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

Designing the Evaluation of a Conservation Education Service Learning Program

This research project will evaluate the experiences of participants in the Canadian Wildlife Federation’s (CWF) new Canadian Conservation Corps (CCC) program. The CCC is designed to engage Canadians between the age of 18-30 in an experience-based, environmental education program which involves an outdoor excursion, field work with environmental organizations and a public outreach project. The study will combine interviews from 9 CCC participants from three different cohorts with interviews from program facilitators and partner organization leaders to understand the experiences, perspectives and challenges of the participants. This work will result in a clear and deep understanding of improvements for future programming and of the impacts on those involved in the program which will be important for funders, stakeholders and colleagues at the CWF.

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Faculty Supervisor:

Bonnie Shapiro

Student:

Nicholas Butt

Partner:

Canadian Wildlife Federation

Discipline:

Education

Sector:

Education

University:

Program:

Accelerate

Policy Optimization in Parameter Space

Model-free Reinforcement Learning (RL) has recently demonstrated its great potential in solving difficult intelligent tasks. However, developing a successful RL model requires an extensive model tuning and tremendous training samples. Theoretical analysis of these RL methods, more specifically policy optimization methods, only stay in a simple setting where the learning happens in the policy space. This project attempts to advance the analysis of the policy optimization methods to a more realistic setting in the parameter space. We will mainly focus on the convergence properties of the model and the unification of value and policy in the parameter space. New algorithms in policy optimization are expected to originate from the analysis.

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Faculty Supervisor:

Dale Schuurmans

Student:

Jincheng Mei

Partner:

Borealis AI

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Development of a clinical protocol for the validation of a detection test for ??amyloid aggregates in retinal scan images.

Alzheimer’s disease (AD) is a neurodegenerative disorder that is difficult to detect early before apparent manifestations of cognitive decline. Current detection methods rely on expensive and hardly accessible imaging techniques (PET scans). Optina Diagnostics developed a new camera as well as innovative image processing methods to propose a non-invasive test to identify the presence of AD biomarkers in evaluated individuals. The objective of this collaborative effort is to develop a clinical protocol to test this new technology. We will define best research design for validation and establish the clinical value of the new test for the detection of early signs of neurodegenerative disorder. This will provide an opportunity of training for a student in a clinical environment and with a partner from the industry in relation to a cutting-edge technology. For Optina Diagnostics, it is an opportunity to mobilize expertise in clinical research, epidemiology and evaluation of new modes of intervention, in order to prepare a sound application for FDA approval of their technology.

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Faculty Supervisor:

Marie Beauséjour

Student:

Mohamed Sangaré

Partner:

Optina Diagnostics

Discipline:

Epidemiology / Public health and policy

Sector:

Medical devices

University:

Université de Montréal

Program:

Accelerate

A case study on the business impact of personalized shopping experience on retail stores

This research will determine what factors are the most significant when it comes to the personalized shopping experience from the perspective of both customers and retailers, and shed light on opportunities and options for North American retailers to innovate within the retail space. We will conduct surveys with both retailers and customers to determine the level of value that each cohort places on the various factors and data points that can improve the personalized shopping experience. Three pilot-studies will be conducted in retailers stores in which new data to provide personalized shopping experiences will be provided to retailers through the addition of a sensor that tracks customer behaviours, and through the collection of POS data to show previous purchasing patterns. We will survey these retailers at the end of the 45-day pilot-studies to uncover how retailers might use this additional data to provide a personalized shopping experience, what factors or data points they found most helpful, and what areas of opportunity they believe exist in the retail landscape in North America. The results of this study will help inform future innovations from RetailDeep in its mission to provide customer-based data to retailers that will support the personalized shopping experience.

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Faculty Supervisor:

Gordon Fullerton

Student:

Zhenni Ge

Partner:

RetailDeep.com

Discipline:

Business

Sector:

Finance, insurance and business

University:

Saint Mary's University

Program:

Accelerate

UppstArt – A blockchain-based e-commerce system for the sale of online art.

UppstArt is a blockchain-based system for arts e-commerce. UppstArt integrates Ethereum blockchain to handle the online sale of art. UppstArt allows buyers to track the ownership provenance of artworks and resell their purchases any time. UppstArt also allows artists receive a royalty percentage every time their artworks are resold (Pending Canadian Legislation Artist Resale Rights). UppstArt is directed to living artists that produce original paintings. In the future, UppstArt will be expanded for other artistic works. For the general public, this research is significant because it will allow having reliable information of artworks before buying them. This project is motivated by the initiative to allow artists to receive a royalty percentage when their work is resold. Sometimes the value of artwork increases but the artist does not receive any reward. 

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Faculty Supervisor:

Ralph Deters

Student:

Mayra Samaniego

Partner:

Adappcity

Discipline:

Computer science

Sector:

Finance, insurance and business

University:

University of Saskatchewan

Program:

Accelerate

Mortality Rate Modeling: A study of the effectiveness of longevity and mortality linked financial derivatives as hedging instruments in a pension risk management strategy

Private pension plan sponsors wishing to manage their longevity risk transfer it to insurers through annuity contracts or bespoke longevity swap arrangements. They increasingly rely on such contracts to reduce their risk exposure. For example, annuity purchase activity from Canadian private pension plans expanded from $1 billion in 2012 to almost $2.7 billion in 2016 (Willis Towers Watson (2017)). This trend is expected to accelerate as interest rates increase, since it will improve the financial position of defined benefit pension plans and at the same time it will lower the costs of buying annuities.

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Faculty Supervisor:

Alain Belanger

Student:

Hugo Carrier

Partner:

Addenda Capital

Discipline:

Business

Sector:

Finance, insurance and business

University:

Université de Sherbrooke

Program:

Accelerate

Development of improved power quality detection methods suitable for modern applications

Discontinuities of service, variations in voltage magnitude, and distortions in AC voltage waveforms constitute the different aspects poor power quality. A poor quality of power supply can cause malfunction of sensitive equipment and interrupt industrial processes, resulting in significant economic losses. Utilities and consumers are taking actions to maintain the power quality set by the standards. Monitoring of power quality at all levels in the power system is necessary to ensure adherence to standards, but specialized power quality monitoring equipment are expensive. Cost of monitoring can be reduced if monitoring functions are integrated to multifunction devices such as fault recorders or protection relays. However, most advanced power quality event detection methods require significant computing power and their implementation on multifunction devices is challenging. The proposed research aims to develop improved power quality detection methods suitable to implement on a resource constrained computing environment.

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Faculty Supervisor:

Athula Rajapakse

Student:

Jagannath Wijekoon

Partner:

ERL Phase Power Technologies

Discipline:

Engineering - computer / electrical

Sector:

Energy

University:

University of Manitoba

Program:

Accelerate

Accessible data platform for dynamic experience study of lifestyle underwriting

We seek to replace or enhance the traditional underwriting approach (namely identification of insureds via a pre-defined fixed set of risk criteria) with one based on a set of dynamic protocols that are responsive to human behavioral factors for continual health improvement. We seek to provide a live and interactive in-market research dataset that can be used to explore the benefit of and improve data-driven approaches (namely artificial intelligence or AI) for immediate use in life & health insurance product development and actuarial risk assessment.

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Faculty Supervisor:

Ken Seng Tan

Student:

Fan XIA

Partner:

Besurance Corporation

Discipline:

Computer science

Sector:

Finance, insurance and business

University:

University of Waterloo

Program:

Accelerate

Efficient face recognition for wearable camera devices

Titan Sécurité Inc. has deployed wearable video camera devices for security and surveillance applications, and seeks to accurately detect and recognize objects appearing in captured videos. This project focuses on video-based face recognition (FR), where facial trajectories captured with video cameras are compare against one (or few) reference stills for each individual of interest. The performance of these FR systems is typically poor due to complex and changing video surveillance environments, e.g., variations of facial appearance due to pose, illumination, blur, etc. Given the state-of-the-art accuracy achieved with deep learning architectures on many challenging visual recognition problems, Titan Sécurité Inc. seeks to design Siamese networks based on deep convolutional neural networks (CNNs) for still-to-video FR. However, since these networks represent complex solutions for real-time applications, this project seeks to develop specialized techniques to reduce their time and memory complexity. These include advanced techniques for reducing search time, selecting features, and pruning parameter. In particular, this project will focus on developing filter-level pruning techniques that can simultaneously accelerate and compress a CNN based on information extracted from its layers.

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Faculty Supervisor:

Éric Granger

Student:

Hugo Lemoine St-André

Partner:

Titan Securite

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

École de technologie supérieure

Program:

Accelerate

Parallel Radiofrequency Transmission Technology for Magnetic Resonance Imaging at 3 Tesla – Year two

Funds are requested for one fellow to work in the laboratory of Dr. Simon Graham at Sunnybrook Research Institute, Toronto, in partnership with Siemens Healthcare Limited. The fellow will work on development of prototype instrumentation that will enable a technique called “parallel radiofrequency transmission (PTX)” to be implemented flexibly for research purposes on a Siemens 3 T MRI system at the Institute. The fellow will also support preliminary testing of the instrumentation towards the long-term goal of developing robust new PTX approaches for safe imaging of patients with implanted medical devices, such as deep brain stimulators, without tissue damage caused by localized heating effects. The fellow will also undergo Siemens training and skills development to support the breadth of MRI technology research ongoing at the Institute. At the end of term, the fellow will be in an excellent position to apply to become an MRI Collaboration Scientist at Siemens.

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Faculty Supervisor:

Simon Graham

Student:

Pei-Shan Wei

Partner:

Siemens Healthcare Ltd.

Discipline:

Medicine

Sector:

Medical devices

University:

Program:

Elevate

Energy storage integration into electrified vehicle systems for shared and transit mobility applications

The deployment of electric and alternatively-fuelled vehicles in urban transportation constitutes a core component of current federal and provincial policies vis-a-vis Climate Action Strategies across Canada.
In the heavy-duty vehicle context specifically, the lack of standardized charging infrastructure combined with a lack of understanding as to the value of integrated energy storage devices to reduce or eliminate demand/delivery charges for high-powered charging constitutes an ongoing technology barrier to electric transit integration.
These important challenges will be addressed in this proposal through the partnership between the fellow and the Canadian Urban Transit Research & Innovation Consortium (CUTRIC). The active intervention of utilities and industry members will enable the researcher to capture proprietary data and technical information not normally available to academic researchers who are studying electrified transit and energy storage systems outside of industry partnerships.
TO BE CONT’D

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Faculty Supervisor:

Richard Chahine

Student:

Cristina Guzman

Partner:

Canadian Urban Transit Research and Innovation Consortium

Discipline:

Engineering - computer / electrical

Sector:

Alternative energy

University:

Program:

Elevate

Security and Resiliency of Prairie Industrial Water Supplies

This research problem examines the security and resiliency of prairie industrial water supplies in a changing climate. The water-consumptive industries in the Prairie Provinces are a major contributor to the national economy, but they depend on secure and reliable water supplies in a region characterized by dry climate. The most challenging future scenario for these industries, and the prairie economy in general, is a prolonged drought in a warmer climate. The objective of this project is to support planned adaptation to climate change in the Prairie Provinces’ energy and mining industries. The research will involve using tree rings to reconstruct past water levels in the Souris, Saskatchewan and Qu’Appelle River basins, and using climate models to predict future climate and water supplies. This new scientific knowledge will be translated so that it can be applied to risk assessment and adaptation planning in the mining and energy sectors.

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Faculty Supervisor:

David Sauchyn

Student:

Sunil Gurrapu

Partner:

Water Security Agency

Discipline:

Environmental sciences

Sector:

Natural resources

University:

Program:

Accelerate

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