Projets novateurs réalisés

Development of an Unmanned Glider for Autonomous Soaring

We will develop an algorithm for a small unmanned glider (a plane-shaped drone) to soar autonomously. Gliders gain energy from the atmosphere by flying in circles in streams of rising air, called thermals. This allows them to stay aloft for extended periods of time, in the range of several hours. Detecting thermals, as well as harvesting energy from them, with an automatic pilot, is the challenge that we are tackling in this work. We will write the program, as well as test it, both in a flight simulator and in the field (with a remote-controlled drone, flying autonomously). The partner organization builds glider-based autopilot solutions for the purpose of monitoring large and remote areas such as forests and fields, hence the need for a reliable thermalling flight controller.

Investigation of Bicycle Traffic Flow Parameters

Bicycle and pedestrian counts are important data for the planning and design of safe roads. However, installing pedestrian and bicycle counters across an entire city road network is not financially viable. Therefore, a good option is to estimate counts at the network scale, using knowledge from a handful of pedestrian and bicycle counters (strategically placed) and trip data from users who willingly share their position from their smartphones.
Eco-Counter, the partner organization, is a world leader is the development of automated pedestrian and cyclist counting systems. The company is expanding into data services and requires additional tools and methods to help their clients (transportation agencies) manage and visualize their data. This project will help Eco-Counter achieve this objective.

Phase-field crystal modeling of the interplay between nanostructure changes and ion transport in lithium-ion battery electrodes

The focus of my research is modelling graphene-hBN (graphene-hexagonal-boron nitride), a novel two-dimensional ceramic with the ability to allow for the measurement of both plastic and elastic strain. Mixing graphene with hBN augments the properties of the ceramic by making it stronger and more versatile. When exposed to an elastic strain, the ceramic will exhibit higher conductivity, but under plastic strain, it loses conductivity. Due its exceptional chemical and thermal stability, graphene-hBN is considered a material of the future, sometimes called the “miracle material” due to its exceptional mechanical and electric properties. For example, it can be used in a wide range of applications from reinforcing structures such as alloys, steel and plastics, to, due its excellent electric properties, electronics (in particular sensors under biological and chemical conditions and in quantum dot devices).

Computational modeling offers a versatile approach to investigate the properties of this new promising material. In this study we develop a new model to study phase transformations and morphological changes in graphene-hBN. The so-called phase-field crystal (PFC) method is used as the basis. TO BE CON’T

Les microplastiques et contaminants associés dans l’estuaire et le golfe du Saint-Laurent : caractérisation environnementale, sociale et bioaccumulation ou dépuration chez les bivalves

Ce projet propose de définir les enjeux socio-environnementaux et socio-économiques reliés à la présence de microplastiques (MP) marins dans l’estuaire et le golfe du Saint-Laurent, une région hydrologique complexe sous-étudiée vis-à-vis cette problématique. Ceci permettra d’orienter les décisions visant à protéger l’environnement tout en soutenant les secteurs maritimes, notamment ceux reliés aux ressources marines commerciales. Ainsi, ce projet intersectoriel, multipartenaire et innovant a pour but d’étudier :
• L’abondance et la diversité des MP et de certains autres contaminants associés dans l’eau et chez deux espèces sentinelles de mollusques;
• La représentation sociale qu’ont les populations littorales à ce sujet.
Ce projet propose donc de déterminer les bases de cette problématique afin de soumettre, avec l’implication des communautés côtières, de nouveaux projets et des solutions intersectorielles pour mieux comprendre les effets environnementaux des MP et pérenniser l’industrie de la pêche, de l’aquaculture et de la transformation des produits marins au Québec

Application of Real-time Axis-based SLAM in Geotechnical Engineering

Mining operations evaluate tunnel stability by looking at orientations of exposed rock faces and extrapolating the underlying rock formation. The most widespread and traditional method for determining these characteristics involve hand measurements with static tools. This process is both time intensive and prone to error. Additionally, it may be dangerous to measure rock faces that are hard to reach. The goal of this project is to implement a novel algorithm that can quickly and automatically determine sets of parallel rock faces and their orientations. This research will help successfully develop the RockMass Mapper which will be the company’s flagship product and is scheduled to launch in early 2020. The mining economy contributes to nearly 20% of Canada’s GDP and RockMass’s solution will improve the overall impact the mining industry will have on Canada’s economy while also improving the working conditions of those involved.

An investigation of consensus and performance in distributed systems

Within a tiered or zoned architecture, new business constraints for regional data residency, imply a need for new architectural patterns. These constraints are not arbitrary: they arise directly from customers demand to ensure their confidential data stays within their defined borders unless required otherwise (e.g. data originating in the U.S. is intended for an E.U. entity). An architecturally clean approach is to negotiate the movement of data only at the persistence layer. The objective is to review the industry landscape for suitable replacements or to otherwise alter the existing design to facilitate the replication of data based on these requirements. Specific targets for consistency latency (with defined upper bounds) and parity performance for read and write rates must be met. Parity performance is measured via msg/s and is achieved if the modified/replacement system has at least the same performance profile (average, median, and 99% percentile) as the existing system.

Measurement of Surface Adhesion using Non-Contact Atomic Force Microscopy

Surface energy is a critical parameter in determining the frictional energy dissipation that occurs when two surfaces slide across each other. In this project, the influence of single atomic layers of lubricants will be placed on metallic surfaces and the impact on surface energy will be measured using non-contact atomic force microscopy. In previous studies conducted at the University of Calgary, it has been calculated that traditional methods of determining surface energy, such as through pull-off experiments and water contact angle experiments, will not provide the required accuracy and precision that is necessary to detect the change in surface energy that occurs when one atomic layer of lubricant is used, compared with two atomic layers of the lubricant. Through this collaboration, we expect to gain access to instruments that will be capable of measuring surface energy with the required precision and accuracy.

A General Approach to Interpret and Explain a Deep Neural Network Model

Deep neural networks have demonstrated state-of-the-art modeling accuracy on a wide range of real-life problems, with some cases surpassing human performance. Despite the promise of deep neural networks as an enabling technology for a large number of industries and fields, there are two particular key challenges in the design of deep neural networks in real-world, operational scenarios. First, the design of deep neural networks is a very time consuming process for a machine learning expert, and often results in complex, non-optimal deep neural networks. Secondly, many of the deep neural networks designed in recent years, while achieving high accuracy, are often very complex with high millions to billions of parameters, making such networks intractable for real-time scenarios on mobile device, IoT devices, and other embedded devices. TO BE CONT’D

Fast Implementation of Machine Learning Algorithms for Event Sequence Data in order to improve Customer Experience

Every day millions of customers move through the sales cycles of companies, this generates large sources of event data. This project aims to discover, understand and predict the journeys of their customers. On one hand, the project is interested in describing the data at a higher level. This means to apply machine learning techniques, namely clustering, and sequence embedding, in order to group similar behaviors together and allow the user to focus analysis on different aspects of the data, such as users of a specific age. On the other hand, this project focuses on dealing with real-time data, i.e. data that is coming into the system continuously. This platform could be applied to sales optimization, business process mining, churn analysis and so on.

iNurse, iTeach: Using Mobile Applications in Client Education.

The use of mobile devices, such as the iPad are quickly becoming an ideal tool for client education. Currently available medical applications range from nutrition guides, calorie counting and exercise programs to blood pressure monitoring and diabetes management tools. These consumer-oriented health applications are believed to empower clients to become active participants in their health care. The use of mobile technology will provide health care providers and clients with easily accessible, current up-to-date health information within the hospital setting or in other home and community environments. Health care professionals will have an opportunity to use technology to educate clients on how to effectively utilize these medical applications and validate that the information is current and up-to-date. This research project will focus on the health care professional and the impact of the use of medical and health applications on the clients’ and family’s enhanced understanding of their condition, diagnosis, treatment plan………….TBC

Stratégies de légitimation des États de facto : l’industrie touristique en République moldave du Dniester

Afin d’opérer sa séparation de facto avec la République de Moldova en 1992 et l’établissement de son « État », la Transnistrie – ou République moldave du Dniester – conçoit depuis maintenant près de 30 ans plusieurs politiques de construction de l’État, de l’identité et de légitimation. Les récents développements d’une industrie touristique transnistrienne autonome sont révélateurs des différentes options employées par cet État de facto afin de sortir de son isolement relatif. Avec l’arrivée de plusieurs touristes et la construction de nouvelles infrastructures, la Transnistrie tente de projeter une certaine forme de légitimité à l’intérieur comme à l’extérieur, mais aussi de continuer la réalisation de son « État » et de son identité distincte. La question se pose donc à savoir comment le tourisme, sous toutes ses formes, contribue à la construction et à la légitimation de l’« État » transnistrien depuis la fin de la guerre civile ? Ce projet démontrera comment la Transnistrie, par son industrie touristique, génère non seulement des revenus, mais tente aussi de se distinguer de l’État-centre, la République de Moldova. TO BE CON’T

Enabling Purchase of Residential Homes at Scale

Properly buys and sells homes directly from consumers. For our business to be successful, we must be able to predict two things when making a home purchasing decision:
? The price it would sell for on the open market
? How much time it will spend on the market to sell at that price
These two variables are correlated: price can affect time-on-market, and time-on-market can affect price. There are many other factors at play as well. In the broader market, these complex real estate decisions are largely made using human judgement, based on experience and expertise. We want to increase the speed and accuracy of these predictions using applied data science, in order to reduce the business risk of home buying decisions, and to allow making them at scale.