Innovative Projects Realized

Explore thousands of successful projects resulting from collaboration between organizations and post-secondary talent.

30156 Completed Projects

2861
AB
5059
BC
812
MB
673
NL
842
SK
8957
ON
9368
QC
96
PE
579
NB
1120
NS

Projects by Category

Modélisation du marché d’espaces publicitaires en ligne avec des séries temporelles

Les espaces publicitaires que l’on retrouve sur la plupart des page web font partie d’un marché similaire à celui des actions. Les espaces publicitaire sont mis à l’enchère sur une plateforme d’échanges électroniques et les publicistes misent sur ces espaces afin d’y afficher du contenu ou de les revendre. Dans ce marché relativement nouveau, les stratégies d’achat et de revente sont souvent déterminées par un agent qui surveille le marché et qui adapte ses stratégies en fonction de ses observations.
L’objectif de ce projet est d’utiliser les connaissances existantes en mathématiques financières pour modéliser ce marché et de développer des outils quantitatifs d’aide à la décision. Ces outils permettront d’optimiser les stratégies pour rendre les entreprises plus profitables et, en conséquence, rendre ce marché plus efficient.

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Faculty Supervisor:

Clarence Simard

Student:

Partner:

All Time Digital

Discipline:

Mathematics

Sector:

Information and cultural industries

University:

Université du Québec à Montréal

Program:

Accelerate

Improving the Performance and Convergence Rate of Transformer-Based Language Models

The pre-trained Bi-directional Encoder Representation from Transformers (BERT) model had proven to be a milestone in the field of Neural Machine Translation, achieving new state-of-the-art performances on many tasks in the field of Natural Language Processing. Despite its success, it has been noticed that there are still a lot of room for improvement, both in terms of training efficiency and structural design. The proposed research project would explore the detailed design decision of BERT on many levels, and optimize them wherever possible. The expected result would be an improved language model that achieves higher performance on NLP tasks while using less computational resources.

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Faculty Supervisor:

Jimmy Ba

Student:

Partner:

Layer 6 AI

Discipline:

Computer science

Sector:

Finance and Insurance; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

The (re)regulation of cannabis: a comparative analysis between the Netherlands and Canada

Research has shown that the traditional field of crime and cannabis control is disrupted. Hence, several jurisdictions are moving towards decriminalization and even legalization of cannabis. The Netherlands was one of the first countries to adopt such an alternative approach by allowing small retail of recreational cannabis. In October 2018, Canada decided to legalize cannabis. Yet, both countries are still struggling to establish a well-functioning cannabis policy that focuses as much on public health and human rights as it does on crime control.
This research aims to investigate the regulation of cannabis in the Netherlands and Canada, examine the area of conflict with the international drug treaties and provide an explanation for the regulation of cannabis in the Netherlands and Canada. TO BE CONT’D

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Faculty Supervisor:

Nicole O'Byrne;Karla O'Regan

Student:

Partner:

University of Groningen

Discipline:

Sociology

Sector:

Education

University:

University of New Brunswick

Program:

Globalink Research Award

Evaluation of monitoring data for predictive maintenance of energy production assets

Hydro-Québec has data acquisition systems for a multitude of sensors, some of which have been installed since almost 20 years in its electrical generation equipment (turbine-generator units – TGU). The collected data is primarily used to ensure that the information is adequate in the event of an equipment breakdown or for specific behavioral studies. Data from monitoring systems are little used in routine maintenance management activities, often due to lack of time and adequate and/or effective analysis methods.
Equipment maintenance is an important part of Hydro-Québec’s equipment management activities. The creation and maintenance of a surveillance system is a major investment for the company. With the development of machine learning analysis approaches, the goal is to provide operators with a clearer view of real-time asset status and predictions about their potential for use.

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Faculty Supervisor:

Yoshua Bengio

Student:

Partner:

Institut de Recherche Hydro-Québec

Discipline:

Computer science

Sector:

Professional, scientific and technical services; Utilities

University:

Université de Montréal

Program:

Accelerate

Feature extraction from 2D and 3D images of flocs

Flocculation is a chemical treatment to improve the settling of fine particulate suspensions in the food, chemical, agricultural, and mineral processing industries. The size, shape, and density of the flocs influences their mechanical strength and settling rate; which are critically related to the efficiency of solid-liquid separation processes. The objective of this research is to test the theoretical predictions of 3D structure from 2D images of flocs by a direct comparison of 3D predictions and 3D measurements of floc structure. The broader context of the proposed work is to improve our understanding of the mechanisms of densification and thickening in difficult-to-treat mineral suspensions so that improved solid-liquid separation (SLS) treatment methods and technology may be designed. From an environmental standpoint, improved SLS processes result in decreased fresh water needs, a smaller ecological footprint, and a decreased likelihood of tailings spills.

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Faculty Supervisor:

Marek Pawlik

Student:

Partner:

Delft University of Technology

Discipline:

Engineering

Sector:

Education

University:

The University of British Columbia

Program:

Globalink Research Award

Ground-Based Remotely Piloted Aerial Vehicle (RPAV) Tracking System

Drone Delivery Canada (DDC) designs and operates high performance Remotely Piloted Aerial Systems (RPAS) to deliver payloads between depots and warehouses. The DDC engineering department is looking to design and deploy a ground-based system to track and point at the Remotely Piloted Aerial Vehicle (RPAV) during flight in real-time. However, DDC’s RPAS must be able to operate in remote areas making the use of communication technology infrastructure difficult due to the need to be able to have communication between the RPAS and ground control station (GCS) over long distances without the use of communication relay nodes or heavier, more powerful communication modules on the RPAV. To solve this challenge, advanced communication equipment such as high-gain antenna(s) will be needed in addition to novel antenna tracking algorithms for the system to be interfaced with the GCS to receive telemetry data from the RPAV. The end product is a robotic tracking system which applies positional feedback data from the RPAV (such as altitude and GPS location) to a control system to dynamically point an antenna at the RPAV throughout flight. Review of existing literature on tracking systems and required infrastructure/resources would be performed to guide the design process. TO BE CON’T

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Faculty Supervisor:

Kamran Behdinan

Student:

Partner:

Drone Delivery Canada

Discipline:

Engineering

Sector:

Transportation and warehousing

University:

University of Toronto

Program:

Accelerate

Analysis of infusions pump usage data to reduce drug errors and enhance maintenance

An infusion pump is a medical device that automatically administers set amounts of drugs into a patient intravenously. Drug errors occur when an incorrect dose or dose rate is programmed. To avoid such errors, a drug library is created providing dose limits for every drug. “Smart” infusion pumps provide a tracking system, saving all pump usage data, messages and errors in a database. A study on “smart” infusion pumps indicates that drug errors and drug events were detected by the drug library; however, poor compliance to the library did not reduce drug error rates if users failed to adjust dose based on the drug library. Research will be conducted, leveraging the data provided by “smart” infusion pumps to improve compliance, and in turn reduce drug errors and associated complications. In addition, research will be conducted to use infusion pump data to enhance maintenance schedules and potentially predict device failures.

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Faculty Supervisor:

Adrian Chan

Student:

Partner:

Children's Hospital of Eastern Ontario

Discipline:

Engineering

Sector:

Health and Related Sciences & Technology

University:

Carleton University

Program:

Accelerate

Développement d’un système d’acquisition de données sportives & gestion informatique de la clientèle pour les centres sportif

Le centre d’escalade Vertige développe présentement un système de gestion de centres sportifs. Ce système se voudrait être combiné avec une technologie sportive innovante servant à analyser les performances de grimpeurs et/ou de sauteurs dans plusieurs optiques. Pour le grimpeur ou le sauteur, cela servira à améliorer la rétroaction aux entrainements, à quantifier les séances pour l’optimisation de celles-ci et à avoir des statistiques sur ses performances. Pour le gestionnaire du centre, cela permettra de savoir quels modules sont les plus appréciés et ainsi d’adapter le centre à sa clientèle. Ça servira aussi à gérer les abonnements et entrées de façon efficace et permettra aux employés de se concentrer sur le service client plutôt que sur l’admission de ceux-ci et la gestion financière.

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Faculty Supervisor:

François Ferland

Student:

Partner:

Vertige Escalade Inc;Centre Récréatif O-Volt Inc

Discipline:

Engineering

Sector:

Arts, entertainment and recreation

University:

Université de Sherbrooke

Program:

Accelerate

VR-based testing station for impairment screening

In this project, a VR-based testing station for impairment screening will be implemented. The station includes a Virtual Reality (VR) goggle (to be updated to Augmented Reality, AR, later), biophysiological measurement sensors, and an integration algorithm to integrate the result of measurement with scene construction of the VR system to implement dynamic scene rendering. The project will undergo several steps including basic scene construction and depth creation to model simple scenarios for the user and to research the implementation (and effect) of different related tests on level of impairment; sophisticated rendering algorithm creation to automatically design scenes based on the application; and dynamic scene construction to receive feedback from other measurement sensors and to implement a dynamic algorithm to update the scenes based on user’s reaction.

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Faculty Supervisor:

Jiannan Wang

Student:

Partner:

Cannsight Technologies

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology; Technology; Information and Communications Technology

University:

Simon Fraser University

Program:

Accelerate

Understanding cell-cell interactions with deep learning-based profiling

The aim is to understand how fibroblasts, the most common connective tissue in animals, and cancer cells interact with each other through image analysis. These co-culture imaging screens, containing fibroblasts and cancer cells, will help identify novel signaling mechanism involved in cancer. The objective is to apply deep learning techniques to these image-based assays to study interactions between and identify novel therapeutics that can make cancer therapies more effective.

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Faculty Supervisor:

Jimmy Ba

Student:

Partner:

Phenomic AI Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Question-to-question semantic similarity for Question Answering System

Question Answering (QA) system automatically answer questions raised by users in natural languages, and it is a crucial component of a human-machine conversation system. A typical QA system collects human written question-answer groups and structures them in a database system. However, in order to answer questions that are semantically similar to the questions stored in the database but are worded differently, the QA system needs to be able to calculate the semantic similarity between different questions. In this research project, the intern will explore different techniques used in question-to-question semantic similarity measurement and try to improve upon the state- of-the-art performance. From participating in this project, RSVP Technology Inc. could seed for more opportunities to collaborate with Canadian community to improve the quality of QA systems used in many other fields and products, such as customer service chatbots and smart home device. Further, this project could serve as the foundation for next step research and development.

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Faculty Supervisor:

Graeme Hirst

Student:

Partner:

RSVP Technologies Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Off-Policy Reinforcement Learning (RL) for a Production Robotics Application

Kindred offers eCommerce retailers a solution to assist with rapid order fulfilment from their distribution centres. The solution (SORT) is a combination of a so-called put-wall and a humanoid robot. The robot picks up items from orders, scans them, and puts each item in a cubby of the put-wall according to the scan code. The robot comprises a gripper, a 6-degree-of-freedom arm, and a stereo vision module, as well as other electronics and mechanical housing. The proposed research will explore machine learning techniques based on reinforcement learning to feed data recorded from Kindred’s production robots back into learning algorithms in order to generate new better ways for those robots to pick, scan, and stow those eCommerce customers’ orders.

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Faculty Supervisor:

Florian Shkruti

Student:

Partner:

Kindred AI

Discipline:

Computer science

Sector:

Technology; Commercial Services

University:

University of Toronto

Program:

Accelerate

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