Innovative Projects Realized

Ground Truth

The goal of the project is to develop a predictive model that will help to make a better estimate of the relative altitude using only the barometer sensor inside the Notio device. This measurement of a precise relative altitude is crucial since it is used to compute the slope, which is an important data for the cyclist and will make the Notio even better. The difficulty of this task comes from the fact that the measurement of the altitude from the sensor has a tendency to drift and thus the computed slope also does. A dataset using more precise equipments for the altitude measurement (which can’t be used in the Notio device for various reasons) is used to train a model to help correct the altitude of the Notio sensor.

The effects of grapevine microclimate manipulations on Pinot Gris glycoside aroma compound abundance and diversity

This research project is a partnership between The Wine Islands Grower’s Association, and the University of Victoria. The goal of the project is to better understand how vineyard management techniques effect wine grape quality prior to wine processing. Pinot gris is the most important white wine varietal grown on Vancouver Island and throughout British Columbia. To understand how vineyard techniques affect quality gas chromatography and mass spectrometry will be used to identify and qualify the aromatic compounds present at harvest to maximize the potential of the grapes. Providing BC growers with standard operating procedures for vineyard management techniques to improve grape and wine quality is paramount.

Assessing Small Mammal Community Assemblages and PopulationDynamics across Multiple Geographic Scales on the Canadian Prairies

Small mammals (mice and voles) are a vital component of most terrestrial

ecosystems; however, their communities have not been studied on large geographic scales

because of limits imposed by conventional sampling methods (i.e., trapping). This project will

take advantage of great-horned and burrowing owls as efficient surveyors of small mammal

comunities. Specifically, small mammal community composition analyses will be based on

50,000 small mammals identified in owl pellets collected from sites spanning 380,000 km’ of

southern Alberta and Saskatchewan. Data on such a large scale would be completely

unattainable through traditional trapping methods. The objectives of the project include (a)

understanding how variation in habitat affects small mammal communities; (b) identifying

relationships between small mammal species composition and human land use; and (c)

understanding changes in small mammal communities over time. This research will offer

unprecedented insight into environmental factors affecting small mammal distribution and

abundance.

Normes environnementales globales et Solutions pour l’Océan mondial

Mon mémoire de maîtrise invite à repenser la coopération mondiale au-delà des frontières nationales et appelle à l’intégration d’une plus large gamme d’acteurs autour des tables de décisions internationales. Pour ce faire, je m’intéresse au cas de l’environnement et, plus particulièrement, à la pollution du plastique dans les océans. À travers ce cas d’étude, mes recherches s’orientent vers l’étude des obstacles présents et futurs auxquels fait et fera face la communauté mondiale pour adresses les enjeux environnementaux globaux. Ce faisant, mon mémoire de maîtrise est porté par un objectif d’améliorer l’état actuel des connaissances sur les problèmes transnationaux, l’émergence des normes coopératives et la gouvernance environnementale mondiale.

Translational Oncology Research Data Analysis and Quality Assurance

The proposed project seeks to better understand and study the effective application of data processing tools and quality assurance systems in clinical research. Data from clinical trials is crucial in the pharmaceutical industry because it provides indicators of safety and efficacy of drug treatments for patients. However, processing and reporting on the large amounts of data available has become increasingly difficult, especially for organizations that run complex trials in an international setting. The project will develop customized reports with MediData digital platform and test their implementation.
The secondary goal of the project will be to research and support the development of a coordinated quality assurance system compliant with Good Clinical Practice (GCP) guidelines. The project will both support the development and test the quality control system that address the needs of a global clinical research organization. The quality control system developed as part of the project will be compatible with the MasterControl digital platform.
The project’s methodology will include literature review, data collection and review in order to answer the research questions and achieve project objectives.

Development of an IoT-based sleep monitoring system for optimal sleep quality

The proposed project will develop a system that combines ambient environmental sensors with sleep detection methods to measure sleep quality and allow the user to improve their focus during activities of their daily lives. Observing sleep patterns through all the stages of sleep to model the users body clock and quality of sleep. In addition, using environmental sensors to help users identify optimal sleeping conditions. This will be done by developing an algorithm that will estimate sleeping patterns with environmental sensors and produce a report to optimize their sleep. A hardware prototype will be created as a proof of concept so that the user could conveniently use it in their own home to adjust their sleep and environment. The benefit of this project will let people become more aware of their sleeping patterns and increase their sleep quality for a more active daily life.

2020 International Ice Hockey Federation Women’s World Championship: Legacy Development Project

The International Ice Hockey Federation’s (IIHF) Women’s World Championship tournament is the most significant women’s hockey event outside of the Winter Olympic Games. International events like this are often considered to bring benefits to the local sport community; however, research has systematically demonstrated that hosting alone will not guarantee a legacy. In order to deliver sport development and participation related benefits, host organizations must engage in extensive planning to leverage the event and incorporate hosting into an integrated strategy that matches identified and existing needs. This project will develop, along with partners Hockey Nova Scotia and Hockey Canada, a plan to help identify potential legacy initiatives, collaborate with stakeholders on leveraging legacy-related interventions, and to evaluate the impact of hosting the 2020 International Ice Hockey Federation (IIHF) on the development of ice hockey for women and girls in Nova Scotia.

Multiplexing detection of infectious diseases biomarkers using fluorescence-encoded microspheres

Infectious diseases from a variety of pathogens can lead to serious health conditions such as sepsis. To improve the prognosis and decrease the mortality rate of infectious diseases, point-of-care testing (POCT) of blood biomarkers is a critical approach. Testing of biomarkers such as C-reactive protein, IL-6 and procalcitonin could help identify the type of pathogen and classify the progression stage of infection. This project focuses on developing microsphere-suspension based detection technology, combining with fluorescence detection device for the quantitative measurement of infectious disease biomarkers. This research project holds potential to provide healthcare access to people in remote areas, local communities and emergency rooms.

Detection of corroded tringles in Michelin tires

The research project entails finding a method to detect corroded steel wires used in the manufacturing of tires at Michelin in Bridgewater. This would be achieved using methods that are proven safe for humans and the environment. Also, the detection equipment will be integrated in the existing Michelin facilities in Bridgwater, NS, Canada.

Influence de la composition du sirop d’érable sur la cristallisation de ses sucres

Le sirop d’érable canadien est reconnu pour sa grande qualité. Depuis 2015, il est classé en quatre catégories selon sa couleur, au lieu de cinq auparavant. Il est consommé tel quel ou peut être transformé en produits, comme le beurre ou la tire d’érable. Ces produits requièrent un contrôle de la cristallisation du sucre dans le sirop. Ce projet vise à comprendre l’impact de la composition du sirop sur sa couleur et sur la fabrication des produits de l’érable. Les résultats permettront de prédire le type de produit à fabriquer en fonction du profil du sirop. Les retombées aideront de diversité l’offre canadienne de l’érable.

The virtual fitting of clothing to a custom avatar

The proposed research involves the development of software capable of fitting a piece of clothing to an avatar based upon customer measurements. The development would include the creation of avatar geometry from photographic information and the computer simulation of fabric based on physics. The project would also include interfacing with clothing merchants through the partner organization in order to obtain sample clothing data (material type and dimensions). For this project, the focus would be developing the software for t-shirts. Anticipated future projects would extend the software to other clothing articles and the graphical rendering of the simulated fit onto the avatar. The partner organization would benefit from obtaining software marketable to both the individual consumer and retail vendors for internet shopping. The software would reduce the occurrence on product returns which improves customer experience and reduces costs for the vendor.

Approximate Online Bilevel Optimization for Learning Data Augmentation

In this project we aim to automatically learn an augmenter network by using an approximate online bilevel optimization procedure. We plan to learn a augmenter network that generates a distribution of transformations that minimizes the loss on a validation set. By unfolding the gradients of the training loss, we will optimize the loss on validation with respect to the data augmentation parameters. In this way we can provide a general solution for an efficient and automatic data augmentation that is learned jointly with the training of the model. We expect that the proposed joint training to produce a classification performance comparable with standard data augmentation techniques, without the need of an expensive external validation loop on the hyper-parameters.