Innovative Projects Realized

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

31915 Completed Projects

3006
AB
5256
BC
878
MB
703
NL
913
SK
9488
ON
9950
QC
98
PE
629
NB
1215
NS

Projects by Category

Data analytics in asset management of erosion control structures

The project aims to study the use of advanced machine learning algorithms to enhance the ability of TRCA to detect shoreline erosion. Accurate detection of shoreline erosion will have significant contribution to optimizing TRCA asset management plans. This will enable TRCA to work with local communities to reduce the impacts of climate change on shoreline erosion, which in turn will help safe shoreline properties and community spaces from increased erosion. The accurate perdition of erosion will also enable TRCA to develop effective plans to protect natural habitat. It will also support building a long-term investment plan to protect shorelines.

View Full Project Description
Faculty Supervisor:

Tamer El-Diraby

Student:

Partner:

Toronto and Region Conservation Authority (Vaughan, ON)

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Public administration

University:

University of Toronto

Program:

Accelerate

Life Signs and Self-Harming Human Gestures, Actions and Behaviors Monitoring with Combined Physiological and Physical Indicators

Suicide is one of the most important causes of deaths in the prison environment, both in Canada and internationally. Rates of attempts of suicide and self-harm have been on the rise in recent years. To address this problem, there is a real and immediate need for an automated, private, and effective monitoring system that can detect attempts of suicide and self-harm in real-time. This project aims to develop monitoring solutions by studying common behavioral patterns and assessing vital signs changes with multi-modal sensing technologies and state-of-the-art artificial intelligence algorithms to detect gestures, actions or behaviors that can be classified as posing a threat to a prisoner’s health and safety. Innovative, reliable and trusted solutions are designed for triggering alarms when the conditions are met in order to assist prison environment’s personnel while ensuring the respect of prisoners’ privacy and confidentiality.

View Full Project Description
Faculty Supervisor:

Pierre Payeur

Student:

Partner:

Spectronix Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of Ottawa

Program:

Accelerate

Machine Learning Techniques for Short-Term Electric Load Forecasting

Once produced, electricity is difficult to store in large quantities. Hence, accurate electric load forecasting is of critical importance to balance production and consumption for modern power grids integrating more and more intermittent renewable energy and variable loads such as electric vehicles. Short-term electric load forecasting for local areas is also of interest to efficiently respond to demand at the distribution level. Any significant forecasting error can result in reliability issues, loss of opportunity, or additional costs to the business, while improvements in forecasting performance could benefit both the consumers and utility companies by optimizing resources and costs. Most of the current short-term load forecasting algorithms assume that the load consumption and energy generation patterns are stationary, which is not the case in real world. In this project, we plan to use recent progress in machine learning to improve the performance and robustness of short-term electric load forecasting.

View Full Project Description
Faculty Supervisor:

Benoit Boulet;Di Wu

Student:

Partner:

Hydro-Quebec

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Utilities

University:

McGill University

Program:

Accelerate

Gameplay Test Automation with Reinforcement Learning

To ensure high performance for AMD’s graphics cards, the company performs extensive testing on computer game titles. Most gameplay testing is done manually, which results in significant effort and cost expenditure. Thus, this project’s objective is to develop a program capable of learning to automatically play a modern video game. Rather than aiming to optimize performance in the game, the goal is to automate basic actions to explore various graphical scenes the game offers, for the purpose of testing graphics cards. This program will run using reinforcement learning, a type of machine learning focused on enabling a program to learn how to navigate environments by offering it rewards for taking optimal actions. As such, the project’s success would also result in a deeper understanding of reinforcement learning algorithms in practice.

View Full Project Description
Faculty Supervisor:

Amir-massoud Farahmand

Student:

Partner:

AMD Canada

Discipline:

Computer science

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Developing MVP Driven Cloud Service Space

Smartphones are becoming a combination of a mobile phone, a digital wallet and mini personal computer all rolled into one place which adds to the need for such virtual space. Virtualization of space or what is generally known as Mobile Virtualization Platform (MVP) include servers, storage and applications into a single solution to eliminate complexity, lowers costs, and frees up management time. However, when assessing MVP solutions, user’s always point to solutions that are easier to use, set up, and administer. This research project is an attempt to provide mobile manageability, security, cost, compliance, application development and deployment over cloud space with a thin software layer (VMware MVP) that will be embedded on a mobile phone to decouple the applications and data from the underlying Smartphone hardware and enabling users to deploy applications and software stack on a wide variety of emulators without worrying about the underlying hardware differences.

View Full Project Description
Faculty Supervisor:

Sabah Mohammed;Jinan Fiaidhi

Student:

Partner:

VurBox

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Lakehead University

Program:

Accelerate

Large-scale Inversion of geophysical data

When using geophysical methods to gain insight into the structure of earth, large geophysical data sets are collected. Since the earth is a 3D structure, the data must be interpreted and processed in 3D to be of the most value in the exploration process. This research will develop the capability to invert large gravity, magnetics, and airborne EM datasets accurately and in a reasonable timeframe. This requires the research and development of inversion software, data visualization and QC software, and inversion setup scripts. Currently much of the time spent on inversion projects is spent manipulating and formatting the data and additional inversion input files. By researching and developing efficient workflow procedures and robust and automated scripts to create the necessary inversion input files, this setup process can be streamlined and made much more efficient. The partner organization, Computational Geosciences Inc., provides modeling and data processing services for the resource sector.

View Full Project Description
Faculty Supervisor:

Eldad Haber

Student:

Partner:

Computational GeoSciences Inc

Discipline:

Mathematics

Sector:

Mining

University:

The University of British Columbia

Program:

Accelerate

Automated Domain Specific Essay Scoring

Blees AI is an educational innovator who believe in transforming and improving the teaching experience and in turn positively impact students learning and maximize their potential. The goal of this project and the company is to provide a complete version of the software platform to the educators to assist their essay grading. Blees AI’s client a large certification body representing a Canadian profession experienced 84% scoring accuracy, 13x faster scoring results, and a 60% cost reduction opportunity by applying its technology. The company automates the process of grading students’ essays and instantly provides consistent and personalized feedback at scale. Also, the company is looking to build automated personalized feedback modules to the students using its data. The AI models do not evaluate sentence structures, or grammar, format, but evaluate the context of the student responses and look for the key concepts that educators are looking for.

View Full Project Description
Faculty Supervisor:

Linbo Wang

Student:

Partner:

Blees AI

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Development of multilayer composite systems via co-extrusion process

Extreme conditions in Oil and Gas industry often impose conflicting demands for the optimum mechanical properties as well as long term corrosion resistance. The internal and external side of the pipeline are usually exposed to different environments and are required to possess different characteristics. It is difficult to find a single material to satisfy all above requirements. Therefore, the utilization of the conventional polymers could force undesirable modifications to the pipe characteristics or the operating conditions. An effective approach, to overcome this problem, could be the use of a composite (multilayers) pipe where the different layers strongly bonded to ensure stability in the extreme operating conditions. In this project, we aim to investigate key parameters of the co-extrusion, select the proper materials for the manufacturing of multilayers systems, and characterize the multilayer structures as wells as properties of final multilayer systems.

View Full Project Description
Faculty Supervisor:

Patrick C Lee;Hani Naguib

Student:

Partner:

Shawcor Ltd (AB)

Discipline:

Engineering

Sector:

Manufacturing; Mining; Other services (except public administration); Professional, scientific and technical services; Transportation and warehousing

University:

University of Toronto

Program:

Accelerate

Adversarial Robustness of Deep Learning Algorithms on Next-Gen AI Accelerators

Neural Networks play a key role in many modern technologies such as self-driving cars, drones, malware detection, and face recognition. For each of these technologies security and reliability is paramount. Unfortunately, researchers have shown that it is possible to reliably fool the neural networks behind these applications. Which makes identifying the best methods to defend a neural network against an attacker deadset on confusing it very important. This research seeks to compare how various proposed defense methods perform when tested on hardware designed specifically for accelerating neural networks and in doing so develop quick, power efficient defense methods for users of next-gen AMD AI Accelerators.

View Full Project Description
Faculty Supervisor:

Gennady Pekhimenko

Student:

Partner:

AMD Canada

Discipline:

Computer science

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Design and Development of the Mortgauge Payout Evaluator API

At Mortgauge, we want to make it easier for Canadians to build wealth as homeowners. Through this project, we will research and develop enhancements to our Payout Evaluator tool in order to further empower our users to act on opportunities to save on mortgage rates or leverage their home equity to build wealth. We will then build out an API framework that enables us to amplify the reach of these insights in collaboration with our referral partners by making our tools easily integratable into their infrastructure.

View Full Project Description
Faculty Supervisor:

Albert Danison;Jigisha Patel

Student:

Partner:

Mortgauge

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

George Brown College of Applied Arts and Technology

Program:

Accelerate

Conception du réseau logistique et des politiques de gestion d’inventaire chez HydroTech Mining

Le problème sur lequel nous travaillerons consiste à développer un outil d’aide à la décision pour supporter la croissance de HydroTech. L’entreprise est actuellement implantée dans trois régions au Canada (Québec, Ontario et Colombie-Britannique) et souhaiterait poursuivre sa croissance en Ontario et Colombie-Britanique. À cet effet, la question se pose de mieux prévoir la demande dans ces marchés en expansion, mais aussi de savoir si le réseau logistique actuel doit être repensé, afin d’optimiser les flux de pièces et marchandises. Dans un premier temps des méthodes de prévision de la demande seront développées, et les prévisions de demande ainsi obtenues viendront nourrir un modèle mathématique développé pour définir le réseau logistique futur. Lors de la modélisation mathématique, des contraintes réelles de l’entreprise seront introduites et plusieurs stratégies de croissance seront testées. Le projet consistera donc à fournir des recommandations quant au réseau logistique futur à mettre en place en fonction de l’état de la croissance des opérations de HydroTech. Des analyses seront menées pour évaluer la robustesse des réseaux logistiques face à la variation de certains paramètres. Cela mènera à des recommandations concrètes pour l’entreprise.

View Full Project Description
Faculty Supervisor:

Matthieu Gruson;Marilène Cherkesly

Student:

Partner:

Hydrotech Mining inc

Discipline:

Business

Sector:

Manufacturing

University:

Université du Québec à Montréal

Program:

Accelerate

A DNA-based approach for evaluating the impacts of wood waste on benthic biodiversity

Coastal habitats are critical for the health, livelihoods and social well-being of Canadian communities. In British Columbia, log handling and storage areas create wood waste that falls to the seafloor and does not decompose, degrading important habitats and substantially reducing the number of species can live there. To manage the impacts of habitat degradation, it is important to have accurate methods for measuring species diversity, but marine animals are difficult and time-consuming to identify and many remain unknown to science. One possible solution is to measure species diversity using DNA barcoding, which uses short sequences of DNA to rapidly identify species. When applied to environmental samples such as seafloor sediments, barcoding can identify many species at once, substantially increasing efficiency and reducing the costs of monitoring. This project will address key challenges in applying DNA-based methods to monitoring marine environments. We will build a reference library of DNA barcodes for seafloor animals, refine methods for using DNA to study the impact of wood waste, and evaluate the strengths and limitations of DNA-based methods compared to visual identification. In doing so, we will significantly improve our ability to monitor marine species, thereby improving policy and planning strategies for TOBECONT.

View Full Project Description
Faculty Supervisor:

Paul Hebert

Student:

Partner:

Biologica Environmental Services Ltd

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

University of Guelph

Program:

Accelerate

Previous
1…1,7101,7111,7121,7131,7141,7151,716…2,660
Next

Join a thriving innovation ecosystem.

Subscribe to our newsletter now!

Subscribe

Mitacs — Return on Innovation

Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.