Projets novateurs réalisés

Nanostructured cathode material for high performance all-solid-state sodium battery – Year two

Lithium batteries are almost universally integrated in modern electronics applications due to their high efficiency and scalability. However, the liquid lithium-ion electrolyte utilized in these batteries has a multitude of safety concerns including flammability, toxicity, and the ability to explode if short-circuited. These issues lead to problems while operating in hazardous environments, more stringent transportation regulations, and pose a threat to the personal safety of consumers. Geometric Energy Corporation is working in conjunction with the Thangadurai Group to address these problems by developing an all-solid-state sodium-ion battery system. This system will be non-flammable, non-explosive, and non-toxic, eliminating these issues that are present in liquid-lithium batteries. Our battery technology will be applicable anywhere a liquid-lithium-ion battery is currently used, including the electric vehicle sector, personal electronics, and grid-scale applications. TO BE CONT’D

Optimization of Cloud Computing Applications through Cloud Data Mining and Analytics

Cloud Computing allows consumers to reduce the costs associated with the purchase

and maintenance of computing infrastructure, platform and software resources by

exploiting on-demand resource provisioning and pay-per-use payment models.

Although, cloud computing has offered significant benefits to the IT industry, the

research on cloud computing is currently at its infancy, with many issues still to be

addressed. In this project, we will investigate current cloud computing services and

state-of-the-art Analytics and Data mining techniques to research and develop a data

mining engine for improving cost efficiencies and optimizations in cloud resource

consumption. A full analysis will be conducted on applying analytics and data mining

techniques on cloud computing platform and infrastructure in enterprise IT and the

impact of business units within the organization. Recommendations for flagging high

cloud data transfer charges, unused cloud resources, operational efficiencies, and

areas of automation are encapsulated into a final report.

Nanostructured cathode material for high performance all-solid-state sodium battery

Lithium batteries are almost universally integrated in modern electronics applications due to their high efficiency and scalability. However, the liquid lithium-ion electrolyte utilized in these batteries has a multitude of safety concerns including flammability, toxicity, and the ability to explode if short-circuited. These issues lead to problems while operating in hazardous environments, more stringent transportation regulations, and pose a threat to the personal safety of consumers. Geometric Energy Corporation is working in conjunction with the Thangadurai Group to address these problems by developing an all-solid-state sodium-ion battery system. This system will be non-flammable, non-explosive, and non-toxic, eliminating these issues that are present in liquid-lithium batteries. Our battery technology will be applicable anywhere a liquid-lithium-ion battery is currently used, including the electric vehicle sector, personal electronics, and grid-scale applications. TO BE CONT’D

Building and supporting healthy educational communities: From kindergarten through university

Health and education have been shown to be interconnected and co-dependent. This research project will connect the work being done on Comprehensive School Health in the K-12 setting with similar work beginning in the post-secondary setting. Ever Active Schools has collaborated with school health teams for over 10 years (Healthy Active School Symposia – HASS). The data they have gathered from students, teachers and administrators will be an invaluable asset to the developmental process of embedding comprehensive school health in the Faculty of Education at the University of Alberta. This project will begin with analysis of the HASS data, then move to apply the results to both the K-12 and post-secondary settings. TO BE CONT’D

Caractérisation des poudres alimentaires lors de l’ensachage industriel en vue d’une prédiction des cadences optimales des équipements

Ce projet de maîtrise en ingénierie mécanique a pour but de prédire les paramètres optimaux nécessaires à mettre une poudre alimentaire dans un sac de transport via une machine industrielle. Cette étude passe donc par une recherche approfondie dans la mesure des paramètres comportementaux d’une poudre c’est-à-dire sa manière de s’écouler, de se compacter, etc. Grâce à des mesures pertinentes, il sera alors possible de créer une base de données intelligente qui donnera, pour une poudre inconnue, les débits optimaux que l’on peut obtenir lors de la mise en sacs, en fonction des paramètres mesurés sur la poudre inconnue. Cet outil va permettre au partenaire de réduire considérablement son temps d’étude d’une nouvelle poudre à ensacher et lui fera donc gagner en efficacité et en rapidité tant au sein de son équipe qu’auprès de ses clients.

Aboveground Storage Tank(AST) testing using statistical approach

Based on the original Statistical Inventory Reconciliation(SIR) Test Method (Quantitative), K-folds cross validation is used to increase P(D) and decrease P(FA) by adjusting K, which are related to bias and standard deviation. There is a trade-off between bias and variance, with very flexible models (overfit) having low bias and high variance, and relatively rigid models(underfit) having high bias and low variance. When K is larger, we have lower bias and larger standard deviation. Also, K-folds cross validation is very useful, when data size is small. So this methods has a higher validity with a lower data requirement.

Geochemical analysis of low-sulphidation epithermal deposits on the La Victoria property in the Ancash Department of the Republic of Peru

This MITACS research project will focus on analytical techniques related to determining the gold geochemistry of the La Victoria property in the western Peruvian Andes. The La Victoria property is found along a major gold belt that is host to some world class deposits such as Yanacocha, Lagunas Norte, La Arena and Shahuindo. Based on initial exploration techniques, two major mineralized areas (San Markito and Rufina) have been identified as important for gold exploration. The next stage of exploration is to examine the geochemistry of surface samples and drill cores to determine the mineralogical and geochemical signatures of the gold mineralization and to put these into a consistent model. Essentially, the mineralogical and geochemical data obtained in this study will be beneficial for continued exploration of the La Victoria property and will provide the exploration company with vital information about both areas of interest.

Object Recognition for Large-Scale and Weakly-Labelled Medical Image Data

The main objective of this research project is to investigate, develop and evaluate state-of-the-art image processing and machine learning algorithms, which are suitable for accurate modeling and recognition from large-scale medical image datasets that are weakly labeled. In particular, we will focus on the learning of recognition models in medical image computing applications that are of high interest/priority to Corstem, for instance, finding automatically the left and right ventricles in short- and long-axis cardiac magnetic resonance (MRI) sequences, which yields diagnosis measures that are of high interest to clinicians. Learning recognition models in medical image computing typically leads to difficult computational problems, where imaging data sets are weakly annotated.
This set of projects will leverage some limited and targeted interactions with medical experts, as needed, to set anatomical constraints and to drive advanced learning methods. TO BE CONT’D

Automatic Modulation Classification using Deep Learning for wireless security applications

Applications of wireless security approaches are increasing in number by the day. One such application is detection and interception of rogue aerial intruders. Drone technology is growing at a tremendous pace and is expected to be a $12 billion industry by 2021. Coupled with this growth comes the increasing threat of rogue intruders disrupting day-to-day activities and sensitive infrastructure. Towards this end, rogue drone detection has become an important industry by itself and focuses on securing the airspace around us. In this project, we aim to build a deep learning framework augmented with traditional signal processing techniques to model and classify unknown wireless signals. This model would be able to learn from the time domain information of the signal (amplitude, phase) and be robust in conditions with varying Signal-to-Noise Ratio (SNR). TO BE CONT’D

Statistical machine learning methods applied to ATB data for credit risk modelling

Machine learning (ML) is a method of training a computer to learn from data and predict future outcomes based on existing patterns in the data. This project aims to utilize various ML methods as new and potentially better analytics and predictive tools in the area of credit risk management for ATB. Given that data quality and flows change over time, a new framework built on Google Cloud Platform to update the machine learning models will also be developed. Additionally, considering the possibilities that the ML models may favour certain subgroup, e.g., defined by race and geography, new strategies to test and correct for model fairness will be established. In summary, we would improve default risk prediction accuracy with the help of leading edge ML methods and expand the field of credit risk management by providing model updates through Google Cloud Platform and establishing model fairness strategies.

Development of an RNAi Approach to Control Wireworms on PEI

Wireworm (click beetle) has becoming a devastating pest for the potato industry in Prince Edward Island (PEI) and many places in the country and around the world. It is a soil born insect that can penetrate a large number of potato tubers, degrading their quality for processing. The problem is more difficult to control after the ban of soil fumigation was in place in PEI. This proposed work is aimed at developing the RNAi technology by targeting the key genes for insects’ survival and development to control the insect population. The long term goal of the project is to deliver the RNAi molecules through the cover (bait) crops that are used to rotate in the potato fields. This specific proposal is the continuation of a two year project. To date, promising candidate genes have been cloned and DNA sequenced. TO BE CONT’D

Assessing the effectiveness of shorebird roost site conservation strategies through the employment of Open Standards for the Practice of Conservation

The purpose of this project is to conserve critical shorebird roost habitats at high tide in the Minas Basin of the Bay of Fundy in collaboration with recreational beach users, local businesses and tourism operators. The project seeks to develop long-term solutions for creating safe spaces for roosting shorebirds by identifying innovative strategies whose effectiveness can be measured using the Open Standards for the Practice of Conservation protocol. This work will allow BSC to develop an in-house capacity for the protocol as well as a proof of concept guide.