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.
People with the autoimmune disease scleroderma are vulnerable in COVID-19 due to frailty, lung involvement, and immunosuppression; they are representative of vulnerable groups in terms of COVID-19 mental health ramifications. No previous randomized controlled trials have tested mental health interventions during infectious disease outbreaks. We leveraged our existing ongoing cohort of over 2,000 people with scleroderma and existing partnerships to launch a new cohort, the Scleroderma Patient-centered Intervention Network (SPIN)-COVID- 19 Cohort.
Cost-effective clean energy production is one of the most urgent economic and societal issues facing Canada today. Hydro-Québec is a world-leader in clean hydro-electric energy production – an essentially carbon-free source of energy.
This project is about using artificial intelligence to interpret agricultural remote sensing data. We will develop new means to integrate repeated imagery data of targeted agricultural fields to pinpoint agronomically significant anomalies (e.g., water or nutrient stress, crop pathology, weeds, etc.) and provide field managers easy to follow recommendations guiding development of the most cost effective plans to treat these anomalies.