Grants and Contributions

Operating costs : The project is aimed at creating and starting up innovative businesses in the Estrie region and in the Brome-Missisquoi and Haute-Yamaska RCMs in Montérégie.

Anthropogenic carbon dioxide release can be offset by capture and conversion of carbon dioxide to urea, the most important nitrogenous fertilizer. However, industrial synthesis of urea from carbon dioxide and ammonia is not effective for carbon dioxide reduction because it requires a large input of energy. This project aims to develop catalysts that will produce urea more efficiently from carbon dioxide and nitrogen in an electrochemical membrane reactor. Catalysts based on metals and oxides will be screened in a multi-cathode cell to establish relationships between their activities for electrochemical reduction of carbon dioxide and nitrogen, and the determine the optimum combinations and conditions for formation of urea. The work is expected to establish the feasibility of this process, and provide knowledge that will guide the development of a membrane reactor

The optical transmitter is a critically important component in optical satellite communications, which includes a high-power high-bit-rate laser, a telescope, and a driving circuit. The current satellite communication based on visible and near-infrared (NIR) light does not work for all-weather (such as fog, rain and snow). Optical satellite communication at mid-IR atmospheric windows (3-5 mm and 8-12 mm) is essential for practical all-weather networks since absorption is much smaller for wavelengths in mid-IR atmospheric windows than for visible and NIR wavelengths. Unfortunately, mid-IR lasers with high optical power and high bit rate are not available on the market. In this project, mid-IR transmitters suitable for all-weather optical satellite communications will be developed. The developed mid-IR transmitters will be tested under various weather conditions. System performance of the developed mid-IR transmitters will be tested and radiation hardness testing and real world (space) implementation will be conducted.

In ground networks, when a user moves from one network to another or between the domains of a single network, the handover management process handles the user connection to the network. The state-of-the-art mobility management protocols provide seamless handover and keep the user connection continuous when the user mobility is at low speed. In satellite networks the handover management faces two challenges: 1) The very high speed of the satellites, and 2) the absence of fixed network nodes to handle the handover process. Through the use of machine learning (ML) techniques, promising improvements will be achieved in the handover management process of satellite networks. As satellites might move in groups (swarm operation), there is a need to handle their handover as a group. The proposed approaches will aim to reduce the handover process delays and control messages overhead.

To deliver packets between non-neighboring satellites using intermediate satellites, efficient routing protocols are required. There exist a high number of routing protocols in the literature, however, they were not designed for the highly dynamic, three-dimensional, fully distributed, and heterogeneous environment of the envisioned satellite networks. As the satellite network changes rapidly, it is necessary to keep learning the topology of the network in order to make the correct routing decisions. In this regard, online machine learning methods will play a significant role in improving the accuracy of routing decisions.
In this project the team will use machine learning to determine routing algorithms for LEO satellite networks.

The optical transmitter is a critically important component in optical satellite communications, which includes a high-power high-bit-rate laser, a telescope, and a driving circuit. The current satellite communication based on visible and near-infrared (NIR) light does not work for all-weather (such as fog, rain and snow). Optical satellite communication at mid-IR atmospheric windows (3-5 mm and 8-12 mm) is essential for practical all-weather networks since absorption is much smaller for wavelengths in mid-IR atmospheric windows than for visible and NIR wavelengths. Unfortunately, mid-IR lasers with high optical power and high bit rate are not available on the market. This project will provide a practical Made-in-Canada approach to mid-IR sources for all-weather optical satellite communications based on novel materials. The developed mid-IR transmitters will be tested under various weather conditions, with system performance of the developed mid-IR transmitters, radiation hardness and real world (space) implementation conducted.

To assess the functionality of novel NRC-developed chimeric antigen receptors (CARs) targeting leukemia in primary NK cells and assessment of CAR-NK potential for therapeutic development, expanded primary NK cells will be transduced, and their anti-tumor response will be evaluated in vitro and in vivo.

This project aims to develop digital twin technologies for aircraft structural life-cycle management. The digital twin can mirror aircraft structural lifecycle by predicting its performance and long-term behaviors, and support the optimal maintenance decision making. Integrated with industrial Internet of Things, a digital twin ecosystem can reduce maintenance costs and downtime. The outcome of the research will enhance Canada’s global competitiveness in both the MRO and ISS sectors to face the challenges from emerging economies.

Precision medicine is an emerging approach for disease treatment and prevention by delivering personalized care to individual patients taking into consideration their personal circumstance in terms of genetic makeup, environment, and lifestyle. Advancement in precision healthcare is tied to technological advancements, such as big data storage and analysis, sensor technologies, the Internet of Things, etc. Despite the rapid advancement of precision medicine and the considerable promises that it entails, several underlying technological challenges have not yet received the attention they deserve. One such area of great importance is the security and privacy of precision health related data. Specifically, the security and privacy risks of introducing precision health related data, such as the human genome, in patient’s electronic health record (EHR), are not well understood.
The purpose of the current project is to develop an Artificial Intelligence-based framework that will take as input precision health related data, such as the human genomic sequence, and produce a different representation that is privacy-proven and can be appended safely to patient’s EHR. This new representation will provide enough information that can be used and analyzed by the different precision health professionals, while not being reversible. The new framework will leverage advances in deep neural network and homomorphic encryption techniques.

HCI research involving simulating (digital twinning) IoTenabled objects and how users may interact with them. In the context of field maintenance of complex IoT-enabled objects, the question arises of exactly how a technician would interact with the IoT-derived data and other maintenance information. The project will look at the data and information by having the technician interact with a digital twin of the object and related IoT data. To identify effective ways in which a technician would interact with this digital twin and its data, the project will implement 3D interaction and visualization techniques and conduct human experiments to assess the efficacy of those techniques. While the project will focus on aircraft engine maintenance use cases, the more general HCI research issue is how to best support technicians in their interaction with IoT-enabled devices. Consequently, the results of this project would apply to a variety of other domains as well.