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

Quantification of biomolecular interactions

The aim of this project is to develop novel optical and microfluidic methods to extract quantitative metrics from biological interactions. Such quantitative information will be used to understand biophysical processes – such as binding of antibodies to a cell or tissue surface as well as improving decision making in diagnostic pathology. Towards these aims, we will develop methods that leverage novel microfluidic platforms for precise fluid control and optical tools for high sensitivity molecular detection. We expect this work to be integrated into existing workflows for quantitative measurements in various aspects of biological research.

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Faculty Supervisor:

Govind Kaigala

Student:

Partner:

ETH Zurich

Discipline:

Life Sciences

Sector:

Education

University:

The University of British Columbia

Program:

Globalink Research Award

Characterizing the wood construction waste stream in BC and evaluating the mechanical performance of new recycled products

Wood waste from Construction, Renovation and Demolition (CR&D) contributes about 7 % of the total waste sent to Canada’s municipal solid waste (MSW, or landfill) sites, according to a 2013 report from Natural Resources Canada (NRCan).
This preliminary project aims to consider viable alternatives to utilize waste wood to manufacture engineered wood products for the construction industry that account for variations in dimensions, moisture content, wood species, etc. The objective is to characterize the raw material available suitable for reuse in engineered wood products and assess their mechanical properties.
This project should demonstrate the potential for the recycling of wood waste, thus contributing to mitigate climate change by reducing the pressure on Canadian forests and the need for landfilling wood waste.

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Faculty Supervisor:

Julie Cool

Student:

Partner:

Urbanjacks

Discipline:

Earth science

Sector:

Sustainability & the Environment; Manufacturing and Construction; Clean Technology

University:

The University of British Columbia

Program:

Accelerate

Multi-hazard Risk & Resilience Assessment for Real Asset Decision-Support (continuation) – Asset Interdependency Risk

The proposed project is an extension of a previous project “Multi-hazard Risk & Resilience Assessment for Real Asset Decision-Support” which aims to develop a methodology and software for performing rapid high-resolution multi-hazard risk assessment for asset portfolios by combining commercial/publicly available hazard models with high-resolution vulnerabilities derived from multi-physics simulations of different asset archetypes. The proposed project adds a component that captures cascading risk due to interdependency through the use of network models, which will be implemented as part of the risk assessment software.
The expected outcome of the project will be a network module that can be integrated into Kinetica Risk’s existing risk assessment software to carry out portfolio risk studies. The project will also result in academic publications in high-resolution network models for capturing cascading risk.

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Faculty Supervisor:

David Bristow

Student:

Partner:

Kinetica Risk

Discipline:

Engineering

Sector:

Environmental Science and Technology; Sustainability & the Environment; Construction

University:

University of Victoria

Program:

Accelerate

A Flexible Development Pipeline for Optimal Anomaly Detection in Derivative Markets

When a previously trained machine learning model is put into production, the production phase begins where said model makes predictions on the inputs provided to it. When the distribution of production data changes over time, we talk about data drift. Then the model is likely to become less efficient, or even obsolete. The project consists of building an intelligent system capable of alerting in the event of a data drift that would have a significant impact on the system.

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Faculty Supervisor:

Maxime Lamothe;Foutse Khomh;Heng Li

Student:

Partner:

Bourse de Montréal

Discipline:

Engineering

Sector:

Finance and Insurance

University:

Polytechnique Montréal

Program:

Accelerate

Using Artificial Intelligence to Classify Interpersonal Skills

This MITACS BSI project represents a collaboration between Skillsetter.com (an online interpersonal skills training company and Partner Organization) and members of the University of Calgary’s Department of Computer Science (Professor Richard Zaho [Academic Supervisor] and Mohamad Elzhobi [PhD Student and Project Intern]) aimed at developing a machine learning model to classify aspects of judgmentalness from video recordings. The intern will develop a theoretical framework for integrating multimodal data with text-based classification models, create a text-based machine learning model to classify different aspects of judgmentalness from video recording transcripts, and integrate the model with existing services and data pipelines at Skillsetter. The project’s primary impact on the partner organization will be to substantially enhance the commercialization of their technology, including supporting their entry into new international markets. The project’s secondary impact will be to directly advance knowledge about the ability of machine learning algorithms to classify nuanced and contextual interpersonal communications.

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Faculty Supervisor:

Richard Zhao

Student:

Partner:

Skillsetter

Discipline:

Computer science

Sector:

Information and cultural industries

University:

University of Calgary

Program:

Business Strategy Internship

Software Acceleration of Video Noise Filtering and its integration into real-time video applications

The project is mainly in the domain of achieving real-time computational speed of methods to remove noise from video signals (for example, those taken by a professional cinema camera). Specifically, in this project, we propose first to improve the speed of current technology that we have developed in previous MITACS projects, in order to make it commercially valuable and second to integrate this new real-time technology into video applications that require noise-free inputs in order for them to have high performance output. The architecture of personal computers allows us to use several processing units simultaneously and this lead to speed up significantly but the program code should be well managed to use all of these resources together. Since currently our code does not have this feature and processing speed is slower than desired, by modifying the program we expect to meet the acceptable speed. We are targeting two important applications: video compression and face recognition. Early investigation shows that we will noticeably improve their performance by either removing noise from their input or estimating that noise.

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Faculty Supervisor:

Aishy Amer

Student:

Partner:

TandemLaunch Inc

Discipline:

Computer science

Sector:

Finance and Insurance; Professional, scientific and technical services

University:

Concordia University

Program:

Accelerate

Real-time food analysis using deep learning for Diabetes Self -Monitoring Phase 2

Our proposed research is to create an algorithm capable of pre-evaluating diabetes patients’ meals before they consume them with the snap of a picture. We are attempting to accomplish this goal by employing AI, machine learning as well as computer vision for real-time analysis. Our goal is to analyse a user’s meal to return an accurate carb count and offer portion size adjustments to reduce their blood sugar fluctuations. By developing a model that uses these technologies, we believe we can create an algorithm that will revolutionize how diabetes patients manage their condition and allow users to maintain consistent and healthier blood sugar levels. This research will greatly benefit the partner organization as it will help accelerate the growth of development heavily on the technology side to bring this to a level where it can be commercialized to generate revenue and used by others.

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Faculty Supervisor:

Lueder Kahrs;Naimul Khan

Student:

Partner:

Glucose Vision

Discipline:

Engineering

Sector:

Health and Related Sciences & Technology

University:

Toronto Metropolitan University; University of Toronto

Program:

Accelerate

Development of a dynamic control strategy for automated window shades

Commercial and multi-unit residential buildings located in cities with high sun exposure often experience challenges regulating indoor temperature due to the uneven distribution of solar radiation and localized overheating. For buildings located in cold climate zones, localized overheating – and in turn, localized indoor cooling – during the winter heating season is often overshadowed by the need to meet the building’s heating demand. This study proposes the use of smart shading systems to regulate the amount and duration of sun exposure within an indoor space. A dynamic control strategy will be developed to reduce both whole-building and localized cooling demand during the day and regulate indoor thermal conditions overnight. In partnership with AI Shading, results from this study will directly impact the technological development of the next phase of the AI Shading technology to improve its applicability for implementation in commercial buildings within Canada.

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Faculty Supervisor:

Lexuan Zhong

Student:

Partner:

AI Shading

Discipline:

Engineering

Sector:

Information and cultural industries; Manufacturing; Professional, scientific and technical services

University:

University of Alberta

Program:

Accelerate

Design and Development of a Secure and Reliable Online Platform for Real-Estate Transactions

The primary objective of the project is to investigate, design and prototype a secure and reliable online tool that enables real-estate buyers to pay deposits on real-estate transactions electronically via credit or debit card. In addition, it will allow real-estate brokers to log in and view transaction statistics. The tool will provide a user friendly online form to accept buyer’s input from any devices (laptops/desktops/tablets/smartphones). The buyer data will be sent to the ExactDeposit server for further processing. The credit card and brokerage information will be sent to a 3rd party gateway for payment processing once the data filtering has been completed. This system will give the realestate buyer – a secure, yet simple and flexible means of sending deposit funds and give participating brokerages the ability access transaction statistics. The project will be a crucial part in the partner organization’s road to commercialization of the product. The proposal has the potential to train valuable HQP in the areas of software integration technologies and cutting edge research in secure and reliable communication protocols.

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Faculty Supervisor:

Srinivas Sampalli

Student:

Partner:

ExactDeposit Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Dalhousie University

Program:

Accelerate

Hybrid Multifunctional 3D bioprinting systems

Successfully fabricating complex living tissue structures demands carefully designed tissue scaffolds that mimic complex native tissues, which 3D bioprinting aims to achieve by creating 3D mimics of natural tissues. The fabrication of functional 3D tissues is challenging due to the limitation of oxygen and nutrient transport to cells inside the printed scaffold. Among the numerous bioprinting techniques recently developed and advanced, none of them are adequate to fabricate tissues with multiple cells and 3D blood vessel networks. The extrusion bioprinting has evolved to a freeform reversible embedding of suspended hydrogels (FRESH), enabling suspending 3D structure printing in the supporting gel bath. Stereolithography (SLA) bioprinting also has evolved to a volumetric SLA (VSLA) bioprinting, enabling high-speed 3D bioprinting by the light projection of 2D slices of the pre-loaded 3D models from all possible angles, while the bioink container rotates around itself. Here, we will develop a hybrid bioprinting system by integrating the FRESH extrusion bioprinting with the VSLA bioprinting system to create a solid organ (e.g., liver) with blood vessel networks. The integration requires elaborately tuned physical properties of bioinks to be suitable for the bioprinting process while maintaining the essential biological characteristics (e.g., biocompatibility, cell adhesion, and cell interactions).

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Faculty Supervisor:

Keekyoung Kim

Student:

Partner:

Instituto Tecnológico y de Estudios Superiores de Monterrey (ITESM)

Discipline:

Engineering

Sector:

Health and Related Sciences & Technology; Advanced Manufacturing

University:

University of Calgary

Program:

Globalink Research Award

Efficacy of two mental well-being interventions provided by CheckingIn, a smart device application

Smart device applications show great promise as a means to improve the mental health and well-being of users. CheckingIn is one such app. CheckingIn monitors self-reported daily mood states of users and suggests exercises to improve user’s mental health and well-being. Although the app’s well-being programs are scientifically informed, there is a general lack of research investigating how effective such apps are in improving mental health. Therefore, CheckingIn requires more research to examine the effectiveness of their app’s mental
health programs. The goal of the present project is to evaluate changes in the mental health of application users after completing either of CheckingIn’s new 30-day mindfulness programs. In doing so CheckingIn will be able to determine how effective the programs are. The results will ultimately improve the quality of well-being programs provided to application users.

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Faculty Supervisor:

Alan Kingstone

Student:

Partner:

CheckingIn

Discipline:

Sociology

Sector:

Professional, scientific and technical services

University:

The University of British Columbia

Program:

Accelerate

Adaptation et validation d’un logiciel pour la formation des infirmières

Menya Solutions est en train de développer un logiciel de simulation du raisonnement clinique appelé pour le moment DITREX, destiné à la formation des infirmières. Il leur permettra de parfaire les habiletés d’évaluation et de raisonnement clinique. Sachant que le développement de ces habiletés requiert beaucoup de pratique, l’Association Canadienne des Écoles en Sciences Infirmières recommande aux écoles infirmières d’investir dans l’utilisation des simulateurs haute-fidélité et virtuels (ACESI, 2010). Cependant, à part les mannequins de haute fidélité qui sont utilisés dans certaines écoles infirmières, il n’existe pas de logiciel de simulation pour pratiquer l’évaluation clinique par les infirmières ni pour se familiariser à la documentation électronique des soins infirmiers. Le logiciel de simulation de Menya Solutions vient combler ce besoin. Ce projet de recherche consistera à investiguer et développer le contenu pédagogique nécessaire pour le logiciel de simulation et à valider l’outil auprès des étudiantes infirmières au baccalauréat.

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Faculty Supervisor:

Sylvie Jetté

Student:

Partner:

Menya Solutions

Discipline:

Life Sciences

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

Université de Sherbrooke

Program:

Accelerate

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