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

Numerical Simulation of Turbulence and In-Situ Tidal Turbine Performance in Minas Passage

Turbulence is a significant issue at every site being considered for instream tidal energy development. This turbulent flow creates fluctuating forces on tidal turbine blades and support structures, reducing turbine performance and shortening turbine lifespan. Thus, improving and validating numerical models of turbulence and turbine operation in turbulent flow is necessary to better predict device operation and, thus, develop efficient and financially viable tidal energy projects. This project will extend existing numerical models to predict the impact of turbulence in Minas Passage on tidal turbine performance with the long-term goal of reducing the overall cost of energy production for the region. Numerical simulations will rely on EXN/Aero software developed and commercialized by Envenio, providing an opportunity to expand software capability and visibility in the environmental modelling sector.

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Faculty Supervisor:

Tiger Jeans

Student:

Farhad Baratchi

Partner:

Envenio Inc.

Discipline:

Engineering - mechanical

Sector:

Energy

University:

Program:

Accelerate

Automated Extraction of Chemical Synthesis Procedures Using Machine Learning

The project involves the development of a system to automate the extraction of synthesis procedures from the texts of organic chemistry journal articles that describe explicit, experimental syntheses of organic compounds and their corresponding properties.

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Faculty Supervisor:

Jian Tang

Student:

Michael Guarino

Partner:

CognitiveChem Solutions Inc

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Monitoring the genetic variation and population structure of White bear populations in British Columbia to inform ecotourism and resource management

Spirit bears are a valuable symbol of the Great Bear Rainforest in British Columbia. These white bears are an economically and culturally important resource that require effective monitoring to ensure their perpetuation. Safeguarding the future continuation of white bears additionally requires understanding both how the white bear allele is perpetuated and how healthy these populations are. One important component of population health is genetic variation. Genetically variable populations are able to adapt to changing threats better than genetically depauperate groups. Using genetic markers, this study will investigate the health of white bear populations on islands in the Great Bear Rainforest. This information will be provided to our First Nation and Ecoutorism partners, and to Raincoast Conservation Foundation.

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Faculty Supervisor:

Chris Darimont

Student:

Lauren Henson

Partner:

Raincoast Conservation Foundation

Discipline:

Geography / Geology / Earth science

Sector:

Natural resources

University:

Program:

Accelerate

Development of transparent near-eye display using a sparse microlens array and lightfield principles

Head-mounted displays (HMDs) allow a convenient delivery of visualized data to the user. HMDs in the form of glasses and goggles (otherwise known as smart glasses and goggles), such as Vuzix Blade and Epson Moverio [1-3], have been introduced but the public acceptance of these devices have been rather lackluster. Part of the sluggish acceptance may be attributed to the still-high device costs (>$1000) and a large form-factor, owning largely to the fact that these devices utilize unique and sophisticated optics on dedicated and non-retrofittable platforms. In this research, we want to design a universal HMD optics that can be placed in the line of sight of the user and is thin and compact enough to be retroffitable on conventional eyewear, using microlens arrays (MLAs) and the lightfield principles.

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Faculty Supervisor:

Boris Stoeber

Student:

Hongbae Sam Park

Partner:

Form Athletica Inc

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Program:

Accelerate

Detection of spacecraft maneuvers based on publicly available data

Detecting maneuvers is important to a number of operational needs, such as to ease tracking of active satellites, discern and forecast the regular activity of a satellite, and detect deviations from nominal maneuver patterns. Although data on the orbits of satellites are publicly available on the internet, their precision is low and uncertain, which makes the task of detecting maneuvers complicated. The aim of this project is to develop and implement new techniques to recognize when maneuvers have occurred, and to give a first estimate of the magnitude and direction of such maneuvers. The developed algorithms will be tested against published maneuver histories, and integrated into software developed in-house at GlobVision.

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Faculty Supervisor:

James Forbes

Student:

Eleonora Botta

Partner:

GlobVision

Discipline:

Engineering - mechanical

Sector:

Aerospace and defense

University:

Program:

Accelerate

BoneTape: A Novel Craniomaxillofacial Fixation Technology

Craniofacial fractures require stabilization to restore appearance, facilitate healing and improve patient’s outcomes. The current standard of care is rigid titanium plates, which require drilling, screws and bending of titanium plates with plyers during surgery to conform to the complex geometry of the face. This is an over engineered solution that is not only difficult to use, but also results in complications including pain and discomfort which require follow-up surgeries in as many as 50% of facial fracture fixation procedures. Cohesys is developing a bio-absorbable flexible tape and adhesive system, BoneTapeTM, that is easier for clinicians to use to use and will result in better patient outcomes. We are looking to leverage the creativity and talents of top Canadian biomedical researchers to transition our prototype into a licenced medical device that can make an impact where it matters – in the hands of surgeons who perform these procedures every week.

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Faculty Supervisor:

Paul Santerre

Student:

Alexander Lausch

Partner:

Cohesys Inc.

Discipline:

Engineering - biomedical

Sector:

Medical devices

University:

Program:

Accelerate

A Finite Element Framework for Non-linear Material Constitutive Modelling of Superalloy Additive Manufactured Parts

Due to its versatility, time and cost saving, additive manufacturing (AM) technology, and more specifically selective laser melting process (SLM), is replacing conventional manufacturing processes, particularly for producing complex geometry components. In this technology, the near net shape parts are incrementally built by fusing layers of powder material using an intensive heating source/ Structural stress analysis and lifing assessment via finite element (FE) analysis are well-accepted modern engineering practices within product development procedures. The use of this solution method reduces trial and error costs as well as risks of failure, among other. Because of the unique microstructure/texture of the additively manufactured superalloy products, the resulting mechanical properties are highly anisotropic as opposed to conventionally manufactured parts which are commonly isotropic. Consequently, efficiently predicting the mechanical properties and functional performance of SLM components through FE simulations become crucial. In this context, the main objective of this project is to create a reliable FE simulation framework for predicting operating performance of SLM manufactured gas turbine hot section components for Siemens Canada. In this research, advanced phenomenological material constitutive models for additive manufacturing applications will be identified and developed. Also, numerical predictions will be validated against experimental data.

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Faculty Supervisor:

Mathias Legrand

Student:

Omid Majidi

Partner:

Siemens Canada

Discipline:

Engineering - mechanical

Sector:

Energy

University:

Program:

Elevate

Named Entities Recognition for Customer Service Automated System

This project aims at creating a robust, efficient and reliable tool for Named Entities Recognition (NER) from vast amounts of textual data related to the customer service.
Named entities recognition, a subtask of information extraction, seeks to locate and classify elements in text into pre-defined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.
Moreover, those extracted named entities will be mapped to existing concepts of an ontology.
The development of such tool will enable easier and quicker decision-making in the customer service for the industrial partner.

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Faculty Supervisor:

Fatiha Sadat

Student:

Ghaith Dekhili

Partner:

Thales Canada Inc.

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Signal recognition with machine learning using wavelet features

The emerging techniques of machine learning and artificial intelligence are making revolutionary changes in all kinds of the industrial world. As a high-tech business solution company, Quartic.ai uses these modern techniques to help industrial manufactory companies work more efficiently. One of the challenging problems is to make the computer automatically recognize the status and behavior of the machine from the data collected by different sensors, so that people can record the history of the machine and conduct further analysis. This project tries to develop some algorithms to achieve this goal using the state-of-the-art machine learning technology. The algorithms developed will help the computer learn the patterns of the sensor data first, and recognize/detect the behaviors of the machine automatically.

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Faculty Supervisor:

Bin Han

Student:

Chenzhe Diao

Partner:

Quartic.ai Canada Inc

Discipline:

Mathematics

Sector:

Information and communications technologies

University:

Program:

Accelerate

Anomaly Detection in Land Vehicle Traffic Activity

This project’s objective is to develop a capability to detect and describe anomalous situations in ground vehicle traffic. Anomalous situations are described as substantial/important changes from the traffic frequently observed for a particular route and/or time. In this sense, anomaly can be quantitatively measured by the degree of predictability of current traffic given historical observations. In the use case of interest, information from traffic will be captured from a GMTI sensor performing recurrent surveillances (1-3 hours per day, multiple days per week) over the same area. By developing such capability, Thales wishes to create a new service offer (based on existing but still un tapped historical data): a decision support capability for GMTI analysts that will draw their attention on suspicious activities that would normally be unnoticed. This project does not deal with raw data processing or tracking problems, but uses vehicles tracks as input data.

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Faculty Supervisor:

Lijun Sun

Student:

Xudong Wang

Partner:

Thales Canada Inc.

Discipline:

Engineering - civil

Sector:

Information and communications technologies

University:

Program:

Accelerate

From Data Collection, Maintenance and Analysis to Effective Air Traffic Management

Skyplan Services Ltd. is a company active in the air traffic management domain. The company is interested in expanding their current working application by benefiting from advanced technology to develop an integrated environment and solutions for air traffic management in order to provide better service at the international arena. Students to be involved in this project will build a data repository to host data to be collected, cleaned, built, integrated and processed for knowledge discovery which will guide more focused decision making. A Web-based and a mobile app multilingual communication platform will be developed to help in connecting a variety of data sources and domains. The target is to maintain data privacy by enforcing security in communication. Intelligent data processing and analysis techniques will be employed to benefit the best from data. This includes behavior analysis, trend prediction, etc.

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Faculty Supervisor:

Reda Alhajj

Student:

Alper Aksac

Partner:

Skyplan

Discipline:

Computer science

Sector:

Aerospace and defense

University:

Program:

Accelerate

In Vitro Screening and Validation of Phyto-Cannabinoids in Glaucoma

Glaucoma is the second leading cause of blindness in the world, mainly induced by increased pressure in the eye. Marijuana has been shown to reduce such pressure, thus benefit glaucoma patients. In this project, we test several components from marijuana extracts that are unlikely to cause psychoactive symptoms, for their therapeutic effects on glaucoma. This project is likely to be the solid base of a future drug that could help lots of glaucoma patients and meet the need of the market.

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Faculty Supervisor:

Ujendra Kumar

Student:

Shenglong Zou

Partner:

InMed Pharmaceuticals Inc.

Discipline:

Pharmacy / Pharmacology

Sector:

Pharmaceuticals

University:

Program:

Accelerate

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