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

Investigating the geomechanics and public safety risks associated with the instability potential of popular geotourist sea stack rock formations

Sea stacks are natural pillars of rock that are usually found on shorelines. They are fascinating formations that are popular destinations for geotourism, including at the Hopewell Rocks Provincial Park, New Brunswick and Flowerpot Island in Fathom Five Marine Park, Ontario. As they experience continuous wave and tidal erosion, sea stacks eventually collapse. Given their popularity, there is an elevated risk of injury or fatality to visitors that is managed by Parks by limiting access to sea stacks that are closer to collapse. This research project aims to improve predictions of when and where sea stacks will collapse by studying the physical properties of the rocks and using computer models to simulate various collapse scenarios. The intern will benefit from the partner organization’s expertise in field data collection using drone-based photography, their knowledge of structural geology that applies to these rock formations, and their local knowledge of the Hopewell Rocks.

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Faculty Supervisor:

Jennifer Day

Student:

Amanda Hyslop

Partner:

Terrane Geoscience Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Queen's University

Program:

Accelerate

Characterization and behavioural studies on Psilocybe mushrooms and related psychotropic compounds

This MITACs proposal seeks funding to establish an internship cluster dedicated to researching novel psychedelic compounds for the purposes of treating clients suffering from PTSD and anxiety using its comprehensive psilocybin-assisted psychotherapy treatment model. The objective of this one-year project is to characterize the chemical composition of psychedelic mushrooms, optimizing extraction process, testing extracts and active compounds on insect model, in order to advance the science around the use of psilocybin in psychedelic-assisted psychotherapy. Specifically, intern will investigate how unique compounds present in specific mushroom strains work together to facilitate treatment.

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Faculty Supervisor:

Nicoletta Faraone;Suzie Currie;Kirk Hillier

Student:

Dayna Forsyth

Partner:

Halucenex Life Sciences Inc.

Discipline:

Biology

Sector:

Professional, scientific and technical services

University:

Acadia University

Program:

Accelerate

Analysis of the potential application of the shortwave infrared and near-infrared cameras to the weld temperature measurements

Temperature is a critical parameter in welding and related processes such as metal additive manufacturing. The real-time temperature measurement systems based on infrared thermal cameras have a potential to significantly improve the existing process control systems and, consequently, the quality of the welds and additive manufactured products. Conventional thermal cameras work in midwave (MWIR) and longwave infrared (LWIR) part of the infrared spectrum. They require sophisticated sensor systems and special optics. Cameras that work in the part of the infrared spectrum which is closer to visible light (shortwave infrared SWIR and near-infrared NIR) are less sensitive at lower temperatures, but potentially may be used for temperature measurements at temperatures typical in welding. They can use regular glass optics and their sensors do not require cooling to low temperatures and are made of less sophisticated materials which makes them significantly cheaper and easier to use. These features of the SWIR and NIR camera systems indicate their potential for the real-time welding and metal additive manufacturing temperature measurement systems. The objective of the project is to study the ability of the camera systems working in the SWIR and NIR spectrum ranges to capture the thermal emission during welding and provide temperature measurement.

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Faculty Supervisor:

Patricio Mendez

Student:

Dmytro Havrylov

Partner:

Xiris Automation Inc.

Discipline:

Engineering - chemical / biological

Sector:

Manufacturing

University:

University of Alberta

Program:

Accelerate

DEEP LEARNING (DL) BASED APPLICATION FOR FRESH PRODUCE YIELD AND PRICE FORECASTING BASED ON SATELLITE IMAGES AND STATION-BASED PARAMETERS

Loblaw Companies Limited (LCL) supplies all fresh produce (FP) to South Western Ontario stores from Waterloo Distribution Center (DC). DC decides prices and quantities to meet FP demand. Timed fair priced orders minimize waste, bring prosperity to growers, consumers and FP trades. Factors affecting prices are highly uncertain due to environmental and socio-economic effects such as income, labor, trade, globalization and climate change which makes price prediction challenging. Immediate produce past prices are used to predict future prices in univariant models, other multivariant models consider price most influential factors as input attributes to predict future prices. Machine learning models deployed for price forecasting can currently be outperformed by univariant models. In this project multivariant Machine learning models finetuned by deploying online learning are tested against mathematical univariant models and their effectiveness is assessed using accuracy measures; micro cent improvement in each transaction save hundreds of millions of dollars for Canada.

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Faculty Supervisor:

Fakhri Karray

Student:

Lobna Nassar

Partner:

Loblaw Company Limited

Discipline:

Engineering - computer / electrical

Sector:

Service industry

University:

University of Waterloo

Program:

Optimizing Virtual Care Solutions Capabilities to Address Cognitive Impairment

Optimizing an integrated virtual care technology solution to expand capabilities to manage patients with cognitive impairment. These enhancements will improve remote monitoring and management of patients with Alzheimer’s and Dementia. The project will result in the development of additional tools and proprietary applications to enable comprehensive assessments in the areas of stroke and other neurological conditions.

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Faculty Supervisor:

Miranda Kirby

Student:

Saeed Babadi

Partner:

ForaHealthyMe

Discipline:

Physics / Astronomy

Sector:

Manufacturing

University:

Ryerson University

Program:

Accelerate

Real-time food analysis using deep learning for Diabetes Self -Monitoring

Our proposed research is to create an algorithm capable of pre-evaluating diabetes patients’ meals before they consume them with the snap of a picture. We are attempting to accomplish this goal by employing AI, machine learning as well as computer vision for real-time analysis. Our goal is to analyse a users meal to return an accurate carb count and offer portion size adjustments to reduce their blood sugar fluctuations. By developing a model that uses these technologies, we believe we can create an algorithm that will revolutionize how diabetes patients manage their condition and allow users to maintain consistent and healthier blood sugar levels. This research will greatly benefit the partner organization as it will help accelerate the growth of development heavily on the technology side to bring this to a level where it can be commercialized to generate revenue and used by others.

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Faculty Supervisor:

Naimul Khan

Student:

Liam Bell;Osama Muhammad;Muhammed Ashad Khan

Partner:

Glucose Vision

Discipline:

Engineering - biomedical

Sector:

Health care and social assistance

University:

Ryerson University

Program:

Continuous calibration, interpolation and predictive analytics using Machine Learning

The company provides devices to measure quality of curing light used in dental procedures. These devices produce high volume and high velocity data. This data is then used to forecast a future state and to make a well informed business/operation decision according to an expected future state. One of the challenges in application of such technology is to improve prediction accuracy of the forecast. The company has developed prediction models based on physical chemistry that have been very useful to both the vendors and dentists. This project will design a generalized framework based on machine learning and artificial intelligence methodologies to improve prediction accuracy that will ultimately result in reduced operating costs and higher yields in dental procedures.

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Faculty Supervisor:

Pawan Lingras

Student:

Akshaykumar Mashalkar;Raj Bhupendrakumar Chauhan

Partner:

BlueLight Analytics

Discipline:

Computer science

Sector:

Other

University:

Saint Mary's University

Program:

Accelerate

Characterizing Offshore Lobster Biology and Estimating Tag Recaptures and Reporting Rates in Lobster Fishing Areas 33 and 34

Little information is known about offshore lobster. Knowledge gaps exist in general population demographics, maturity rates, and movement patterns (migrations). Most notably, nothing is known about offshore lobster in this region especially during the closed season. This project will form a collaboration between captains and their crews and researchers to collect data on the offshore fishery. The primary project goal is to ensure a healthy, viable, and sustainable fishery. Through data collection and lobster tagging training, captains and crews will participate in the core areas of collecting data on their fishery. Lobster biological characteristics, environmental (temperature), oceanographic (depth), and weather information will be collected. Lobster will be tagged and movement patterns will be mapped using a capture-mark-recapture activity. Captures of tagged lobster are anticipated to be reported by other captains. Lobster information and movement patterns will be related to other information through modeling exercises.

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Faculty Supervisor:

Trevor Avery

Student:

Alysa Czenze

Partner:

Brazil Rock

Discipline:

Biology

Sector:

Other

University:

Acadia University

Program:

Accelerate

Investigating a dispersive process of nanocellulose reinforced silk protein bone screws to increase its strength

Bone fracture is usually fixed with metal made plates and rods to secure the bone. After the bone heals the plates and rods are usually required to be taken out. The propose of this research is to find a way to create a biomaterial that is strong enough to hold the bones, and can dissolve after the bone is healed, or to convert to another material that will have no effect on normal body function. The partner organization thus partnered with Prof. Sain at university of Toronto to find ways of disperse and create the desired material.

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Faculty Supervisor:

Mohini Sain

Student:

Yinan Liu

Partner:

Osteoway Inc

Discipline:

Engineering - mechanical

Sector:

Manufacturing

University:

University of Toronto

Program:

Discovering new microbes and metabolisms in deep sea sediments using metagenomic sequencing

Microorganisms living in marine seafloor sediments are of scientific interest for many reasons including their role in cycling nutrients, their metabolic diversity, and the relatively few investigations of their existence due to their habitation of such an extreme and isolated environment. In addition, subsurface microbes can provide insight into their surrounding environment, including signalling the presence of hydrocarbon seeps. Hydrocarbon seepage from subsurface petroleum reservoirs to marine surface sediments alters the immediate microbial community by selecting for and enhancing the growth of microbes that degrade and feed on hydrocarbons. By analyzing the genetic material of subsurface microbes, the types of microbial species present and their potential functions in this extreme environment can be elucidated. Certain microbial species and certain genes can indicate the potential presence of a hydrocarbon seep and thus aid in de-risking offshore oil and gas exploration. In this project two “bioassays” will be established. The first will target microbial species that are preferentially found at hydrocarbon seep sites. The second will target microbial genes that are involved in hydrocarbon biodegradation. Samples to identify these microbial species and genes will be collected in partnership with Offshore Energy Research Association (OERA), on the Scotian Slope, offshore Nova Scotia.

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Faculty Supervisor:

Casey Hubert

Student:

Jackie Zorz

Partner:

Offshore Energy Research Association of Nova Scotia

Discipline:

Biology

Sector:

University:

University of Calgary

Program:

Evaluating Oil Sands Process-Affected Water Seepage to Waterbodies by Implementing Traditional Knowledge and Western Science

An Indigenous community living near Alberta’s oil-sand mines is concerned that mining operations may be harming the rivers and lakes where they fish, hunt and trap. The research looks at finding the locations along the edges of rivers and lakes where there is evidence that mining operations may be affecting these waterbodies. The unique part of the research is that Indigenous knowledge, which is the wisdom owned by the community, and Western science are being combined in complementary and respectful ways to find the affected locations along the edges of rivers and lakes. This research will help the community in its relationships with companies that operate the oil-sands mines.

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Faculty Supervisor:

Gopal Achari

Student:

Ron Thiessen

Partner:

Fort McKay Metis Community Association

Discipline:

Engineering - civil

Sector:

University:

University of Calgary

Program:

Model-Based Security Compliance-By-Design for Low-Earth Orbit Satellite Operations Segments

Low-earth orbit (LEO) satellite constellations require high levels of security and resilience to provide high quality, reliable and trustworthy global connectivity services to customers. For these systems to develop customer trust and find widespread use, developers must demonstrate compliance to a variety of security standards, policies, and regulations. However, because these systems are very large and complex, it is difficult to clearly and effectively show how the system satisfies all of its compliance requirements throughout its development lifetime. In this project, we aim to develop an architecture and design framework for the operations segment of the Telesat LEO satellite constellation. The operations segment is responsible for connecting the overall LEO system by directing the other system components cooperate to deliver the service. The framework seeks to support security compliance-by-design and help developers trace security compliance requirements to the design of the operations segment so that they can easily show where and how such requirements are satisfied. This support can provide a competitive advantage and help solidify Telesat’s position as a Canadian leader in LEO satellite communications.

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Faculty Supervisor:

Jason Jaskolka

Student:

Sana’a Abdel Ra’uof Ali Alwidian

Partner:

Telesat Canada

Discipline:

Engineering - computer / electrical

Sector:

Information and cultural industries

University:

Carleton University

Program:

Accelerate

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