Innovative Projects Realized

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

29670 Completed Projects

2811
AB
4990
BC
801
MB
663
NL
825
SK
8841
ON
9197
QC
95
PE
568
NB
1088
NS

Projects by Category

Understanding of Technology Adoptions for Prefabricated Construction for Efficiency and Productivity Improvements

Prefabricated and modular construction has gained popularity compared to traditional stick-built construction, due to its benefits of shortened schedule, increased safety, benefits to the environment, and minimization of an unpleasant outdoor construction activities. In typical prefabricated construction, the building construction project is designed and converted into shop drawings for manufacturing purposes, which are conducted manually or with assistance of automated machinery. Technologies (both digital and machines) can greatly improve efficiency and productivity, and ultimately the profitability for the company. However, for companies to adopt new technologies, they need to understand their existing processes, and build a business case for implementing the new technologies. Furthermore, the organizations need to understand the economic impacts and barriers in the areas of: (1) integration of BIM-based design for manufacturing; (2) impacts of machineries on the production line; and (3) Lean manufacturing-oriented thinking to facilitate the technology implementation. The industry partner is in the process of increasing their production capacity through technology innovations and intend to explore the opportunities in those three areas with the University of New Brunswick (UNB) Off-site Construction Research Centre (OCRC).

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Faculty Supervisor:

Zhen Lei

Student:

Partner:

Ironwood

Discipline:

Engineering

Sector:

Construction and infrastructure

University:

University of New Brunswick

Program:

Accelerate

Tissue Simulation Speedup

Physics-based simulation has been receiving a great deal of attention as it is of interest for various branches such as the film industry, computer game development, and biomedical research, etc. Especially for the professions that are interested in modeling creatures and producing correlated visual effects such as 3D animation, tissue simulation becomes a principled approach to make an animated character come to life by modeling the deformation of skin and muscle. Currently, the tissue simulation software is robust and full featured, but it is not particularly fast. Improving the speed of the simulator may be the most urgent request from customers and the company’s internal users. The desired outcome is to achieve fast-speed simulation meanwhile maintaining accuracy and robustness so that it can help emerging artists, award-winning studios, and global brands create better results.

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Faculty Supervisor:

David I.W. Levin

Student:

Partner:

Ziva Dynamics Inc

Discipline:

Computer science

Sector:

Information and cultural industries

University:

University of Toronto

Program:

Accelerate

Optimisation du traitement physico-chimique par décantation à floc lesté pour l’enlèvement des métaux d’effluents miniers par méthodologie de surface de réponse

Le partenariat avec l’entreprise a comme objectif principal de répondre à la problématique du traitement des eaux de ruissellement dans un contexte minier. Le projet comporte une extrême variabilité des débits d’effluent à traiter. L’évaluation des solutions d’optimisation se fera en fonction de données réelles. Ce partenariat permettra notamment d’améliorer la captation des métaux par l’optimisation du traitement en place par décantation à floc lesté. À terme de la recherche, l’usine de traitement sera en mesure de fonctionner à sa pleine capacité par une optimisation de son fonctionnement selon divers critères techniques, économiques et environnementaux. L’approche proposée permet de valider les paramètres ayant un réel impact maximal sur la qualité de l’eau rejetée au milieu récepteu

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Faculty Supervisor:

Hubert Cabana

Student:

Partner:

FNX-INNOV

Discipline:

Engineering

Sector:

Construction and infrastructure

University:

Université de Sherbrooke

Program:

Accelerate

Energy and exergy analysis of refrigeration systems for refrigerated cabinets

Major efforts have been undertaken to reduce ozone depletion substances (ODS) used in refrigeration systems such as hydrofluorocarbons (HFC). Carbon dioxide (CO2) is recognized as safe, economic and environmentally sustainable. Commercial transcritical CO2 (R744) refrigeration systems have emerged as leading technologies in food retails, especially in cold climate countries. Major research and development challenge is undergoing to increase the performances of R744 cycles in order to compete energetically with HFC based systems. Hence, it is of prime importance to develop a numerical tool and analyze data measurements for the purpose of characterizing the performance of R744 refrigeration food cabinets in Canada. Then, a further analysis of the cycle configuration, operating conditions and design parameters are deemed necessary to optimize its performances. This project aims at developing a numerical tool validated by experimental measurement in order to predict and optimize the performances and operation conditions of CO2 transcritical refrigeration cycles.

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Faculty Supervisor:

Sébastien Poncet

Student:

Partner:

Arneg Canada

Discipline:

Engineering

Sector:

Manufacturing

University:

Université de Sherbrooke

Program:

Accelerate

Dosage of admixture for 3D printing based on optimization

Many chemicals can be used for the mix design of concrete 3D printing. With a traditional experimental approach, the number of experiments to conduct is exponential. The objective of this study is to develop a cement-based material suitable for 3D concrete printing applications by utilizing optimization and predictive algorithms. The question to answer is if the artificial intelligence can add value in the mix design by reducing the material and time required to develop a mix with the desirable properties. After collecting enough data in the lab, a predictive algorithm will be developed to predict the properties of the mixes. The optimization algorithm will then rely on the predicted values until the desired composition is obtained.

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Faculty Supervisor:

Claudiane Ouellet-Plamondon

Student:

Partner:

Master Builders Solutions Canada

Discipline:

Engineering

Sector:

Manufacturing

University:

École de technologie supérieure

Program:

Accelerate

An end-to-end IoT framework for reliable remote and contactless measurement of biometric data

Veyetals online smartphone-based application that can be downloaded through the Apple Store or the Google Play Store. It uses Remote Photoplethysmography (rPPG) technique to extract Blood Volume Pulse (BVP) signal from a face video captured using smart phone camera, and then applies multiple computational algorithms to measure heart rate, heart rate variability, oxygen saturation, and stress level from the BVP signal. In the proposed multidisciplinary research project, Queen’s researchers will collaborate with the industry partner to enhance the Veyetals platform to add new capabilities such as body temperature and blood pressure measurement and increase its overall performance and accuracy. New face video and signal processing techniques will be developed to improve signal extraction, remove noise, and apply advanced error handling routines to enhance the robustness and reliability of the Veyetals application. The improved Veyetals application can be used for public health monitoring, at care facilities, or for providing online medical advice through services such as YourDoctorsOnline where doctors can obtain patients’ vital signs to provide a diagnosis. The proposed work will greatly increase the performance and business value of the Veyetals application and leverage remote health care services in Ontario and around the globe through online health care platforms.

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Faculty Supervisor:

Farhana Zulkernine

Student:

Partner:

MarkiTech

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology; Professional, scientific and technical services

University:

Queen's University

Program:

Accelerate

Predicting population level effects of microplastics ingestion on the behaviour of fishes

High numbers of microplastics are currently present in freshwater lakes, rivers and ponds. Fish are known to mistake microplastic particles for food, then behave differently after eating them. As changes in behaviour are often the first line of defense against human-induced rapid environmental changes, monitoring these behavioural changes can tell us how pollutants, like microplastics, are likely to affect populations. This research will model stream-dwelling salmonids consuming microplastic particles using agent-based models, to explore how microplastic ingestion will scale up to affect populations, and to identify the characteristics that will make a salmonid population the most and least resistant to the effects of microplastic pollution. This study will help inform effective conservation targets, to help mitigate harm caused by microplastics, and to reduce ecosystem-level effects caused by declines in salmonid populations. Educational materials targeting all ages will also inform the public on microplastics impacts in freshwater ecosystems.

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Faculty Supervisor:

Christina Semeniuk

Student:

Partner:

rare Charitable Research Reserve

Discipline:

Life Sciences

Sector:

Arts, entertainment and recreation; Other services (except public administration)

University:

University of Windsor

Program:

Accelerate

Assessment of neuromuscular, cardiorespiratory, and cognitive performance in running and hopping tasks using wearable sensors

The objectives of this research project are (a) to record neuromuscular, cardiorespiratory, and cognitive related data using wearable sensors during running and hopping as two basic activities of many sports; and (b) convert the data to medically meaningful indicators of athletic performance and risk of injury using signal processing and machine learning algorithms. The project outcomes will fuel iKinesia’s ongoing research to develop and enhance its solution for Canadian athletes and sports teams at both national level and private clubs. iKinesia’s solution will benefit athletes and coaches to improve and personalize assessment and training, augment game strategy, capture noteworthy players, reduce the risk of sports related injuries, and accurately determine the return to play time post injury.

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Faculty Supervisor:

David St-Onge;Rachid Aissaoui

Student:

Partner:

iKinesia Inc

Discipline:

Engineering

Sector:

Manufacturing

University:

École de technologie supérieure

Program:

Accelerate

Cloud platform for machine learning

Surgical Safety Technologies aims to provide healthcare professionals with the opportunity to perform research in areas of surgical performance and education and implement evidence-based solutions to improve patient safety. Search on video content would an ideal functionality to assist with healthcare professionals’ research. This project uses computer vision model to rank the relevance of the surgical video. In addition, this project aims to improve the user experience of the product by analyzing the metadata generated by the cloud platform and developing an efficient data visualization technique.

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Faculty Supervisor:

Maryam Mehri Dehnavi

Student:

Partner:

Surgical Safety Technologies Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Multi-Object Tracking in Production Environments

Multi-object tracking has many uses cases in autonomous driving, robotics and security. Tracking is important as it allows us reason about the dynamic world and make actionable decisions based-off predicted object trajectories. As an example, in autonomous driving, not only the location of objects is important but also their predicted future trajectories are needed in order to make informed decisions. It is important to understand the hardware constraints in order to develop trackers that work in real-time. Understanding the necessary camera frame rate needed to perform actionable real-time tracking is an example of such hardware constraint. For robotics, developing adequate tracking solutions that satisfy hardware constraint will allow the verification that objects are moving as they should.

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Faculty Supervisor:

Florian Shkurti

Student:

Partner:

Kindred AI

Discipline:

Computer science

Sector:

Artificial Intelligence

University:

University of Toronto

Program:

Accelerate

Numerical Assessment of Structural Behaviours of High-Performance Building Envelope Panels

The Nexii building system consists of a structural lightweight pre-cast sandwich panel system that attaches to an existing or new base building structure. In an effort to make the panels lighter and improve the building envelope performance, the new generation of Nexii panels use FRP instead of steel framing among several other modification to the panel assembly. This study aims at assessing structural performance of the panel under the gravity and wind loading through a comprehensive parametric study using numerical method and Finite Element (FE) simulations.

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Faculty Supervisor:

Min Sun

Student:

Partner:

Nexii Building Solutions

Discipline:

Engineering

Sector:

Construction and infrastructure

University:

University of Victoria

Program:

Accelerate

A Predictive Cluster-based Machine Learning Pricing Model

Dynamic pricing models create price by assessing total cost, demand, and timing to customize the price to the moment. The models enable both buyers and sellers to settle a price that is very custom to their specific needs. Bison Transport Inc. has a network model that monitors profit and a pricing engine that monitors margin. The network model needs to evolve in critical ways to facilitate dynamic pricing. The current model allows viewing of the network from a variety of vantage points- region, customer, driver, asset, service type and time (day of week, time of day, season of year). The pricing engine, however, is not flexible. The current direct programming-based solutions incorporated into the pricing engine for dynamic pricing cannot adapt to the changing and unpredictable market conditions. Deletion or addition of information are not possible unless the programming code is directly modified. This is tedious. The solution is, therefore, automating the software. We propose to use computer automating tools from machine learning, that will allow the computer to learn from the input data set and predict future prices without human intervention. These tools can perform real time data analysis and optimize prices to changing demand and market conditions.

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Faculty Supervisor:

Parimala Thulasiraman

Student:

Partner:

Bison Transport Inc

Discipline:

Computer science

Sector:

Transportation and warehousing

University:

University of Manitoba

Program:

Accelerate

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