Innovative Projects Realized

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

13270 Completed Projects

1072
AB
2795
BC
430
MB
106
NF
348
SK
4184
ON
2671
QC
43
PE
209
NB
474
NS

Projects by Category

10%
Computer science
9%
Engineering
1%
Engineering - biomedical
4%
Engineering - chemical / biological

Energy management and optimization of airport terminals with the focus on green and net-zero energy solutions

This project is a collaboration between Vantage Airport Group Ltd. and Dr. Krishna Vijayaraghavan research group at SFU to identify best current sustainable practices in the airport industry. The focus of this project is on the economic and environmental aspect of sustainability, and this research aims to identify the best terminal and hub design practices that can minimize the energy consumption with the focus on green and net-zero energy solutions. The ultimate goal of this project is to contribute to Vantage’s long-term efforts to develop and operate airport terminals with a minimum total cost of ownership and environmental impact by identifying existing best practices in airport terminals design and operations. Since 1994 Vancouver-based Vantage Airport Group has developed or operated 31 airports around the world and currently manages ten assets in Canada, the US, and overseas. For development projects, Vantage typically teams up with investors, architects, and builders and contributes to the project as an experienced airport operator and owner representative.

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Faculty Supervisor:

Krishna Vijayaraghavan

Student:

Sasan Ebrahimi

Partner:

Vantage Airport Group Ltd.

Discipline:

Engineering - mechanical

Sector:

University:

Program:

Accelerate

Temporally consistent employee group labels

Analytical applications in large organizations across even intermediate time ranges are often made complex, costly or even impractical due to temporal inconsistencies in the available data. The ever-changing nature of organizations causes categorical labels in data to change over time. This is particularly true for HR data, as the organization adjusts to changes in skillsets, market and operations. This project aims at establishing automated methods of defining consistent employee group labelling across time. Such consistent labels will allow organizations to make better organizational decisions as more historical data becomes available for analysis.

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Faculty Supervisor:

Daniel Coombs

Student:

Rebeca Cardim Falcão

Partner:

Visier Solutions Inc

Discipline:

Mathematics

Sector:

Information and cultural industries

University:

Program:

Accelerate

Revitalizing Indigenous traditional management of salmon – evaluating risk, opportunities and needs for re-emergence of traditional terminal fisheries

First Nations have fished for salmon in British Columbia for more than 10,000 years. Traditionally, many First Nations fisheries were conducted using weirs – fences constructed in the river – or stone fish traps, and these traditional technologies were used for thousands of years to manage and harvest salmon. However, with the arrival of commercial fishing in British Columbia, these traditional technologies were banned under the Fisheries Act, and salmon fishing is now primarily conducted in marine waters, targeting hundreds of co-migrating populations. With declines in abundance of many wild salmon stocks, conservation concerns for at-risk populations has led to reductions in fishing opportunity in First Nations subsistence, commercial, and sport fisheries. Our project will provide scientific analysis and data collection to support the Heiltsuk Nation as they work to rebuild a traditional terminal fishery for salmon in the Kunsoot River. TO BE CONT’D

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Faculty Supervisor:

Natalie Ban

Student:

William I Atlas

Partner:

Qqs Projects Society

Discipline:

Environmental sciences

Sector:

University:

Program:

Accelerate

New Catalysts and Chemical Processes for the Pharmaceutical Industry

Synthetic chemistry is used to assemble the small organic molecules that comprise many of the important consumer products used by society every day. Such fine chemicals are used to assemble drugs, agrochemicals, electronics, materials, dyes, etc.. Metal catalysts are part of the arsenal that the synthetic chemist uses to make such prized small molecules and Total Synthesis Ltd. (TSL), the sponsor of this research, is a globally recognized leader in catalyst invention and sales. The interns on this project will work in the company lab to develop methods to prepare palladium-based catalysts at various scales and to evaluate these catalysts in new chemical transformations being invented in the academic lab. TO BE CONT’D

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Faculty Supervisor:

Jeffrey Manthorpe

Student:

Nicholas Andrella;Nalin Chandrasoma;Uttam Kumar Das;Mathieu Morin

Partner:

Total Synthesis Ltd

Discipline:

Chemistry

Sector:

Manufacturing

University:

Program:

Accelerate

Computational and Experimental Evaluation of Leading-Edge Droops for Performance Enhancement of Wind Turbine Rotors

Wind power is becoming an important source of clean electricity. However, wind turbines operating at low wind conditions suffer from reduced efficiency and reduced electricity generation. Leading-edge droops are small extensions added to the front of the blade that can potentially improve the turbine efficiency under low wind speeds. The Mitacs project will investigate how aviation-inspired leading edge droops affect the aerodynamic characteristics of wind turbine blades. The research will use computer simulations to evaluate the effect of droops on the performance of various wind turbine families and to optimize the droop geometry for a selected family. The findings from the computer simulations will be compared to field measurements of droops added to a small-scale wind turbine.

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Faculty Supervisor:

Joshua Brinkerhoff

Student:

Saeed Nazari

Partner:

Harrison Blade Solutions

Discipline:

Engineering - other

Sector:

Manufacturing

University:

Program:

Accelerate

Interior Construction Progress Monitoring Using Computer Vision and BIM

Progress monitoring is an essential task in all construction projects. A proper progress monitoring can minimize the level of project overruns, which is very common among construction projects but incurring significant loss to public and private funds annually. Manual methods of progress tracking are too infrequent to represent continuous and live insights about the workplace. Recent advancements in the area of computer vision and machine learning inspired researchers to use these techniques for development of new automated progress monitoring systems. The main objective in these types of research is to use different types of cameras, and by capturing videos or photos, monitor and measure the progress during construction. Reaching to this objective becomes more complicated when the construction occurs indoor and many objects obstruct and confuse the vision of cameras. TO BE CONT’D

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Faculty Supervisor:

Brenda McCabe

Student:

Seyedfarid Mirahadi

Partner:

Indus.Ai

Discipline:

Engineering - civil

Sector:

Information and cultural industries

University:

Program:

Accelerate

Development of a human immortalized FSHD cell line to study the epigenetic targeting of DUX4 to treat FSHD

Facioscapulohumeral muscular dystrophy (FSHD) is a debilitating disease resulting from the loss of skeletal muscles mainly in the face, shoulder blades and upper arms. FSHD results from expression of the protein DUX4
which causes skeletal muscle cell death and therefore loss of muscles. This project aims to immortalize isolated skeletal muscle cells from patients with FSHD and produce long-lived cells that can be used to screen
pharmaceutical compounds for their ability to inhibit the expression of DUX4 and therefore treat FSHD. The completion of this project will result in a new tool to advance Resverlogix’s efforts in FSHD drug development.

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Faculty Supervisor:

Robin Yates

Student:

Rhiannon Campden

Partner:

ResVerlogix

Discipline:

Biochemistry / Molecular biology

Sector:

University:

Program:

Accelerate

Validation of Cartilage Segmentation Accuracy using Deep Learning

Osteoarthritis is a degenerative disease that affects the majority of seniors and costs the Canadian economy > $30 Billion/year. The hallmark of osteoarthritis is damaged cartilage. Our understanding of how and why cartilage degenerates is not well understood. In order to identify how to stop or prevent cartilage degeneration we must have accurate and reproducible ways to measure cartilage health. The industry partner NeuralSeg has developed an algorithm that utilizes a form of artificial intelligence to measure cartilage health. The proposed research project consists of two phases. Phase one identifies the accuracy of the analysis performed by the algorithm. Phase two identifies how reproducible the measures of cartilage health are. These benchmarks are a necessary step before this technology can be used for research and eventually be deployed to the clinic.

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Faculty Supervisor:

Monica R. Maly

Student:

Anthony Gatti

Partner:

NeuralSeg Ltd.

Discipline:

Other

Sector:

University:

Program:

Accelerate

Value Engineering Feasibility Study for System Controller Chassis/Connector Redesign

Electronic assemblies are used to control various systems in an aircraft. Under normal operating conditions, these undergo vibration, and therefore have an expected life span. Different designs are analyzed to reduce production cost, and these designs must ensure that the electronic components contained within the hardware can tolerate the same operating conditions without failure. With time continuous research projects are being conducted to produce products with the same quality and lower costs, and this is one of them.

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Faculty Supervisor:

Kamran Behdinan

Student:

Youssef Makarious

Partner:

Honeywell Aerospace

Discipline:

Engineering - mechanical

Sector:

Manufacturing

University:

Program:

Accelerate

Development of a New Test Method to Evaluate the Impact of Curing on the Near-surface Chloride Penetration Resistance of Concrete

The rate at which chlorides from deicer salts penetrate into concrete towards the reinforcing steel has a strong influence on the time-to-corrosion and service life of concrete structures. Thus, the permeability of the concrete cover layer protecting the reinforcement has to be minimized especially in severe exposure conditions. In addition to the type of concrete, the permeability of the concrete cover is influenced by early-age curing (keeping the concrete warm and wet to maintain cement hydration that fills in pores). There are currently no standard test methods for evaluating the impact of curing on surface durability and resistance to chloride ingress, so the purpose of this research is to develop a test method that can allow precast concrete manufacturers and owners of structures to determine adequate curing regimes. This will help prevent premature corrosion of steel reinforcement in concrete structure

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Faculty Supervisor:

Douglas Hooton

Student:

Majed Karam

Partner:

Canadian Precast Prestressed Concrete Institute

Discipline:

Engineering - civil

Sector:

Construction and infrastructure

University:

Program:

Accelerate

Evaluation and optimization of a mine water treatment system

Currently, mine water treatment systems within the Sydney Coalfield extract and treat mine water from depth with the aim to gradually ‘flush’ the mine pools of its acid-generating products and achieve good water quality over the long-term. However, since the deep, lower quality mine water is always being treated, significant annual operational costs (>$1 million) are being incurred. This project will evaluate the hydrodynamics and hydrogeochemistry of the mine pools and investigate treatment approaches that instead focus on the shallow, higher quality mine water. It is envisaged that this project may lead to a more efficient and cost-effective approach to treat mine water, both within the Sydney Coalfield, and throughout other mining regions throughout Canada.

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Faculty Supervisor:

Christopher Power

Student:

Patrick Merritt

Partner:

CBCL Ltd.

Discipline:

Engineering - civil

Sector:

University:

Program:

Accelerate

Machine Learning Approach for Real-time Assessment of Voltage Stability Using Multiple Indicators Derived from Wide Area Synchrophasor Measurements

Voltage instability is one of the major causes of many blackouts such as Canada-United State blackout (2003), Sweden-Denmark blackout (2003), India blackout (2012), and Turkey blackout (2015). If reliable methods are available for online voltage stability assessment, operators can be warned and automated corrective actions can be initiated to prevent voltage collapse. Although, a large number of Voltage Stability Indices (VSIs) are reported in literature, they are not practically applicable for real-time monitoring or not sufficiently reliable under all operating conditions. This research proposal envisages the development of Composite Voltage Stability Indices (CVSIs) combining the strengths of previously proposed Voltage Stability Indices computable from wide area synchrophasor measurements. Advanced machine learning techniques will be applied to derive CVSIs. It is expected that such a CVSI would be more reliable and applicable under wide range of conditions. The machine learning based algorithms for calculating CVSIs would be trained using the data generated through offline simulation and then tested using synchrophasor data generated through real-time simulations performed on RTDS real-time simulator. The proposed research will provide training opportunities for one M.Sc. student, while demonstrating and testing the synchrophasor capabilities of the simulators developed by the partner organization.

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Faculty Supervisor:

Athula Rajapakse

Student:

Kalana Dharmapala

Partner:

RTDS Technologies Inc.

Discipline:

Engineering - computer / electrical

Sector:

Manufacturing

University:

Program:

Accelerate

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