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

Use of nanoparticles, phase change materials, and antifreeze admixtures for cold weather concreting

In cold regions, freezing temperatures impede construction activities or put them on hold until warmer seasons, as concreting activities under such conditions are quite challenging. This leads to considerable socioeconomic losses. This research will advance the current knowledge on cold weather concreting, which is a critical issue for Canada and other cold regions. It will mobilize novel projects on the use of innovative materials and protection methods for concreting at a range of low and freezing temperatures, which is envisioned to enable longer construction of concrete elements/infrastructure in late fall, winter and early spring with better productivity rates and offer developmental solutions to northern parts of Canada. Throughout the course of this research program, practical outcomes (e.g. innovative concrete mixture designs/protection techniques for cold weather) can be incorporated in construction specifications for concrete in Canadian jurisdictions and standards for concrete, with a measurable impact on the construction industry.

View Full Project Description
Faculty Supervisor:

Mohamed T Bassuoni

Student:

Ahmed Yasien Soliman

Partner:

City of Winnipeg

Discipline:

Engineering - civil

Sector:

Administrative and support, waste management and remediation services

University:

University of Manitoba

Program:

Further improvements of image analysis for multiplexed microarrays – Part 3

Microarray testing allows high-volume analysis. This work will develop tools for accelerated analysis and modifications to surfaces used within the partner facilities. The goal is to enhance the performance of current assay designs and to inform and guide the next-generation of assay designs (ie 384 well plates) which will support the partner’s technology leadership position. After implementing a print run and analysis using the current quality control protocols, data will be compared with existing results. As well, based on both past work and next stage results these will be modified to provide reports in the same format as current reports and expand current assay options to 384 well plates

View Full Project Description
Faculty Supervisor:

Pierre Sullivan

Student:

Simeng Chen

Partner:

SQI Diagnostics Systems Inc

Discipline:

Engineering - mechanical

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Integrating thermal storage in hybrid renewable mine energy systems; a techno-economic feasibility assessment

As a major contributor to Canadian industrial carbon emissions, miners are placing more emphasis on decarbonization efforts by developing greener strategies and sourcing cleaner energy for their mining operations. Despite some progress, decarbonization attempts by mining companies have been underwhelming mainly due to the financial challenges of renewable system implementations. This study aims to perceive an all-inclusive hybrid hydrogen-renewable storage energy system which can be financially competitive with the conventional mine energy system (usually diesel-fueled). Accordingly, the present research for the first time offers a novel solution to achieve full decarbonization of mine power system by integrating a multi-storage (Battery/Fuel Cell/Thermal Storage) and renewable power generation systems (i.e. wind or solar) for application in remote mine sites. Given the heavy reliance on diesel fuel in remote mines deployment of the proposed hybrid renewable system offers a significant potential for carbon savings.

View Full Project Description
Faculty Supervisor:

Seyed Ali Ghoreishi-Madiseh

Student:

Mohammad Amin Shadi Diznab;Hosein Kalantari

Partner:

HATCH Ltd.

Discipline:

Engineering

Sector:

University:

University of British Columbia

Program:

Accelerate

Accelerating discovery through high-throughput experimentation and machine learning – Year two

Canonical methods of molecular discovery and reaction optimization rely on “trial-and-error” approaches and slow experimentation with low discovery rates. By harnessing high-throughput experimentation (HTE) with machine learning (ML) methods, artificial intelligence (AI) and robotics, we have the potential to dramatically accelerate the discovery and preparation of next generation molecules and materials. We will extract, unify, and transform data from literature into actionable intelligence, and generate a robust workflow for the automated synthesis of catalysts and resins at NOVA Chemicals. Through ML models, we will leverage newly-generated data to guide experiments and simulations, enabling rapid molecule development, and culminate in the inverse design of molecules and materials targeting function rather than a particular molecular structure. By combining the expertise, software, and hardware tools of the Hein Lab with the instrumentation and extensive database at NOVA Chemicals, we will create a closed-loop, self-driving laboratory that will (i) be capable of implementing a diverse range of chemical workflows and (ii) create datasets that will be leveraged by AI, allowing users to navigate complex structure-function relationships and experimental landscapes.

View Full Project Description
Faculty Supervisor:

Jason Hein

Student:

Wei Ling Chiu

Partner:

NOVA Chemicals

Discipline:

Chemistry

Sector:

Manufacturing

University:

University of British Columbia

Program:

Elevate

Multi-agent reinforcement learning for decentralized UAV/UGV cooperative exploration – Year two

Over the last decade, artificial intelligence has flourished. From a research niche, it has been developed into a versatile tool, seemingly on route to bring automation into every aspect of human life. At the same time, robotics technology has also advanced significantly, and inexpensive multi-robot systems promise to accomplish all those tasks that require both physical parallelism and inherent fault tolerance—such as surveillance and extreme-environment exploration. Decentralized control laws are key to achieve reliability of these systems (as they eliminate the risks posed by single-points-of-failure). Yet, the effective synthesis of (i) machine learning, (ii) multi-robot approaches, and (iii) field robotics is no small task. Previous machine learning and distributed control research rarely ventures beyond computer simulations. GDLS-C and the University of Toronto will investigate how to effectively use multi-agent reinforcement learning in field robotics. GDLS-C’s goal is to improve situational awareness of ground vehicles by using swarms of Unmanned Aerial Vehicles (UAV). Learning decentralized cooperation strategies will improve the resilience of these multi-robot systems—potentially faced with adversarial environments—and, ultimately, the safety of their human operators. Answering our research questions will also enable large collections of robots to learn how to interact with one another—beyond the point human designers can attain.

View Full Project Description
Faculty Supervisor:

Angela Schoellig

Student:

Jacopo Panerati

Partner:

General Dynamics Land Systems - Canada

Discipline:

Engineering

Sector:

Manufacturing

University:

University of Toronto

Program:

Elevate

Program evaluation for JUMP Math: An empirical assessment of a resource for math education – Year two

Canadian math scores are in decline. Numerous studies have demonstrated the importance of numerical proficiency for outcomes such as health, employability and financial stability. Therefore, the effectiveness of a child’s math education is key to future success. It is of utmost importance, then, to identify effective math education programs. The proposed project will evaluate JUMP Math – a not-for-profit math curriculum – in a selection of schools within the Thames Valley District School Board (TVDSB). Along with investigating growth on several key numerical outcomes measures, this study will determine the effectiveness of JUMP Math for reducing anxiety regarding math in both children and teachers. These data will provide valuable information on the effectiveness of the JUMP Math program. Another focus is a literature review to communicate the evidence base for JUMP Math. This will provide clear links to the research that supports design and implementation of JUMP Math. In sum, our collaboration with JUMP Math will provide an empirical investigation and validation of an alternative curriculum for math pedagogy; work that holds great promise to positively impact the state of math education in Canada.

View Full Project Description
Faculty Supervisor:

Daniel Ansari

Student:

Celia Goffin

Partner:

JUMP Math

Discipline:

Psychology

Sector:

Education

University:

Western University

Program:

Elevate

Understanding the impact of changes to blood donation deferral screening and criteria for men who have sex with men – Year two

Available evidence suggests that up to 71% of individuals will require blood or blood products at some point in their lives. To meet this demand, Canadian Blood Services estimates that approximately 100,000 new donors are required annually. However, current blood donation guidelines in Canada require a 3-month deferral period for men who have sex with men (MSM) due to the elevated incidence of HIV in this population, guidelines many see as discriminatory. Given the the improvement in HIV testing technology in recent years, re-evaluation of these guidelines would optimize donor eligibility. The proposed project will examine attitudes to revised donation guidelines among the general population and blood users, using a bilingual, representative national survey and semi-structured interviews with blood users. This will provide a better understanding of how moving towards gender-blind, behaviour-based screening of potential donors, and the inclusion of sensitive questions in the screening questionnaire, may affect donation rates and therefore the sufficiency of blood and blood products in Canada. This research will allow the partner organization, the Community-Based Research Centre for Gay Men’s Health, who have been studying the issue for several years, to develop recommendations for Canadian Blood Services and Héma-Québec regarding screening and deferral criteria.

View Full Project Description
Faculty Supervisor:

Nathan Lachowsky

Student:

Karyn Fulcher

Partner:

Community-Based Research Centre

Discipline:

Epidemiology / Public health and policy

Sector:

University:

University of Victoria

Program:

Elevate

Internet-based mental state monitoring using patient’s textual data – Year two

Among all chronic diseases, mental health issues have the highest burden on health care systems. However, unlike other chronic diseases, like Diabetes or hypertension, no monitoring procedures exist to monitor patients’ mental health status to prevent relapse and crisis situations. It is therefore necessary to develop cheap, convenient and accessible monitoring systems that could be used outside clinical setting. Most mental health diseases demonstrate a range of physical and behavioral symptoms (e.g. change in tone, posture and use of words, aka psychomotor symptoms) that could be measured using smart devices prevalently used by patients. Recent Internet-based methods of care delivery (eg online psychotherapy) provide the opportunity to utilize such digital evaluations of behavior (behavioral phenotyping) for long-term and remote monitoring of mental health status. Our proposal is to process digital behavioral data generated by the patients in an online platform (i.e. text, voice and video feedback) using machine learning approaches to develop an algorithm to predict their mental status. Furthermore, using recent advancements of deep learning in natural language processing, we are going to generate more personalized therapy content for patient interactions to improve the quality of the care.

View Full Project Description
Faculty Supervisor:

Nazanin Alavai

Student:

Amirhossein Shirazi

Partner:

OPTT

Discipline:

Psychology

Sector:

Health care and social assistance

University:

Queen's University

Program:

Elevate

Exploring race representation and the racial wage gap in Southern Ontario’s Tech Industry

There is a lack of “diversity” in Canada’s tech industry and this is what the research is based on. The lack of research considering racial diversity in the tech industry in Canada leaves a significant gap in understanding issues that would be critical in addressing such a lack of diversity. This research will explore how, to what extent, race is represented in Southwestern Ontario’s tech industry. In so doing, it will determine if, and if so, to what extent, there is there is a race equity pay gap. It will provide a baseline assessment of to what extent race is represented in SWO’s tech industry. Important to this research is understanding how intersection race, gender, age and other such factors intersect. The research will take place in three phases over a one-year period using both qualitative and quantitative measures. The research will have three potential outputs with relevance to both the tech industry and academia.

View Full Project Description
Faculty Supervisor:

Kathy Hogarth

Student:

Hervege Ngweyin;Anne-Marie Bola Oladosu

Partner:

Innovate Inclusion

Discipline:

Social work

Sector:

Professional, scientific and technical services

University:

University of Waterloo

Program:

Accelerate

An electrochemical microfluidic sensor for cannabinoid detection

Cannabis legalization creates a pressing need to improve existing screening methods. Currently, the two devices approved by the office other the attorney general of Canada (i.e. the Drager 5000 and SoToxa) have not been embraced by the vast majority of police forces who deemed both options unaffordable, difficult to use and inaccurate. The present project aims to create a next generation cannabis detection device capable of accurately assessing the blood concentration of THC. This will be accomplished by harnessing recent advances in the fields of analytical chemistry and micro engineering to create a portable, accurate, affordable and easy-to-use detection kit.

View Full Project Description
Faculty Supervisor:

Steve Shih

Student:

Oriol Ymbern;Roberto Duca

Partner:

Strem Biotechnologie

Discipline:

Engineering - computer / electrical

Sector:

Professional, scientific and technical services

University:

Concordia University

Program:

Accelerate

Thin-film photocatalyst development for Solar-Driven GHG Conversion to Fuels

Solar-driven dry-reforming is an ideal solution for recycling greenhouse gasses (GHGs) while producing valuable chemical feedstock. These anthropogenic emissions of the GHGs are the leading cause of global climate change. Furthermore, these emissions are related to the manufacture of fuels and carbon-based products. Solar fuels technology addresses both of these issues. Solistra is developing photocatalyst technology in partnership with NRC, through the Materials for Clean Fuels Challenge program, and the University of Toronto’s Solar Fuels group. Photocatalysts, nanomaterials engineering to directly use solar energy, can convert carbon dioxide and methane into the same carbon-based consumer products we rely on every day using sunlight. This technology represents an advancement toward a clean and carbon recycling economy.

View Full Project Description
Faculty Supervisor:

Geoffrey Ozin;Benjamin Hatton

Student:

Young Feng Li

Partner:

Solistra

Discipline:

Chemistry

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Applied research in dynamic risk analytics using machine learning

Risk analysis has a primary role in safety-critical industries such as oil and gas explorations, marine and pipeline transportations, and downstream operations. This essential task is facing a series of challenges due to the increased complexity and volume of generated data. Due to the recent advancements in cloud computing power provided by the two giants: Google and Amazon, a powerful web-platform for advanced risk analytics and predictions is currently under development. SRCube Technologies Inc. is the first web platform to provide a direct link between applied research in machine learning for risk engineering and the petroleum and chemical industry. Engineers and executives can track current and predictive industrial risks such as toxic releases, fire hazards and explosions due to loss of containment. Real-time process analytics are generated, visualized, and processed to predict dangerous escalations based on research-proven models using machine-learning techniques.

View Full Project Description
Faculty Supervisor:

Carlos Bazan

Student:

Mohammed Taleb Berrouane

Partner:

SRCube Technologies Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Memorial University of Newfoundland

Program:

Previous
1…323324325326327328329…1,106
Next

Join a thriving innovation ecosystem.

Subscribe to our newsletter now!

Subscribe