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

Optimizing heuristics for spin-glass problems for diverse solutions

Optimization problems, such as finding the shortest or fastest path to a destination are ubiquitous in industry. Hower, for some industrial applications it may be desirable to have a set of few diverse, yet nearly optimal solutions. The goal of this project is to create new optimization problem solvers that focus on both quality and diversity of the solutions proposed. These solvers will subsequently be used to assess the performance of the D-Wave quantum annealer processor.

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Faculty Supervisor:

Malcolm Kennett

Student:

Alex Zucca

Partner:

D-Wave Systems Inc.

Discipline:

Physics / Astronomy

Sector:

Manufacturing

University:

University of Toronto

Program:

Accelerate

Off-Policy Reinforcement Learning (RL) for a Production Robotics Application

Kindred offers eCommerce retailers a solution to assist with rapid order fulfilment from their distribution centres. The solution (SORT) is a combination of a so-called put-wall and a humanoid robot. The robot picks up items from orders, scans them, and puts each item in a cubby of the put-wall according to the scan code. The robot comprises a gripper, a 6-degree-of-freedom arm, and a stereo vision module, as well as other electronics and mechanical housing. The proposed research will explore machine learning techniques based on reinforcement learning to feed data recorded from Kindred’s production robots back into learning algorithms in order to generate new better ways for those robots to pick, scan, and stow those eCommerce customers’ orders.

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Faculty Supervisor:

Florian Shkruti

Student:

Bryan Chan

Partner:

Kindred Systems Inc

Discipline:

Computer science

Sector:

University:

University of Toronto

Program:

Accelerate

Visual attention in deep learning for detection and classification

Visual attention refers to the mechanism of dynamically and selectively focusing on a subset of the visual input stimuli for detailed analysis, which is part of the visual perception process of the early primate vision. It has been successfully integrated into the design and implementation of many artificial visual recognition systems with applications to image classification, object detection, object sequence recognition, as well as image captioning and visual question answering. This research will explore various visual attention mechanisms with the goal to improve the generalization, robustness and efficiency of various DCNN models and algorithms developed for Epson’s computer vision and machine learning core technologies and products.

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Faculty Supervisor:

Sanja Fidler

Student:

Jing Huang

Partner:

Epson Canada Ltd

Discipline:

Computer science

Sector:

Manufacturing

University:

University of Toronto

Program:

Accelerate

Express Scripting Technology: Scratch for SOTI SNAP and IoT

SOTI has developed a software product called SOTI SNAP that is designed to allow anyone to create an app with no programming or technical knowledge. SOTI SNAP allows users to drag and drop widgets onto a canvas and connect them together to create an app. Apps generated with SOTI SNAP have cross platform capabilities, they can run on Android and iOS based devices. Currently SNAP apps that require programming logic, must use JavaScript, but using JavaScript requires technical skills. SOTI would like to add a visual language capability that is similar to visual block programming frameworks like Scratch and Blockly. This capability would fit well with our zero-code goal, and minimal technical skill requirement for developing apps with SOTI SNAP.

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Faculty Supervisor:

Syed Ishtiaque Ahmed;Khai Truong

Student:

Jamie Beverley

Partner:

SOTI Inc

Discipline:

Computer science

Sector:

Information and cultural industries

University:

University of Toronto

Program:

Accelerate

Real VR Hands and Interaction with Virtual Objects

Serving as the most widely-used body part for communication, hand is a very important tool for human to interact with the world. Especially with the continuing development of virtual reality and augmented reality, hand pose information has gradually become an indispensable component for improving users’ experience in interacting with computing devices. Therefore, this project aims at enabling an expressive virtual hand reconstruction to increase immersion and presence in VR experiences. The capacitive sensor that will be utilized in this project is supported by the project partner, Tactual Labs who, by the end of this project, will benefit by having its current innovative capacitive controller more intelligent.

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Faculty Supervisor:

David I.W. Levin

Student:

Shihang Zhu

Partner:

Tactual Labs Co.

Discipline:

Computer science

Sector:

University:

University of Toronto

Program:

Accelerate

Enabling Purchase of Residential Homes at Scale

Properly buys and sells homes directly from consumers. For our business to be successful, we must be able to predict two things when making a home purchasing decision: ? The price it would sell for on the open market ? How much time it will spend on the market to sell at that price These two variables are correlated: price can affect time-on-market, and time-on-market can affect price. There are many other factors at play as well. In the broader market, these complex real estate decisions are largely made using human judgement, based on experience and expertise. We want to increase the speed and accuracy of these predictions using applied data science, in order to reduce the business risk of home buying decisions, and to allow making them at scale.

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Faculty Supervisor:

Nathan Taback

Student:

Bharadwaj Janarthanan

Partner:

Properly

Discipline:

Computer science

Sector:

Real estate and rental and leasing

University:

University of Toronto

Program:

Accelerate

An investigation of consensus and performance in distributed systems

Within a tiered or zoned architecture, new business constraints for regional data residency, imply a need for new architectural patterns. These constraints are not arbitrary: they arise directly from customers demand to ensure their confidential data stays within their defined borders unless required otherwise (e.g. data originating in the U.S. is intended for an E.U. entity). An architecturally clean approach is to negotiate the movement of data only at the persistence layer. The objective is to review the industry landscape for suitable replacements or to otherwise alter the existing design to facilitate the replication of data based on these requirements. Specific targets for consistency latency (with defined upper bounds) and parity performance for read and write rates must be met. Parity performance is measured via msg/s and is achieved if the modified/replacement system has at least the same performance profile (average, median, and 99% percentile) as the existing system.

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Faculty Supervisor:

Matt Medland

Student:

Duc Truong

Partner:

Ethoca Technologies

Discipline:

Computer science

Sector:

University:

University of Toronto

Program:

Accelerate

Predicting Scleral Lens Rotation Based on Corneoscleral Toricity

Patients with corneal disease often require treatment with scleral lenses. Unlike regular soft contact lenses, these lenses are much larger and have a space between the cornea and the lens that is filled with fluid before lens application. These lenses are extremely useful in cases of extremely ocular dryness and in patients with irregular corneas. Adjusting these lenses to perfectly mold the surface of the eye is of the utmost importance to ensure that the patient is comfortable and sees well with their lenses. Current techniques to adjust scleral lenses involve a “trial and error” fitting technique, which takes a lot of chair time for both the patient and the practitioner. This study aims to evaluate the ways to fit lenses empirically using two topographers, instruments that provide information on the shape of the eye, ultimately improving lens fittings for practitioners, patients and contact lens manufacturers.

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Faculty Supervisor:

Langis Michaud

Student:

Gabriella Courey

Partner:

Les Laboratoires Blanchard Inc

Discipline:

Medicine

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

Neuroimaging biomarkers of Parkinson’s disease identified through brain, brainstem and spinal cord imaging

In the current functional and structural neuroimaging project, we aim to identify functional and structural changes that correlate with disease presence and its severity  staging) and that can serve as a basis for future development of PD neuroimaging biomarkers. To achieve this objective we will use our expertise in functional neuroimaging of the cervical spinal cord (CSC), brainstem and brain (simultaneously), as well as in micro-structural neuroimaging of the spinal cord. The PDQ will play an essential role in  subject recruitment, while also benefiting from the research outcomes and gained knowledge from the project. This work has great potential for future development of  clinical applications as the novel PD biomarkers developed here can help diagnose the disease, assess its severity or prodromal identification of at-risk individuals.

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Faculty Supervisor:

Julien Doyon

Student:

Linda S. Dahlberg

Partner:

Parkinson Québec

Discipline:

Medicine

Sector:

Life sciences

University:

McGill University

Program:

Accelerate

Characterization of Boiler Fly Ash to Match Producers with Beneficial End Uses

Many mills are currently using biomass (hog fuel, bark, sawdust, demolition waste etc.) for heat and electricity generation due to its greenhouse gas neutral status. The combustion of such feedstock generates ash residues the properties of which vary widely with the properties of feedstock and the boiler operating conditions. As such, majority of the ash generated in Canada is currently landfilled. The proposed project aims to characterize ash from various sources and match ash properties with the requirements of the beneficial end uses, such as cement replacement in the construction industry, soil amendment, forest/rural road ingredient and as a source material to recover saleable/useable products. The partner organizations, BC Pulp and Paper Bioproducts Alliance member mills, will benefit from the mill-specific ash utilization opportunities identified through this research. The mills will be able to market ash and ash-derived products while helping towards eliminating the old and deep rooted landfilling practice.

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Faculty Supervisor:

Sumi Siddiqua

Student:

Abu Sayed Md. Kamal

Partner:

Discipline:

Engineering - other

Sector:

Agriculture

University:

Program:

Accelerate

Toward Ore-Specific Sensor-Based Sorting Systems in Mining

A two-year, multi-disciplinary research project requiring MSc, PhD and PDF researchers across Computer Science, Earth and Ocean Science, and Mining Engineering is proposed, working with an industrial sponsor MineSense, focused on the development of new sensors for advanced sensor sorting and so-called ‘non-grade’ applications in previously unaddressed high capacity, low grade mining situations. Specific objectives include, through advanced ore characterization, sensor development, algorithm development (including use of AI) and advanced system design and evaluation to:
• Improve the ability of sensors to respond to different mineralogical compositions of an ore/orebody
• Incorporate knowledge of these characteristics into algorithms for either rock or bulk sorting or grade or non-grade parameters to develop more intelligent sensing, and ultimately sorting, systems
• Integrate developed sensor systems into ore-specific sensing and sorting applications in the industry, including ore recovery, waste rejection and non-grade parameter control.

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Faculty Supervisor:

Bern Klein;David Poole;Lee Groat

Student:

Lindsey Abdale;Natalia Martino

Partner:

MineSense Technologies

Discipline:

Engineering

Sector:

Mining and quarrying

University:

Program:

Accelerate

Use of temperature and activity monitoring system as predictor for parturition and estrus

Monitoring dairy cows individually around the time of calving and during lactation has the potential to identify calving difficulties or cows at risk of developing disease, as well as cows in estrus to create alerts for dairy farmers. Therefore, there has been an increase in research investigating methods to accurately predict timing of calving, disease diagnosis, and estrus detection via activity and temperature monitoring. Recently, a novel cattle activity and core temperature monitoring system that uses new sensor technology has been developed. The purpose of this proposal is to create opportunities to improve transition cow health by more accurately predict calving time and cows at risk for developing diseases.
Herdstrong is relatively small start-up, but with a very active R&D department and products in several operations and Universities particularly across North America. TO BE CONT’D

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Faculty Supervisor:

Ronaldo Luis Aoki Cerri

Student:

Janet Bauer

Partner:

Herdstrong

Discipline:

Food science

Sector:

University:

Program:

Accelerate

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