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

Caractérisation et amélioration des performances des dispositifs à particules suspendues pour les vitres intelligentes

Les matériaux intelligents chromogéniques sont très importants pour les applications domotiques telles que les fenêtres intelligentes. Ces produits sont essentiels pour les applications d’économie d’énergie telles que les bâtiments durables. Parmi ces matériaux, les matériaux activés électriquement (électrochromes) offrent des avantages tels qu’un fonctionnement simple et linéaire. En comparaison avec les autres dispositifs électrochromes à base d’oxydes de métaux de transition sur substrats de verre, les dispositifs à matrice polymère organique sur substrat souple apportent des solutions beaucoup plus simples, souples et économiques. Des nanoparticules organométalliques photoactives dispersées dans des matrices polymères sont utilisées comme couche active de photocommutation (PS) dans un dispositif à particules en suspension (SPD). De plus, ces dispositifs s’adaptent aux techniques de fabrication à faible coût, telles que la sérigraphie. Malgré des performances plus élevées, les SPD ont reçu beaucoup moins d’attention parmi les chercheurs. D’autres études sur l’amélioration des SPD pour les applications PS en termes de meilleur transfert de charge entre les électrodes et la matrice polymère, une transmission optique plus élevée, des temps de commutation et une consommation d’énergie faibles, et la longévité sont essentielles pour la durabilité et la viabilité commerciale.

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Faculty Supervisor:

Ricardo Izquierdo

Student:

Partner:

1-Material Inc

Discipline:

Engineering

Sector:

Manufacturing

University:

École de technologie supérieure

Program:

Accelerate

Towards 3-D spot recovery in laser diode-based photoacoustic imaging

Photoacoustic (PA) imaging has shown a great potential in cancer detection, tissue characterization, and therapeutic applications. Compact, portable, cheap, and high frequency, but low-power, Laser Diode has been a new venue for PA imaging. Laser diode-based PA imaging can be a supplement for image guidance during prostate studies and nerve or cancer imaging procedure or used for the localization of a needle target. To accurately localize and/or image the spot, a small region of interested is illuminated, and US and PA signals are collected simultaneously. Point source or spot imaging using the conventional US transducer is needed for these image guidance procedures. Preliminary study for the laser spot recovery has been performed in [Moradi et al, SPIE, 2021] using 2-D clinical transducer, in this project, 1) 3-D spot recovery using T-shaped arrays of the prostate transrectal ultrasound transducer is investigated, 2) machine learning algorithms will be employed to increase signal-to-noise ratio of the signal recorded with the laser diodes, 3) multi-spectral PA spot recovery will also be discussed.

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Faculty Supervisor:

Septimiu (Tim) Salcudean

Student:

Partner:

Eindhoven University of Technology

Discipline:

Engineering

Sector:

Education

University:

The University of British Columbia

Program:

Globalink Research Award

Energy-Efficient Precast Concrete Wall and Floor Connections

Developing energy-efficient and durable floor and wall systems is critical to modern precast building construction, particularly with extreme weather conditions in Canada and the associated high costs of heating. This project addresses the structural performance of two components critical to energy efficiency of buildings as a result of their low thermal conductivity, namely double-wythe ultra-high performance concrete (UHPC) walls and cantilevered balcony component of precast concrete floors. Both components incorporate a thermal insulation system, which is sandwiched between the two wythes of the walls, and incorporated as a thermal break at the junction between the balcony and the rest of the concrete floor. Outcomes of this study will assist the industrial partner in introducing and promoting novel precast concrete components that are thermally efficient and structurally sound, as well as establishing a design guide, which is expected to benefit the Canadian construction sector.

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Faculty Supervisor:

Amir Fam

Student:

Partner:

Canadian Precast/Prestressed Concrete Institute

Discipline:

Engineering

Sector:

Construction and infrastructure; Professional, scientific and technical services

University:

Queen's University

Program:

Accelerate

An Automated Object Detection and Tracking through Multi-Modal Image Fusion

This research project will develop an automated maritime object detection and tracking system using fused images from infrared and visible RGB sources. The developed system can be deployed and used in patrol vessels for maritime surveillance. As the system takes advantage of both infrared and visible RGB images, it is capable of running for 24 hours all over the year under various illumination and different weather conditions. By efficiently and effectively detecting and tracking maritime objects, the system will provide timely and accurate information about maritime objects. The partner organization can take this system as the maritime surveillance software solution and attract more investments and collaborations. Overall, the developed system will enhance the maritime surveillance software system and benefit Canadian ocean safety.

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Faculty Supervisor:

Zheng Liu

Student:

Partner:

TerraSense Analytics Ltd

Discipline:

Engineering

Sector:

Agriculture; Professional, scientific and technical services

University:

The University of British Columbia - Okanagan

Program:

Accelerate

Selective metal separation from steel slags and municipal solid waste incinerator ash (MSWIA) by intensified solvent extraction and precipitation

This project tackles heavy metals removal from two end-of-life streams, notably steel slags (SS) and municipal solid waste (MSW). These slags could be valorized and used in cement and concrete materials after eliminating heavy metals and other toxic components. Intensified cost-effective processes have the potential to be green alternatives for extracting and precipitating heavy metals from steel slags and municipal solid waste incinerated ash (MSWIA), using less or no organic solvents saving time and energy, and improving extraction results over traditional separation methods. This research project focuses on valorizing two types of wastes; steel slags (basic oxygen furnace, BOF, electric arc furnace, EAF and Klockner Oxygen Blown Maxhutte process, KOBM) and MSWIA applying process intensification principles.

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Faculty Supervisor:

Daria Camilla Boffito

Student:

Partner:

Carbon Upcycling Technologies Inc

Discipline:

Engineering

Sector:

Construction and infrastructure; Manufacturing; Professional, scientific and technical services

University:

Polytechnique Montréal

Program:

Accelerate

Setting of critical-metal bearing ores in the Betts Cove and Tilt Cove volcanogenic massive sulfide (VMS) deposits, Baie Verte, Newfoundland Appalachians

The green transition requires the sourcing of critical metals like copper, zinc, cobalt, and nickel for use in climate change mitigating technology. This internship will train one Masters-level student to study the geological setting and localization of these metals associated with ancient submarine volcanic rocks in the Tilt Cove and Betts Cove regions of the Baie Verte Peninsula, Newfoundland and Labrador. The work will be aimed at understanding the setting of critical metal ores, what metals are present, how they formed, and use various techniques ranging from field work looking at rocks in the field as well as subsurface core from previous drilling. The work will also involved advanced statistical analysis of the metals present in the mineralization to determine elemental abundances, their relationships, and how the various metals in the ore originated. The work will increase our understanding of the origin of critical metals in submarine volcanic-associated ore deposits and create potential tools that can increase our efficiency and ability to find further sources of these metals in similar settings in Canada and globally

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Faculty Supervisor:

Stephen Piercey

Student:

Partner:

Signal Gold

Discipline:

Earth science

Sector:

Mining; Professional, scientific and technical services

University:

Memorial University of Newfoundland

Program:

Accelerate

Automotive Modular Real-time Edge Computing on Embedded Hardware

As vehicle systems become more advanced, accurate information about vehicle states, parameters, and surrounding operating conditions becomes paramount for vehicle health monitoring and driver control systems. This information is vital for the production, cost, and efficiency of the vehicles, as well as a key to improving passenger safety. In commercial vehicles, not all parameters of interest are directly measurable using sensors, because of sensor costs or design constraints. This research aims to bring developed estimation algorithms to embedded hardware for application to commercial vehicles.

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Faculty Supervisor:

Amir Khajepour

Student:

Partner:

Audesse

Discipline:

Engineering

Sector:

Manufacturing

University:

University of Waterloo

Program:

Accelerate

Develop Cost-effective Road Condition Detection Algorithm for Off-road Trucks

The goal of this project is to develop a more advanced cost-effective algorithm that can detect the road condition. The pressures signal from the pressure transducers installed on the struts of the off-road trucks are acquired and analyzed. The relationship between the road condition and pressure changes will be studied and researched. Based on the study and research, a road condition monitoring data model can be developed, and bad road condition event concept can be developed and defined. The data model can be implemented in a real-time mobile controller. Implementation of the data model (algorithm) can report an event of bad road condition. The bad road condition event can be sent to management control center via a wireless communication. Mining management team can dispatch maintenance equipment to repair the section of the bad road on time

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Faculty Supervisor:

Simon Yang

Student:

Partner:

ACCA Solutions Ltd

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of Guelph

Program:

Accelerate

Research and Design of In-Memory Database for Vehicles’ Telematics Based on IoT Big Data Environment

Road safety affects everyone, not just Geotab customers. With several years of driving and environmental data from over 2 million vehicles, we have an opportunity to make our customers safer, as well as our communities and cities. To reduce accidents, we need to understand both the driving behavioral patterns that are predictive of accidents and the environmental factors involved. Thus, the data infrastructure should be capable of processing a series of real-time video and telematics data, as well as time-series historical records generated from existing machine learning models to respond to real-world incidents with low latency. However, data communication between the data warehouse and the models/applications is inefficient, an in-memory database is then considered to improve the data communication efficiency. Due to the nature of driving data that it is changing all the time, the structure of the database should be carefully designed and analyzed.

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Faculty Supervisor:

Ben Liang

Student:

Partner:

Geotab Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services; Transportation and warehousing

University:

University of Toronto

Program:

Accelerate

La qualité des emplois d’économie circulaire dans le contexte des entreprises d’économie sociale du Québec

Dans le cadre de l’e?tude sur les synergies entre e?conomie sociale et e?conomie circulaire, la question de l’emploi se pose ine?vitablement. En effet, la circularisation des chai?nes de valeur industrielles transforme le nombre et la nature des ro?les de ses acteurs. Toutefois, si la litte?rature s’accorde pour dire que cette mutation structurelle sera cre?atrice d’emplois, la question de leur qualite? se pose. Ainsi, ce projet s’interesse aux spe?cificite?s des emplois circulaires dans les entreprises d’e?conomie sociale en termes de bien-e?tre, de sante? et de se?curite?, de stabilite? et de de?sirabilite?, et de compe?tences. Il vise ultimement à identifier, comprendre et partager les bonnes pratiques liées à l’emploi dans les chaînes de valeur circulaires.

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Faculty Supervisor:

Sophie Bernard

Student:

Partner:

Territoires innovants en économie sociale et solidaire

Discipline:

Business

Sector:

Professional, scientific and technical services

University:

Polytechnique Montréal

Program:

Accelerate

Using machine learning to allocate stratified care in an electronic cognitive behavioural therapy program for depression

Depression is a leading cause of disability worldwide, yet only one third of patients with depression receive care. This is because the traditional in-person format of mental health care delivery can be inaccessible, inefficient, and expensive. Electronic cognitive behavioural therapy (e-CBT) has been shown to be an effective solution to expand care access, efficiency, and affordability. Combining depression-based e-CBT with artificial intelligence, this study aims to develop an effective decision-making model that matches an individual’s needs with the right amount of care. Our goal at OPTT is to develop a platform that makes e-CBT more efficient and highly accessible to individuals facing mental health issues and this project will use artificial intelligence and tailored interventions to efficiently allocate mental health care resources. At a time when mental health challenges are on the rise due to the pandemic, this technique could scale up care capacity without sacrificing the quality of care.

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Faculty Supervisor:

Nazanin Alavi

Student:

Partner:

OPTT

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology; Information and cultural industries

University:

Queen's University

Program:

Accelerate

Design and optimization of a solar-air heating system for window applications

The increased pressure of natural resource depletion and environmental issues have largely promoted the search of renewable energy sources such as solar energy. Solar energy can be converted to electric power through photovoltaics (PV), but this requires PV panels and the latter have efficiencies as low as 20%. An alternative way is solar heating systems that use air or fluid to transfer the heat energy from solar irradiation to the indoor environment with a much higher energy conversion efficiency (70%). However, current solar thermal applications have not yet targeted residential and commercial window applications -although these contribute to the largest portion of heat loss in buildings. Aiming at developing more energy-efficient solar-thermo products, Innovative North and University of Guelph are in partnership to develop a novel solar air heating system for window applications. In particular, the solar air heating system will be optimized regarding material selection, weight reduction, and manufacturability as well as integration with venting and control systems with maximized energy-efficiency and lower production cost. A systematic approach will be adopted for the design – that is, mechanical, electrical, and manufacturing aspects of the design will be addressed by the proposed partnership. The proposed project will directly address

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Faculty Supervisor:

Sheng Yang

Student:

Partner:

Innovative North

Discipline:

Engineering

Sector:

Construction and infrastructure

University:

University of Guelph

Program:

Business Strategy Internship

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