Innovative Projects Realized

Detection of Cloud Network Traffic Abnormalities

This research project aims to develop a technique for detecting and analyzing security incidents in their early stages, reducing the potential impact on an organization’s operations. Conventional methods of deep packet inspection (DPI) and network monitoring solutions only identify frequently occurring traffic patterns, and security threats are often not detected until it’s too late. The project investigates a new approach that takes a “horizontal perspective” to detect outliers by identifying packets with out-of-distribution attributes and a “vertical perspective” to detect unusual patterns formed by common packets during a certain interval. The project will also develop countermeasures for specific attacks and general abnormalities. The expected benefit for the partner organization is early detection of security incidents, reducing the risk of data breaches and damage to the organization’s operations.

Étude de la variation des propriétés physiques et chimiques du bois du pin sylvestre

Le pin sylvestre (Pinus sylvestris) fait partie des espèces les plus dominantes de la forêt méditerranéenne et il est connu pour sa croissance rapide et sa capacité d’adaptation à divers sites écologiques. Dans cette étude, plusieurs méthodes seront utilisées (densitomètre à rayon X et la spectroscopie proche infrarouge (NIRS)) pour déterminer les propriétés physiques (densité) et chimiques (cellulose, hémicellulose, lignine et extractibles) de cette essence. L’objectif général de ce travail est d’étudier les différents caractères de la qualité du bois du pin sylvestre et leurs variations. Plus précisément, nous allons évaluer la croissance, la densité des cernes et les caractéristiques chimiques de cette essence. Les programme d’amélioration génétique des arbres forestiers profitent bien des résultats de cette étude sur les propriétés du bois pour les intégrer comme critères de sélection.

Navigation and dynamic obstacle avoidance for UAVs in cluttered indoor GPS-denied environments

With the evolution of unmanned aerial vehicles (UAVs) in recent years, more and more researchers are setting their sights on the application research of indoor environment. Indoor applications include industrial facility inspection, warehouse inventory management, health sector, search and rescue, among others. However, the use of UAVs in these applications requires continuous high-accuracy positioning and pose information, and consequently an efficient obstacle avoidance algorithm. The current implementation uses Visual Inertial Odometry (VIO) to compute pose information, rapidly exploring Random Trees (RRTs) for path planning, and the PX4 stack for navigation. The goal of the project is to design an obstacle avoidance system which can avoid both static and dynamic (slow and fast moving) objects while executing optimal path planning strategy.

Wi-Fi SSID Based Positioning System

The use of indoor location-aware applications such as augmented reality, social networking, health care monitoring, asset tracking, and inventory control is on the rise. However, accurately locating Wi-Fi based devices within buildings can be a challenge, particularly in areas where GPS signals are unavailable. This research project focuses on finding ways to locate indoor devices with high precision using Wi-Fi signal patterns and strength, combined with GPS markers gathered from other devices. By analyzing a large dataset of mobile devices, the goal is to identify the most effective techniques for accurately locating Wi-Fi devices in GPS-denied environments

Out of Distribution Detection in Deep Generative Models

As generative models become increasingly prominent in machine learning, the need for accurately detecting out-of-distribution data has become crucial. The primary objective of this research is to develop an approach that can identify when the program encounters data that is vastly different from what it was trained on. In machine learning, programs may make errors when they encounter data that is dissimilar to what they have learned. To tackle this issue, we will investigate various techniques utilizing deep generative models to help the program comprehend what types of data it should expect to encounter. However, even the most sophisticated deep generative models may occasionally mistake new data as similar to old data, leading to inaccurate predictions. Therefore, we aim to investigate the underlying reasons for this phenomenon and explore potential solutions to address this issue.

A putative chloroplast Ustilago maydis effector causes morphological changes in Arabidopsis thaliana

Plants have several ways of defending themselves from plant pathogens including physical structures such as thick cuticles and defense hormones such as salicylic acid. The latter induces a cascade of plant defense responses that can ultimately lead to resistance. Similarly, to successfully invade their host, plant pathogens secrete a cocktail of proteins called effectors that favour the virulence of the pathogen. We identified seven candidate effector proteins from the corn smut fungus, Ustilago maydis, that potentially target the maize host’s chloroplasts, which are the organelles where the biosynthesis of salicylic acid happens. The overexpression of these seven potential effectors in the non-host Arabidopsis thaliana led to the foundation of this project. When overexpressed in A. thaliana, I found that one potential chloroplast effector caused a substantial morphological change in the plant, despite no changes in its susceptibility to the bacterial pathogen Pseudomonas syringae pv, maculicola. I plan to use the exchange in Germany to identify the plant interacting partner of this effector protein. This will further our understanding of the role of different effector proteins in causing morphological changes in their host in order to facilitate their development in planta.

Exploring an interactive multisensory physical movements model during and beyond COVID-19: a case study of children with special needs

The COVID-19 pandemic has forced children to quickly adapt to home-based or virtual learning; however, a number of researchers have identified challenges and difficulties with applying and using technology. More importantly, there has been a significant rise in the rates of mental illness occurring as a result of COVID-19 and children are now experiencing increased mental health and physical challenges. This study is to conduct ongoing interdisciplinary research which aims to assist children by adopting a novel multisensory model during and beyond COVID-19. The primary expected outcome is to provide an innovative method combining different senses and emerging technologies to improve children’s attention and enhance teaching effectiveness. Qualitative and quantitative data will be collected to assist practitioners and parents who currently have no related reference to serve in the process of creating evidence-based guidelines. The research findings will broaden the scope of current literature focusing on the need to emphasize the importance of empirical investigations into how best to use the multisensory model within the learning environment.

Arctic Research Foundation– Polar Data Analyst Project

The Arctic Research Foundation (ARF) is a private, non-profit organization creating a new kind of scientific infrastructure for the Canadian Arctic, through its operation of efficient, cutting-edge research vessels and self-powered mobile labs. This phase will concentrate on extending and improving the functionality of the platform, and will include developing processes to mint DOIs and a means for researchers to request large file uploads. In addition, a framework for developing new types of visualization will be defined and implemented along with UX improvements to the admin area of the platform.

Development of Data Collection Procedures

1JustCity’s West End Drop-in provides support to West End community members seven days a week. The main part of their organization is a drop-in lunch program, but there is a constant push for their organization to do more than that. They provide Indigenous Cultural Programming, offer Housing Support, and aim to create a community where people can form an identity outside of poverty. 1JustCity is growing quickly, and although grateful for opportunities to increase funding, there is an increased expectation of tracking data that has been a challenge to keep up with. This project will help the organization develop a concrete data collection process, allowing 1JustCity in the West End to track and save information about daily visitors. The project will work to improve the internal processes of data collection within the organization by having a more efficient and effective way to track data. This will allow the organization to increase their sources of funding.

Sales Process for medical technology

Foqus has built a software solution that can speed up MRI scans and is applying for regulatory clearance to commercialize the software. This project focuses on the sales process of the product and facilitates taking the software to the market.

Brett McGonigal – Studying Advanced Manufacturing Strategies for Autoinjectors

The intern will participate in an interdisciplinary project on advanced manufacturing strategies for autoinjectors, which have become increasingly popular due to their convenience and reliability in administering medications. While the Epipen is a well-known example, other medications, such as Noxalone, use this delivery system.

The scale and extent of autoinjector manufacturing in Canada are unclear, and further research is required to determine the current status. One significant obstacle with the current manufacturing process is the high cost associated with producing autoinjectors, primarily attributed to material, assembly, quality control, and regulatory compliance costs. Identifying the specific parts of the manufacturing process contributing to the high cost is crucial in finding ways to overcome them.

The intern will collaborate closely with the Cansbridge Fellowship, the partner organization, and their academic supervisor, Dr. Alan Ableson, to investigate the latest technological advancements in advanced manufacturing in Canada to optimize cost efficiency. For example, the project will explore whether automation can streamline the assembly process or whether 3D printing can reduce production costs by removing the need for expensive molds.

The intern will benefit from the Cansbridge Fellowship’s extensive alumni network, which includes leaders from top innovation-driven organizations like Google, Tesla, and numerous startups.

Paul Yang — AI/ML Data Analysis of Fashion Consumer Trends in Social Media: An Impact Report for Canadian Businesses

The project, “AI/ML Data Analysis of Fashion Consumer Trends in Social Media: An Impact Report for Canadian Businesses,” aims to analyze fashion consumer trends on social media platforms and produce an impactful report for Canadian businesses. The intern will employ and develop advanced data analysis techniques to collect and process data from various social media sources to gain insights into the latest fashion trends, which will be presented visually in an analytical report. This project is of great importance to Canadian businesses as it will provide a comprehensive overview of what consumers are looking for, enabling companies to improve their products and services accordingly, leading to an improvement in the economy. The intern will work in collaboration with the partner organization, Cansbridge Fellowship, and academic supervisor, Alan Ableson. The project’s direct benefit to the partner organization is the production of a real-world case study, which can be used to assist other project’s within the Cansbridge Fellowship program as a whole. This will further attract Canada’s best leaders in entrepreneurship and leadership.