Innovative Projects Realized

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

29670 Completed Projects

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Projects by Category

Application of machine learning techniques to control surface quality of as-printed wire arc additive manufactured components

Nowadays, the wire arc additive manufacturing is making its path toward providing benefits to aerospace, defense, and oil and gas sectors, ascribed to the process capacity to fabricate components with minimum waste of material and lead time. However, the main challenges associated with the WAAM that have hindered the wide-spread application of the technology include the irregular and random quality of the WAAM fabricated surfaces. The mission of this project is to control aforementioned irregularities in fabrication by implementing machine learning-based algorithms and modify the process parameters to achieve a defect-free part with high surface quality. The anticipated trained machine learning method in this project will foster the progress toward completion of an autonomous in-situ defect recognition and correction (AIDRAC) system, which is the primary goal of the intern.

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Faculty Supervisor:

Ali Nasiri

Student:

Partner:

Springboard Atlantic Inc.

Discipline:

Engineering

Sector:

Technology; Manufacturing and Construction; Advanced Manufacturing

University:

Memorial University of Newfoundland

Program:

Accelerate

Discover anomaly signatures from time series data of telecommunication networks

Failures in a telecommunication network harm the communication quality. Once happened, if the system cannot solve it by self-healing, such anomaly may even result in serious problem and result in massive economic loss. In this project, we will design and develop a system to predict these failures in advance using the status values of the hardware facilities. Our goal is to build a completed data processing, model building and training system to predict facility failures automatically for production-level deployment with strict evaluation criteria (precision > 80%, which means for all the positive prediction our model gives, at least 80% of them is correct; recall > 10%, which means for all real failures in the network, our model could predict at least 10% of them). The success of this project will expand Ciena’s capability to develop superior products for anomaly prediction services.

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Faculty Supervisor:

Yan Liu

Student:

Partner:

Ciena Corporation (Ottawa, ON)

Discipline:

Computer science

Sector:

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

University:

Concordia University

Program:

Accelerate

Élaboration d’un modèle théorique valide à la base d’un moteur intelligent pour le développement professionnel en formation.

Le projet vise à développer un dispositif intelligent permettant l’autodiagnostic et l’auto-orientation de personnes formatrices qui souhaitent développer leurs compétences professionnelles et plus spécifiquement leurs compétences dans l’usage du numérique pour enseigner et apprendre. Ce dispositif innovant s’appuyera sur un moteur intelligent rigoureux fondé sur un cadre théorique en éducation validé. La stagière contribuera à la validation scientifique du modèle théorique ainsi qu’à sa mise en application dans un contexte d’autoformation. Ce projet permettra de donner une force théorique et scientifique aux développements informatiques en cours, de consolider les interactions à la base d’un robot intelligent ainsi que d’augmenter la valeur commerciale des développements et des produits de l’entreprise.

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Faculty Supervisor:

Florian Meyer

Student:

Partner:

Optania Solutions Inc

Discipline:

Sociology

Sector:

Professional, scientific and technical services

University:

Université de Sherbrooke

Program:

Accelerate

Mechanistic study of tellurium/carbon cathode in liquid and solid-state lithium-tellurium batteries

Lithium-tellurium (Li-Te) batteries provide higher volumetric energy density than current lithium-ion batteries and are considered as one of the most promising energy storage technology for emerging applications in electric vehicles, implantable medical devices, and Internet of things. The development of stable tellurium/carbon (Te/C) cathodes is the key towards durable Li-Te batteries. However, the reaction mechanism of Te/C with Li ions remain unknown due to complexity involved from the porous carbon, pore size, and the type of electrolytes, limiting the design of practical Li-Te batteries with high energy and power densities. This collaborative project between the University of British Columbia (Canada) and National Cheng Kung University (Taiwan) will fill this knowledge gap by using advanced in-situ characterization techniques to track real-time reactions between Te/ C and Li ions. The new knowledge obtained in this project will not only advance fundamental understanding on solid-state chemistry, but also accelerate the development and commercialization of Li-Te batteries for practical applications .

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Faculty Supervisor:

Jian Liu

Student:

Partner:

National Cheng Kung University

Discipline:

Engineering

Sector:

Clean Technology; Nanotechnology; Green/Alternative Energy

University:

The University of British Columbia - Okanagan

Program:

Globalink Research Award

Investigation of Water-in-Oil Emulsion on CSI Solvent Dissolution and Ex-solution Performance for Heavy Oil

This research work will establish a systematic workflow for analyzing transient equilibrium foamy oil phase behavior by coupling the CCEC tests, depletion rate and presence of water-in-oil emulsion which are seldom performed for heavy oil. It will provide a strong connection and comparison with previously studies which was conducted in the absence of water-in-oil emulsion. It will create a strong connection between phase behavior with fluid properties, operating conditions and kinetics. A concrete database which contains large amount of laboratory transient equilibrium data for selected heavy oil reservoirs can be established. The effects of different content of water-in-oil emulsion on the heavy oil production performance will be evaluated.

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Faculty Supervisor:

Na Jenna Jia

Student:

Partner:

Petroleum Technology Research Centre

Discipline:

Engineering

Sector:

Mining; Professional, scientific and technical services

University:

University of Regina

Program:

Accelerate

Promoting Gender Equality through Social Innovation

From women-only taxi companies in New Delhi to smokeless stoves in Uganda, innovation can transform the lives of women and girls around the globe. While it is well known that social innovation and women’s empowerment are each processes that drive change, there is little research to date connecting social innovation to the empowerment of women and girls. This project will identify how social innovation connects to women’s empowerment through a partnership with MATCH International. This project will support MATCH through the launch, development and growth of the Women’s Fund for Social Innovation. As the first of its kind in Canada, the fund will invest in innovation projects proposed by women and girls in the global South.

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Faculty Supervisor:

Bipasha Baruah

Student:

Partner:

Match International

Discipline:

Sociology

Sector:

Other services (except public administration)

University:

Western University

Program:

Accelerate

Effects of Partial Shading on Bifacial Photovoltaic Modules

This research project will analyze the effects of partial shading, which photovoltaic modules are subject to. The objective of this research is to elucidate the mechanisms of degradation of partial shading. Investigations in photovoltaic systems will involve the collection and analysis of production data. Measurements of the individual I-V curve of the modules of a tracker will be performed. Through these results it will be possible to identify if any module will present degradation and eventually correlate them with its location. In conclusion, it is hoped that this research project will enable the exchange of knowledge and experiences between research groups and, ultimately, contribute to research and innovation in the photovoltaic sector.

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Faculty Supervisor:

Maxime Darnon

Student:

Partner:

Universidade Tecnológica Federal do Paraná

Discipline:

Engineering

Sector:

Education

University:

Université de Sherbrooke

Program:

Globalink Research Award

AR Marine Visualization System

A sailor needs to obtain real-time information about the status of the sailboat and also derive an accurate approximation of the behaviour of the surrounding environment, in order to safely and successfully sail a boat. We also aim to investigate how providing vital knowledge using an immersive approach will affect the cognitive load on the sailor, thus potentially enhancing efficiency and safety at sea.

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Faculty Supervisor:

Bruce Kapron

Student:

Partner:

Springboard Atlantic Inc.

Discipline:

Computer science

Sector:

Automotive; Technology; Water

University:

University of Victoria

Program:

Accelerate

Data Markets for Blockchain-based Federated Learning in Health Care

Artificial intelligence (AI) is a promising technology for patient-centric health care, for example, to diagnose diseases, or to recommend therapies. However, deploying AI in health care is challenging. The creation of AI systems typically requires large amounts of data, but health data is kept private due to high privacy requirements. Furthermore, AI in health care should not only benefit majorities in society but also benefit underrepresented groups. Toward this end, this research project aims to combine federated learning with blockchain to develop responsible AI for health care. Federated learning enables the creation of AI systems without disclosing private data. A blockchain infrastructure enables a transparent and fair information system. Furthermore, a blockchain enables a data marketplace through a token economy, where individuals are financially incentivized for sharing unique data for the creation of AI systems. The goal of the project is to demonstrate a practical implementation for responsible AI in health care, and its benefits. Ultimately, it can transform the Canadian health care system.

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Faculty Supervisor:

Ivan Beschastnikh

Student:

Partner:

Karlsruher Institut für Technologie

Discipline:

Computer science

Sector:

Education

University:

The University of British Columbia

Program:

Globalink Research Award

Machine Learning for Turbulence Modelling

Machine learning has revolutionized a variety of fields in the past decade, due to increasing availability of data and processing power. Engineering simulations of most industrially relevant fluid flows (e.g aircraft design and turbomachinery) require modelling of turbulent fluctuations in the flow. For the turbulence modelling community, which has seen widespread stagnation, machine learning offers a clear path to improve model accuracy, estimate uncertainty, and develop new data driven models. While the potential for machine learning in the field of turbulence modelling is clear, the number of thorough investigations is limited. This project involves a detailed investigation into improvements and testing of a neural network architecture for turbulence modelling, and implementation of this method in a practical engineering simulation setting. The project represents a critical leap forward in revolutionizing the field of turbulence modelling augmentation by machine learning.

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Faculty Supervisor:

Fue-Sang Lien

Student:

Partner:

University of Manchester

Discipline:

Engineering

Sector:

Education

University:

University of Waterloo

Program:

Globalink Research Award

Automatic Species Identification in Underwater Environments

Knowledge of the geographic distribution and identification of species is essential for the conservation of biodiversity. With advances in technology and greater accessibility of equipment capable of recording underwater, it was possible to obtain data efficiently. However, it leads to an immense volume of information collected, which requires exhaustive manual processing that requires label, time and money. That is why the creation of tools capable of assisting in the monitoring of these species is so important. In this project, we propose to fill a gap in the literature on the development of species monitoring systems in underwater environments. The proposed approach aims not only to identify the existing species in the training set, but also to be able to identify new species that can be registered in that environment. We intend to explore several methodologies using clustering, dissimilarity and convolutional neural networks. We will also present a new set of public data from high resolution underwater videos that will be available to the scientific community.

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Faculty Supervisor:

Alessandro Lameiras Koerich

Student:

Partner:

Federal University of Parana

Discipline:

Computer science

Sector:

Artificial Intelligence; Ocean Tech; Life Sciences (not health)

University:

École de technologie supérieure

Program:

Globalink Research Award

Enhancing climate change resilience in viticulture using information in long-term records

Winegrape phenology (the timing of seasonal events – leafout, flowering, harvest) has traditionally been an important tool for winegrowers to plan vineyard management. With warming, however, winegrape phenology has advanced significantly, impacting the type and quality of wine different vineyards can produce. How large the impact of climate change has been, and will be, depends on several factors including which winegrape varieties (or cultivars, such as Pinot noir or Syrah) a vineyard has planted. Shifts in phenology could change the suitability of varieties currently planted, but we have data on only a few widely grown varieties. We propose to use long-term records from the Unité Expérimentale du Domaine de Vassal, a French research vineyard, to examine how climate change affects winegrape phenology of roughly 200 varieties. This unique diversity of varieties will allow us to include typically understudied varieties in our research and help develop tools and information for winegrowers to adapt their vineyards to climate change.

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Faculty Supervisor:

Elizabeth Wolkovich

Student:

Partner:

INRAE Occitanie-Montpellier Research Centre

Discipline:

Life Sciences

Sector:

Agriculture and Food; Sustainability & the Environment; Life Sciences (not health)

University:

The University of British Columbia

Program:

Globalink Research Award

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