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

Utilization of Machine Learning in an Automated Framework for Evaluation and Management of Information Security Risk

During the internship in collaboration with RootCellar Technologies, research will be conducted towards the design of an adaptive machine-learning solution and its integration with the existing RootCellar framework for automated evaluation and management of information security risk in small and medium size enterprise networks. The existing framework is very advanced in terms of end-point risk monitoring as well as its compliance with the NIST CVSS System. However, the part of the framework that deals with the final aggregation and ranking of individual risk-scores is suboptimal in its design and does not allow for an easy integration of feedback/expertise provided by the end-user. The objective of this research is to make the existing framework: 1) network adaptive: by arriving at the most optimal risk-score aggregation/ranking model for each particular network, and 2) time adaptive: by allowing that the risk-score aggregation/ranking model of each network be easily updated as new data becomes available. These improvements would result in a significant enhancement of the company’s present-day product. TO BE CONT’D

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Faculty Supervisor:

Natalija Vlajic

Student:

Pooria Madani

Partner:

Root Cellar Technologies Corporation

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

York University

Program:

Accelerate

Foundation design system for FortisBC’s power poles

Traditional design method for the foundation of the transmission poles simply assigns a standard set of depths based on the length and diameter of poles. Although, this method has proven to be conservative and reliable, but it does not incorporate site-specific soil properties, water table, and weather conditions in its calculations. As a result, a new foundation design system which will integrate site specific conditions for each pole will provide more safe, economical, and reliable performance of transmission poles for the long-term benefit of FortisBC.

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Faculty Supervisor:

Sumi Siddiqua

Student:

Amin Bigdeli

Partner:

FortisBC

Discipline:

Engineering

Sector:

Construction and infrastructure

University:

Program:

Accelerate

Image matching for purposes of consumer recommendation

The purpose of this project is to develop a highly accurate e-commerce recommender system able to select products across databases and recommend them to prospective customers both in real-time and off-line. Leveraging the historical inventory of sold products, browsing history, purchase history, and expressed preferences helps the recommender to formulate highly accurate product suggestions to find closest matches to what a consumer is looking for. Consumers will instantly be shown other products similar to what she/he is looking at as well as an assortment of other products that complement it. For example, a prospective customer looking at blouses of a certain type will be shown other blouses of the same look and style as well as other well-matched pants, bracelets, earrings and purse.

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Faculty Supervisor:

Robert Bergevin

Student:

Maryam Ziaeefard

Partner:

Stradigi Ventures

Discipline:

Engineering - computer / electrical

Sector:

Digital media

University:

Program:

Accelerate

Developing Prediction Models on London Stock Exchange (LSE) Equitiesand Indicies using Microsoft Azure Machine Learning and Data Mining

I am to import ten year’s worth of amassed historical data on news events, price movement of equities and public sentiment metrics to Microsoft Azure platform for study and analysis through the latest Data Mining techniques with an Economics point of view to uncover the hidden correlation and casualty between events and price movement of global markets in multiple timeframes (three hours, daily, weekly, monthly and yearly). Predictive models based on the studies will be developed, compared, evaluated, and incorporated into industry partner’s website as real-time market news analysis engine to be offered as a paid service.

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Faculty Supervisor:

Fred Popowich

Student:

Bernard Lin

Partner:

EOTPRO

Discipline:

Computer science

Sector:

Digital media

University:

Simon Fraser University

Program:

Accelerate

Applications of Neural Network Curve Fitting Methods for Least-squares Monte Carlo Simulations in Financial Risk Management

Monte Carlo simulation methods are commonly used as a risk management tool to estimate the risk exposure of financial asset portfolios. However, the traditional brute-force Monte Carlo (BFMC) method is often very timeconsuming, which makes it difficult to serve the risk management needs of modern insurance industry. An alternative approach, the least-squares Monte Carlo (LSMC) method, could substantially reduce the computation cost by fitting a proxy function of liabilities using simple nonlinear regression methods. However, the LSMC does not work well in capturing the true risk properties of hedged assets and some other variable annuities. To improve the LSMC method, we propose to use neural network method during the curve fitting process. Since the neural network method allows more curve-fitting flexibility, the proxy functions of liabilities and Greeks are expected to be improved. By using the improved proxy functions to predict the liabilities and Greeks, the risk exposure of financial portfolios can be more properly quantified.

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Faculty Supervisor:

Longhai Li

Student:

Yunyang Wang

Partner:

Aon Securities Inc.

Discipline:

Mathematics

Sector:

University:

University of Saskatchewan

Program:

Accelerate

Improving usage pattern quality by comparing different sequential pattern mining methods and the effect of considering additional user information

Frequent usage patterns generated can provide valuable information for several applications such as platform restructuring and recommendation. In this project, we aim to compare different practical methods, and to investigate the effect of user identity and user intention information on them. To that end, a technique and a framework need to be developed, in which frequent patterns are composed of more refined analysis result instead of simple frequent sequences of basic operations over all users’ behavior. The outcome of this project is expected to improve the user experience for the partner organization’s product and such methods can be also used in various relevant applications.

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Faculty Supervisor:

Fred Popowich

Student:

Zelin Tian

Partner:

Kinematicsoup Technologies Inc

Discipline:

Computer science

Sector:

Media and communications

University:

Simon Fraser University

Program:

Accelerate

Gearbox fault detection and failure prediction

The objective of the project is to develop an automated monitoring system to accurately and reliably detect deterioration within gearboxes operating on an industrial forming line. This will involve reviewing, developing and testing one or more methodologies based on vibration signal measurement and analysis. In particular the work will focus on exploring existing potential methods, defining the capabilities of different sensors that could be used in the given environment and developing appropriate vibration signal analysis algorithms for gearbox deterioration detection and decision making. The final result of the work will be a system suitable for implementation on an actual forming line.
The participating sponsor anticipates to benefit from the project through participation and guidance of an investigation into the possibility of developing a new automated, accurate and reliable gearbox deterioration detection system. A successful system could be used in a wide range of applications where machinery condition inspection is done manually or not at all. Reliable detection of gearbox deterioration will result in improved efficiency and therefore lower costs.

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Faculty Supervisor:

Chris Mechefske

Student:

Chenyi Jin

Partner:

Co-Ex-Tec

Discipline:

Engineering - mechanical

Sector:

Automotive and transportation

University:

Queen's University

Program:

Accelerate

Investigation of cohesive-bed erodibility and water quality in semi-alluvial rivers

Rivers in much of eastern Canada flow through regions comprised of cohesive glacial sediments, including glaciomarine clays and glacial tills. Given the glacial history of Canada, many if not most of its rivers can be characterized as semi-alluvial.
Management of these rivers in terms of sediment load is a difficult challenge, because relatively little is known about their stable channel geometry. The proposed research plans to improve and evaluate both laboratory and field techniques to estimate critical shear stress of cohesive river bed sediments and monitor suspended solids concentration in correlation with observed erosion in semi-alluvial rivers in Eastern Canada.
The proposed research will strategically improve techniques to estimate critical shear stress of cohesive river bed sediments. Both laboratory and field techniques to measure these parameters will be developed, tested, and utilized by the intern and partner organization.

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Faculty Supervisor:

Colin Rennie

Student:

Carlo Zaro Custodio

Partner:

AATech Scientific Inc

Discipline:

Engineering - civil

Sector:

Natural resources

University:

University of Ottawa

Program:

Accelerate

Building Information Modeling Enabled Dependency Risk Analysis for Resilient Building Design and Operation

This project integrates building information models (BIM) with RiskLogik’s proprietary risk and resilience solutions to progress the design of complex buildings. This is accomplished by supporting the improved design of the interrelationships between building systems, such as mechanical, electrical, communications, and security systems and the operations that reside within the buildings. As a result, performance is improved by ensuring a resilient network of interconnections that reduces system conflicts and cascade effects.

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Faculty Supervisor:

Brenda McCabe

Student:

David Bristow

Partner:

Deep Logic Solutions Inc

Discipline:

Engineering - civil

Sector:

Environmental industry

University:

University of Toronto

Program:

Accelerate

Resilience of modern skyscrapers subject to natural hazards – Year two

The structural performance of skyscrapers subjected to natural hazards such as strong winds and earthquakes has significant effects on the resilience of a city because of the recent boom in the construction of skyscrapers around the world. However, resilience is currently not explicitly considered in the design of tall buildings. Studies show that modern tall buildings can suffer significant damage due to natural hazards and they might need to be closed for up to 2–3 years for repair. This has serious socio-economic repercussions. Therefore, this research is first aimed at developing a comprehensive framework for evaluating resilience of modern skyscrapers. The research will then investigate methods of enhancing tall-building resilience using the Viscoelastic Coupling Damper, which is Kinetica’s ground breaking technology. Kinetica is a leader in the design of tall buildings and the findings of this research will create working platforms for Kinetica to enhance its competitiveness worldwide.

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Faculty Supervisor:

Evan Bentz

Student:

Deepak Raj Pant

Partner:

Kinetica Dynamics Inc.

Discipline:

Engineering - civil

Sector:

Construction and infrastructure

University:

University of Toronto

Program:

Elevate

Sustainable Urban Development: Homebuyer Expectations and Implementation Challenges

The main objective of this research project is to synthesize and evaluate the published and grey literatures on consumer perceptions of green real estate development and sustainable community design features. Current research on homebuyers perceptions, priorities, motivations, and willingness-to-pay has yet to be consolidated. The complex nature of these topics scatters research across several disciplinary sectors, making it difficult to integrate the data and make an informed decision for sustainable real estate investment. This synthesis will offer a comprehensive organizational model through which consumer engagement can be visualized, analyzed, and better understood. This research will seek to identify demand-driven incentives for sustainable community development that will hopefully act as a catalyst for the adoption of these sustainable development ideals in Canada. Arbutus Properties will use this research to inform future investigations into the ways in which sustainable real estate development can appeal to suburban buyers in Saskatoon.

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Faculty Supervisor:

Maged Senbel

Student:

Anna Tirng-Ann Zhuo

Partner:

Arbutus Properties

Discipline:

Urban studies

Sector:

Construction and infrastructure

University:

University of British Columbia

Program:

Accelerate

3-D UAV Magnetometry for Improved Target Characterization in Mineral Exploration

Geophysical exploration is one of the primary forms of preliminary site investigation used to characterize ore potential and the economic viability of newly discovered mineral deposits. The current platforms for collecting magnetic data include dense coverage but low resolution airborne surveys and high resolution but low coverage terrestrial surveys. The recent
proliferation of Unmanned Aerial Vehicles (UAV) offers an opportunity to fill the observation gap inherent in conventional
survey methods. This project will build upon the UAV magnetometer platforms developed at Queen’s University as well as
the UAV operational expertise of the industry partner (Sumac Geomatics Inc.). The main objectives of this project are to
demonstrate the effectiveness and feasibility of UAV magnetometry to perform high-resolution 3-D magnetic gradient
surveys and to develop optimized survey strategies for improved target characterization including adaptive sensing. By the end of this project, the intern will be equipped with state-of-the-art technological and processing knowledge for nonintrusive site investigations and enhanced mineral exploration strategies. The partner organization will continue to be at the forefront of remote sensing technologies for enhanced/improved information products, which is a critical component of their ongoing success in a competitive marketplace.

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Faculty Supervisor:

Alexander Braun

Student:

Callum Walter

Partner:

Sumac Geomatics Incorporated

Discipline:

Engineering

Sector:

Environmental industry

University:

Queen's University

Program:

Accelerate

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