Projets novateurs réalisés

Peg City Car Co-op – Private Fleet Management Project

My Partner organization- Peg City Car Co-op operates a carsharing service locally in Winnipeg.

The goal of the project is to establish a business case for the Peg City Car Co-op to achieve their goal of working with the City of Winnipeg to manage part of the city’s fleet of non-specialized vehicles. The project is divided into four stages. The first stage is to conduct a literature review on the history, variants and benefits of carsharing. This will help me know more about the industry and how it came to be. In the second stage where I will reach out to various other carsharing organizations that have already reached Fleet management agreements or worked with Municipalities. This questionnaire will help to know the challenges faced by those organizations, and the outcomes achieved, which will be documented in the stage two literature review. After establishing precedents, in the third stage, I will engage with the Winnipeg Fleet Management Agency (WFM) to know their needs and how the Co-op could help them. The final stage will consist of preparing a business case outlining the costs, benefits and challenges to overcome, including a set of steps to bring the plan to action.

Schedulability Analysis of Real-Time Systems Using Metaheuristic Search and Machine Learning

Schedulability analysis aims at determining whether task executions complete before their specified deadlines. It is an important activity in developing real-time systems. However, in practice, engineers have had difficulties applying existing techniques mainly because the working assumptions of existing methods are often not valid in their systems. Specifically, uncertainties in real-time systems and hybrid scheduling policies that combine standard scheduling policies have not been fully studied in the literature. This project develops an approach that analyzes the schedulability problem of real-time systems by accounting for such uncertainties and complex scheduling policies applied in practice. Our approach combines a metaheuristic search algorithm for generating worst-case scheduling scenarios with a machine learning technique for inferring a probability of deadline misses. To evaluate the practical usefulness of our work, we apply our approach to real systems developed by our industry partner, BlackBerry.

Quantitative, and qualitative evaluation of the Halo Ai stress reduction music application

The main objective of this research project is to evaluate and analyze the effect of music in well-being and state of flow of the human participants. First, we intend to design a set of scenarios, in which we collect qualitative and quantitative data via micro-phenomenology and biofeedback techniques. Then we analyze the collected data to support or reject hypotheses, such as: (1) A single use of Halo AI reduces user stress levels, and (2) repeated use of Halo Ai amplifies this effect. Finally, we will write a report in the form of a publishable research paper and document the process and the results.

Ontario Back To Play Research Project

The expected research seeks to understand the current state of youth sport offerings in Ontario, Canada. Through a survey of youth (ages 6-29), parents and organizations involved with sport in Ontario, the back to play survey aims to understand the current state of youth sport in the aftermath of the COVID-19 pandemic. Access to opportunity, whether or not youth are comfortable engaging or reengaging in sport, and whether social or economic factors such as race, income or geography influence how or who is return to play, will be explored. The survey itself will include both quantitative and qualitative items for analysis, with key findings to be shared publicly and to inform future research, policy, funding strategy.

Exploring perceptions on the QUIT smartcase to guide continued development of this digitally enhanced technology for reducing and quitting dependence on e-cigarettes.

There is growing evidence that e-cigarettes, also known as vaping, have led to an upsurge health risk to young people in Canada, including long-term harm to brain development and respiratory health. Due to such adverse effects, more and more young people indicate a desire to quit. However, such desires often become unsuccessful due to the lack of resources available to assist young people in reducing and quitting e-cigarettes.
The objective of this research project is to explore young people’s perceptions of the “QUIT smartcase” for reducing and stop using e-cigarettes. The QUIT smartcase prototype consists of a case wherein a user puts their pack of cigarettes or e-cigarette into the case and schedule time for when the case can be opened to access them. A user study will be conducted to explore the usefulness of the QUIT smartcase prototype and generate recommendations to design fully functional QUIT smartcase for a gradual reduction of e-cigarette.

Perception de la ville de Val d’Or par les femmes autochtones

Mon projet de recherche se concentre sur la perception de la ville de Montréal qu’ont les femmes autochtones. Je vais, au travers de ma problématique, essayer de saisir en quoi cette perception de la ville est liée aux rapports de domination, et plus particulièrement les violences subies. A travers plusieurs sous-questions et à l’aide du cadre théorique de la géographie de la peur, je vais tenter de comprendre comment les femmes autochtones perçoivent la ville de Montréal, comment cet espace est vécu et s’il existe une localisation privilégiée pour ces femmes, c’est-à-dire des quartiers qu’elles préfèrent fréquenter ou loger.
A l’aide d’une méthodologie basée en partie sur des entretiens compréhensifs en compagnie de femmes autochtones vivant à Montréal et de l’observation, je vais chercher à répondre à ma problématique de départ.

Data-Driven Control of an Ultracompact Industrial Robot

In recent years, automation has become more accessible to small- and medium-sized businesses, leading to an increase in popularity of ultra-compact and easy-to-integrate industrial robot arms like Mecademic’s Meca500. However, because of their size constraints, it is harder for these robots to accurately follow a programmed path. This research project aims to improve the path-tracking performance of Mecademic’s Meca500 robot by fusing state-of-the-art machine learning techniques with modern control design techniques. Improving the path-accuracy of the Meca500 will strengthen Mecademic’s competitive advantage in the fast-paced industrial automation market.

Enhancing visualization of manufacturing complexity highlights on CAD file

Currently, the service provided by GRAD4 has an interface for visualization of CAD models. However, there is still room for improvement in speed and comprehensibility for the users: both manufacturers and buyers. The main challenge of implementing the improvements is in relatively high computational cost of such visualizations: while a regular PC handles such task efficiently, web-based tools tend to have difficulties when modelling 3D object with similar performance. Based on this, it is proposed 1) to investigate ways of improving geometric representations of CAD models on a web-page; 2) to retrieve and analyze user feedback on the viewer and take according actions; and 3) to identify future venues for viewer development.

Performance map of effect of composition and heat treatment on the work output from shape memory alloys.

Shape memory alloys are materials which, when cooled are easily stretched, but when heated, return to its previously “remembered” shape with a high force. This allows for a simple heat engine to be made, which can use hot and cold sources to create motion and do work. The amount of work that can be done by a shape memory alloy is dependant on its composition, geometry and how the material is processed prior to use. To properly a design a shape memory alloy device, it is important to determine how it will perform, taking those previously mentioned factors into account, but also heating and cooling rates. The objective of this proposal is to develop a testing facility tailored to shape memory alloys, accounting for the factors which affect performance in application. This apparatus is unique in that it allows for a complete characterization of the performance of the material in a single step.

Comparison of electronic cognitive behavioural therapy to other treatment modalities at a specialized mental health hospital

There is a need for research on electronic CBT (eCBT) in real-world settings, particularly during the COVID-19 pandemic as access to face-to-face treatment is limited. Understanding the challenges in delivering eCBT will be crucial for improving access to care during the current pandemic, and in preparing a response to future pandemics. The intern will lead an evaluation and continuous improvement of the eCBT program at Ontario Shores Centre for Mental Health Sciences. This project will involve three phases: (1) A comparison of client characteristics and outcomes in different modalities of CBT (individual face-to-face, group, eCBT, and mixed); (2) an analysis of client adherence to treatment; and (3) an economic evaluation of eCBT compared face-to-face treatment.

Photonic Cognitive Processor for Next Generation Artificial Intelligence Hardware

Artificial Intelligence (AI) is transforming our lives in the same way as the advent of the Internet and cellular phones has done. However, it takes thousands of CPUs and GPUs, and many weeks to train the neural networks in AI hardware. Traditional CPUs, GPUs, and brain-inspired electronics will not be powerful enough to train the neural networks of the near future. To radically impact the next generation of AI hardware, I propose to develop a fundamental technology: a photonic cognitive processor that uses light (instead of electrons). By employing photonic networks, I will test my processor with standard benchmark tasks on pattern recognition such as MNIST. I propose to use a special purpose GEMM compiler that will efficiently perform small matrix multiplications on enormous matrices. Results will be compared to electronic counterparts in terms of speed, precision, and energy efficiency.

Creating a Map of Adipose Tissue Distribution and Automated Analysis for Plastic Surgery

The surgical practices involving fat are constantly evolving at the cutting edge of technology. Procedures involving fat manipulation, such as liposuction, contain a degree of subjectivity which is mainly guided by the surgeon’s visual or tactile perception of the underlying fat. This lack of subjectivity raises an important issue regarding the surgeon’s ability to replicate his work and predict the final results of the procedures. Moreover, there is currently no absolute method to measure the volume of the fat in real time effectively. Current works will demonstrate that the thickness of fat can be automatically and objectively measured in real time in the operating room using ultrasound technology and an in-house software. The benefit to the partner organisation is the development of an optimized ultrasound assessment tool using artificial intelligence to provide surgeons and patients with a repeatable, predictable service and, ultimately, greater confidence in the planning and execution of invasive surgical procedures in plastic surgery.