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

Development of new techniques for power system model validation and calibration

Dynamic modeling is one of the most important tools for the power system operation and planning purposes. In order to study the behavior of the system, which is subjected to disturbances, a valid knowledge of parameters of system components is essentially required. The objective of this project is to propose an applicable algorithm to identify the parameters of the power system components’ models. For the identification purpose, the actual power systems’ subsections data collected by phasor measurement units (PMUs) are employed. Afterward, the acquired model needs to be validated to assess the capability of the model in performing accurately in different operation conditions. This study is in line with the intern’s research topic, which is modeling, identification, and control of power generation systems. Moreover, this project will help the Powertech to add a tool to their software to predict the behavior of the power systems’ components.

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Faculty Supervisor:

Jeffery Pieper

Student:

Peyman Sindareh Esfahani

Partner:

Powertech

Discipline:

Engineering - mechanical

Sector:

Information and communications technologies

University:

Program:

Accelerate

Prototype Behavior Based Integrity Verification (BBIV)

Web computing, in which the world-wide web is itself employed as a distributed computing platform, is entering a stage of rapid expansion with the advent of Open Web Platform so that programs that once worked only a native environment on desktop, tablets or phones can now work from within a browser itself. There is therefore a need for a new form of protection for apps. Existing security models are inadequate to address emerging threats to applications in low-cost, highly-exposed environments, because they fail to protect data and code on exposed consumer terminal devices of the network, which are vulnerable to attacks directly accessing their hardware and software. Irdeto developed a new approach for protecting such applications based on novel methods of integrity-verification, which permit the implemented validated integrity to serve as a root of trust. TO BE CONT’D

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Faculty Supervisor:

Azzedine Boukerche

Student:

Fan Zhang

Partner:

Irdeto Canada

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Program:

Accelerate

Investigation of magma conduits and their relationships to Cu-Pd mineralization at W-Horizon of the Marathon deposit, ON, Canada

Copper and palladium (Cu-Pd) mineralization at the Marathon Deposit are associated with gabbro rocks. It is fundamentally important to be able to distinguish among the different types of gabbros, because only those of the Marathon Series are host to mineralization. This is accomplished through logging drill core, whole rock geochemistry and mineralogy. Mineralization at the W Horizon (the highest grade mineralization at Marathon) is believed to have formed in a conduit system (flowing magma) but the distributions of gabbros in W Horizon need to be determined in order to develop a 3D model, which can then be applied to guide future exploration. The goal of the current study is to develop a 3D model of the W Horizon. TO BE CONT’D

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Faculty Supervisor:

Robert Linnen

Student:

Yonghua Cao

Partner:

Stillwater Canada Inc

Discipline:

Geography / Geology / Earth science

Sector:

Natural resources

University:

Program:

Accelerate

Review of Dry Comminution Technologies and Innovations

Goldcorp recently announced a new initiative referred to as H2zero with the goal of reducing water usage in their mining operations by 80 to 100%. Mineral processing and specifically comminution and mineral separation are the main consumers of water. This research focuses on dry comminution technologies and represents a first step towards advancing dry comminution as part of a longer term goal and research intersect. A literature review will be conducted to compile information about existing and novel dry comminution technologies. The review will consider technologies used across a range o industries and to assess their suitability for metal mining.

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Faculty Supervisor:

Bern Klein

Student:

Mengdie Zhang

Partner:

Goldcorp Inc.

Discipline:

Engineering

Sector:

Mining and quarrying

University:

Program:

Accelerate

Portfolio Strategies under Scenario Optimization

This project concentrates on the scenario optimization method which does not need to make any assumption for the underlying asset distribution and directly incorporate such uncertainty into the objective or constraint functions through stochastic programming. The scenario optimization is performed under different parameters and constraints while Markowitz and Black-Litterman model are taken as the benchmarks to evaluate if the scenario optimization can outperform the traditional methods with the same input exchange-traded funds (ETF) data. The efficient frontier of the portfolio determined by the scenario optimization is shown as well to compare with the traditional methods. A hypothesis test is conducted to see whether we can efficiently map the scenario optimization to Black-Litterman model. RiskGrid Technologies will benefit from participation in the internship as the realization of the approach will be direcly used to improve the services for the customers of RiskGrid Open Eikon App. TO BE CONT’D

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Faculty Supervisor:

Traian Pirvu

Student:

Chengwei Qin

Partner:

RiskGrid Technologies Inc.

Discipline:

Mathematics

Sector:

University:

Program:

Accelerate

The impact of correlations on VaR

Energy companies are in the business of turning energy from one form into another. For example, a gas-fired power station turns chemical potential energy stored in the natural gas into electrical energy. A natural gas storage facility allows energy (held in the form of natural gas) to be stored at one point in time and recovered at a later time. A gas pipeline moves energy from one location to another. The result is that the financial risks faced by an energy company involve a large portfolio of spreads – differences between energy prices. These prices will vary stochastically through time, and will typically be related to each other in some way. This project will involve studying those relationships and how they affect specific measures of the risk faced by the company from possible future movements in those prices.

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Faculty Supervisor:

Tony Ware

Student:

Liang Chen

Partner:

TransAlta

Discipline:

Mathematics

Sector:

Energy

University:

Program:

Accelerate

Generative Models for Financial Time-Series Predictions

The intern will work on applying new advances from the field of Machine Learning to models which make predictions about time-series data. The models have the desirable property modeling the distribution of outcomes in a way that we can sample from, allowing us to account for uncertainty in the model’s predictions. By making more accurate predictions with more accurate gauges of uncertainty, Electronica will be able to construct portfolios which give more desirable risk-adjusted returns to investors.

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Faculty Supervisor:

David Duvenaud

Student:

Jonathan Lorraine

Partner:

Electronica AI Inc

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Extracting supplier information from the web

Using web crawling technology in coordination with state of the art machine learning techniques, the project aims to mine useful, structured information about the world’s suppliers from the web. Recent advances in artificial intelligence have increased the viability of such autonomous systems for extracting coherent information from arbitrary human-produced content. By leveraging these technologies, our goal is to build improved supplier discovery and recommendation systems. Such systems would enable manufacturers to meet the right suppliers faster, thus putting their products on the market sooner. The methods and processes we will develop might be transferable to different text mining tasks on other subjects as well.

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Faculty Supervisor:

Frank Rudzicz

Student:

Mete Kemertas

Partner:

Tealbook inc

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Automated Detection and Classification of Adverse Events in Surgery

During surgeries, it is important to keep track of what is happening with the patient, the steps being taken during the surgery by the operating staff, and unforeseen events that occur. All the previous correspond to the surgical workflow. Keeping track of the workflow is essential to achieving a better and safer surgery. In the past, computational tools have been developed to track each step the surgeon takes during the surgery, and dividing the separate surgical phases. However, the adverse events have not been tracked. This project aims to develop computer software capable of detecting adverse events that occur during surgery and classifying their severity. This way, it is expected to assess surgical performance at a faster pace. The advances that are achieved from this internship will improve surgical skills, improve patient outcomes and reduce unnecessary healthcare costs.

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Faculty Supervisor:

Babak Taati

Student:

Juliana De La Vega Fernandez

Partner:

Surgical Safety Technologies Inc

Discipline:

Computer science

Sector:

Medical devices

University:

Program:

Accelerate

Deep Collaborative Filtering using two stage information Retrieval

The company wants to develop a state of art recommendation system for the clients. A recommendation system is a piece of software that provides products’ suggestions to customers on a website. For example the products suggestions that can be seen on Amazon’s web page are generated by its recommendation engine.
The typical recommendation engines work by utilizing the existing user-product preferences information. They recommend products to a user by comparing his preferences to other similar users’ preferences. The typical example of this is Users who bought item-A also bought item-B. This suffers from the problem of cold-start. This happens say when a user logins for the first time and has no preference information.
We propose to solve this problem by using user-content information and using a technique called deep learning. TO BE CONT’D

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Faculty Supervisor:

Richard Zemel

Student:

Himanshu Rai

Partner:

Milq

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Standard Response Documents Application

Developing a model for a system can come with a lot of uncertainty, especially in the early stages of development. Recent research has be done into removing uncertainty during early stage models. Doctalk plans to use modern research to develop a viable product for market, while contributing to the process of the research being applied to the development of the product.

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Faculty Supervisor:

Marsha Chechik

Student:

Jamie Beith

Partner:

Doctalk Inc

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Labeling a user’s speech in real-time for always-on VoIP

TurnMeUp is an iOS app for always-on voice communications. Users leave the app running in the background and can talk to the recipient (also using the app) at any time. This app is especially useful for coworkers listening to their own music in the background without needing to enter and exit voice call sessions manually. To conserve bandwidth and ensure that users listen to music without being unnecessarily interrupted, TurnMeUp sends voice signals to the recipient only if the user is speaking. The purpose of this project is to improve the algorithm used for detecting when the user is speaking (as opposed to background noise or other people speaking) in real time, using contemporary machine learning methods. This project will potentially improve the performance of TurnMeUp; to the greatest extent possible, it will ensure that the entirety of a user’s speech, and nothing else, is sent to the recipient.

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Faculty Supervisor:

Frank Rudzicz

Student:

Willie Chang

Partner:

Synervoz Communications Inc

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

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