This project aims at creating a model for forecasting ice formation in the St. Lawrence Seaway between the Welland Canal and Quebec City. This will improve drastically the planning of all maritime operations during the winter transition period, before the freeze-up.
Since Amazon robotics expanded the use of drones to package deliveries to customers, drone applications have been expanded to many industries along with its ability to perform various tasks autonomously. The fundamental technology of drones’ autonomy comes from perceiving its surrounding, creating its own map based on onboard sensors and estimate its location within the map.
Genacol Canada Corporation Inc. manufactures a collagen hydrolysate (CH) product, sourced from bovine (Genacol® Original Formula), that has shown promising and positive results in three clinical trials for decreasing joint pain, as well as having a positive effect on articular cartilage. Following digestion, and first pass metabolism, CHs release peptides that have been shown to be the health promoting component of the supplement. New research trends have suggested that joint pain is a multi-tissue disease, and not only attributed to articular degradation.
Dialogue is Canada's leading telemedicine provider, founded to improve well-being by using technology to deliver excellent care. Dialogue is a pioneer of virtual health care dedicated exclusively to organizations that want to improve the health and well-being of their members and families.
As Unmanned Aerial Vehicles (UAVs) become more ubiquitous, a special class of UAVs known as Unmanned Aerial Gliders (UAGs) promises to offer more efficient flight by using atmospheric energy to remain afloat. In order to facilitate the usage of UAGs in various applications, researchers have developed algorithms which allow for autonomous flight of UAGs. The developed algorithms, however, still lag in performance as compared to piloted UAGs, and require an extensive amount of calibration upfront, making them difficult to implement on gliders of various sizes and properties.
Automatically assessing a pilot performance during a flight training session is a capability that can enhance the flight instructor during his duty. From data gathered during a flight maneuver, we are looking for a way to automatically assess pilot performance to augment instructor performance and provide objectivity during flight training assessment.
The treatment of chronic conditions accounted for 58% of the annual healthcare spend in Canada in 2012, primarily through the use of pharmaceuticals. However, these are generally best suited to treat acute diseases, as with chronic use, side effects can accumulate over time while therapeutic effects diminish. Neuromodulation of the Peripheral Nervous System (PNS) represents a promising and adaptable treatment alternative to pharmaceuticals in many cases.
In this postdoc, we plan to focus on computer vision tasks where existing deep learning methods require lots of labeled samples to work well. Acquiring labeled samples is time-consuming and often impractical. Thus, we investigate three different classes of methods to alleviate the label scarcity problem: active learning, weakly-supervised learning, and few-shot learning. In active learning, the goal is to label the most important samples to maximize the performance of the model while reducing labeling costs. In weakly supervised learning, the goal is to train models using weak labels.
The development of autonomous unmanned aerial vehicles (UAVs) is a growing area of interest. An important step in the creation of a fully autonomous flying vehicle is the ability to precisely and smoothly land on a target. The goal of this research is to develop a UAV landing system that is able to track and land on a moving platform. The project will involve developing a guidance and control system that can plan a descent trajectory and track it down to the platform. The proposed system will be robust to strong wind gusts and still provide a smooth touchdown to avoid damage.
The goal of the project is to understand how athletes have been affected by the COVID-19 pandemic. Through online surveys and interviews, the researchers hope to learn about the way
the pandemic. They also want to know what athletes are doing to cope and how their families and coaches are supporting them. Study findings will guide the partner organization in their development of holistic recommendations and automated tools optimized through artificial intelligence.