Innovative Projects Realized

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

30508 Completed Projects

2882
AB
5105
BC
825
MB
681
NL
860
SK
9051
ON
9491
QC
97
PE
586
NB
1141
NS

Projects by Category

Innovations in Marine Food Systems

Coastal communities in British Columbia are economic hubs and gateways to marine resources. The health of these communities’ depends on having sustainable food systems. How these communities’ access and eat local marine foods can be threatened by economic and environmental pressures – including the impact global climate change has on marine environments. In order to thrive, communities are innovating to ensure they can access and eat nourishing local food. The objective of this research is to better understand the role that innovations play in providing food security in local marine food systems in small coastal BC communities. We will be exploring how novel ideas, technologies, or systems come to be, and what combinations of actions, assets, and experience are leading to these innovations’ successes. By engaging with communities to explore the forefront of innovation in local marine food systems, we aim to generate knowledge that benefits other communities facing similar challenges

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Faculty Supervisor:

Ann Dale

Student:

Partner:

Ecotrust Canada

Discipline:

Sociology

Sector:

Agriculture; Other services (except public administration); Professional, scientific and technical services

University:

Royal Roads University

Program:

Accelerate

Identification of Key Microbes from Spontaneous Beer to Improve Mixed Fermentations

Sour beers, traditionally made via spontaneous fermentation, are growing in popularity; particularly examples produced using the modern technique of mixed fermentation. These mixed fermentation beers present unique challenges during production. We hypothesize that by examining traditional process spontaneously fermented beer, we can identify new methods and strategies for improving the quality of mixed fermentation beers. Blind Enthusiasm operates a brew house uniquely built to produce both traditional method spontaneous beer, as well as mixed beer. In order to improve mixed fermentation beer it is important to understand how different populations of microorganisms develop during fermentation in spontaneous beer. Through this collaboration we hope to understand how spontaneous fermentation progresses and how individual microbes from these beers impact the final flavor of the beer.

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Faculty Supervisor:

Benjamin Willing

Student:

Partner:

Blind Enthusiasm Brewing Company

Discipline:

Life Sciences

Sector:

Manufacturing

University:

University of Alberta

Program:

Accelerate

Démonstration d’outils pour la récolte d’échantillons foliaires par drones dans des environnements réalistes

De nombreux écosystèmes sur la terre sont encore extrêmement difficiles, voire impossibles, d’accès pour la communauté scientifique. Cela affecte considérablement la quantité et la qualité des recherches en cours, ainsi que les efforts de conservation pouvant être déployés. Ces environnements difficiles d’accès comprennent entre autres la canopée des forêts et les falaises. Pour atteindre la cime d’arbre mature, les techniques d’échantillonnage couramment utilisées sont archaïques. Elles incluent les perches télescopiques (limitées à 10 mètres de portée en hauteur et utilisation non ergonomique causant des blessures), les fusils à pompe (dangereux et maintenant illégal sur la majorité des territoires), les grimpeurs (coûteux et physiquement exigeant), les grues (coûteux et accès limité), l’abattage d’arbre (complexe et impossible dans plusieurs projets de suivi) ou encore l’hélicoptère (coûteux et complexe). En premier lieu, ce projet a pour but de terminer le développement d’un échantillonneur à portée verticale suspendu sous un drone, principalement pour la cime des arbres et, en deuxième lieu, de commencer le développement d’un échantillonneur à portée latérale, qui cible plutôt le côté des arbres et les plantes sur les falaises. Ces nouveaux outils représentent une percée majeure qui révolutionnera les protocoles d’échantillonnage en sciences environnementales.

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Faculty Supervisor:

David Rancourt;Alexis Lussier Desbiens

Student:

Partner:

DeLeaves Drone

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Université de Sherbrooke

Program:

Accelerate

CFD simulation and analysis of gas separation using dual-spinning disk reactors

Reducing greenhouse gas (GHG) emissions is an important global issue as GHGs from human activities are a significant contributor to global climate change. Greenhouse gases cause climate change such as rising average temperatures, extreme weather events. Carbon dioxide is the primary greenhouse gas, responsible for about three-quarters of emissions. This project aims to reduce global carbon emissions by extracting CO2 from waste gas streams and converting it into fuel. For this, Vorsana’s radial counter-flow device: a low-cost and efficient gas separation method based on the concept of flow between two parallel counter-rotating disks at an axial distance, is used. As the disks rotate, a complex turbulent vortex system is created in the gap. The rapidly rotating flow inside each vortex applies localized centripetal accelerations on the gases and forces the light gas fraction towards the axis and heavy gas fraction towards the periphery, where they are collected through outlet channels.

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Faculty Supervisor:

Joshua Brinkerhoff

Student:

Partner:

Vorsana

Discipline:

Engineering

Sector:

Manufacturing

University:

The University of British Columbia - Okanagan

Program:

Accelerate

Detection for Smart Home Devices’ Environment with Neural Network

As smart home and artificial intelligence technologies are developing rapidly, smart home devices contribute to better living quality and safer spaces. These smart devices are intelligent agents. They receive a variety of signals through sensors placed in ecobee’s thermostats, light switches and other smart devices and controls the heating and cooling, lighting, as well as providing important notifications. In this project, we would like to analyze sensory data and develop various machine learning solutions for characterization of the devices’ environment (e.g. object detection and audio classification). Currently, machine learning and deep learning algorithms have achieved significant improvement in building intelligent agents. We will apply them to assist ecobee’s products to better understand the environment and make better decisions.

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Faculty Supervisor:

Roger Grosse

Student:

Partner:

Ecobee Inc

Discipline:

Computer science

Sector:

Manufacturing

University:

University of Toronto

Program:

Accelerate

Detection and Characterization of the Synovium in Musculoskeletal Ultrasound

Arthritis is a chronic disease that severely decreases the quality of life and affects almost 4.6 million Canadians, costing $33 billion for the Canadian economy every year. Affected individuals experience pain and disability through an extended period of time. Rheumatoid arthritis (RA), a common form of arthritis, is an autoimmune disease characterized by the inflammation of the synovium, or synovial membrane, a connective tissue that provides a cushion between bones and tendons and muscle around a joint. Characterization of the synovium is crucial for the management of RA as well as a useful marker of treatment efficiency. Ultrasound imaging is an affordable non-invasive technique to assess the synovium for thickening. The examination of the images is often contingent on sonographers and clinicians. Therefore, the current research proposes to utilize deep learning to accurately and reliably assess the synovium on ultrasound images, and then implement the network in the software implementable in medical settings.

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Faculty Supervisor:

Pascal Tyrrell

Student:

Partner:

16 Bit

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Energy Optimization Design Tool for Data-driven Building Massing

This project enhances the impact of a basic tool for early stage design decisions such as orientation,and building envelope composition on energy performance of the building. The proposed project adds the capabilities of the basic tool to drive, not only decisions regarding the design articulation of the building envelope, but also the design of the building geometry as a whole, in order to optimize the overall energy performance of the building. The tool will aim as well at integrating advanced building envelope systems (such as ventilated facades) with other building systems (e.g. HVAC/ electrical system).

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Faculty Supervisor:

Caroline Hachem-Vermette;Joshua M Taron

Student:

Partner:

DIALOG

Discipline:

Sociology

Sector:

Sustainability & the Environment; Clean Technology

University:

University of Calgary

Program:

Accelerate

Intelligent Piezoelectric Transduction System for In-situ Health Monitoring of Concrete Elements

Concrete elements are extensively used in urban structures including high-rises, bridges, dams and tunnels, and testing their quality and monitoring their health is of extremely high importance in the industry.
In this project, use of piezoelectric transducers along with wireless communication and memory elements is suggested to overcome the limitations with the current methods. Piezoelectric elements are embedded in a concrete element at the time of construction. They will later generate alternating forces in the concrete and sense the reaction of the elements. This reaction will be used to make conclusions about the health conditions of the concrete elements.
Advanced computer modelling and computational techniques will be adopted to find a correlation between the concrete health conditions and its reaction to force disturbances. The final product will be able to communicate the results with the outside and help the engineers and inspectors know about the quality and lifetime of the structure.

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Faculty Supervisor:

Mehrdad Moallem

Student:

Partner:

Concrete MRI

Discipline:

Engineering

Sector:

Construction and infrastructure

University:

Simon Fraser University

Program:

Accelerate

Planora – Confections d’horaires

Planora est une entreprise qui offre une solution informatique pour
optimiser les horaires de personnels dans une large gamme de secteurs d’activites tels que
les centres de contacts, les systemes de la sante, les services de la protection & des
urgences, les ventes au detail, etc. Cette solution est constituee de deux modules distincts.
O’une part, un module de prevision de la demande et de dimensionnement est utilise pour
trouver Ie nombre d’employes optimal a chaque periode. O’autre part, un module
d’optimisation des horaires est lance a partir du dimensionnement pour construire un horaire
respectant divers lots de contraintes desires par Ie gestionnaire. Le projet consiste donc a
fusionner Ie dimensionnement avec Ie module d’optimisation afin de rendre plus realiste
I’impact d’un manque ou d’un surplus de ressources a une periode don nee lors de
I’optimisation. O’une part, ce projet permettra a I’organisation partenaire d’enrichir et
d’ameliorer la robustesse du modele mathematique et d’autre part, il permettra de faciliter la…

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Faculty Supervisor:

Sylvain Perron

Student:

Partner:

Planora

Discipline:

Business

Sector:

University:

HEC Montréal

Program:

Accelerate

Designing and developing a real-time medical data transfer system for space health usage

In recent years there has been a rapid increase in companies providing rocket launch services for private space travel (SpaceX, Blue Origin and Virgin Galactic to name a few). Access to space by private citizens means that companies will need to collect health information from the private astronauts as well monitor their health while in space. This information will have to be transmitted from space to the ground in a secure manner so that individual health information remains private. Mission Control Space Services (Mission Control) is a company which specializes in the transmission of data from remote sources using satellites. The intern is in the Aerospace Physiology Laboratory which conducts research in astronaut health and will have a demonstration CubeSat orbiting the earth in 2021. They will use their combined information construct a communication system that will be used to transmit secure health data to and from orbit.

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Faculty Supervisor:

Andrew P. Blaber

Student:

Partner:

Mission Control

Discipline:

Life Sciences

Sector:

Manufacturing; Professional, scientific and technical services

University:

Simon Fraser University

Program:

Accelerate

Casting and Production of HT9 for Advanced Reactor Fuel Cladding

In recent years there has been renewed interest in nuclear power for the purposes of combating climate change. ARC Nuclear’s ARC-100 reactor design requires extensive research and development to deploy a prototype. Significant effort is needed to develop the materials to be used in the reactor’s core. HT9 is a specialty steel alloy which has shown some promising results in the past and therefore has been selected for use in the ARC-100. The main objective of this project is to manufacture a batch of HT9 and test its properties, to ensure that it can withstand the environment of a nuclear reactor core. This knowledge will allow ARC Nuclear to make an informed choice: whether they should use HT9 and begin its commercial production; or, pursue different materials instead.

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Faculty Supervisor:

William Cook

Student:

Partner:

ARC Nuclear Canada Inc;CanmetMATERIALS

Discipline:

Engineering

Sector:

Utilities

University:

University of New Brunswick

Program:

Accelerate

Ultrasound image analysis for identifying blood in an existing effusion in knee joint

Ultrasound, an inexpensive, accessible and portable device is gaining popularity in various disease diagnoses. In this project, we aim to analyze the ultrasound images generated for knee joint for diagnosing hemarthrosis (joint bleeding), a common clinical event in patients with severe hemophilia. We aim to analyze the images using machine learning and deep learning techniques. As the major challenge in this case is the scarcity of number of cases with hemophilia, we plan to use one-shot learning techniques, which use neural networks to learn the similarity features between images and need only few training images. The learned model will be deployed in 16 Bit software for diagnosing hemarthrosis in hemophilic patients.

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Faculty Supervisor:

Vassilios Tzerpos

Student:

Partner:

16 Bit

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

York University

Program:

Accelerate

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