Innovative Projects Realized

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

13270 Completed Projects

1072
AB
2795
BC
430
MB
106
NF
348
SK
4184
ON
2671
QC
43
PE
209
NB
474
NS

Projects by Category

10%
Computer science
9%
Engineering
1%
Engineering - biomedical
4%
Engineering - chemical / biological

Design Automation and Optimization Using Artificial Intelligence

The goal of our proposal is to develop three automated processes in the field of construction using artificial intelligence. The first process is to develop a method that can convert two-dimensional drawings into three-dimensional models that can be further manipulated on a computer. The second process is to optimize the cutting of raw materials– such as panels and stiffeners– to reduce the overall wastage, as well as optimize the transportation process of these materials to the resulting construction site. The third process is to design a method that can ensure objective and accurate calculation of costs for construction projects based on the multiple parameters included in the design. This project is situated at a Manitoba-based construction firm, Greenstone Solutions. The benefits to the company will include reductions in manufacturing time, costs, and wastage, which will result in increased productivity, efficiency, and reputation.

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Faculty Supervisor:

Vijay Mago;Pawan Lingras;Muntasir Billah

Student:

Andrew Fisher

Partner:

Greenstone Building Products

Discipline:

Computer science

Sector:

Construction and infrastructure

University:

Program:

Accelerate

Machine Learning for Practical and Scalable Regression Test Selection and Prioritization

In the context of systems with a large codebase, Continuous Integration (CI) significantly reduces integration problems, speed up development time, and shorten release time. While regression testing is widely practiced in the context of CI, it can be time-consuming and resource intensive for large codebases where the execution of test cases is time and resource intensive. In this project, we try to devise and apply Machine Learning-based solutions for three critical problems related to regression testing in the context of CI: (1) test selection and prioritization, (2) test case minimization, (3) automatic refactoring and generation of test cases.

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Faculty Supervisor:

Lionel Briand

Student:

Nafiseh Kahani

Partner:

Huawei Technologies Canada

Discipline:

Engineering - computer / electrical

Sector:

University:

University of Ottawa

Program:

Accelerate

Dino Island: Improving Executive Functioning in Very Young Children with Autism Spectrum Disorder

Many children with Autism Spectrum Disorders (ASD) have problems with attention and executive functions (EF). Cognitive interventions have great potential to improve attention/EF and related skills (e.g., academic learning, social function, behaviour etc.), but few such interventions exist with even fewer that can be delivered at home. In light of COVID-19, parent-delivered interventions are crucial for continuity of healthcare for children with ASD. We will evaluate the efficacy of a new attention/EF intervention (Dino Island), as delivered by parents at home to their children with ASD. Half of the children will complete a 12-week attention/EF intervention whereas the other half will complete a control intervention. We will conduct pre- and post- testing to assess cognitive, academic, and behavioral outcomes. We will also gather information on the use of an on-line telehealth tool (TelerooTM) for delivering home-based interventions.

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Faculty Supervisor:

Sarah Macoun

Student:

John Sheehan;Buse Bedir;Jessica Lewis

Partner:

The Uncomplicated Family Inc.

Discipline:

Psychology

Sector:

Health care and social assistance

University:

University of Victoria

Program:

Accelerate

Establishing productivities of violacein production in recombinant E.coli using CSTR batch processing

Material Futures Inc. has developed a bioprocess to manufacture coloured pigments using cells, instead of petroleum. The process is carbon neutral, renewable, and has the potential to eliminate water pollution resulting from current dyeing methods. Working with students at the University of Waterloo, Material Futures Inc. will be scaling their bioprocess while exposing students to the industrial applications of metabolic engineering and bioprocess engineering. Two students will gain exposure to applied research and development at a top-tier Canadian institution under the guidance of expert researchers and industry leaders to develop their professional skillsets.

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Faculty Supervisor:

Roderick Slavcev

Student:

Iris Redinger

Partner:

Material Futures Lab

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of Waterloo

Program:

Non-thermal sono-treatment improves the liquefaction and saccharification process and presence of phytochemicals of Cannabis sativa

In beer manufacturing, the liquefaction and saccharification process utilizes heat, alkaline and acid washes to break down complex sugars into wort (simple sugars) leading to destruction of desirable bioactive compounds. Beer manufacturers are keen to use green technology and non-starch plant materials from agri-food by-products. Province Brands has developed its proprietary methods for brewing from cannabis plant waste (stems, stalks, roots). In partnership with Province Brands, this research aims to a) convert plant by-products (non-starch sources) to fermentable sugars using high power- low frequency ultrasound as a non-thermal green treatment; b) maximize retention of bioactive compounds in the final brews; c) improve the sensory properties of the finished products. This project is an excellent opportunity for the intern to apply their critical thinking skills to develop an innovative product and will put Province Brands of Canada at the forefront of the market by investing in research and through knowledge transfer.

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Faculty Supervisor:

Farah Hosseinian

Student:

Kelly Dornan;Minfang Luo;Matheuzs Poluchowski;Winifred Akoetey

Partner:

Province Brands of Canada

Discipline:

Chemistry

Sector:

Agriculture

University:

Carleton University

Program:

Accelerate

Practical implementation of an anisotropic rock mass strength model for rock slope stability analysis

As mine pit slope become higher, the implications of accurately predicted slope angles becomes greater for worker safety, environmental impact and economics. Over the past decade, data analysis and computational methods have resulted in significant research developments in this area. Utilizing these some of thee methods requires a high level of field data and large computational resources. For many projects, this may not be warranted or available. This project will study the state-of-the-art approaches and develop a practical workflow implementation that captures the most important features of current research findings. In addition, this study will explore some of the research gaps, such as scale effects and spatial variability, and include these within the analysis tools to provide a holistic analysis approach consistent with geological observations.

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Faculty Supervisor:

Andrew Corkum

Student:

Ryan Ziebarth

Partner:

BGC Engineering Inc

Discipline:

Engineering - civil

Sector:

Professional, scientific and technical services

University:

Dalhousie University

Program:

Multi-Camera Calibration and Stitching Under Automated Scenarios

Many vehicles use multiple cameras to provide an unobstructed view to the operator and provide information about the environment around them. As these cameras are placed sparsely around the vehicle, the video sequences are not easily mapped to a regular surface; therefore, distortion from the irregular mapping process provides an insufficient reproduction of the exterior environment. In addition, currently employed mapping methods are based on mapping points at infinity and thus while background objects are generally correct, foreground objects become distorted, especially when transferring from one camera to the another. Our objective is to develop a projection and stitching system that is able to correctly map images into a view space that is relatively correct for the operator, while maintaining an overall easy-to-use and low-power operation, in a real-time environment.

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Faculty Supervisor:

Chris Joslin

Student:

Kaveh Rouhandeh

Partner:

General Dynamics Mission Systems - Canada

Discipline:

Other

Sector:

University:

Carleton University

Program:

Accelerate

Investigating the benefits of natural habitats and farmland heterogeneity for the diversity and abundance of insect pollinators in southern Ontario

To address the increasingly important problem of global insect pollinator declines, this project will investigate the relationship between three different natural habitat types (hedgerow, forest patch and restored prairie grass) and their impacts on wild pollinator biodiversity in Canada. This will be studied through the use of Malaise traps place on agricultural land adjacent to these key habitats to monitor for changes in abundance and diversity of native pollinators. While the importance of non-native, managed honeybees as crop pollinators has been well studied, there is still a critical need to understand what habitats are needed to support wild bee and fly pollinators, taking into account their nesting and foraging requirements. The CWF will benefit from this work by receiving a biodiversity analysis of the farmland adjacent habitat, which will be used to inform public education, knowledge transfer to agricultural producers, and influence government policy that could improve pollination services.

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Faculty Supervisor:

Nigel Raine;Andrew Young

Student:

Samantha Reynolds

Partner:

Canadian Wildlife Federation

Discipline:

Environmental sciences

Sector:

University:

University of Guelph

Program:

Integration of Simultaneous Localization And Mapping (SLAM) to improve workflow of reconstruction projects and space utilization

The focus of this project will be how modern technologies, specifically static and mobile laser scanners, drone photogrammetry, and Virtual Reality (VR) can be applied to solve issues related to renovating and utilizing (repurposing) old buildings. This is a multi-disciplinary approach with college interns from Geomatics Engineering and Architecture Engineering programs.

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Faculty Supervisor:

Blair Bridger;Deirdre Snook

Student:

Marina Charlie Dalton

Partner:

People of the Dawn Indigenous Friendship Centre

Discipline:

Engineering

Sector:

Other

University:

College of the North Atlantic

Program:

Accelerate

The Affect of Fun on the Adoption of Micromobility

This research project aims to understand what factors are driving the increased usage of shared micromobility vehicles – primarily escooters – in city centres. In particular, this research seeks to understand the notion of fun as it influences how people chose to get around within a city. The findings of this inquiry will help cities understand and in turn be better prepared in planning for and in incorporating new modes into transportation strategies. Ultimately, this research intends to help cities become more integrated and better equipped to meet demands around sustainability, congestion, affordability and increased population density. In partnering with Onpoint Strategic Group to complete this research project, the results will help them remain relevant and provide new avenues of revenue in their focus on disruptive technology.

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Faculty Supervisor:

Ingrid Kajzer-Mitchell

Student:

Karly Nygaard-Petersen

Partner:

OnPoint

Discipline:

Other

Sector:

Professional, scientific and technical services

University:

Royal Roads University

Program:

Accelerate

Characterization of Free/Bound Water of Mature Fine Tailings Using Differential Scanning Calorimetry

Oil sand tailings could take ages to naturally dewater enough to be reclaimed. Finding ways to accelerate dewatering fluid tailings is crucial to improving overall reclamation planning and performance. The goal of the proposed project is to establish a new thermoanalytical technique for quantification and analysis of free and bound water contents in both individual MFT clays and flocs using Differential Scanning Calorimetry (DSC). These techniques will be beneficial in the study of the effectiveness of different concentrations of flocculant, coagulant or a mixture of both on the tailings settling rates. A higher grade of effectiveness of these substances results in a larger amount of water that could be reincorporated immediately into the recovery process. This work will also contribute to the design of novel water-soluble polymer compositions to achieve rapid flocculation and dewatering, therefore to increase the efficiency of tailings separation.

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Faculty Supervisor:

Seyed Hossein Hejazi

Student:

Yalda Zamani Keteklahijani

Partner:

Suncor Energy Inc

Discipline:

Engineering - chemical / biological

Sector:

University:

University of Calgary

Program:

Accelerate

Unmanned Aerial Vehicle Swarm Collaboration for Weed Control in Field Crops

Precision.ai is building solutions to minimize chemical consumption while maintaining weed control through Intelligent UAV based application. Precision.ai has working survey drones that can fly a field, capture images and use AI to map weeds to be sprayed later. Precision.ai also has “See & spray” drones that can fly a field, identify weeds and spray them. We now need to scale our capabilities through drone swarming. The required speed and coverage will require an autonomous and collaborative swarm of drones (or a combination of more capable drones and/or more efficient field coverage). This includes smart autonomous adaptive path planning for the multi-UAV swarm. This includes improved (optimized) field coverage. We need growers to be able to configure missions to optimize coverage, minimize chemical use, minimize mission time and optimize weed suppression based on their crop and field scenarios. We expect field size to mean that drones will need to autonomously reload and recharge at points in their missions. This will likely require other agents in the swarm to adapt their flight / spray paths for complete coverage.

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Faculty Supervisor:

Malek Mouhob

Student:

Ali Moltajaei Farid

Partner:

Precision.ai

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Regina

Program:

Accelerate

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