and unlabelled traffic flows. We show that our solution performs competitively compared to previous approaches while using less data and simpler features. Copyright © 2010 ACM.

Investigating the hydrothermal growth of zinc oxide nanostructures through seed layer control

Controlling the growth of ZnO nanostructures for photovoltaic applications will ensure greater device efficiency and parameter control. This paper reports on methods to engineer the morphology and tailor the nanostructure growth direction through the hydrothermal synthesis method. Effective control is achieved through the use of a sputtered zinc layer together with modifications of the growth solution. These nanostructures have been developed with a view to incorporation into excitonic solar cells, and methods to improve surface stability using a fully aqueous synthesis method will be discussed. © by Oldenbourg Wissenschaftsverlag, München.