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

Multiproduct production routing problem under vehicle capacity uncertainty

This project aims at providing new and tractable models and solution procedures to optimize production, inventory, distribution and routing decisions simultaneously. This type of problem, commonly called Production Routing Problem (PRP), is especially important in the context of Vendor Managed Inventory (VMI), in which the supplier manages the inventories of retailers and decides on the quantities of replenishment. The proposed approaches will account for the uncertainty in the vehicle capacity, a setting that has not been studied yet. Moreover, a case study will be performed. We expect to provide the partner organization with models and tools to solve related problems in the field of operations research, which can potentially provide multiple benefits, such as better resource utilization, better service and product quality, and increase in profits.

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Faculty Supervisor:

Michel Gendreau;Walter Rei

Student:

Partner:

Conseil 2.0

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Polytechnique Montréal

Program:

Accelerate

Développement d’une méthodologie d’analyse en temps réel de l’état des connexions boulonnées d’un simulateur de vol

Les simulateurs de vols sont devenus des instruments indispensables pour former les pilotes et membres d’équipages. Le leader mondial dans la conception de ces simulateurs est CAE, une entreprise Canadienne comptant plus de 10 000 salariées. Cependant, afin de garder une longueur d’avance sur ses concurrents, il est nécessaire de constamment ajouter de nouvelles fonctionnalités aux simulateurs et de rester à la pointe de la technologie.
Pour cette raison, CAE souhaite développer un système permettant de prévoir à l’avance l’endommagement des connexions boulonnées de ses simulateurs et d’informer l’utilisateur qu’une opération de maintenance est nécessaire. Cette fonctionnalité, unique aux simulateurs de CAE, permettra d’éviter des oublis de maintenance, donc de rallonger la durée de vie des simulateurs.

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Faculty Supervisor:

Alain Batailly;Annie Ross

Student:

Partner:

CAE

Discipline:

Engineering

Sector:

Aerospace; Health and Related Sciences & Technology

University:

Polytechnique Montréal

Program:

Accelerate

Auditory spatial and memory processing in the blind

Early loss of vision is accompanied by widespread cross-modal changes in the brain, in that ‘visual’ areas of the brain show responses to nonvisual stimuli. This raises the possibility of using the reorganization associated with vision loss to investigate how auditory events may be encoded and retrieved from memory. The proposed project will examine the relationship between vision loss and memory and spatial auditory processing, using both behaviour and imaging methodology. In part 1, we will recruit congenitally blind participants and age-matched controls to an internet-based study in order to assess their memory for sound objects and sound location. In part 2, we will test a subset of congenitally blind individuals on their auditory memory and spatial abilities and relate their performance to previously acquired structural and functional data obtained from these participants. Results from these studies will offer a refined and more general description of brain plasticity in congenitally blind individuals and should provide key features of organizational processes.

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Faculty Supervisor:

Morris Moskovitch

Student:

Partner:

University of Oxford

Discipline:

Life Sciences

Sector:

Life Sciences (not health); Technology; Health and Related Sciences & Technology

University:

University of Toronto

Program:

Globalink Research Award

A Deep Learning Method for Detecting Defaults on Assembly Line

With the advancement of methods based on artificial-intelligence, computer vision and deep learning, activities concerning progress monitoring, safety management, and quality control can be automated that leads to saving in time and cost. With the collaboration with partner industry, computer-vision approaches are going to be employed in order to find an improved method that utilizes new machine learning techniques to detect defaults on prefabricated production line. The results will help with improving the quality level of products and reducing potential project risks. The area is relatively young; especially for construction industry; and a lot of R&D works need to be done in order to increase the efficiency of the current procedures. As the market for AI-based quality monitoring tools is rapidly growing, the results of this study will greatly benefit construction industry by offering a possible improved procedure to detect quality issues in production line.

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Faculty Supervisor:

Ali Motamedi;Daniel Forgues

Student:

Partner:

Groupe Canam

Discipline:

Engineering

Sector:

Construction and infrastructure; Manufacturing

University:

École de technologie supérieure

Program:

Accelerate

Multimodality thermal cancer treatment using targeted liposomes and temperature monitoring using photoacoustics and ultrasound

The problem of cancer chemotherapy is the severe toxic side effects on healthy tissues. The use of liposomes as a chemotherapeutic drug carrier can accumulate at the tumour site, but the timely release of the drug has remained as a problem. Liposome systems that respond to hyperthermal temperature is one of the approaches for triggered release of drugs from liposomes. The combined use of drug-containing thermosensitive liposomes and localized heating methods can selectively deposit drugs in the heated region. The effectiveness of the combined treatment will be based on the liposome efficiently releasing the drug load at the target while being able to heat the target at a predefined temperature and period. The technical development from this project will be a platform for CancerRx’s future commercialization, and the developed thermometry function can be added to VisualSonics’ VEVOLAZR system to expand the market need.

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Faculty Supervisor:

Michael Kolios;Jahan TAVAKKOLI;Carl Kumaradas

Student:

Partner:

FUJIFILM VisualSonics;Cancer Rx

Discipline:

Physics

Sector:

Life Sciences (not health); Nanotechnology; Health and Related Sciences & Technology

University:

Toronto Metropolitan University

Program:

Accelerate

Real world testing of online cognitive training and rehabilitation programs

With an aging population on the rise, the prevalence of cognitive decline is expected to increase substantially. Goal Management Training® (GMT) and the Memory and Aging Program® (MAP) are cognitive interventions that have been studied extensively and applied clinically to address these needs. Although previous research has demonstrated efficacy of the in-person versions of both MAP and GMT, significant barriers exist in the utilization of these programs. In light of these challenges, online versions of MAP and GMT have recently been developed. The current project aims to evaluate if online versions of MAP and GMT are successful at improving cognitive functioning, knowledge about normal cognitive aging, engagement in lifestyle behaviours/strategies to mitigate cognitive changes and reducing intention to seek unnecessary medical attention for memory-related concerns. Findings will be used for knowledge dissemination and will aid in the commercialization of these evidence-based online cognitive interventions.

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Faculty Supervisor:

Alexandra J Fiocco

Student:

Partner:

Cogniciti

Discipline:

Life Sciences

Sector:

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

University:

Toronto Metropolitan University

Program:

Accelerate

Improve workplace wellbeing using AI and organizational behavior software platform

behaviours within a given organization. This will allow to remedy and/or remove the counterproductive, and to enhance and enable positive behaviours, civility and engagement. The platform that will be developed is unique and distinctly not social feedback nor 360-degree feedback. The cloud-based systems that will be developed should detect, measure, map, correct, and sustain the quality of interactions and culture among members of an organization or a work group. We will define, guide and integrate artificial intelligence (AI) approaches to enhance the system functionalities for healthy work environments, and organizational remediation of counterproductive and bad behaviours (e.g. harassment, bullying, intolerance, discrimination, targeting, etc.).

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Faculty Supervisor:

Nizar Bouguila

Student:

Partner:

Beslogic

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Concordia University

Program:

Accelerate

Free-Piston Direct-Drive Thermal Engine for Waste Heat Applications

The research project proposed here takes a radically new approach to on-vehicle electric power generation, with the potential for developing a scalable, flexible, and conceptually very efficient method without the need for complex mechanical off-take. The new method is called the Free-Piston Direct-Drive Linear Generator, a type of Rankine Cycle expansion engine. Excess thermal energy is used to heat a fluid via a heat exchanger, and the energy in this high-pressure and high-temperature fluid is then converted into work via the free-piston expansion engine. A Free-Piston Expander (FPE) provides this approach with flexibility and a very broad dynamic range of operating points. Unlike a more familiar reciprocating piston, there is no connecting rod or crankshaft; the piston trajectory is completely unconstrained and key parameters impacting thermodynamic performance, such as expansion ratio (ER) can be varied over an enormous range in real-time. Furthermore, by optimizing the piston trajectory (position versus time) in each stroke, it is possible to smooth the power output to nearly steady conditions. The FPE embodiment of the Rankine cycle expander is likely to be optimal in the range of 10-1000kW.

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Faculty Supervisor:

Luis Rodrigues;Charles Basenga Kiyanda;Chunyan Lai;Pragasen Pillay

Student:

Partner:

NovoPower International

Discipline:

Engineering

Sector:

Manufacturing; Utilities

University:

Concordia University

Program:

Accelerate

Étude comparative de points d’opérations sans charge des turbines hydrauliques à l’échelle modèle – Partie 2

Le projet réalisé dans le cadre de stages Mitacs vise à renforcer et augmenter les connaissances sur la dynamique de l’écoulement traversant les turbines hydrauliques de type Francis lorsqu’elles sont opérées en régimes hors production (sans charge). Ces conditions d’opération de plus en plus fréquentes et faisant partie intégrante des régimes transitoires, telles que les arrêts ou démarrages de turbine, sont parmi les plus dommageables pour les turbines. Donc, améliorer la compréhension permettra, à long terme, d’allonger la durée de vie des machines, d’augmenter leur fiabilité et de réduire les coûts d’opération. L’association des connaissances et compétences de l’Université Laval et Andritz Hydro permettra d’approfondir les connaissances sur les diverses réponses des machines aux opérations sans charge en présence ou non de cavitation.

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Faculty Supervisor:

Claire Deschênes;Sébastien Houde

Student:

Partner:

ANDRITZ Canada Inc.

Discipline:

Engineering

Sector:

Energy and Utilities; Green/Alternative Energy; Sustainability & the Environment

University:

Université Laval

Program:

Accelerate

Deep Neural Networks for applications in public safety

Deep Neural networks have revolutionized machine learning and in particular computer vision. The revolution was achieved by a combination of big data, graphical processing units and advances in numerical optimization. In this work we propose to extend and develop machine learning techniques, focusing on deep learning methods for public health and safety applications. We will use and extend deep learning methodology to deal with 3D seismic and electromagnetic data for signals that are emitted for public safety

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Faculty Supervisor:

Eldad Haber

Student:

Partner:

Xtract AI

Discipline:

Earth science

Sector:

Professional, scientific and technical services

University:

The University of British Columbia

Program:

Accelerate

Development of a Methane Flux Sensor

This project supports development of a laser-based sensor capable of accurately measuring methane releases from oil and gas wells, an important source of greenhouse gas emissions. This sensor will lead to a more accurate understanding of greenhouse gas emissions and enable mitigation actions in the upstream oil and gas industry. This is a critical step in developing effective climate change policy which affects all Canadians. By supporting this project, INO wants to ensure the technological and commercial viability of this important new sensor. Success in these objectives will give Canadian industries rapid access to a state-of-the-art tool enabling them to quantify and reduce methane emissions, which is critical to helping Canada meet its stated commitments to reducing oil and gas sector methane emissions by 40–45% by 2025.

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Faculty Supervisor:

Matthew Johnson

Student:

Partner:

National Optics Institute (QC)

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Carleton University

Program:

Accelerate

Non-state governance of violence: a comparative analysis of two communities in the South Zone of Recife, Brazil

The objective of this project is to compare and analyse the forms of non-state governance of violence
in two poor communities in the southern region of Recife, Northeast Brazil. Although they are close
to each other and have similar structural contexts, each of these communities has specific governance
arrangements with disparate day-to-day consequences. The working hypothesis is that, in contexts of
poverty and inequality and selective action by police organisations, societal patterns emerge that
favour the consolidation of non-state forms of governance of violence. In this way, this project aims to amplify knowledge of violence prevention programmes focusing on the specificities of urban poverty areas.The biggest challenge for the public intervention aimed at in this project to occur, is to directly establish a constructive dialogue between those with institutions of repression and the justice system with the residents of these communities.

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Faculty Supervisor:

Karen Schwartz;Jean Daudelin

Student:

Partner:

Universidade Federal de Pernambuco

Discipline:

Sociology

Sector:

Education

University:

Carleton University

Program:

Globalink Research Award

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