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

Catalyst development and characterization for microwave reactors

Hydrogen is a clean energy and its demand continues to rise rapidly in recent years. While hydrogen can be produced at low cost at large scale, the significant cost and complexity of distribution significantly increases its cost when used in smaller quantities in the emerging fuel cell electric vehicle market. Microwave technology is an alternative technology to produce hydrogen on-site, on-demand, which is right for cost effective small-scale minimal emissions. This proposed project is to develop a robust catalyst for microwave-assisted hydrogen production. It will not only help Canada to produce on-site clean hydrogen energy that can be used in remote regions and to reduce emissions, but also assist the industrial partner to promote its competitiveness in the relevant market. In addition, a highly qualified personnel (HQP) will be trained in an industrial-oriented setting.

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Faculty Supervisor:

Ying Zheng

Student:

Shima Masoumi

Partner:

Nuionic Technologies

Discipline:

Engineering - chemical / biological

Sector:

Manufacturing

University:

Western University

Program:

Developing an interfacing platform technology for silicon-micromachined JFET biosensors

Biosensors are can detect a variety of molecules in a rapid and highly sensitive manner. A new biosensing technology was developed to allow scientists to customize the biomolecular target they wanted to detect, called an open-gated silicon junction field effect transistor (JFET). However, this technology lacks user friendly packaging needed accommodate its use in diverse research settings. This can discourage people from using and building new sensing platforms. A customizable packaging platform to house the JFET technology would provide the tools needed for any scientist to develop their own unique biosensor. The aim of this project is to develop and validate a suite 3D printable housing designs that meet the need for high customizability and rapid small-scale production. A standardized plug-and-play approach to take away the frustration from the basic manufacturing and prototyping may encourage more researchers to adopt and develop new applications that will accelerate Canadian development of important tools including COVID diagnostics.

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Faculty Supervisor:

Christopher Moraes

Student:

Stephanie Mok

Partner:

CMC Microsystems And Applied Nanotools

Discipline:

Engineering - chemical / biological

Sector:

Professional, scientific and technical services

University:

McGill University

Program:

Ultra-high-quality optical coatings fabricated using plasma-assisted reactive magnetron sputtering

The proposed project is a collaboration between the Bradley research group at McMaster University and Intlvac, located in Halton Hills, ON, on the development of novel deposition methods and thin film materials for high performance optical coatings. Intlvac has a long history of developing state of the art deposition systems for coatings and thin films, in research and industrial applications. The Bradley group has extensive experience in thin film deposition and development of high optical quality materials for microphotonic devices. Intlvac is seeking to advance complex multilayer optical coatings technology and ultra-high-quality dielectric films, which will lead to economic growth and highly qualified personnel (HQP) training in this growing sector in Canada. The intern will work at both Intlvac and McMaster University to develop deposition techniques and applications for Intlvac and use the extensive optical and material characterization equipment available in the Bradley lab, the Centre for Emerging Device Technologies (CEDT) and Canadian Centre for Electron Microscopy (CCEM) at McMaster University.

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Faculty Supervisor:

Jonathan Bradley

Student:

Jeremy Miller

Partner:

Intlvac

Discipline:

Engineering

Sector:

Manufacturing

University:

McMaster University

Program:

Community-based water monitoring and two-eyed seeing in the St. Marys Area of Concern

This project aims to bring Indigenous and Western ways of knowing together to generate actionable community-based data and information to influence local and regional water management and related decisions in the St. Marys River Area of Concern. Led collaboratively by the Garden River First Nation, NORDIK Institute (Algoma University), and Waterlution, delivered in partnership with four other organizations, we propose a pilot project that will train Indigenous community members to monitor water quality over a condensed monitoring season (i.e., as a proof of concept). Data will be uploaded to openly accessible databases and will be interpreted and shared with members of the Garden River community, the Algoma University academic community, and others involved in the St. Marys River Area of Concern. The goal is to make community-derived information actionable by decision makers, while improving the community’s ability to self-govern its resources. Monitoring will contribute to an understanding of current conditions (a ‘baseline’ before further industry enters) in the Garden River and nearshore St. Marys River, while providing context for training basic monitoring skills.

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Faculty Supervisor:

Istvan Imre

Student:

Elaine Ho

Partner:

Waterlution

Discipline:

Biology

Sector:

Education

University:

Algoma University

Program:

Reducing the effect of magnetic disturbance in inertial measurement based indoor localization

Using inertial measurement units (IMUs) has been one of the main techniques for indoor localization. In many cases, this type of methods uses accelerometer, gyroscope, and/or magnetometer that are available in commercial mobile devices, such as smart phone. One main advantage of using this type of techniques is that no separate infrastructure deployment is needed. However, its performance is affected by noise that exists in the measurement data. When tracking pedestrians, the moving speed and direction are usually estimated separately. Muldi Vision Ltd. (MDV), the industrial partner organization, has been providing localization services for workers in industrial environments. When using their current product in manufacturing plants, the accuracy of the IMU-based localization method is highly affected by magnetic disturbance. The objective of this work is to find methods to reduce the negative effect of magnetic disturbance on the accuracy of the sensor orientation estimate and therefore improve the localization accuracy.

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Faculty Supervisor:

Dongmei Zhao

Student:

YiQiong Miao

Partner:

Muldi Vision Ltd.

Discipline:

Engineering - computer / electrical

Sector:

Professional, scientific and technical services

University:

McMaster University

Program:

Accelerate

Advancing TTC Ridership Analytics and Revenue Forecasting Tools for Improved Transit Planning

(TTC) for improved public transit planning and better transit service delivery. With the implementation of PRESTO Card, TTC now generates real-time data on how often and where transit riders interact with the TTC’s infrastructure and network. PRESTO Card data allows new ways to capture transit demand in real-time and makes it possible to deploy state-of-the-art data science and predictive analytics to develop ridership forecasts for varying time horizons. The ridership forecasts could then be used to generate forecasts for farebox revenue. This project will, therefore, contribute to a significant improvement in transit planning from revenue and resource utilization perspectives. The project will also contribute to the mentoring and training of two graduate students at Ryerson University who will lead the two inter-related projects of transit ridership and revenue forecasting.

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Faculty Supervisor:

Murtaza Haider

Student:

Abir Saumik;Yichun Du

Partner:

Toronto Transit Commission

Discipline:

Business

Sector:

Transportation and warehousing

University:

Ryerson University

Program:

Accelerate

Using long-term monitoring data to quantify the impact of white-tailed deer reduction on vegetation and avian communities at Long Point, Ontario

Through analysis of vegetation data collected between 1991 and 2021 in Long Point National Wildlife Area, it is the goal of the research to identify trends and changes in sand dune vegetation communities following a reduction of white-tailed deer browsing pressure. By evaluating the rate and level of change in vegetation diversity, structure, and composition, recommendations can be identified for land managers to assist in achieving effective management of protected areas in relation to deer populations and resulting community impacts.

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Faculty Supervisor:

Ryan Norris

Student:

Joshua K Pickering

Partner:

Nature Conservancy Canada

Discipline:

Biology

Sector:

University:

University of Guelph

Program:

Threats to urban-forest sustainability in Halifax Regional Municipality

Our research aims to provide reliable information to HRM’s urban forestry staff for decision-making to enhance the sustainability of the city’s tree population. One student will investigate the factors that contribute to poor health and mortality of new street trees, aiming to assist the urban forestry staff to alleviate these factors in future plantings. Another student will document the prevalence of hemlock trees in HRM’s six large wooded parks, aiming to assist urban forestry staff to determine whether actions will be needed to cope with the impending arrival of the insect pest known as the hemlock woolly adelgid.

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Faculty Supervisor:

Peter Duinker

Student:

Tyler Doucet

Partner:

Halifax Regional Municipality

Discipline:

Environmental sciences

Sector:

Other

University:

Dalhousie University

Program:

Accelerate

Developing an Efficient Ensemble Machine Learning Model for Evaluating Construction Project Bidding Quality and Optimal Winning Strategies

PledgX is interested in building a solution that aims to optimize the bidding process to maximize key performance indicators for contactors and vendors. For bidding optimization, several strategies and methods have been proposed; however, with the massive amount of available bidding datasets, the quality and performance of such methods are questionable. Machine learning introduces intelligent solutions to optimize the bidding decision, however these solutions are applicable to a range of prediction or classification tasks. Thus, ensemble modelling is introduced for efficient performance and to overcome drawbacks for individual modeling. In this project, we propose a novel data-driven bidding model based on ensemble predictive learning, which extracts sophisticated features and learns to bid automatically using the collected data. The model is composed of sub-models aggregated to form a more robust global model. The proposed ensemble learning model enables PledgX to learn complex rules of bidding with optimized overall bidding performance.

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Faculty Supervisor:

Rasha Kashef

Student:

Alireza Ghasemieh

Partner:

PledgX

Discipline:

Engineering - biomedical

Sector:

Information and cultural industries

University:

Ryerson University

Program:

Accelerate

Root associated microbiome of trees growing in a fractured bedrock toluene phytoremediation site – Year two

Phytoremediation is a promising in-situ technology that uses plants and its associated microorganisms (particularly bacteria and fungi) to clean up contaminated soils. The efficacy of these processes however, requires an in-depth knowledge on the diversity of microbial communities closely interacting with plant roots. Several studies have demonstrated that plants growing in contaminated soils select for competent microorganisms able to degrade these contaminants. Although phytoremediation has received great attention in recent years, research to-date has been limited to typical unconsolidated sediments and its efficacy has yet to be shown in fractured bedrock environments. Therefore, the proposed research will aim to provide a practical evaluation on phytoremediation of petroleum hydrocarbons in fractured rock environments. Together, the results in this project will fill knowledge gaps in the scientific literature and evaluate phytoremediation systems as a viable remediation option for our industry partner in a toluene-impacted site. In addition, the proposed research will also provide new insights for industries and regulators to further develop rapid and cost-effective monitoring strategies for phytoremediation performance evaluation at other impacted sites.

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Faculty Supervisor:

Kari Dunfield

Student:

Eduardo Kovalski Mitter

Partner:

BP Corporation North America Inc.

Discipline:

Environmental sciences

Sector:

University:

University of Guelph

Program:

Elevate

Synthesis and Development of Low-Cost and High-Quality Graphene Nanostructures

Graphene is the thinnest, lightest, strongest, and a highly conductive material discovered to date, which makes it attractive for diverse applications, ranging from energy storage devices, electronics and automotive to construction. Despite the unique properties of graphene and its potential application in various industries, the widespread application of graphene is still limited due to the high cost of starting materials, high production cost, or low production volume. In this project, we will develop cost-effective methods to synthesize high-quality and low-cost graphene on a large-scale from inexpensive and abundant natural carbon resources. It is anticipated that the outcome of this project will help the industry partner to produce high-quality graphene at a lower cost and reduce environmental footprints.

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Faculty Supervisor:

Al Meldrum

Student:

Razieh Firouzihaji

Partner:

Discipline:

Physics / Astronomy

Sector:

Other

University:

University of Alberta

Program:

Smart Work Zone Management – Year two

Construction zones are one of the leading contributors to Toronto’s ever-growing congestion. The aim of this study is to develop an integrated construction zone traffic management framework to minimize disruption of the traffic and reduce the effect in terms of congestion. This study leverages historical and real data collected from on-board construction trucks provided by the partner organization to find an insight as to how far upstream and downstream of the work zone congestion propagates. Using such information, it is then possible to develop novel prediction models determining the impact zone for future construction zones and selecting optimal work zone size and staging of vehicles and equipment. In addition to the prediction model as part of this collaboration, an innovative anticipatory vehicle routing algorithm will be developed that not only help motorist to avoid construction zones but also guides them to their destination while minimizing travel time and utilizing road network more efficiently.

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Faculty Supervisor:

Bilal Farooq

Student:

Amjad Dehman

Partner:

Lazaret Capital

Discipline:

Engineering - civil

Sector:

Management of companies and enterprises

University:

Ryerson University

Program:

Elevate

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