Projets novateurs réalisés

A Novel AI-Based LED Grow Light

Indoor farming offers the opportunity to grow produce close to the consumer and has recently been gaining attention due to its efficiency, controllability, and sustainability. In indoor farming, food is grown indoors in a controllable environment, with stable environmental conditions. This approach reduces the land footprint required, the demand for energy and water, the nutrient input requirements, and can eliminate the need for pesticides. Food can be locally produced, picked, packed, and quickly delivered to neighbouring communities.
In this research program, next-generation AI-based LED grow lights tailored for future vertical farming will be designed and developed. The proposed solution will offer high efficiency and great flexibility as it can adjust the light intensity and spectrum accordingly. The firmware embedded in the proposed AI-based LED grow lights can identify issues in the growth process and adaptively change the outputs.

Socia-Economic Mine Closure (SEMC) Guideline: An Evaluation of theLocal Economic Development (LED) Initiatives Implemented by Mining Companies toAddress the Adverse Results of Mine Closure.

This MIT ACS-Accelerate proposal is the first involving a multiple stage project to be developed as part

of my PhD investigation. Through the completion of the full research project, the initiatives

implemented by mining companies to address the socio-economic impact imposed on local

communities when a mine ceases operation will be analyzed. Additionally, a socio-economic mine

closure guideline will be developed to assist both mining communities and mining companies in the

anticipation and mitigation of the impacts that mine closure has on communities. Although the

methodology for the full research includes surveys and interviews with community members, company

representatives and other key stakeholders, this present proposal will not involve the phase of

engagement with these subjects.

Optimization Design and In-lab characterization of Optical Phased Arrays (OPAs)

We are proposing to help design the experimental setup to characterize chip-scale Optical Phased Arrays (OPAs). OPAs – a photonic device used for optical beam forming and beam steering – have been widely studied for LiDAR, optical sensing, free-space communication and more. Building on previously prototyped phase array antenna design at Honeywell, the team aims to develop next-generation micro-photonic phase arrays that can steer beams with minimum output of 2 Watt (optical power) for data rates on the order of 10 Gbps and acceptable bit error rates. Our goal is to establish and conduct in-lab characterization of the newly design OPA to validate and verify design parameters predicted through simulation studies, as well as to provide recommendations for optimization of the OPA design.

Development of Technologies and Methods for Metabolite collection and processing

Metabolomics is an emerging field of research that provides insight into health and disease by studying the levels of various small molecules (metabolites) in the body. In this project, we are developing new tools that will improve and standardize methods for collecting and stabilizing fecal samples for metabolomics studies based on the analysis of fecal samples. Ultimate goals of the project are improved workflows for analyzing fecal samples for metabolomics studies, and kits which will permit easy home-collection of samples. The kits will also stabilize the samples at room temperature. This will provide new methods which can be used to study various diseases and/or the health of gut bacteria, or develop new diagnostic tests for identifying diseases in patients.

Development of prediction models in smart buildings using data driven approaches through Artificial Intelligence

BrainBox AI is working on the mandate to provide better solutions for optimized control of HVAC systems modeled through data using AI. The Intern will continue our research at BrainBox AI and to continue to develop data driven approaches for an optimized operation of HVAC systems in design and implementation of scalable AI frameworks for AI needs in the ongoing projects at BrainBox AI. The Intern will apply AI (including ML/DL/RL), data mining and statistical approaches for the creation of scalable predictive models. Evaluate the performance of predictive models and integrate in existing AI frameworks identify, analyze, and interpret trends or patterns in complex data sets. The solution to achieve this objective will be created by applying AI algorithms.

Analyse des réponses gravimétriques en forage du système Gravilog

La gravimétrie est une méthode de géophysique appliquée qui utilise la loi d’attraction

Universelle pour sonder le sol. Cette méthode est utilisée pour calculer les changements de

densité des roches. Puisque les gisements miniers de la région de l’Abitibi sont beaucoup

plus denses que la roche l’entourant, il est possible de les détecter à l’aide de cette méthode.

Récemment, un nouvel instrument de mesure, le Gravilog, a été développé par la compagnie

Scintrex. Le Gravilog est un gravimètre qui mesure la variation d’attraction dans un forage. À

l’aide de cet instrument, il est possible d’observer des gisements profonds et de mieux les

positionnés. En plus, la méthode gravimétrique peut être utilisée en complémentarité avec

d’autres méthodes géophysiques en forage déjà utilisées par Abitibi Géophysique pour mieux

positionner et dimensionner un gisement. Cependant, l’utilisation de cet instrument est

coûteuse, alors il faut bien optimiser son utilisation. Pour ce faire, trois aspects seront

analysés.

Graph-based learning and inference: models and algorithms

Learning from relational data is crucial for modeling the processes found in many application domains ranging from computational biology to social networks. In this project, we propose to work on developing modeling techniques that combine the advantages of the approaches found in two fields of study: Machine Learning (through graph neural networks) and Statistical Learning (through statistical relational learning methods). By combining the advantages of both approaches, we aim to obtain better prediction results for an array of problems such as classification and link prediction in relational data.

Unsupervised Learning of 3D Scenes from Images using a View-based Representation

We’d like to address the issue of 3D reconstruction from 2D images. This means developing a machine learning algorithm that can take a regular photo as an input and generate a full 3-dimensional reconstruction of the contents of the photo. Such technology can be used creatively or to help the coming generation of robots better understand their surroundings.

Surface property optimization of an insulation coating for glass

The handling and storage of perishables like vaccines are critical to retain medical potency and effectiveness for patient administration. The delivery of vaccines can succumb to several pitfalls such as physical damage, losses in transit and expiration. One of the primary causes of vaccine wastage is a result of heat and freezing of the biological products. These sensitive perishables quickly spoil if not maintained in the correct temperature range of a refrigerator that is kept to +2°C to +8°C. [1] As a result of vaccines being exposed to temperatures outside of this 6-degree range can reduce the protection that vaccines provide against such diseases like diphtheria, pertussis and tetanus. Clear visual differences can be observed from within vials that show large precipitates of crystalline structures of the affected vaccine when exposed to freezing temperatures. [2] These unfavorable conditions irreversibly ruin the vaccine for immunization. Ensuring proper vaccine temperature conditions in-transit have mainly been done through cold-chain delivery and the use/management of expensive purpose-built refrigerators.

Les didacticiels de films interactifs : entre intégration diégétique et instructions procédurales

Se situant dans la lignée des études du jeu vidéo en sciences humaines, mon projet de recherche prend pour objet les didacticiels, c’est-à-dire les niveaux ou instants de jeu “apprenant” à l’utilisateur comment interagir avec le logiciel (“appuyez sur la touche X pour sauter”). Il se concentre sur le genre du cinéma interactif, afin de replacer des œuvres contemporaines – comme “Black Mirror : Bandersnatch” (David Slade, 2018) sur Netflix – dans la filiation des films interactifs nommés FMV (Full Motion Video). Ces productions hybrides, mêlant interactivité et images en prise de vues réelles, sont apparues au sein de l’industrie vidéoludique il y a bientôt trente ans, avec des œuvres brouillant chacune à sa façon les frontières entre cinéma et jeu vidéo. Ces “films-jeux” et leurs manuels, boîtiers et autres notices d’emploi sont conservés au LUDOV (Laboratoire Universitaire de Documentation et d’Observation Vidéoludiques) de l’Université de Montréal. Ma recherche permettra de redécouvrir ce corpus et d’analyser, à partir de leur(s) didacticiel(s), les stratégies employées par les développeurs pour s’adresser au joueur/spectateur et nouer des “contrats de lecture” ludiques, dont on étudiera toutes les modalités et paradoxes énonciatifs.

The Impact of Emerging Technologies on Defense Policy

How technology relates to Canadian national defence policy, strategy, and planning is not well understood. To date, much of the research on emerging technology in the military focuses on concerns over ethics, privacy, human rights, and governance, largely overshadowing equally important research on the way technology intersects with military strategy and tactics, defence recruitment and training, and alliance politics and national policy. Our project is inherently forward looking: its overarching objective is to think creatively about emerging technology and to link developments therein to Canadian defence.

Lay Sisters and Brothers in Medieval German Convents

Medieval nuns were supported by a wide range of people, including lay sisters and brothers who performed much of the physical and manual work of the convent. Yet scholarship has long ignored these members of the monastic community. My research attempts to address this gap by focusing on lay sisters and brothers in fourteenth- and fifteenth-century German convents. I would like to request funding to spend three months in southern Germany next summer, from the beginning of May 2020 through to the end of July 2020. During this time, I will search through archival documents from medieval convents, including deeds, visitation records, account books, necrologies, and convent statutes, to find references to lay sisters and brothers in various archives in southern Germany. I am a third-year PhD student at the Centre for Medieval Studies in the University of Toronto, and this ground-breaking research will help to nuance our understanding of women’s daily lives, religiosity, and the intersections of gender and class in medieval Europe––subjects that have historically been neglected by historians. I believe that undertaking this research next summer will provide me with the remaining research necessary for my dissertation and greatly contribute to the study of female monasticism.