Projets novateurs réalisés

A Nanomaterial-Integrated Paper Microfluidic Device for Detecting and Predicting Myocardial Infarctions

Cardiovascular disease kills 17.9 million people globally each year. 85% of these deaths were caused by either a myocardial infarction (heart attack) or stroke. Current tests for predicting heart attacks provide rapid results when presented with a blood sample, but these samples must be preprocessed using time-consuming methods. This project will develop a paper-based platform to detect heart attack markers in blood without external preprocessing. This will be accomplished by integrating a blood processing device into the testing platform. Ideally, this platform will predict heart attacks within five minutes of drawing blood from patients, facilitating life-saving medical treatment. The partner organization, Mitra Biotechnologies, previously developed a paper-based blood processing device. They are now partnering with researchers at the University of Waterloo with expertise in paper-based medical tests to turn this device into an all-in-one blood processing and testing platform that can be brought to market to save lives.

High-threshold photonic quantum computing

Today, quantum computers of any architecture still operate in the noisy intermediate-scale quantum (NISQ) regime, with systems composed of tens to hundreds of noisy qubits that are not protected from errors. To meaningfully solve challenging problems from industries using quantum computers, we need to make a system that is large enough in scale and robust to errors. The intern will be involved in Xanadu Architecture team’s efforts in investigating promising approaches for tolerating and lowering noise and errors affecting photonic platforms. The aim is to lower the demands on hardware, shortening the time to build a universal, fault-tolerant quantum computer.

Détection des engins perdus pour protéger les espèces aquatiques en péril dans le golfe du Saint-Laurent en utilisant l’intelligence artificielle

La perte d’engins de pêche en mer représente un danger pour la vie marine, en particulier pour les baleines noires qui risquent de s’emmêler dans les cordages. C’est pourquoi la détection des engins de pêche abandonnés est une préoccupation majeure pour les scientifiques et les autorités maritimes. Les avancées dans l’intelligence artificielle offrent une solution prometteuse à ce problème. En utilisant des algorithmes de détection, il est possible d’identifier et de localiser les zones où les engins de pêche fantômes sont susceptibles de se trouver, ce qui facilitera les efforts de récupération. Cela permettra également de réduire le temps et les coûts nécessaires pour ces campagnes de récupération. En fin de compte, l’utilisation de l’intelligence artificielle peut aider à protéger les écosystèmes marins et les espèces qui en dépendent.

AI-based decision support tool for storm damage prediction in Nova Scotia

Nova Scotia Power Inc. (NSPI) is the main provider of electricity in Nova Scotia. The largest disruptor to its system is the weather, which can lead to widespread equipment failure and outages across the grid. NSPI attempts to predict these damages before they occur to decide on the appropriate level of response and allocation of resources to restore service to their customers as soon as possible, during and after a weather event. Currently, NSPI uses a simple MS Excel-based tool to make predictions. However, its accuracy is relatively low. This project investigates whether modern AI/ML tools like Artificial Neural Networks, Random Forests, and Reinforcement Learning can be harnessed to improve the prediction accuracy of storm damage in Nova Scotia. The objective is to develop and test a prototype prediction tool that uses weather data like wind speed and direction, precipitation levels, ground thaw and foliage, as well as system information, e.g., the number of transformers and electricity poles and the distribution of customers across the province, to make systematic predictions about the damage to be experienced in different geographical regions as a result of forecasted extreme weather events.

Investigation of Striped Bass Morone saxatilis, Atlantic Sturgeon Oxyrinchus oxyrinchus, and American Eel Anguilla rostrata movements and migrations in Annapolis River, Nova Scotia

Following the completion of the Annapolis River Tidal Power Generating Station in 1984, the abundance of many native diadromous fish species declined in the river. In 2019, new regulatory directives resulted in the abrupt termination of power generating activities, and tide gates that had barred access to the river were left open for the first time in 35 years. In a collaboration, Nova Scotia Power, Clean Annapolis River Project, and Acadia University are currently assessing the recolonization and habitat use by COSEWIC designated species of concern including Striped Bass and Atlantic Sturgeon and will soon monitor the movements of American Eel within the system. The goal of this project is to monitor river recolonization including spawning habitats and to collect data on critical habitat.

Lobed Mixer and Inter-Turbine Duct Aerodynamics

The intern will assess computer-based simulation tools used by a leading Canadian aerospace organization to determine the accuracy of these tools. Specifically, the objective is to establish the extent to which these engineering tools can be used to capture the performance benefits of novel aerodynamic design strategies recently developed at Carleton University for aerodynamic shaping of inter-turbine ducts and compact exhaust diffusers, which are two components used on gas-turbine engines. Upon confirming sufficient accuracy of the simulation tools for this purpose, the partner organization will be able to integrate the research findings developed at Carleton University into the design of their gas turbine engines.

Reduced order modeling and machine-learning techniques for environmental flows problem

The project involves enhancing and extending the in-house fluid flow solver using advanced mathematical and computing framework to be undertaken at the host university. The current in-house solver code can be efficiently applied to aerospace, marine and environmental flow problems. The collaboration with host university will result in a more advanced version of the code due to the implementation of mathematics based reduced order models which will speed-up the computations without any loss of physics. The reduced order modeling method has proven to reduce the complexity of the problem from millions of elements to just tens or hundreds of elements with almost similar accuracy. Another part of the research collaboration consists of application of machine learning based models to the in-house flow solver. An artificial neural network will be fed in physics of the fluid flow problem and using training data it will be trained to “learn” how to solve the physics for the particular problem. These are the main objectives of the proposed research project which will be completed in the time frame of 24 weeks under the host supervisor.

Assessment and Optimization of Chemical Extraction of Critical Metals from Li-ion Batteries (LIB) Waste

The proposed research project aims to explore a cost-effective and eco-friendly method for recycling lithium-ion batteries (LIBs) used in electric devices and electric vehicles. Lithium, cobalt, nickel, and copper, which are considered critical metals, are present in LIBs, and they are essential to the operation of electric vehicles and devices. After their lifetime, these batteries often end up in landfills, leading to significant environmental hazards. By developing an effective recycling process, it will be possible to recover valuable metals and reduce the amount of hazardous waste generated. This research project will contribute to a more sustainable and eco-friendly approach to battery recycling, which can help to reduce the environmental impact of waste batteries. The results of this project will be valuable for various organizations working in the field of battery recycling and recovery of critical metals.

Application of Artificial Intelligence in Human Fall Detection

Falls are a significant public health concern, particularly among older adults, who are more vulnerable to injury and death resulting from falls. According to the World Health Organization (WHO), falls are the second leading cause of accidental or unintentional injury deaths worldwide. With the global population aging rapidly, there is an urgent need to develop reliable and accurate fall detection systems.

The current approaches for fall detection include wearable and non-wearable devices, such as sensors, cameras, and accelerometers. However, these methods often suffer from limitations, such as low accuracy, false alarms, and inability to detect certain types of falls, such as falls from a seated position or falls on soft surfaces. The use of artificial intelligence (AI) algorithms has shown great potential in addressing these limitations by providing more accurate and reliable fall detection.

The application of AI in fall detection is a growing research area, with many recent studies exploring the use of machine learning, deep learning, and computer vision algorithms in human fall detection. These studies have shown that AI algorithms can significantly improve the accuracy and reliability of fall detection systems, particularly when combined with wearable and non-wearable devices.

Samantha Lin – Circular Economy in Canadian Fabric Manufacturing Industry

The intern will be participating in a project analyzing and growing upon a case study of advanced manufacturing circular economy initiatives in the global fabric manufacturing industry. They will create a proposal for how to integrate a circular economy in Canada’s manufacturing industry, utilizing key success stories, identifying vital partners in the current industry, and conducting research into best practices to work with existing practices and integrate new technologies and solutions. In addition, by collaborating with key project partners, including the Cansbridge Fellowship and the intern’s academic supervisor (Dr. Marcus Taylor), they will utilize their analytical sustainability perspectives and knowledge about development and production best practices to assess the feasibility and create a roadmap for advanced manufacturing integration in Canadian industries. The partner organization will provide direct mentorship through its alumni network with expertise spanning multiple sectors and different scales of impact. The direct benefit to the partner organization is the advanced manufacturing circular economy proposal that the organization’s alumni can integrate into existing and upcoming startups and business initiatives.

Mobilisation et accompagnement pour l’évaluation participative des projets du système alimentaire montréalais

La démarche d’évaluation participative des projets du volet alimentaire de Montréal en commun (MEC), un programme porté par le Laboratoire d’innovation urbaine de la Ville de Montréal (LIUM) issu de la candidature gagnante de la Ville au Défi des villes intelligentes du Canada, est appelé à changer d’échelle dans le cadre de ce projet de recherche, de mobilisation et d’accompagnement. Celui-ci vise en effet à élargir l’utilisation des outils d’évaluation développés dans ce contexte et à favoriser l’intégration de pratiques d’évaluation dans plusieurs organisations du système alimentaire montréalais. Ce projet contribuera à l’atteinte de deux des principaux objectifs et défis du LIUM, soit la pérennisation des projets et outils numériques développés dans le cadre de MEC et la mise à disposition de ces outils au plus grand nombre d’organisations montréalaises. Cela s’inscrit dans la vision du Défi des villes intelligentes du Canada de mettre les données aux services de la collectivité. Le projet d’élargissement de la démarche d’évaluation participative représente, d’une part, un héritage qui sera laissé par MEC, mais aussi une contribution à la consolidation des projets dans le système alimentaire montréalais.

Partner – Development of retail operations strategy for increased business demands

The Brackley Bay Oyster Company is a family-owned, boutique oyster packing and shipping facility. We ship wholesale to domestic markets, we have a federal CFIA license to allow us to ship internationally, and also have a retail location at our packing facility, which has been in operation for 3 years. After seeing the success of the retail operation, we decided to modify our business strategy and focus more on the retail sector. We had a piece of very valuable commercial property in a busy tourism location in Brackley Beach, in an underserved area, so we built a new larger, better served location and plan to open in June 2023.
Our expertise is in the oyster growing, processing and packing side of our business. We need help in the retail organizational skills and customer experience, human resource management and and the daily operation management that will be required. This project will assist in us, not only bringing someone on with this skill set, will help us learn and train an intern to continue work with us on a full-time, year round basis.