Innovative Projects Realized

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

31132 Completed Projects

2940
AB
5159
BC
837
MB
685
NL
882
SK
9291
ON
9695
QC
97
PE
601
NB
1161
NS

Projects by Category

AGROBOT: An Autonomous Robotics System for Automated weed detection and eradication

The research project involves three interns collaborating on an innovative solution to improve farm management by automating weed control. Intern #1 is tasked with developing a smart system that uses machine learning and
computer vision to distinguish between crops and weeds, ensuring the robot can identify what needs to be removed without damaging valuable plants. The other two students will focus on the robot’s movement and
operation: one will refine how the robot’s arms move precisely to target and eliminate weeds based on the identifications made by the first student’s system. The second will work on optimizing the robot’s ability to navigate
and plan its actions in the dynamic outdoor environment of a farm. This collaborative effort aims to create a robot that can autonomously keep fields free of weeds, reducing the need for chemical herbicides and manual labor.
For the industry partner, this project promises a state-of-the-art agricultural tool that enhances efficiency, sustainability, and crop yield, potentially transforming modern precision farming practices.

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Faculty Supervisor:

Mehrdad Saif

Student:

Partner:

BHF Agrobot

Discipline:

Engineering

Sector:

Agriculture; Manufacturing

University:

University of Windsor

Program:

Accelerate

Development of the anti-fouling hard marine coatings with increased resistance to grooming and cleaning

This proposal outlines the development of a novel polymeric hard marine paint with antifouling and easy-to-clean performance, mechanically compatible with the state of the art in-field grooming and cleaning technologies. This proposal targets reaching a 120 days of biofouling prevention in static deployment conditions, the chemical composition of the novel paint non-inclusive of toxins or biocides, typically utilized in the conventional anti-fouling systems to maintain the foul-free paint finishes. The significance of this research lies in its potential to redefine marine coatings, offering an environmentally friendly alternative to current biocide-reliant solutions, with the main objectives encompassing such novel and highly industry-sought improvements, as the superior mechanical durability, 0-toxicity to the aquatic environment, and applicability to the most wear-demanding market of ice-resistant marine paints.

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Faculty Supervisor:

Kevin Plucknett

Student:

Partner:

GIT Coatings

Discipline:

Physics

Sector:

Manufacturing; Professional, scientific and technical services

University:

Dalhousie University

Program:

Accelerate

Selective Dissolution And Precipitation For PVC Recovery In A Circular Vinyl Economy

This project aims to revolutionize PVC recycling in a circular vinyl economy by further developing and optimizing a novel approach called Selective Dissolution and Precipitation (SDP). PVC, a widely used plastic, poses recycling challenges due to its complex composition, especially in multilayer materials, like coated fabrics. SDP involves selectively dissolving PVC from mixed plastic waste and then precipitating it back in a purified form. This innovative method not only facilitates efficient PVC recovery but also minimizes environmental impact compared to traditional recycling methods. The project will investigate optimal solvents for selective PVC dissolution, optimize the reuse of recycled PVC from the process, and assess the economic and environmental feasibility of scaling up SDP for industrial. Successful implementation of SDP could significantly contribute to closing the PVC recycling loop, promoting sustainability, reducing landfill waste for multilayer systems, and increasing recycled content to reduce the reliance on virgin components, thus advancing the goals of a circular economy.

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Faculty Supervisor:

Li Xi

Student:

Partner:

Oligomaster Inc

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services

University:

McMaster University

Program:

Accelerate

Assessing impingement risk and patient outcomes after total hip arthroplasty with optimized acetabular cup placement

Total hip replacement usually provides good outcomes for patients. Sometimes, if one part of the artificial hip is placed incorrectly, it can cause the hip to dislocate and require a second surgery to fix the implant. New computer software has been developed by our partner to guide the surgeon during the operation to place the artificial hip in the best spot for each patient. We will use x-rays taken with the patient in different leg positions after surgery to see if using this software will lower the risk of the hip dislocating, compared to patients who had hip replacement without the software. CT imaging will be conducted to gain a better understanding of possible bone and muscle quality factors influencing dislocation. Patients will also complete a questionnaire asking about their outcomes. If patients who had hip replacement with the software have better outcomes and less dislocation risk, this will provide evidence the partner can use to help support the use of the software more routinely in our healthcare system.

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Faculty Supervisor:

Brent Lanting;Matthew Teeter

Student:

Partner:

Smith+Nephew

Discipline:

Life Sciences

Sector:

Manufacturing

University:

Lawson Health Research Institute

Program:

Accelerate

Assessing generalization training of the Puppet Academy system

A resource to measure collaborative practice and generalized learning within minutes needs to be easy of use, effective and provide intrinsic motivation to engage users. This project advances the company’s commercialization opportunities by expanding entry into larger organizations and provides an additional opportunity to expand market reach. In the future, the product can be used to conduct collaborative research within training institutions and as a training tool for pre-professional collaborative education and practice.

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Faculty Supervisor:

Lizbeth Escobedo

Student:

Partner:

SaySo Communication

Discipline:

Computer science

Sector:

Education

University:

Dalhousie University

Program:

Accelerate

Preventing ice accretion on electrical utility infrastructure by surface engineering

Every winter Quebecers, Canadians and basically anyone living in northern climates suffer from icing related failures (power blackouts, fallen trees, blocked roads, etc.). In the best case, meteorological forecasts and HQ’s ice-removal measures are timely, costly, and the society does not experience the severe effect caused by harsh winter weather. Currently applied active ice protection or removal systems are highly labor-intensive, energy­consuming and/or polluting processes. In the proposed collaborative research between McGill and Hydro-Quebec, we will translate promising laboratory research results obtained from finely woven metallic fabric with excellent passive ice-shedding ability to real world utility infrastructure. In this context, two master students will be trained and obtain valuable insights to the research environment at Hydro-Quebec’s industrial research center IREQ and at McGill University.

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Faculty Supervisor:

Anne-Marie Kietzig

Student:

Partner:

Hydro-Quebec

Discipline:

Engineering

Sector:

Sustainability and the Environment; Energy and Utilities; Clean Technology

University:

McGill University

Program:

Accelerate

Reinforcement-Based Large Language Models to Enhance Energy Management Platform

Reinforcement Learning in Large Language Models (RL-LLM) is revolutionizing how AI understands and responds to human language, improving virtual assistants in various fields. It teaches models to follow instructions effectively, even with vague prompts, reducing inappropriate responses and false information. RL-LLM’s application extends to sectors like healthcare, finance, and online shopping, enhancing product suggestions and aiding quick medical information comprehension.
The project further explores RL’s role in energy management, aiming to refine algorithms for decision-making accuracy. It integrates IoT and Edge Computing for efficient data acquisition, develops adaptive algorithms for changing energy patterns, and ensures ethical AI implementation. Ultimately, the project aims to advance Edgecom’s energy management platform for smarter, more efficient, and responsible solutions.
AI is a key development theme for Edgecom moving forward and this project will help supplement our limited expertise and experience in this field. This project will provide significant benefit to the company in the form of increased innovation and product development capacity, an improved product offering, and further differentiation versus competition.

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Faculty Supervisor:

Alireza Bakhshai

Student:

Partner:

Edgecom Energy

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Queen's University

Program:

Accelerate

AI based multi-phase flow meter

The persistent challenge faced by oil producers in measuring multi-phase flows, characterized by a complex mixture of oil, gas, and water from wells, has prompted the development of an AI-based multi-phase flow meter by MLCan. Current approaches to real-time precision are hindered by the inherent complexity of these flows, leading to costly and less accurate measurements. Additionally, conventional techniques involve phase separation, posing environmental risks and generating increased waste. MLCan’s innovative meter, equipped with pressure and temperature sensors, bypasses the need for phase separation, capturing flow data for individual components. Processed through an AI algorithm, this data enables the meter to provide real-time and online estimations. The research aims to comprehensively study oil wells in Alberta, creating a dataset for various oil resources and implementing machine learning algorithms for evaluation.

View Full Project Description
Faculty Supervisor:

Hadis Karimipour;Eric Limacher;Andy Knight

Student:

Partner:

MLCan

Discipline:

Engineering

Sector:

Mining

University:

University of Calgary

Program:

Accelerate

Identifying the impact of social determinants of health on the incidence and outcomes associated with acute kidney injury using a machine learning approach

Acute kidney injury (AKI) is a syndrome which involves a sudden decrease in kidney function because of functional or structural impairment. It is a global issue and affects more than 7% of hospitalization in Canada. Unfortunately, although there has been improvement in the recognition and management of AKI, it continues to be a disease associated with poor outcomes. Furthermore, we know that the social determinants of health have a significant impact on outcomes; however, there is very limited information available in AKI. This project will utilize machine learning to identify high risk populations with AKI and determine the intersection with the social determinants of health. The partner organization will have the opportunity to generate important information to help guide future health policy decisions for patients with AKI.

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Faculty Supervisor:

Dean Eurich

Student:

Partner:

OKAKI

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology; Information and cultural industries; Professional, scientific and technical services

University:

University of Alberta

Program:

Accelerate

Distributed Energy Resources Management for Grid Modernization and Grid Transformation Applications

Ambitious 100% net-zero emissions targets, the growing proliferation of distributed energy resources including wind, solar photovoltaic and battery energy storage, and a rise in modern loads such as high capacity electric vehicle charging stations are ushering Canadian utility companies through a major transformation in how electrical distribution grids are managed and operated. Consequently, the traditional passive and mostly static distribution networks are rapidly evolving into more complex, active, and dynamically changing systems. This poses several challenges to distribution system utilities in how they operate, manage and optimize the use of distributed energy resources within their system. This research project will help address these challenges within the context of grid modernization and grid transformation applications.

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Faculty Supervisor:

Gregory Kish;Omid Ardakanian

Student:

Partner:

EPCOR Water Services Inc

Discipline:

Engineering

Sector:

Utilities

University:

University of Alberta

Program:

Accelerate

Probabilistic safety evaluation of the effective area methods for the assessment of crack and corrosion defects in pipelines

Pipeline integrity management involves effectively managing various threats, including cracking and corrosion. Crack and corrosion threat management has evolved keeping pace with advancements in the Inline Inspection (ILI) technologies which are primary tools for detection and sizing of crack and corrosion features on a pipeline. With the high-resolution inspection data obtained through ILI tools, the ILI vendors can report features’ profiles with varying depths along the length of crack and corrosion features. Compared to the past, when features were reported with a single maximum depth value that was assumed for the full length, the pipeline operators conservatively assumed the maximum feature’s depth in their assessment analysis. The goal from this research project is to develop an approach to utilize features’ profiles in the reliability analysis of crack and corrosion threats.

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Faculty Supervisor:

Samer Adeeb

Student:

Partner:

Enbridge Employee Services Canada Inc.

Discipline:

Engineering

Sector:

Energy and Utilities; Oil and Gas

University:

University of Alberta

Program:

Accelerate

Examining the Effects of Cannabis-based Edibles on Cognitive Health and Simulated Driving Performance in Young Drivers

There is increasing evidence showing an increase in motor vehicle crashes associated with young drivers who consume cannabis. While studies show that smoking or vaping cannabis is associated with impaired driving performance, there have been no studies examining the influence of edibles on driving performance. Additionally, prior studies have not defined the type of cannabis tested in research studies and there is a strong likelihood that different cannabis strains produce different impairments related to driving performance. Using a driving simulator, the objectives of the present study are to 1) to discern the effects of cannabis types (sativa and indica) on driving performance; 2) to determine at what time driving is most impaired; 3) to examine how long driving ability is impaired after edible consumption; and 4) to examine the association between driver perceptions, cognitive health, and driving performance.

View Full Project Description
Faculty Supervisor:

Alexander Crizzle

Student:

Partner:

CAA

Discipline:

Engineering

Sector:

Administrative and support, waste management and remediation services

University:

University of Saskatchewan

Program:

Accelerate

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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.