Similar to current efforts in the automotive industry, there is a substantial interest in developing fully autonomous trains. One of the key steps towards enabling autonomous operation is being able to accurately estimate train position and speed. In addition to this, better estimates will also increase safety and reduce distance between trains, allowing more frequent trains in peak hours. The current project deals with a method of estimating the velocity and position without using GPS measurements, which is the standard method.

Cannafish is a Startup working with hydroponic farmers to include manure and compost in their fertilisation plan. Doing so, they reduce the use of chemical fertilizer in hydroponics while valorizing organic residual matter. However, to replace chemical with organic fertilizers requires good practice and precision to achieve competitive yield. Hence, using a specific ion monitoring strategy in organic hydroponics can allow the preparation of a nutrient solution that is rich in nutrients and optimal for hydroponic plant growth.

Robust perception in all weather conditions is a critical requirement for autonomous vehicles. This project proposes fusing gated and conventional RGB camera images for robust scenes encoding, depth estimation and trajectory prediction. Conventional approaches using lidar and RGB camera fail to perform robustly in rain, fog and snow. By extending existing computer vision algorithms to Gated-RGB camera pair the fusion algorithms developed will utilize features that are robust in one sensor modality but not the other.

Cancer is the leading cause of death in Canada. A promising new way to treat cancer is through the administration of immune cells that target the cancer – an approach called cancer immunotherapy. The goal of this project is to engineer better culture vessels to produce dendritic cells. Dendritic cells are the "gatekeepers" of our immune system. They can have the capacity to activate other immune cells to attack cancers. The project involves two industrial partners: Kanyr Pharma, Inc. and Saint- Gobain, Inc.

The proposed project deals with the extrusion encapsulation of bioactive components using rice and corn protein based food matrix. It is well recognized that the performance properties of natural lake pigment encapsulated products are influenced by the structural properties of the matrix protein used for the extrusion encapsulation. The type, mixture and nature of proteins can have a significant effect on the extrusion encapsulated product as well as its functionality. Significant changes in the product properties can be expected due to the difference in the manufacturing steps.

Currently, the service provided by GRAD4 allows online storing and sharing of computer-aided design (CAD) models. However, there is no interface developed for visualization of CAD models in the service in a fast and comprehensible way to the users: both manufacturers and buyers. The main challenge of such implementations is in relatively high computational cost of such visualizations via tools used for web-development: while a regular PC handles such task efficiently, web-based tools have means insufficient for similar performance and, thus, mostly involve cloud computing.

The primary objective of this collaborative research project is to further advance an established industrial strength framework for the aerodynamic design optimization of Advanced Aerodynamic Systems. A major expense during a three-dimensional aerodynamic shape optimization (ASO) is the cost of obtaining design sensitivities or gradients at each design iteration. The computational cost grows rapidly as the number of cost functions and design variables are added.