Innovations 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

Interpretable dimensionality reduction of multivariate time series data using LSTM based autoencoders

Data collection over time is a common practice in many large organizations- including financial institutions and health care providers- often with the goal of using this data to predict future challenges and opportunities. While this data may contain valuable information, it is often unstructured, coming from different sources and recorded at different times. This lack of structure makes extracting useful information difficult, as most standard statistical and machine learning tools are designed to work with data in a fixed structure. This project will develop a framework for automatically learning a fixed length representation composed of interpretable features from unstructured data collected over time, which requires minimal intervention by human experts. The efficacy of the framework will be evaluated by learning representations for electronic health records, created by the Synthea simulator.

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Faculty Supervisor:

Ting Hu;Yuanzhu Chen

Student:

Kyle Nickerson

Partner:

Verafin Inc.

Discipline:

Computer science

Sector:

Information and cultural industries

University:

Memorial University of Newfoundland

Program:

Accelerate

Development and Characterization of COVID-19 vaccine candidate

The goal of this study will be to characterize a SARS-CoV2 antigen and the formulated drug product that will contain SARS-CoV2 antigen and a squalene-based adjuvant under tight timeline to release the material for COVID-19 vaccine clinical trials.

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Faculty Supervisor:

Yi Sheng;Paola Battiston

Student:

Gabriella Gerzon

Partner:

Sanofi Pasteur

Discipline:

Biology

Sector:

University:

York University

Program:

Accelerate

AAV Vectored Immunoprophylaxis for Prevention of SARS-CoV-2 in the Elderly and Immunocompromised

As of May 1st, 2020, over 3.2 million cases of COVID-19 have been confirmed, resulting in over 233,000 deaths globally. This project aims to provide an alternative vaccine for the prevention and treatment COVID-19 in high risk individuals, mainly the elderly and immunocompromised, who do not respond well to tranditional vaccination. Using a single viral vector platform, we will deliver broadly protective monoclonal antibody genes isolated from human survivors of COVID-19 to provide sustained levels of protection against SARS-CoV-2 infection for all individuals, particularly the elderly and immunocompromised. This project will help Avamab Pharma Inc. bring its first AAV VIP therapy to human clinical trials, with the ultimate goal of providing high risk patient populations with an urgently needed prophylactic against COVID-19.

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Faculty Supervisor:

Sarah Wootton

Student:

Amira Rghei

Partner:

Avamab Pharma Inc

Discipline:

Biology

Sector:

Professional, scientific and technical services

University:

University of Guelph

Program:

Accelerate

Low cost wearable and continuous temperature sensor for COVID-19 pre-screening with remote monitoring (TEMPOS)

This project will undertake the development of a cost-effective, continuous monitoring, wireless temperature sensor. The sensor development undertaken with NURO will integrate the hardware with software algorithms designed to track and deliver a simple output for healthcare monitoring. The device is anticipated to be employed for tracking of COVID-19 in front line workers by monitoring their body temperature as fever is one of its most common symptoms. The device will form an integral part of NURO’s overall platform that is based on human-machine interface and is expected to become the basis for further expansion into other devices for monitoring pulse rate and shape, electrocardiograms etc . Such a comprehensive tool set will allow the monitoring of a wide-ranging conditions affecting human health and TEMPOS is a critical step in current scenario and for future applications.

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Faculty Supervisor:

Vivek Maheshwari;George Shaker

Student:

Hua Fan;Avi Mathur;Hajar Abedi;Martins Akhuokhale

Partner:

NURO

Discipline:

Sector:

Professional, scientific and technical services

University:

University of Waterloo

Program:

Accelerate

What are the factors influencing the socioeconomic impact of COVID-19 among Canadians?

Beyond the devastating physical health crises, COVID-19 and its related social distancing measures has wreaked havoc on the economic-, mental-, and social-health of Canadians. The Association for Canadian Studies has been collecting behavioural, economic, and social data from Canadians weekly since March 9 and continues to do so. Our goal is to identify the socioeconomic factors that have had the most effect on the lives of Canadians. The expected benefits to the organizations, and to Canadians in general, is to have an ongoing, fact-based record of the social, economic and behavioural conditions of Canadians as they have experienced the COVID-19 virus and related social distancing measures.

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Faculty Supervisor:

Lori Wilkinson

Student:

Sally Ogoe

Partner:

Association for Canadian Studies

Discipline:

Resources and environmental management

Sector:

Education

University:

University of Manitoba

Program:

Accelerate

An examination of gamification methods to improve user engagement: A case study of a system to manage expertise during the COVID-19 Pandemic

totaliQ has developed an expertise management platform that helps organisations save significant time and money by providing them with maximum visibility into the individual expertise that each employee within the organization has and where that employee is located. safetyiQ is a free, light version of this system that will allow employees, managers and occupational health and safety professionals to share best practices, templates, lessons learned, Q&A and more, while it auto-generates an inventory of each user’s areas of expertise so that collaborators can identify experts in specific topics within this online community during the COVID-19 pandemic.
Gamification is a technique to increase willingness of the user to contribute to share his knowledge with others. When applying gamification to knowledge management, the idea is to get employees to share knowledge and expertise by making it fun, introducing an element of friendly competition, and shining a light on top performers. The purpose of this project is investigating gamification techniques that maximise user engagement in expertise management platform of totaliQ.

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Faculty Supervisor:

Jennifer Jewer

Student:

Morteza Amiri

Partner:

totaliQ Technology Inc

Discipline:

Resources and environmental management

Sector:

University:

Memorial University of Newfoundland

Program:

Accelerate

Development and optimization of durable, non-release antiviral coatings for public spaces

As of 15 May 2020, more than 4.4 million COVID-19 cases have been reported across 188 countries and territories, resulting in more than 300,000 deaths. According to the World Health Organization reports, one of the main routes for transmission of COVID-19 and other infectious diseases is by contact with surfaces in public spaces that are contaminated with infectious droplets produced by infected people. In order to slow down or stop the spread, cleaning and disinfection of ‘high-touch’ surfaces need to be performed regularly. However, the development of an effective antiviral surface coating provides an additional protection layer against disease transmission. In this work, we aim to develop a non-release antiviral coating. The application of the antiviral coating inactivates the viruses on the surfaces of public spaces such as schools, hospitals and grocery stores. The non-release approach for antiviral coating increases the antiviral efficiency of the coating with more consistency in long-term protection. The active agents have already shown the highest efficiency against the H1N1 virus. They will be examined against the novel coronavirus.

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Faculty Supervisor:

Drew Higgins

Student:

Amir Kazemi

Partner:

Trimis Inc.

Discipline:

Engineering - chemical / biological

Sector:

Manufacturing

University:

McMaster University

Program:

Assessment of anti-viral, anti-bacterial and anti-fungal properties of metal-ion-filament laced 3D-printed personal protective equipment

3D-printed personal protective equipment can provide a locally-sourced manufacturing network to address shortages for Canadian front-line workers during the COVID-19 pandemic, however little is known about the harmful germs that can live on 3D-printed material. Certain metals-ions are known to have anti-microbial properties and can be incorporated into 3D-printed plastics. We will study the anti-microbial properties of metal-laced 3D-printed plastics by assessing the presence of bacteria, and fungi on the plastics, and then determine optimal disinfection times and formulations to reduce contamination on 3D-printed personal protective equipment. We will also test the effectiveness of these metal-laced 3D-printed plastics in killing viruses, including the novel coronavirus.
At DECAP Research and Development Inc. our mission is to design, test and manufacture customizable 3D-printed protective equipment. Currently, our efforts are focused on optimizing and conducting research on 3D-printed personal protective equipment to enhance front-line worker safety and prevent the spread of COVID-19.

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Faculty Supervisor:

Horacio Bach

Student:

Ana Cecilia Lorenzo-Leal

Partner:

DECAP Research and Development Inc

Discipline:

Medicine

Sector:

Professional, scientific and technical services

University:

University of British Columbia

Program:

Accelerate

Novel Portable Sensor to Reveal “Hidden” COVID-19 Infection

In Canada, as of April 21, only 569,878 people (~1.5% of the population) have been tested, with more than 38,413 positive COVID-19 cases identified; yet most people, including the asymptomatic COVID-19 cases, are not eligible for testing. Given that as many as 45% of all COVID-19 cases lack the known symptoms, or so-called asymptomatic cases, up to an estimated 17,000 cases could be asymptomatic and thus endangering public health. Moreover, these symptoms are not observed in the early stages of the disease, even in symptomatic cases.
Early detection and isolation of COVID-19 cases, especially asymptomatic cases, is therefore crucial for controlling this outbreak, and a novel method to identify asymptomatic cases is urgently needed. In response to this urgent situation, we propose a rapid solution to identify asymptomatic and presymptomatic cases through early detection of a “hidden” symptom. We will combine microfluidic, microelectronic, and open-JFET (junction gate field-effect transistor) sensing techniques to develop a safe, low-complexity, rapid, and easy-to-use technology to fight COVID-19. The proposed technology would allow rapid testing at home using a portable sensor to evaluate disease progress or treatment, eliminating the need to break quarantine (which could potentially infect others) as part of follow-up testing.

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Faculty Supervisor:

Ebrahim Ghafar-Zadeh

Student:

Abbas Panahi;Hamed Osouli Tabrizi;Shahin Ebrahimi

Partner:

CMC Microsystems And Applied Nanotools

Discipline:

Engineering - computer / electrical

Sector:

Professional, scientific and technical services

University:

York University

Program:

Accelerate

Improving avalanche forecasts in data-sparse areas with physical snowpack modelling – Year two

Assessing dangerous avalanche conditions requires a reliable stream of weather and snowpack data, which can be difficult and expensive to collect in many remote areas of Canada. Snowpack conditions can be simulated in these areas by coupling weather forecast models with physical snowpack models, however, this method has had limited adoption by avalanche forecasters. The proposed project will increase the adoption of snowpack models by developing a dashboard that allows Avalanche Canada forecasters to visualize spatial snowpack patterns, alarm them of critical changes, and provide an assessment of the model’s accuracy. Novel methods of comparing model output with snow observations will be investigated and spatial clustering methods will offer a new dynamic view of regional snowpack patterns. The project will improve the accuracy and quality of Avalanche Canada’s public safety products and warnings in data-sparse areas.

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Faculty Supervisor:

Pascal Haegeli

Student:

Simon Horton

Partner:

Avalanche Canada

Discipline:

Environmental sciences

Sector:

Arts, entertainment and recreation

University:

Simon Fraser University

Program:

Elevate

Total Versus Bioaccessible Soil Sterilants (Bromacil and Tebuthiuron)

Bioaccessibility of soil sterilants is a limitation in management of sterilant-impacted sites. The term ‘bioaccessibility’ means: what is immediately available, plus that which may become available. Studies have been conducted to examine the bioaccessible fraction of various soil sterilants after different aging periods; however, studies have not been conducted in Alberta. Immobilization technologies such as activated carbon have been applied to sterilant impacted sites in Alberta for decades (Drozdowski et al. 2018). Given the uncertainty associated with the bioaccessibility of soil sterilants over time, there is a reluctance from a regulatory perspective to accept immobilization as a long-term solution for managing sterilant impacted surface soils. There is a need to identify methods for quantifying bioaccessible concentrations of sterilants in soil at different aging times from the application of activated carbon to properly manage sterilant impacted sites.

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Faculty Supervisor:

Sylvie Quideau

Student:

Jackie Maxwell

Partner:

InnoTech Alberta Inc

Discipline:

Resources and environmental management

Sector:

Professional, scientific and technical services

University:

University of Alberta

Program:

Accelerate

Predicting the Fair Market Value of a Real Estate Asset

Finding the right real estate property that grows in value over the next few years is of paramount importance. For doing so, one of the most important factors is to estimate the current value of the property as well as its future value. The goal of this project is to build a data and domain-driven model using machine learning that uses previous real estate data to estimate the value of property and suggest the right property in the right neighborhood for investment. We will build an end-to-end framework to collect and preprocess the data and then predict the value of a property.

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Faculty Supervisor:

Morteza Zihayat;Mehdi Kargar

Student:

Mohammad Iman Zadehnoori

Partner:

BuyProperly Limited

Discipline:

Other

Sector:

Real estate and rental and leasing

University:

Ryerson University

Program:

Accelerate

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