Grants and Contributions

Automatic measurement of river surface velocity and discharge using shore-based and aerial imagery

Pilot study on Stage-Discharge Uncertainty Evaluation

Investigating the combined impact of microplastics and temperature warming on zooplankton health using environmentally relevant conditions.

The objectives of the Youth Employment and Skills Strategy Program (YESSP) are to help all youth navigate through the labour market and to successfully transition into sustained employment. These objectives are attained by supporting the needs of all youth, especially youth facing barriers to develop skills, knowledge and networks through education, skills development and meaningful work experiences.

To develop a national framework for improving and coordinating market research to guide the stakeholders' strategy and decision making for the wood construction industry.

The organization will provide services for victims of human trafficking by connecting them to agencies and the services they require including physical health, mental health, personal identification, housing, employment, addiction counselling and education. The goals of the project are to assist victims exit sex trafficking, improve their quality of life and minimize the risk of them returning to their trafficker.

Deep learning models are increasingly being adopted and rapidly improved for property prediction in intelligent design, including predicting the functionalities of a novel circuit, foreseeing the working behaviors of a new material, and anticipating the reactions of a fresh compound. State-of-the-art deep techniques leverage two building blocks: deep graph neural networks and supervised signals from large amount of labelled data. Graphs are powerful and versatile data structures to model the relationships among objects, and powered by the graph structure, supervised learning leverages large amount of labelled data to construct efficient graph representation, which then enables the prediction model to make accurate decisions on the properties of a new design. Such state-of-the-art graph neural networks for property prediction, rely heavily on supervised learning with large labelled datasets, which is typically expensive and time-consuming.
This Project will examine the development of fundamental techniques for graph representation learning in the presence of limited labeled data, aiming at accurate property prediction of novel designs. The successful outcome of this Project will be a learning framework that enables the efficient learning of graph representation with limited labelled data. Consequently, with improved graph representation, the properties of a new design can be more accurately and effectively evaluated, resulting in meaningfully speeding up the search for desirable designs.

Development of inline liquid-solid separation process, measurements and a control system to enhance discharge water quality and rapid consolidation of resulting tailings.

This project will enhance the Stepscan tile hardware and software capabilities to create a dual-authentication footstep-based biometric reader testing platform, with integrations needed for popular user access control systems.

The Company has been developing a light-weight ground-penetrating radar solution for use with RPAS (drones) for near-surface data collection in large areas and complex ground terrain/structures.