Projets novateurs réalisés

Impact de la sécheresse sur le niveau piézométrique des eaux souterraines de l’hydrosystème du Drâa (Maroc) et l’étude de la concentration en éléments en relation avec la présence des mines abandonnées

Au Maroc, la disponibilité en eau douce constitue un défi majeur, tel le cas dans la région de l’Oued Draa (sud du Maroc), une région dépendante des eaux souterraines pour l’irrigation, l’eau potable et d’autres usages. La sécheresse météorologique, la surexploitation et la pollution menacent cette ressource essentielle. Pour évaluer la résilience et la vulnérabilité de l’hydrosystème du Draa, une méthodologie est proposée. Des mesures régulières du niveau piézométrique, l’utilisation d’indices de télédétection, et une modélisation hydrogéologique via l’intelligence artificielle seront employées. Des échantillons d’eau et de sédiments seront analysés, incluant la détection de métaux lourds, sulfates, nitrates, chlorures, isotopes, et bactéries indicatrices. Une base de données SIG sera établie pour diagnostiquer les aléas hydrologiques et climatiques, en produisant des cartes de risques confrontées à la réalité terrain. L’objectif final est pouvoir faire une gestion intégrée des ressources hydriques Ce projet s’inscrit dans une démarche collaborative qui a été récemment concrétisée par la soumission d’un projet conjoint au Ministère des Relations internationales et Francophonie. En effet, des liens entre nos équipes ont été initiés il y a environ 6 ans dans le cadre de cours de terrain dans le sud du Maroc, offerts aux étudiants du Québec- Maroc.

Additive manufacturing of rare earth hard magnetic materials

This proposal aims to examine the potential of laser powder bed fusion additive manufacturing (AM) in producing rare earth magnetic materials and open the door for manufacturing such components via recycled materials. The AM sector is the fastest-growing area within advanced manufacturing and presents a unique opportunity to boost economic profiles at both provincial and national levels and establish a robust domestic supply chain, particularly for rare earth magnets. The project stakeholders are GreenAge Materials (GreenAge), the National Research Council of Canada (NRC), and the Multi-Scale Additive Manufacturing Lab (MSAM) at the University of Waterloo.

Trust Establishment Mechanism to Isolate the Malicious Nodes in Flying Internet of Drones: From ML-Based Spectrum Fingerprinting Techniques to Honeypot Learning Attack Patterns Predicting

Today’s automobile is more than a mechanical tool; it contains a myriad of computers, sensors, IoT, and embedded nodes. The embedded system is the heart of a vehicle’s electronic system because of its versatility and flexibility. Furthermore, these systems are becoming increasingly sophisticated and interconnected, both to each other and to the Internet. However, with great convenience also comes great concerns about the security and privacy of our digital assets. Unfortunately, the security implications of this complexity and connectivity have mostly been overlooked, even though ignoring security could have disastrous consequences.
The attacks on embedded systems are evolving and becoming more complex and destructive. In this project, we develop a security incident response system for embedded systems designed to recover from attacks without significant interruption, dynamically selecting response actions while being lightweight in computational power, memory, and energy overhead. Furthermore, this project’s overcoming will help Thales identify the problem and associated cyber threat risks and pave the way for more efficient hardware/software solutions.

Détection et caractérisation de la désagrégation du béton par Géoradar

La désagrégation du béton est une des principales causes de dégradation des dalles des tabliers de ponts et des dalles des toits des chambres souterraines. Cette dégradation est invisible car ces dalles sont recouvertes soit d’un béton bitumineux, soit d’un remblai. Le présent projet de recherche a pour objectif d’étendre les applications de la technique du Géoradar à la détection de cette désagrégation. Il fera appel à des travaux expérimentaux en laboratoire et sur site, ainsi qu’à de la simulation numérique. Ce projet permettra à AusculTech d’offrir des services mieux adaptés aux besoins des ingénieurs. Il lui permettra aussi de se positionner en tant que leader au Canada dans le domaine de l’auscultation des dalles en béton armé par Géoradar.

Development of Fitness/Training Plans with AI/Machine Learning

Strontium loaded 3D printed scaffolds for improved bone regeneration

Strong evidence exists indicating the therapeutic potential of strontium in bone health and regeneration, and
perhaps treating metastatic bone disease. Since current standard treatments of large bone defects can still result
in poor outcomes (whether using autografts, allografts, ceramic or acrylic cements). New technologies and
advances in 3D printing with ceramics and other blended bioactive elements like strontium, may provide a new
pathway to improved treatments. There are several stable isotopes of strontium, and here we will test the impact
of scaffolds loaded with strontium on osteoblast and HUVEC adhesion, growth, viability and matrix deposition/tube
formation. Furthermore, we will apply scaffolds to a bone like microenvironment model representing a human 3D
bone-like tissue

PyLLMD – A Conversational Assistant for The World of Work Built on Interactively Programming Language Models

THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW

Machine learning X-ray emission spectra of metal impurities in aluminum alloys

Aluminum alloys are widely used in many industrial applications, including automobile industries, thanks to their outstanding thermal conductivity, lightweight, and low cost. However, the varied range of metal impurities contained in aluminum alloys from different suppliers makes it difficult to control the quality during manufacturing processes. This problem is exacerbated by the lack of a suitable method to characterize the properties of such dilute metallic impurities. The proposed research project will develop a novel method to investigate the properties of metal impurities in aluminum alloys, exploiting the extreme sensitivity of X-ray Spectroscopy on physical properties of dilute impurities. The development of rapid assessment method could make a significant impact on improving aluminum processing in the automobile industry in general.

Innovative Solutions for Pressure Injury Prevention through Introducing a Non- Wearable Smart System

About 8.8% of people in Canada are 65 years or older, and this number is expected to rise by 6.2% by 2036. As people get older, they become more likely to develop pressure injuries (PI) due to changes in nutrition, mobility, and skin health. In healthcare settings, patients at risk of PIs need to change positions every two hours. In Canada, around 26% of patients in all healthcare settings suffer from PIs. In Ontario alone, hospital-acquired PIs cost about $90,000 each year. While there are some products and articles about using telehealth for sleep monitoring, they can be pricey and hard to make. So, our main goal is to create a new smart technology that doesn’t need to be worn, which can detect body positions in bed and suggest when to change positions. Our system will use a special sheet on the bed mattress and a laptop to process the data and give recommendations. We want our technology to be easy to use, accurate, and affordable compared to what’s already out there.

A Lane-level Navigation System with HSLIG Fusion Scheme for Smart Vehicles

As autonomous technology evolves, smart vehicles emerge as a pivotal development for transforming public transportation and minimizing transport costs. Key to their broad acceptance is the resolution of safety, stability, and compatibility issues. This project proposes a cutting-edge solution through a High-Definition (HD) map-aided, multi-sensor fusion approach, aiming at precise lane-level positioning and navigation in dense urban canyons. The objective is to create a resilient sensor fusion scheme that seamlessly merges the onboard sensors with environmental contextual information from High-Definition (HD) maps, achieving lane-level navigation even in urban canyon environments. The expected output for the industry partner will be a prototype of a navigation system that can be deployed on the partner organization’s smart vehicles, facilitating reliable lane-level navigation in typical urban canyon environments.

Integrating graph-based data management into materials acceleration platforms

This research project aims to significantly improve the way data are managed in a specific self-driving laboratory in the AUTODIAL group of Prof. Hattrick-Simpers at the University of Toronto, focusing on discovering new materials that are resistant to corrosion. This class of labs, known as Self-driving labs (SDL) or Materials Acceleration Platforms (MAPs), use advanced technologies, such as AI and automated experiments. The project introduces a new system for organizing and analyzing data using a new approach called a graph database, which is better at handling complex and interconnected information. This upgrade will also involve the use of sophisticated language-processing technologies to better understand and utilize the data collected. The goal is to make the labs more efficient and effective, reducing the time and cost of the experiments and simulations. The project will also compare how these labs communicate in an in-operable fashion with similar labs across Canada and Germany to identify common challenges and solutions, ultimately aiming to accelerate the development of new materials.

2D material band-gap prediction by machine learning

Two-dimensional (2D) semiconductor materials are materials with thickness on the atomic scale that provide unique properties compared to their 3D counterparts. One important property of semiconductors is their band gap, which dictates how the semiconductor material will behave. However, manufacturing and testing 2D semiconductors can be costly and difficult, so the ability to predict the band gap of 2D semiconductors would be very useful to allow researchers to focus efforts on testing materials that will yield desired band gaps. This project aims to train a machine learning algorithm on data from 2D and 3D semiconductors to predict the band gap of new 2D semiconductor materials. This machine learning approach has the potential to be more accurate and quicker to compute than current quantum-mechanics based computations. This project will benefit both institutions by developing the application of machine learning in materials engineering, as well as providing an algorithm that can be used by researchers and engineers to design new 2D semiconductor materials.