Developing CF for video data fit as multivariate functional data. Phase 3

positions available: 2 This project develops filters for functional data, specifically video data, that can be used to extend functional neural networks to convolutional neural networks. Filters work for data with relationships. These have been developed for photos and work for relationships in time series data. We plan to link these two filters into video data to filter unique properties of video (movement, colour change, and effects). To implement this project, we plan to use Python's functional data analysis package, pillow, and pandas. We will produce and distribute a package using GitHub so that interested teams can implement the filters. We will also produce this framework as there is no current format for turning videos into functional data.

Developing CF for video data fit as multivariate functional data. Phase 2

positions available: 2 This project develops filters for functional data, specifically video data, that can be used to extend functional neural networks to convolutional neural networks. Filters work for data with relationships. These have been developed for photos and work for relationships in time series data. We plan to link these two filters into video data to filter unique properties of video (movement, colour change, and effects). To implement this project, we plan to use Python's functional data analysis package, pillow, and pandas. We will produce and distribute a package using GitHub so that interested teams can implement the filters. We will also produce this framework as there is no current format for turning videos into functional data.

Developing CF for video data fit as multivariate functional data. Phase 1

positions available: 2 This project develops filters for functional data, specifically video data, that can be used to extend functional neural networks to convolutional neural networks. Filters work for data with relationships. These have been developed for photos and work for relationships in time series data. We plan to link these two filters into video data to filter unique properties of video (movement, colour change, and effects). To implement this project, we plan to use Python's functional data analysis package, pillow, and pandas. We will produce and distribute a package using GitHub so that interested teams can implement the filters. We will also produce this framework as there is no current format for turning videos into functional data.

Analysing the effect of industrial environmental damage in Canada. Phase 1

positions available: 2 This project aims to quantify/discover environmental damage due to industrial activity in Canada. This will be achieved by combining various datasets from the Canadian government and other certified sources. In this project, we will Look at environmental indicators such as infectious diseases, malformation rates, endocrine and stress responses, genotoxicity, and concentrations of heavy metals, naphthenic acids and polycyclic aromatic hydrocarbons within amphibians as well as the overall ecosystems. Apply functional data clustering and model-based clustering algorithms. Show the current trajectory of these ecosystems towards recovery and regrowth.