Innovative Projects Realized

Explore thousands of successful projects resulting from collaboration between organizations and post-secondary talent.

29670 Completed Projects

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4990
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801
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663
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825
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8841
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9197
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95
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568
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1088
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Projects by Category

Homeowner water quality testing through use of miniaturized ISE and FET-based sensor arrays

Water quality is a key public and environmental health concern. Many rural communities rely on privately owned wells to provide water; however, these wells are not usually tested regularly and may be dangerously contaminated with bacteria or inorganic materials, such as arsenic or lead. A simple, easy-to-use, handheld water quality testing solution would encourage private well owners to regularly test their water by eliminating costly laboratory testing, sample shipping, and potentially inappropriate sample handling. The proposed project is the development of a multi-analyte sensing platform for the detection and quantification of pathogenic bacteria and a host of inorganic contaminants that simply requires the user to dip the sensor in their water sample. Sensor arrays targeting different markets would enable the industry start-up to provide value to various clients, generate return on investment, and promote greater public health.

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Faculty Supervisor:

Ken Oakes

Student:

Partner:

Drinkable

Discipline:

Physics

Sector:

Manufacturing

University:

Cape Breton University

Program:

Accelerate

Sequential anomaly detection with labelingcosts

In a number of data analytics domains, there is a need for detecting the situations when something outside of the normal conditions is happening. The goal of this project is to develop novel algorithms that learn to distinguish these normal and anomalous patterns through minimal interaction with a human user, while allowing complex data patterns such as time-series data. We further take into account a specific asymmetry of labeling costs that is inherent in the problem. Our proposed methods use prediction with expert advice techniques, adapting them to the the structure of the aforementioned problem. We avoide statistical assumptions about the data generating mechanism, while allowing the use of domain knowledge through designing a set of experts used by our algorithms. Our algorithms enhance the effectiveness of Darkhorse Analytics’ anomaly detection system, which is currently based on manual definition of thresholds on specific key values, and also reduce the cost of manual analysis of the data sets by requiring less interaction with human users.

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Faculty Supervisor:

Csaba Szepesvari

Student:

Partner:

Darkhorse Analytics Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Alberta

Program:

Accelerate

Profinite Groups and Cohomology

In mathematics, a group is a set (an accumulation of items) together with a certain operation. For example: the integers with addition, the symmetries of an equilateral triangle. Being one of the simplest algebraic structures, groups are ubiquitous in mathematics and have applications in other disciplines. Informally, a group is called profinite if it can be assembled from finite groups in a certain manner. The above symmetries are a finite group: there are only three reflections along the axes and the rotations around 120, 240 and 360 degrees. On the other hand, cohomology is an algebraic tool that can discern different geometric objects by looking at their “holes”. For example, one can discern a ball from a donut using it. There is a counterpart for groups, so group cohomology is a tool that can discern different groups. This project forms a part in answering a question about group cohomology of profinite groups.

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Faculty Supervisor:

Alejandro Adem

Student:

Partner:

University of Southampton

Discipline:

Mathematics

Sector:

Education

University:

The University of British Columbia

Program:

Globalink Research Award

Estimation visuelle sécuritaire et explicable des conditions routières

Nous avons actuellement un prototype de systeme de vision artificielle base sur des reseau de neurones profonds entra1ne sur notre jeu de donnees proprietaire qui permet d’identifier les zones de construction et de detecter et lire les panneaux de limite de vitesse. Dans notre prochain de R&D, nous souhaitons tirer partie de notre reseau de detection d’objets afin d’identifier les conditions routieres dangereuses ( neige, blizzard, glace, pluie, aveuglement ) ainsi que le contexte de conduite ( autoroute, route rurale, zone urbaine, chemin de terre).

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Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

E-SMART Control Inc.

Discipline:

Computer science

Sector:

Manufacturing

University:

Université de Montréal

Program:

Accelerate

Fabrication and measurement of 2D materials heterostructures

Two-dimentional (2D) materials made of few atomic layers, bring unprecedented quantum properties from their extreme thickness. The possibility to stack layers from different 2D materials into heterostructures is one of the most active areas of research in quantum materials, bringing a wide parameter space for creating previously inaccessible quantum states and device geometries. This project will advance the field of quantum devices based on 2D materials by focusing on heterostructures made from two atomically thin semiconductors WSe2 and MoS2 and insulating hexagonal boron nitride. The goal is to fabricate such ultrathin semiconductor heterostructures, optimize their electrical contacts in devices and understand their optical and electrical properties. To that end, we will combine the complementary expertise of the Canadian and French institutions on the fabrication and characterization sides. The outcome of this project are scientific advances in quantum technologies based on 2D materials, giving Canada a leading edge in this area.

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Faculty Supervisor:

Adina Luican-Mayer

Student:

Partner:

Université Grenoble Alpes

Discipline:

Physics

Sector:

Education

University:

University of Ottawa

Program:

Globalink Research Award

Test-based Fracture Criteria for Pipeline Steels

Steel pipelines as key Canadian infrastructure are often exposed to various geological and environmental conditions that cause defects such as cracks, dents, and gouges in the pipe wall, which can lead to pipe fracture in pipelines, compromising the entire pipeline’s functionality and resulting in significant economic losses, environmental issues or even fatalities. There are several methods to predict the fracture capacity, including experimental testing, analytical approaches, and advanced numerical methods. The use of fixed fracture strain criterion implemented in the computer programs may not appropriately predict the crack propagation path because of the concentration of large strains. Further, fracture criteria that ignore the crack tip constraints and depend on the type of the test specimens used to obtain fracture indices may lead to inconsistent predictions. There is a need to develop and implement more efficient and accurate fracture criteria specific to pipe steel as a function of crack tip constraints, independent of the type of specimens used and the size of pre-cracking. This research project aims at developing calibrated numerical-based fracture criteria for predicting the crack propagation in steel pipelines.

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Faculty Supervisor:

Ali Imanpour;Samer Adeeb;Samer Adeeb;Yong Li;Ali Imanpour

Student:

Partner:

Enbridge Pipelines;C-FER Technologies

Discipline:

Engineering

Sector:

Mining; Professional, scientific and technical services; Transportation and warehousing; Utilities

University:

University of Alberta

Program:

Accelerate

Study of Amaryllidaceae alkaloid pathway genes using comparative Omics analysis

Plant produce large number of the bioactive compounds, among them Amaryllidaceae alkaloids has such potential. Those Amaryllidaceae alkaloids are extracted from Amaryllidaceae plant family. Galantamine is one of the drug which is derived from the Amaryllidaceae alkaloid pathway; and is the only commercialized drug for the treatment of early symptoms of Alzheimer’s disease. Although many Amaryllidaeae alkaloid have potential pharmacological properties, its further research and application is restricted due to its low level of accumulation in plants. Emerging technology in genetic engineering have potential to produce plant derived compounds from heterologous microbial host. The knowledge on biosynthetic pathway of targeted compound is absolute necessity for reconstruction of biosynthetic route in microorganisms. Ongoing biological process can be captured using RNA sequencing technology, co-expression network analysis and computational studies. Hence, our project focus on the identification of biosynthetic pathway genes involved in Amaryllidaceae alkaloid metabolism by comparative transcriptomic analysis.

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Faculty Supervisor:

Isabel Desgagné-Penix

Student:

Partner:

Nanyang Technological University

Discipline:

Life Sciences

Sector:

Education

University:

Université du Québec à Trois-Rivières

Program:

Globalink Research Award

ALT TEX Circular Textile-grade Polymer Synthesis

Generating 10% of global greenhouse gas emissions the fashion industry’s heavy reliance on synthetic,
petroleum-derived, fabrics such as polyester, is projected to create over a quarter of the world’s carbon emissions
by 2050, according to the Ellen MacArthur foundation. Unlike its bio-based counterparts, such as cotton, wool or
silk, polyester does not biodegrade, and its synthetic production is estimated to consume 70 million barrels of oil
each year while it’s disposal creates 55 million annual tons of landfill waste globally.
This project proposes a green polymerization method to generate a circular biosynthetic polymer alternative that
can be manufactured into textiles, similar to the polyester counterpart. The resulting material alternative carries
potential to reduce fashion’s dependence on fossil fuels while the industrial biodegradability opens the door to
circular textile waste diversion.
This research will support ALT TEX in its materials science research on the path to developing commercially
viable, radically circular textiles for the fashion industry.

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Faculty Supervisor:

Parisa Mehrkhodavandi

Student:

Partner:

ALT TEX

Discipline:

Engineering

Sector:

Manufacturing

University:

The University of British Columbia

Program:

Accelerate

“The Space Where Everything Flows”: Room, Trauma and Myth in Youval Shimoni’s Work

My proposed internship is part of a larger research project, my thesis. In my research, I present the first critical, academic discourse on Youval Shimoni’s body of work through an examination of the concepts and aspects of space in his works, and their relation to Israeli reality. In my thesis, Shimoni’s work is mapped for the first time, revealing his unique poetic language, and the philosophical and political themes that reoccur throughout his body of work. I hope this internship will provide me a chance to make significant progress with my thesis while benefiting from the leading researchers that are part of the vital intellectual community at the University of Toronto. By working with the guidance of Professor Naomi Seidman I hope to deepen my knowledge of modern myths and rethink Jewish-Christian relations. I wish to participate in courses and the diverse community of the Department of Religion, the Center of Diaspora and Transnational Studies, and the Center for Jewish Studies at the University of Toronto. In the three months of the internship, I plan to work on my thesis chapter dealing with the myths in Shimoni’s work, while conducting a progress meeting with prof. Seidman, participating in seminars related

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Faculty Supervisor:

Naomi Seidman

Student:

Partner:

Tel Aviv University

Discipline:

Sociology

Sector:

Education; Other

University:

University of Toronto

Program:

Globalink Research Award

Arch-App

The intention of the Arch-App is to create a mobile app that utilizes geo-location and augmented reality technologies to access data (including imagery, videos, and text) on building projects throughout the city in real time. The infrastructure of the Arch-App effectively provides users a resources (ranging from historic imagery to notable interviews) and tour guides to any local landmark in the real world condensed into their smartphones. Currently, there is no such infrastructure which, supports said goals of Arch-App. The Arch-App strives to empower people with more information on any given landmark they may come across including touristic, academic, and recreational initiatives. The intern will be conducting interviews, gathering content from the firms and compiling and uploading information to the app. The app will expose the industry to cutting-edge technology and firms involved will gain exposure to the public, educational and professional level and will have the opportunity to leave a legacy through their work.

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Faculty Supervisor:

Vincent Hui

Student:

Partner:

Ontario Association of Architects

Discipline:

Sociology

Sector:

Professional, scientific and technical services

University:

Toronto Metropolitan University

Program:

Accelerate

Non-Destructive Evaluation of Irradiated Single walled carbon nanotube

Semiconductors have been used in electronics for decades and are an integral part of every circuit. Because of this, evaluating semiconductor performance and tolerance in different environments is crucial. For example, evaluating a semiconductor’s radiation tolerance can be quite useful such as for the radiation detectors in the Large Hadron Collider and for the solar panels on the international space station which are exposed to lots of radiation. Unfortunately, it is still unsure how radiation affects semiconductors, for this reason the student (Nicholas Dallaire) will study the tolerance of single walled carbon nanotube semiconductors. Nicholas will use transient grating spectroscopy (TGS) to evaluate the degradation of the semiconductors which have been exposed to radiation. TGS is a method used to characterize the surfaces of materials in a nondestructive way to determine a material’s elasticity, energy dissipation, thermal diffusivity. The research done will aid in figuring how semiconductors are affected by radiation and help innovate novel circuits which may need protection from radiation, such as satellites.

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Faculty Supervisor:

Benoit Lessard

Student:

Partner:

Massachusetts Institute of Technology

Discipline:

Physics

Sector:

Education

University:

University of Ottawa

Program:

Globalink Research Award

Federated Learning on Sensitive Mobile Data (Python/Java)

At Lerna IA, we are building a private federated learning platform for mobile applications. Our technology allows mobile-first companies to learn about their app users without violating their privacy. Our novel security architecture speeds up the federated learning process by at least 50x, rendering it practical for real-world mobile set-ups by running the whole ML process on thousands of mobile phones in a distributed fashion. We predict the user behavior based on their context, demographics, mood, etc. in order to identify best timing for out-reach, without retrieving any data!
For the purpose of this internship, the core research portion constitutes the selection and tuning of ML algorithms that are suitable for our application – both useful and with an efficient Federated Learning version.

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Faculty Supervisor:

Laurent Charlin

Student:

Partner:

Lerna

Discipline:

Computer science

Sector:

Information and cultural industries

University:

HEC Montréal

Program:

Accelerate

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