Innovative Projects Realized

Good Neighbour App

Community Connectedness, belonging and caring for each other has been fundamental to human wellbeing. In recent decades, our society has moved to replace the importance of human connectedness and mutual support with self-reliance. This has resulted in ever increasing incidences of loneliness, isolation and mental health challenges. Happy Community Project Limited www.happycommunityproject.com was incorporated in 2014. It’s purpose is to increase social connectedness, belonging and caring in communities. Communities that have strong social connectedness, belonging and a culture of looking out for fellow community members has been the foundation of human wellbeing since humans. The Good Neighbour App developed by the Happy Community Project provides citizens a way to make these connections so that they have the social, belonging, mental and physical support they require to live well being lives in their homes. The Good Neighbour App allows Happy Community Project Limited to reach out to people across Canada with a practical way to facilitate these kind of connection.

Étude des déterminants de la santé psychologique de procureurs canadiens

La maladie mentale est un enjeu préoccupant, alors qu’environ de 6,7 millions de canadiens en sont touchés (Camh, s.d.). Dans les milieux professionnels, la détresse psychologique est très présente et les juristes sont parmi ceux qui en vivent le plus (Cadieux et al., 2020). Spécifiquement, les procureurs sont très à risque en raison de la forte charge émotionnelle associée à la nature même de leurs fonctions. À cet effet, l’étude vise à identifier les facteurs de risques et de protections susceptibles d’expliquer la détresse psychologique de ces professionnels et à cerner les leviers d’interventions sur le plan de l’organisation du travail qui devraient être activés. Les résultats de ce projet, en partenariat, serviront à améliorer la santé mentale des procureurs canadiens et à limiter l’impact lié au travail.

Impact de la surface libre sur les performances d’une hydrolienne à aile oscillante passive

Dans le contexte actuel du réchauffement climatique, les méthodes d’exploitation d’énergie renouvelable dans le but de générer de l’électricité sont très recherchées par les pays développés. Au Québec, les nombreux cours d’eau et les grands dénivelés facilitent l’exploitation de l’hydroélectricité.

L’aile oscillante est un concept prometteur, car contrairement aux barrages hydroélectriques, celle-ci ne nécessite pas de grandes structures ni de modifications majeures à l’environnement où elle est installée. Il s’agit d’une méthode d’exploitation d’énergie renouvelable qui permet de générer de l’électricité relativement facilement. Une aile oscillante à mouvement contraint est mise en mouvement par une structure externe à l’aile, alors qu’une aile oscillante est dite passive si son mouvement est généré par le courant d’eau. Elle est généralement retenue par des ressorts et des amortisseurs.

Le projet vise la simulation numérique de l’écoulement autour d’une hydrolienne à aile oscillante passive pour étudier l’impact de la présence de la surface libre et suggérer une profondeur d’installation optimale.

Locating Missing Grave Sites using Remote Piloted Aircraft Systems.

This collaborative project between Vulcan County and the University of Lethbridge will allow an intern to use multispectral and thermal imaging as well as terrain analysis using topography rendered from Structure from Motion (SfM) to locate potential grave sites at a variety of locations in rural Southern Alberta. Sites vary from single plots to larger sites containing victims of the Spanish Flu outbreak in 1918. Application of these technologies provide the intern with an opportunity to recover lost records of graves in a non-destructive and cost-effective manner. By locating these lost graves, municipal records can be updated and detailed maps will prevent potential future disturbance of these sites.

Derivation of germ lineage cells from bovine haploid parthenogenetic embryos

Artificial reproductive technics (ART) have been widely utilized to maximize the use of genomics for animal improvement. In the forefront of novel expectations in ART is the ability to derive sperm and oocytes in vitro by differentiating pluripotent stem cells into germ cells and then into gametes. However, a major hurdle in the derivation of gametes from diploid pluripotent stem cells is to achieve complete meiosis and derive haploid cells that become viable sperm and/or oocytes. Our main goal in this proposal is to derive haploid primordial germ cells from parthenogenetically-derived embryonic cells that can differentiate into haploid primordial germ cells with epigenetic markings suited to produce genomically characterized diploid embryos. Apart from creating an opportunity to develop novel knowledge on the role of haploid genomes in mammalian development, this project will provide a unique tool to accelerate genetic gain by improving the selection accuracy of cattle breeding programs. The proposed project will be performed with close interaction with research staff from SEMEX, thereby enabling that all novel technical and scientific knowledge be effectively transferred to the industry

Developing Machine Learning Methods for RGB Images to Quantify Crop and Weed Populations Across Agricultural Fields

Food security is a global concern due to the ever-increasing population amid limited agricultural land resources. Farmers utilize fertilizers and herbicides to ensure consistent crop establishment. However, overuse of fertilizers and other treatments leads to negative consequences resulting in soil depletion, environmental contamination, and pollution. An effective way to reduce these toxic chemicals is to apply the optimal amount based on crop/weed distribution. Uneven crop distribution adversely impacts crop production, and it is important to identify inconsistent areas within a field. Once inconsistent regions are identified, site-specific treatments can be prescribed. Site-
specific farm management practices require high-resolution quantification of crop establishment like plant stand counting, leaf area estimation, inter-row spacing, inter-plant spacing, plants per crop row and plants per acre. Traditionally, manual scouting is performed to estimate these parameters, which is subjective, time-consuming, costly and not fully representative of the whole field. In this research proposal, we develop methods for quantifying crop establishment and biotic stress mapping using AI methods. Recently, highly accurate deep learning-based object detection methods have been developed. These methods are widely applied for non-agriculture applications like autonomous vehicles and medical imaging.

ROS2 Native Robot Controller for Manufacturing

In 2017, the global market for stand-alone industrial robots reached 2.1 million units, with a value of 8-16 billion USD for installation, service, and programming. This project aims to develop a modular robotic solution for controlling robots in the manufacturing industry, utilizing the open-source Robot Operating System 2 (ROS2) framework. The goal is to create a compact, high-performance system that enables direct communication between robot actuators and the ROS2 framework, to improve automation in the manufacturing industry. This will include the development of real-time controllers that can communicate directly with motor drivers, allowing for flexible configurations and applications to custom hybrid robotic systems, such as autonomous ground vehicles with robotic arms.

LIDAR à deux peignes aéroporté

L’équipe du Centre national d’études spatiales (CNES) à Toulouse a développé un concept de télédétection laser (LIDAR) aéroporté pour la détection et la quantification du dioxyde de carbone (CO2) ultimement à partir d’un satellite. Le projet est en phase initiale et un démonstrateur de laboratoire de même qu’un instrument aéroportés sont en cours de développement ou fonctionnels. Le but du stage est de mettre en application l’expertise en interférométrie à deux peignes de l’Université Laval afin d’améliorer la qualité des mesures. Il s’agit en particulier de mettre en oeuvre certaines approches de traitement du signal, en particulier de correction de phase interférométrique, dans un contexte où le chatoiement (speckle) de la tache laser est problématique.

Measuring International Entrepreneurial Competency

This important research project seeks to a) engage graduate students from National Cheng Kung University (NCKU) in an
immersive entrepreneurial experience in Toronto, Canada in the fields of advanced computing, clean technologies and scientific
innovation and b) measure the development of international entrepreneurial competencies impacted by this experiential learning.
Students will participate in a variety of structured entrepreneurial learning activities and will work with a variety of start-up
employers to learn about Canadian entrepreneurial innovation firsthand. The findings of this evaluation will significantly inform
future international entrepreneurial programming, which is hypothesized to lead to increased entrepreneurial activities in Canada
and by Canadian overseas.

Enhancing the Explainability of Temporal Financial Data

Deep Learning has seen great success in recent years in application to high-dimensional data. In the advent of increased computability, the increase in accuracy of Deep Learning has surpassed traditional statistical methods. Consequently, the complexity of Deep Learning methods are not interpretable (black-box); therefore, are often dismissed in high-risk domains such as health care and finance. Explainable Artificial Intelligence (XAI) aims to increase the interpretability of black-box models through explanations. In finance, forecasting is important for both the researchers and policymakers in financial economics, but the development of XAI for time-series models is limited and often the XAI methods do not take into account the aspect of time in explanations. Therefore, we propose to develop and implement a variant of the deep learning explainer Integrated Gradients to produce explanations for temporal data.

Building a Data Fluent Canadian Cultural Sector

21st century Canada requires a data fluent arts and culture sector. The cultural sectors and their funders must mobilize data to understand the impact and value of their efforts in Canada and abroad, and to improve these efforts. Building a Data Fluent Canadian Cultural Sector will identify existing data collection efforts in Canada, describe data types, collection methods and standards. It will establish who is currently undertaking cultural analytics research and what data they are collecting and producing, connect these researchers, and their data with efforts to build a common database, and encourage their use of such a database with the intention of coordinating significant and ongoing research. We will create a plan for an integrated database of cultural data and researchers; and create a prototype.

Understanding the Baseline and Changes in Canada’s Blue Economy Workforce

Canada is launching a new plan for its oceans, titled the “Blue Economy”, which is focused on economic development that will benefit Canadians equitably and protect the environment. This project is focused on understanding a baseline of who is part of Canada’s ocean industries, and create a monitoring tool to see if diverse Canadians benefit as the Blue Economy plan is implemented. Importantly, Blue Economy plans aim to aid in the economic benefits towards Indigenous people, and this research is focused on understanding how Blue Economy plans can best promote the wellbeing of Canada’s Indigenous people. The partner organization has a mandate to promote a diverse workforce in Canada’s ocean economy, and this research program would directly help the organization achieve that my understanding baseline characteristics and monitoring change.