Projets novateurs réalisés

Assay and Portable Device for the Assessment of an ALS Biomarker

A PhD student from the IMDEA Nanoscience research institute in Spain will travel to Canada for an internship at the University of British Columbia. This internship will initiate a new collaboration to develop materials, methods, and devices for detecting a protein malfunction in white blood cells that reflects the emergence of the neurodegenerative disease, amyotrophic lateral sclerosis (ALS). This disease affects hundreds of thousands of people, has a poorly understood cause, and no cure. The proposed research will create a way of imaging the protein malfunction within blood cells, as a mirror for what is occurring in brain cells during ALS onset, using a smartphone and brightly fluorescent nanoparticles. The internship will enable an effective exchange of knowledge between one group with the resources and expertise for studying ALS biology (Spain) and one group with the tools and expertise for creating the nanoparticles and imaging device (Canada), enabling advances that would not be possible with the collaboration. The technology developed as an outcome of this internship and collaboration will be an important step toward an accessible, effective, and earlier diagnosis of ALS, and will be a valuable tool for searching for and evaluating potential therapies and drugs for ALS.

Planifier des interventions des espèces végétales exotiques envahissantes du mont Saint-Bruno

Les espèces végétales exotiques envahissantes (EVEE) ont été identifiées comme étant une menace majeure à la biodiversité du mont Saint-Bruno, une colline montérégienne abritant des milieux naturels d’importance pour la communauté métropolitaine de Montréal. Depuis 2019, la Sépaq, gestionnaire du parc national du Mont-Saint-Bruno qui protège la moitié de ces milieux naturels, a initié des mesures de contrôle d’EVEE. Cependant, aucune donnée ou plan d’intervention d’EVEE est prévu pour la zone périphérique qui comprend des terres publiques et privées, compromettant l’éventuel succès des efforts en cours. Notre projet vise à combler cette lacune en effectuant une cartographie détaillée de l’occurrence de sept EVEE ciblées et en effectuant une revue de littérature et une consultation avec personnes expertes pour identifier les meilleures techniques de contrôle selon l’espèce et le niveau de menace établi. Ces connaissances serviront à produire un plan d’intervention et son plan d’action 2025-2027 pour mobiliser tous les acteurs du milieu à contrôler les EVEE du mont Saint-Bruno avec une vision concertée.

Big Data Processing and Analysis

Addictive Mobility is a leading online advertising company in Canada. The success of ad campaigns drives the majority of the company revenue. Exploring advanced machine-learning techniques to efficiently control an ad’s performance is crucial to the company strategy. The objective of the proposed project is to optimize the real-time bidding system in the sense that delivery has been carried out in real-time and within a time-interval of 100 ms. As we mentioned, this problem is highly complex and we can break it into several subproblems each of which can be a major area in machine learning.

The Columbia Valley Community Renewable Energy Project

The proposed research will review and evaluate the renewable energy options available for both energy production and carbon dioxide mitigation in the Columbia Valley and how to implement the preferred solution at the community level. The research aims to review relevant academic literature and Canadian case studies, consult with local stakeholders, provide quantitative analysis (e.g., levelized-cost-of-energy, cost-effectiveness analysis) of options, provide qualitative analysis of options where quantitative analysis is not possible, and make
recommendations regarding the options

Machine Learning developer and Product interns working within cross-functional teams to develop and commercialize AI-powered solutions in the Public Services sector (1)

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Advancing Gene Expression Microarray Analysis: Assessing and Enhancing the Linear Combination Test Through Integration with Machine Learning Tools

Understanding which genes are involved in diseases is incredibly important because it helps us develop better treatments. By identifying these genes, scientists can better understand how diseases operate in our bodies and create more effective treatments. This also allows for the creation of personalized treatments based on a person’s unique genes, increasing their chances of recovery. However, finding these genes is a difficult task as there are thousands of genes in the human body and vast amounts of genetic data to sift through. This project aims to enhance a method called the Linear Combination Test (LCT) and use machine learning tools to optimize further its ability to identify disease-related genes, such as those responsible for COVID-19 and cancer. In the second step of this project, the improved LCT will be tested on real-world data to gauge its effectiveness. Additionally, user-friendly software tools will be designed to make it simple for other scientists to use. Our ultimate goal is to help find cures and improve the lives of those who suffer from diseases.

Matrix Inspection Technique for Tubular Components (MITC)

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Development of Automation Innovation

BMM Testlabs is a private independent gaming test laboratory that provides testing, certification, and compliance services for various gaming platforms and jurisdictions around the world . It has been serving the gaming industry for over 40 years. We have opportunities that this project can address, specifically in the area of automation using full stack development and machine learning. Full stack development can automate various business processes, resulting in cost savings and increased productivity. Machine learning can be used to develop chatbots and virtual assistants that can understand and respond to natural language queries, improving customer service. Finally, both machine learning and full stack development can help businesses make data-driven decisions, leading to improved efficiency and competitive advantage.
Currently BMM Testlabs has challenges in obtaining available in-house resources to work fully on these projects. The two projects will complement and integrate with each other. This will help BMM Testlabs become more competitive. We will implement Machine Leaning into our Test Automation Framework utilizing the full-stack development of the front and backend. The machine learning will utilize the abundance of log information to obtain informed decisions about test cases and defects. A chatbot assist in the process.

Autonomous Navigation of a Drywall-Sanding Mobile Manipulator

Nowadays, many building contractors use modular construction, where various parts of a building are partially or completely built on an assembly line and then put together on the delivery site. Building interior modules often involve building and finishing plasterboard walls, including closing joints between panels and hiding fasteners or defects. To do so, a gypsum-based compound is applied, let to dry, and sanded iteratively to achieve a regular surface. This produces a considerable amount of fine dust, which can be a nuisance, especially in the closed space of a manufacturing plant. In this project, we are investigating how a mobile robot can automatically scan a room and position itself to perform the sanding task using a 7 degrees-of-freedom manipulator arm. This project builds on previous successful result in 2D sanding surface identification and sanding tool control, and aims at producing a complete solution for 3D environments.

Enhancing Digital Marketing and Business Intelligence for Hyspecs Eyewear

This project aims to drastically improve Hyspecs Eyewear’s digital marketing strategies and business intelligence capabilities. It seeks to leverage Zach Ketter’s academic background in Business Technology Management and his academic advisor Robb Somach to address real-world business challenges. The project focuses on the development and implementation of innovative digital marketing strategies and the use of analytics to drive strategic decisions, thereby enhancing the company’s market position and customer engagement.

Beyond the Book

The ultimate aim of this project is to design and develop methods and tools for classifying attributes of books such as genre, style, tone, and likelihood of being popular. Towards this end we will make use of various information types available on books and users of the Kobo catalog, including the text, meta-data associated with the text, and user features associated with readers of the text. This is a large undertaking. As a first step, the intern and research team will tackle the problem of genre classification, while keeping in mind that the task is one among a collection of desired automatic tagging tools for books. Through the use of NLP and machine learning techniques, books can be categorized and referred to readers via interests that they have expressed. This project is likely to provide valuable insight into both books and readers, allowing Kobo to provide users with higher quality recommendations, interesting reading lists, and deeper understanding of both books and users.

Projet d’augmentation de l’efficacité énergétique du produit

Fondée en 2020, AWL-E a pour mission de commercialiser des solutions de transmission d’énergie sans fil grâce à sa technologie unique offrant plus de distance, de puissance et d’efficacité à ses utilisateurs. L’entreprise se concentre sur l’implémentation de ses inventions aux applications à fort impact social et économique.
AWL-Électricité travaille actuellement au développement d’un chargeur sans fil dans le domaine de la mobilité en utilisant une technologie de couplage capacitif résonant. Son premier produit consiste à recharger les fauteuils roulants électriques de résidants de centre pour personnes âgées, pour ainsi améliorer leur autonomie et réduire la charge de travail du personnel soignant.

Dans le but de manufacturer et d’amener un premier produit sur le marché, AWL-E cherche à développer un système de recharge à distance. Réalisée grâce à l’implication de nos employés et stagiaires, servira à réduire le temps de recharge des fauteuils roulants électriques et améliorer l’expérience utilisateur. Ce projet est crucial pour l’entreprise car il accélérera sa mise sur le marché. De plus, en ayant des composants plus efficaces, notre produit sera plus attrayant pour les futurs clients et permettra de faire des avancés technologiques afin d’éventuellement viser d’autres marchés.