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

Identifying and Prioritizing Critical to Quality Characteristics

Research In Motion (RIM) is a leading designer, manufacturer and marketer of innovative wireless solutions. High-quality reliable products are critical in today’s competitive manufacturing environment. To help monitor and improve their processes and products, manufacturers collect large amounts of data from a variety of sources including warranty claims, customer surveys, usage data, inspection, reliability testing and the manufacturing process. The goal of this research project is to determine how to model and make sense of these complex data. By combined information across the different data sources rather than looking only at one source at a time, we hope to find ways to improve product reliability, reduce costs and improve quality.

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Faculty Supervisor:

Dr. Stefan Steiner

Student:

Ryan Browne

Partner:

Research in Motion

Discipline:

Statistics / Actuarial sciences

Sector:

Information and communications technologies

University:

University of Waterloo

Program:

Accelerate

Global Warming Catastrophes Impact on Insurance

Scientists believe that global warming will trigger increasingly more frequent and violent storms, heat waves, flooding, tornadoes and cyclones in some areas of the globe, while other areas will slip into cold or drought. Although the effects of climate change will impact every segment of the business community, the insurance industry is especially at risk. Extreme weather events in past years have caused tens of billions of dollars in losses for insurers. In collaboration with AVIVA, a leading property and casualty insurance groups in Canada, this project will involve data collection, merging insurance data with climate data. This will serve as the basis for further advanced statistical modeling and analysis. The results will be used to educate the policyholders about financial risks associated with global warming and help them to avoid or minimize such risk.

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Faculty Supervisor:

Dr. Jose Garrido

Student:

Jun Zhou

Partner:

AVIVA Canada

Discipline:

Mathematics

Sector:

Finance, insurance and business

University:

Concordia University

Program:

Accelerate

Development of Software Algorithms for Efficient Acquisition and Mining of Hyperspectral Imaging Data

For project, the intern will assist with the development of a new software package for a product line of hyperspectral imaging devices for Channel Systems Inc, a developer of technologies for scientific and industrial imaging and measurement. The general purpose of this research is to implement mathematical, statistical, and chemometric tools as software algorithms in order to enhance quality of data and explore both qualitative and quantitative information contained in the hyperspectral data sets. The proposed research is two-fold. First, the intern will develop and test calibration methods to reduce hardware impacts such as light intensity and optical distortion based on photonics statistics and diffraction optics. Secondly, the intern will take part in developing algorithms for the purpose of isolating and extracting specific chemical constituents. The developed software will help visualization of chemical constituents and simplifies tasks for further feature extraction and classification.

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Faculty Supervisor:

Dr. Jitendra Paliwal

Student:

Wenbo Wang

Partner:

Channel Systems Inc.

Discipline:

Engineering

Sector:

Information and communications technologies

University:

University of Manitoba

Program:

Accelerate

Convex Decomposition of a Triangle Soup

Radical Entertainment is a video game developer which creates and develops games for all current and next generation platforms. Collision detection forms an indispensable part in today’s 3D game engines. Due to increasing size of the 3D objects used in game development, high-performance collision detection running directly on these objects requires a significant amount of work. To allow for real-time collision detection between a 3D object and other objects in the game environment, the typical approach is to first find for each object a set of best-fitting convex hulls. Collision detection can then be conservatively, yet efficiently, carried out by detecting the collision between the set of convex hulls. In this project, in collaboration with Radical Entertainment, the intern will focus on 3D objects represented by triangle soups, where a “soup” of triangles without explicit connectivity roughly approximates the surface of an object. The goal is to compute a “good” set of convex hulls for an arbitrary 3D object represented by a triangle soup, where “good” means that the number of convex hulls is small, while the volume enclosed by the convex hulls is also a close approximation to the volume bounded by the original object. The small number of convex hulls helps improve the efficiency of collision detection; while the tightness of bounding by the convex hulls lead to more accurate collision detection.

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Faculty Supervisor:

Dr. Richard Zhang

Student:

Frank Liu

Partner:

Radical Entertainment

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Simon Fraser University

Program:

Accelerate

Active Learning of Hierarchically Parameterized Policies

Next Level Games is a full-service videogame developer based in Vancouver, BC. This intern research project will investigate mathematical solutions to the incredibly difficult problem of sequential decision making in an uncertain, partially observed, multi-agent environment with realistic motor dynamics. The problem is formalized under the reinforcement learning framework, where the agent observes the world state, takes action, receives a reward and observes the new state.

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Faculty Supervisor:

Dr. Nando de Frietas

Student:

Mike Vlad Cora

Partner:

Next Level Games

Discipline:

Computer science

Sector:

Digital media

University:

University of British Columbia

Program:

Accelerate

Access Network Evolution

Considering the introduction of new network services such as high definition television over the internet protocol (IPTV), new technologies should be introduced and planned in the Bell’s access network to improve the access rate. In this project, to maximize the profitability of the network infrastructure, we propose to explore an access network evolution model (a mixed integer mathematical programming model) to help the network engineers to plan Bell’s access network considering the demand over the time and the introduction of new services. The model will be solved for small geographic regions using a commercial mathematical programming solver.

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Faculty Supervisor:

Dr. Steven Chamberland

Student:

Annick Ntareme

Partner:

Bell Canada

Discipline:

Engineering

Sector:

Information and communications technologies

University:

Polytechnique Montréal

Program:

Accelerate

2D Fluoroscopic Image Registration for Target Localization and Patient Monitoring in Image-Guided Radiation Therapy

Many hospitals use image-guided cone-beam computed tomography (CT) to provide qualitative treatment for cancer patients. Although cone-beam CT provides good volumetric images, it takes a while to reconstruct them, which limits its applications because in many cases, real-time spatial information about patient’s internal structures is needed. To overcome this slow image reconstruction problem and missing of the movement data in between the reconstructions, we will use the same cone-beam device to obtain a rapid series of 2D projection images, which have a high temporal resolution but low spatial resolution. In this project, the intern will investigate the possibility and effectiveness of mapping this image sequence onto the high resolution reference 3D image (known as image registration), so that the 3D image provides necessary detail while 2D image sequence gives information about moves and deformations. This will enable continuous detection of inaccuracies in the patient’s position or anatomy, and image guidance for the treatment procedure with precision never possible before. To achieve this, novel algorithmic and software tools for 2D image sequence analysis will be developed. In addition, the team will benchmark existing algorithms for image registration and develop their new variants specifically for our problems, analyze and test these novel methodologies on real large-scale patient data.

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Faculty Supervisor:

Dr. Tamas Terlaky

Student:

Olesya Peshko

Partner:

Princess Margaret Hospital

Discipline:

Engineering

Sector:

Life sciences

University:

McMaster University

Program:

Accelerate

Clustering of Network Data Streams for Alcatel-Lucent

Alcatel-Lucent manufactures telecommunications equipment ranging from telephone handsets to internet routers. The Research & Innovation group within Alcatel-Lucent is mandated with evaluating new technologies and new ideas that may benefit Alcatel products, thereby benefiting Alcatel-Lucent’s customers, and ultimately the end users. One current area of research is the ability to group streams of traffic into clusters, so that similar types of traffic can be treated together, and to allocate resources suitable for the stream. Thus this collaborative internship project targets algorithms to cluster these traffic streams in high dimensional spaces, given the restricted computing power available for such features at the router level.

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Faculty Supervisor:

Dr. Shirley Mills

Student:

Pin Yuan

Partner:

Alcatel Canada Inc.

Discipline:

Mathematics

Sector:

Information and communications technologies

University:

Carleton University

Program:

Accelerate

Process Optimization of Kraft / Dissolving Pulp Manufacturing

Paper is one of the few basic materials for which per-capita demand has not become saturated by the cost of raw materials and energy which are on the rise annually. Energy, wood supply and lower than predicted reliability of equipments leading to production swings have been a major issue for AV Nackawic which came to production in 2006 after a brief closure of 15 months. With change in management the mill has proposed to new fiber line for the production of dissolving grade pulp with a capacity of 600 tons/day with the existing Kraft pulp line of 800 tons/day. With these concerns, this study coincides with our mills expansion project. The intern will closely with senior energy engineer to measure steam supply and consumption for individual stages and calculate overall energy balance to make recommendations for steam usage based on economical benefits. He will also be working along with technical manager in process calculation and new furnish development to accommodate changes from Kraft pulping to dissolving grade pulp. Further this will be a good opportunity for the mill to conduct feasibility study of the proposed project, re-evaluate and re-asses our existing processes and records to make a more reliable and productive environment.

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Faculty Supervisor:

Dr.Yonghao Ni

Student:

Jayakumar Balakrishnan

Partner:

AV Nackawic Inc.

Discipline:

Engineering

Sector:

Pulp and paper

University:

University of New Brunswick

Program:

Accelerate

System Equivalent for Real-Time Digital Simulator

RTDS Technologies Inc is the manufacturer of RTDS® Simulator, which is a fully digital, wide-band real-time simulator for the power industry. The size of the RTDS hardware and subsequently its monetary cost is proportional to the size of the modelled system. The goal of the intern’s research is to develop an efficient yet accurate equivalencing method for the RTDS® Simulator to reduce the hardware cost of the real-time simulation of a large power system.

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Faculty Supervisor:

Dr. Aniruddha Gole

Student:

Xi Lin

Partner:

RTDS Technologies Inc.

Discipline:

Engineering

Sector:

Alternative energy

University:

University of Manitoba

Program:

Accelerate

Protection Strategies for Subsea Wellheads in Iceberg Environments

The design of subsea infrastructure on the Grand Banks and, potentially, Labrador Sea must address the potential interaction with freely floating and gouging icebergs. Current practice is to protect the subsea infrastructure, such as conductors, wellheads and production trees, within massive excavations known as glory holes. For marginal fields or satellite wells, considerations of capital cost, construction cost and risk for developing the reservoir and protecting the assets may not be sufficient for project sanction. Consequently, the development and examination of alternative protection schemes or risk mitigation strategies that satisfy risk, safety and economic targets are required.

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Faculty Supervisor:

Dr. Shawn Kenny

Student:

Kenton Pike

Partner:

C-CORE

Discipline:

Engineering

Sector:

Aerospace and defense

University:

Memorial University of Newfoundland

Program:

Accelerate

Online Learning for Pinpoint Selling

This internship applies data mining and machine learning techniques to increase conversion rates in the interactive online marketing industry by providing personalized recommendation to specific customer segments. It will consist of two parts. The first part is to classify customers according to what message would resonate. The intern will apply some techniques to improve the accuracy of classification. The second part is to order resources from most to least interesting for website. He will mainly apply an information filtering technology called collaborative filtering to recommend resources according to the similarity of customers. This technique will help customers to find new useful information, not just customer interests predefined in user profiles.

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Faculty Supervisor:

Dr. Bruce Spencer

Student:

Biao Wang

Partner:

Pinpoint Selling

Discipline:

Computer science

Sector:

Information and communications technologies

University:

University of New Brunswick

Program:

Accelerate

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