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

Using Machine Learning to Predict 30-Day Risk of Hospitalization, Emergency Visit or Death Among Albertans Who Received Opioid Prescriptions

When utilizing and implementing ML for prediction using administrative health data, two key issues are ML algorithm evaluation and generalizability21. Current approaches evaluate model performance by quantifying how closely the prediction made by the model matches known health outcomes. Evaluation metrics include sensitivity, specificity, and positive predictive value, as well as measures such as the area under the receiver operating characteristic (ROC) curve, the area under the precision-recall curve, and calibration. Because no single measurement reflects all of the desirable properties of a model, several measurements typically are reported to summarize the performance of the model16. Furthermore, model performance ultimately comes down to discrimination and calibration22. Discrimination is usually quantified using a concordance statistic (area under ROC) while calibration is graphically represented as observed to expected ratios.
Generalizability is also an issue that must be acknowledged in ML prediction settings21. ML models trained in one setting may not be valid in another. The same is true for populations. Furthermore, even ML algorithms that are considered generalizable may quickly become outdated as treatment guidelines or the population changes thus requiring model updating and re-evaluation 21.

View Full Project Description
Faculty Supervisor:

Irene Cheng

Student:

Tanya Joon;Navya Gururaj Rao

Partner:

OKAKI

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Alberta

Program:

Accelerate

Performance map of effect of composition and heat treatment on the work output from shape memory alloys.

Shape memory alloys are materials which, when cooled are easily stretched, but when heated, return to its previously “remembered” shape with a high force. This allows for a simple heat engine to be made, which can use hot and cold sources to create motion and do work. The amount of work that can be done by a shape memory alloy is dependant on its composition, geometry and how the material is processed prior to use. To properly a design a shape memory alloy device, it is important to determine how it will perform, taking those previously mentioned factors into account, but also heating and cooling rates. The objective of this proposal is to develop a testing facility tailored to shape memory alloys, accounting for the factors which affect performance in application. This apparatus is unique in that it allows for a complete characterization of the performance of the material in a single step.

View Full Project Description
Faculty Supervisor:

Chan Y. Ching

Student:

Mohamed Hammouda

Partner:

Smarter Alloys Inc

Discipline:

Engineering - mechanical

Sector:

University:

McMaster University

Program:

Accelerate

Schedulability Analysis of Real-Time Systems Using Metaheuristic Search and Machine Learning

Schedulability analysis aims at determining whether task executions complete before their specified deadlines. It is an important activity in developing real-time systems. However, in practice, engineers have had difficulties applying existing techniques mainly because the working assumptions of existing methods are often not valid in their systems. Specifically, uncertainties in real-time systems and hybrid scheduling policies that combine standard scheduling policies have not been fully studied in the literature. This project develops an approach that analyzes the schedulability problem of real-time systems by accounting for such uncertainties and complex scheduling policies applied in practice. Our approach combines a metaheuristic search algorithm for generating worst-case scheduling scenarios with a machine learning technique for inferring a probability of deadline misses. To evaluate the practical usefulness of our work, we apply our approach to real systems developed by our industry partner, BlackBerry.

View Full Project Description
Faculty Supervisor:

Shiva Nejati;Lionel Briand

Student:

Jaekwon Lee

Partner:

Blackberry

Discipline:

Engineering - computer / electrical

Sector:

Manufacturing

University:

University of Ottawa

Program:

Accelerate

Investigation of techno-economic feasibility and environmental benefits of mine waste heat recovery systems for an underground mine in northern british columbia

Underground mining operations are very energy intensive and could require significant use of fossil fuel burning on site for electrification and heat provision, if situated in off-grid areas. Conventionally, these power plants employ diesel gen-sets that can convert only about 35% of the combustion energy to qualified work and discard about 65% of the energy generated as heat through cooling cycles and exhaust. On the other hand, due to harsh climates associated with Canadian winters, such mines require mine intake air heating (or referred as preconditioning in some literature) for preventing the mine shaft and stationary shaft units from freezing (i.e. liners, transportation equipment, ventilation fans and such). Conventionally, these mines employ large-scale fossil burning stations to generate heat at the mine intake. These systems are mostly natural gas and propane based. This project aims to investigate reutilization of the discarded heat from the power plant at the mine intake to save from fossil burning at the shaft intake burners. Earlier studies suggested that mine waste heat systems can save thousands of dollars from operational expenditures with comparably less carbon emissions.

View Full Project Description
Faculty Supervisor:

Seyed Ali Ghoreishi-Madiseh

Student:

Ali Fahrettin Kuyuk

Partner:

Mineit Consulting

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of British Columbia

Program:

Accelerate

Examining the operation of Public Pools and Spas in the COVID-19 Era

The COVID-19 pandemic affected every aspect of our lives, and recreational water facilities were not immune to this with several questions and concerns about potential exposure to the virus at these facilities. This research project aims to understand experiences, needs, and attitudes towards the use of recreational water facilities, namely, public pools and spas during the COVID-19 pandemic. Three groups of interest that will be investigated are: 1) public pool and spa staff, 2) members of the public who regularly use recreational water facilities and 3) public health inspectors (PHIs) who conduct inspections at public pools and spas. The partner organization will utilize results to better understand the challenges and develop strategies to support the pool and spa operation.

View Full Project Description
Faculty Supervisor:

Fatih Sekercioglu;Chun-Yip Hon

Student:

Jessica Castellucci

Partner:

Lowry and Associates

Discipline:

Environmental sciences

Sector:

Transportation and warehousing

University:

Ryerson University

Program:

Accelerate

Anaerobic biodegradation of complex aromatic pollutants by indigenous microbes from a contaminated site in Brazil

This four-month research project proposal aims to contribute to bioremediation projects carried out by the partner industry by evaluating the biodegradability of some halogenated aromatic compounds, such as dichloroaniline (DCA), and chloroaniline (CA), present in a contaminated site. Through some biodegradability tests, we will verify and better understand how native microbes from the mentioned site degrade those compounds in the absence of oxygen, as it occurs in deep soil layers. The microbial community will be evaluated and identified during the research period, in order to understand which microbes are responsible for the various degradation processes. These results will guide the remediation projects that are planned and implemented by the industry.

View Full Project Description
Faculty Supervisor:

Elizabeth Edwards

Student:

Sofia Pimentel Araújo

Partner:

Geosyntec Consultants Inc

Discipline:

Engineering - chemical / biological

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Intensifying Mass Transfer and Flotation Rates in Multiphase Contactors/Reactors

The proposed project mainly focuses on developing an innovative gas/liquid contacting technology that is of critical importance to a wide range of process industries and environmental-management operations. Successful development and implementation of this project are expected to:
• Reduce the environmental impact of a variety of operations that are needed to meet human needs and welfare (e.g. water/wastewater treatment, aquaculture, environmental protection),
• Strengthen BC Research Inc’s position in the areas such as process intensification, advanced wastewater treatment, mineral processing, biotechnology and green chemistry,
• Expand BC Research Inc’s sustainable business opportunities in new areas (e.g. selective flotation and stripping of volatiles from effluents) and enhance job creation for high-tech personnel.

View Full Project Description
Faculty Supervisor:

Adel M. Al-Taweel

Student:

Rong Leng

Partner:

BC Research Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Dalhousie University

Program:

Accelerate

Using community science to protect the butterflies of Canada

Community science is emerging as a powerful tool to answer scientific questions and to involve the public in educational activities. eButterfly, the Montreal Insectarium community science program, is one of the largest citizen science programs on insects in North America, and has great potential to inform management decisions across Canada during these crucial times for biodiversity conservation. During this fellowship, I will use cutting-edge statistical models and capitalize on thousands of checklists collected by volunteers to assess the diversity and status of the butterflies of Canada. These analyses will allow me to recommend areas of conservation priority for the establishment of new protected areas across Canada, and best practices for monitoring, management, and conservation of butterflies. The Montreal Insectarium will gain important insights on how to maximize the use of its large eButterfly dataset, as well as visibility as a scientific hub through the publication of relevant research articles in internationally recognized scientific journals.

View Full Project Description
Faculty Supervisor:

Lenore Fahrig

Student:

Federico Riva

Partner:

Montreal Insectarium

Discipline:

Biology

Sector:

Arts, entertainment and recreation

University:

Carleton University

Program:

Oilsands Mine – Pit wall design optimization

Two primary objectives of this undertaking are (1) to determine whether ore recovery along pit walls can be increased by replacing and/or augmenting current pit wall stability assessments involving two-dimensional static methods with three-dimensional deformation analyses and (2) to differentiate pit wall deformations associated with stress relaxation from those associated with failure. Three-dimensional deformation analyses using traditional and/or semi-custom constitutive models calibrated against (a) laboratory testing data and (b) in-situ monitoring data will be used in this evaluation. The research is expected to benefit CNUL increasing the efficiency, economics and environmental sustainability of its current operations. The knowledge gained from this work is highly applicable to all open-pit mining projects, and thus can contribute to enhancing safe mining practice in Canada and internationally.

View Full Project Description
Faculty Supervisor:

Ward Wilson

Student:

Elena Zabolotnii

Partner:

Canadian Natural Resources Ltd.

Discipline:

Engineering - civil

Sector:

University:

University of Alberta

Program:

Development of a UV-LED air purifier for indoor air

Indoor air is contaminated by a variety of chemical and microbial contaminants. These contaminants can be effectively removed or inactivated using a process known as photocatalysis. In this method, a photocatalyst is chemically activated using ultraviolet (UV) radiation and readily degrade the contaminants when coming in contact with them. The application of newly emerged ultraviolet light-emitting diodes (UV-LEDs) as the radiation source for photocatalytic air purification is investigated in this project. A UV-LED-based air purifier concept is developed, and the engineering design is optimized using simulation tools. Then, a prototype with an optimal design is fabricated and is experimentally tested for removing several chemical and microbial contaminants. The outcome of this work is important for improving the indoor air quality by removing chemical and microbial pollutants in the air.

View Full Project Description
Faculty Supervisor:

Fariborz Taghipour

Student:

Shahriar Rouhani Anaraki

Partner:

Acuva Technologies Inc

Discipline:

Engineering - chemical / biological

Sector:

Manufacturing

University:

University of British Columbia

Program:

Accelerate

Efficient Learning Methods for Multimodal Understanding

The main focus of this research is to develop representation learning architectures and algorithms that can help perform various multimodal understanding tasks, and at the same time reduce the need for human supervision in the form of costly annotations. To achieve this goal, a learning system must be able to: (1) learn new tasks or concepts with a few examples; (2) effectively use the knowledge already acquired by the system; (3) rely on one modality (e.g., text/audio) to fill in gaps in another modality (e.g., vision); and finally (4) retain high performance on all tasks/concepts learned previously. The goal of this research is to develop methods that enable efficient learning for multimodal tasks (e.g., dialogue-based image/video retrieval) in scenarios with limited annotated data. As part of this work, we will focus on answering two key questions: (1) How to leverage the problem structure in order to enable learning algorithms that use limited annotations more effectively? and (2) What are the mechanisms that enable efficient learning from few examples?

View Full Project Description
Faculty Supervisor:

Animesh Garg

Student:

Nikita Dvornik

Partner:

Samsung Electronics Canada

Discipline:

Computer science

Sector:

Manufacturing

University:

University of Toronto

Program:

Accelerate

Does Intervention Work? Assessing the Effectiveness of Business Innovation and Growth Support in Canada

Using the Business Innovation and Growth Support (BIGS) dataset in Statistics Canada’s Linked File Environment (LFE), the proposed research will evaluate the impact of federal business innovation supports on firm growth, innovation (e.g., patent behavior), and propensity to export within and across strategic industries. The findings will inform how the partner organization, and firms like it, understand the program support environment (i.e., what federal supports are available), make strategic decisions (i.e., which programs to target), and plan for the future of the firm’s growth.

View Full Project Description
Faculty Supervisor:

Catherine Beaudry;David Wolfe

Student:

Steven Denney

Partner:

Delvinia

Discipline:

Public administration

Sector:

Management of companies and enterprises

University:

Program:

Previous
1…326327328329330331332…1,106
Next

Join a thriving innovation ecosystem.

Subscribe to our newsletter now!

Subscribe