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

Mechanical characterization of phage-coated implants for the prevention and treatment of periprosthetic joint infections in high risk patients

Caused by planktonic and biofilm drug-resistant bacteria on implants, periprosthetic joint infections (PJI) is one of the most devastating complication in orthopedics and is in line with forecasted rise in joint replacement. From the perspectives of patients, surgeons, hospitals, and health care system, PJI thus present a great unmet medical need, resulting in high morbidity, and even mortality, among affected patients. Therefore, clinicians would find invaluable a technology with a potential to manage PJI on implants. With the rise of antimicrobial resistance (AMR), a new technology to prevent or treat PJI would be invaluable.

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Faculty Supervisor:

L'Hocine Yahia

Student:

Joséphine Tawil

Partner:

Phagelux

Discipline:

Engineering - mechanical

Sector:

University:

École de technologie supérieure

Program:

Accelerate

Reinforcement Learning for Aviation Training

This project seeks to explore the use of a class of artificial intelligence algorithms called reinforcement learning for the purpose of aiding the training of new pilots. In the process, we seek to “teach” an algorithm how to fly an aircraft by exposing the AI pilot to a virtual environment and providing it with flight data and a goal. Alternatively, the algorithm could learn by observing human pilots. If this approach is successful, it could lead to better autopilot systems as well as teaching aids for new pilots by simulating their response to different flying environments and helping instructors select the most appropriate training exercises.

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Faculty Supervisor:

Yoshua Bengio

Student:

Sahar Bahrami

Partner:

Paladin AI Inc

Discipline:

Computer science

Sector:

Aerospace and defense

University:

Université de Montréal

Program:

Accelerate

WP 3.2.2 – Automated Log Analysis

Ciena is a Canadian company leader in engineering and manufacturing networking systems and devices. The company has around 5,000 operable products in its portfolio. The vast majority of Ciena products generate logs during the boot up and the mission mode operations from the various tasks running on their real time operating systems. The company wants thus to increase its software’s capabilities in order to be able to collect any type of log data generated in the production site and linked to other external information to extract actionable knowledge. In this setting, the general goal of the proposed “Automated log analysis” project is about providing Ciena advanced log analysis capabilities, data mining tools and machine learning algorithms to support its product manufacturing operations during new product introduction and production phase as part of the product life cycle as well as for field returns failures analysis.

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Faculty Supervisor:

Naouel Moha

Student:

Fares Ben Slimane

Partner:

Ciena Corp.

Discipline:

Computer science

Sector:

Information and cultural industries

University:

Université du Québec à Montréal

Program:

Accelerate

Characterization of KPI Outliers from Logs Using Data Mining

Ubisoft records the interaction between its customers and its servers in large execution logs (also called traces). Any failure of the system is thus recorded therein. However, the considerable size of these logs considerably hinders their effective use by analysts and developers. We propose an automated method to detect failing executions, and furthermore to characterize the features that are common to clusters of failing instances. The approach will be based an machine learning algorithms, and will produce clusters of failing traces with common features. Since isolating of the features common to a runtime failure is an important part of the overall effort of fixing the issue, the research being proposed here will allow developers to extract actionable information from the traces.

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Faculty Supervisor:

Raphaël Khoury

Student:

Souhail Ben Ali

Partner:

Ubisoft

Discipline:

Computer science

Sector:

Information and cultural industries

University:

Université du Québec à Chicoutimi

Program:

Accelerate

Predicting the Behavior of Loyalty Programs Customers Using Interpretable Patterns Based on Logical Analysis of Data

Aeroplan Inc. (“Aeroplan”) , aims to redesign and optimize its loyalty program Aeroplan via a collaboration with Polytechnique Montréal. Customers affiliated with Aeroplan’s program earn miles through their purchases and can exchange these miles for various gifts. It is essential for Aeroplan to predict customers behavior, to define the causes of certain behaviors and to predict the consequences of applying different policies, e.g. offers or gifts value. In this project, we propose to exploit the historical customers database of Aeroplan to predict customer behavior using Logical Analysis of Data (LAD) as an interpretable machine learning technique. We intend to use the LAD generated patterns to design customized marketing plans. This approach should allow the marketing department at Aeroplan to identify the future behavior of customers along with its cause and to target customers with suitable personalized marketing policies. This should help Aeroplan to avoid problems such as churn or drop in usage rate. The prediction accuracy of our model will be compared to traditional machine learning techniques that are known to perform well in predicting customers behaviors. The proposed marketing policies will be tested on a sample of Aeroplan’s customers using an A/B testing approach.

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Faculty Supervisor:

Antoine Saucier

Student:

Mohamed Ossama Hassan

Partner:

Aeroplan

Discipline:

Engineering - other

Sector:

Service industry

University:

École Polytechnique de Montréal

Program:

Accelerate

Displacement-based Design of Hybrid Steel-timber Structures – Year two

Driven by heightened environmental awareness, the construction industry increasingly strives to utilize materials such as timber with a low-carbon footprint in their life cycle. High-strength mass-timber products, innovative ductile connections, and fast computer-numerically-controlled pre-fabrication, combined with changing legislation create better opportunities to also build tall timber structures. However, low ductility and limited tensile strength of timber are challenges for such buildings particularly in high seismic zones. By contrast, steel exhibits high ductility and tensile strength; the hybridization of steel and timber is an intriguing solution that offers new design opportunities. Current codes and standards, however, do not provide any design guidance for timber-steel hybrid structures. Displacement-based design (DBD) in which the seismic design problem is reduced to the evaluation of the allowed displacement and required strength that ensures all performance objectives are satisfied, has been identified as a promising approach. TO BE CONT’D

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Faculty Supervisor:

Thomas Tannert

Student:

Md Shahnewaz

Partner:

Fast+Epp

Discipline:

Forestry

Sector:

University:

University of Northern British Columbia

Program:

Elevate

Technical and Economic Assessment of Implementing UV Treatment in Potable Reuse Process Trains – Year two

Driven by climate change induced water scarcity, further enhanced by rapid urbanization and population growth, potable water reuse initiatives are gaining interest. Potable reuse involves the indirect or direct use of highly treated municipal wastewater as a municipal drinking water source. Historically, the most commonly installed potable reuse train consisted of microfiltration, reverse osmosis (RO), and ultraviolet (UV) as treatment stages. Today, in many non-coastal geographies, non-RO based alternative advanced water treatment trains such as ozone-biological activated carbon (BAC) are being evaluated. UV plays a significant role in potable reuse trains because of its capability to inactivate pathogens up to 6-log. Thus, given the multiple reuse treatment trains where UV plays an essential role, there is a need to minimize the UV energy consumption and maximize the performance depending on the various upstream treatment trains. TO BE CONT’D

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Faculty Supervisor:

Ajay Ray

Student:

PANKAJ CHOWDHURY

Partner:

Trojan Technologies

Discipline:

Engineering - chemical / biological

Sector:

Construction and infrastructure

University:

Western University

Program:

Elevate

Radio Frequency Identification (RFID) Based Multi Agent System in Banking Environment – Year two

The wide adoption and development of wireless sensing technologies for the monitoring and autonomous identification of financial activities have affected financial institutions in the past decade. However, wider utilization of RFID technologies in the banking sector has introduced challenges regarding the security and privacy of sensitive financial data. The proposed innovations and technological developments will revolutionize the banking sector by increasing efficiency, decreasing cost and provide secure and privacy sensitive financial transactions. In this work, we will deliberately build up a RFID based comprehensive framework and its application to expertly and automatically matching profile of customer and banker according to a number of selected weighted attributes. We will develop a RFID framework which collects, communicates and manages the financial data and customer’s account details securely. TO BE CONT’D

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Faculty Supervisor:

Dimitrios Hatzinakso

Student:

Sonam Kaul

Partner:

RBC Royal Bank

Discipline:

Engineering - computer / electrical

Sector:

University:

University of Toronto

Program:

Elevate

Investigating the scope of dsRNAi in human cells

Long dsRNA is produced by viruses during their replicative cycle. In plants and invertebrates, long dsRNAs block crucial cellular processes through RNA interference (RNAi). In vertebrates, long dsRNA is a potent inducer of critical signaling proteins that regulate antiviral immune responses. Although the RNAi system is conserved in vertebrates, there is little evidence to suggest that it plays a major role in antiviral defense. Moreover, it remains unclear whether long dsRNA can function as a template for RNAi (dsRNAi) in vertebrates. The proposed project aims to determine what human cell types are capable of using long dsRNA for RNAi and test this mechanism for novel therapeutic applications in the human cells.

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Faculty Supervisor:

Stephanie DeWitte-Orr

Student:

Shawna L Semple

Partner:

Centre for the Commercialization of Antibodies and Biologics

Discipline:

Biology

Sector:

University:

Wilfrid Laurier University

Program:

Accelerate

Use of X-rays as an alternative for fungal and bacterial pathogen control in seeds and stored food

The shortage and quality of food to feed the existing and growing population is an issue of global concern. There is also serious problem with the fungal and bacterial pathogens which result in losses of billions of dollars of annual loss of agricultural products. This project is an attempt to mitigate this with the use of X-ray treatment of seeds and organic products in stead of gamma rays or electron beams of a few MeV energies. This project, when completed, has the prospects to develop field-deployable treatment facilities for use around the globe, including developing countries in the tropical zones to mitigate the food shortage of the hungry world.

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Faculty Supervisor:

Chary Rangacharyulu;Venkatesh Meda

Student:

Kaylyn Olshanoski;Moira Elisabeth McCoy

Partner:

NavGaea Consulting Inc

Discipline:

Engineering - other

Sector:

University:

University of Saskatchewan

Program:

Accelerate

Improving avalanche forecasts in data-sparse areas with physical snowpack modelling

Assessing dangerous avalanche conditions requires a reliable stream of weather and snowpack data, which can be difficult and expensive to collect in many remote areas of Canada. Snowpack conditions can be simulated in these areas by coupling weather forecast models with physical snowpack models, however, this method has had limited adoption by avalanche forecasters. The proposed project will increase the adoption of snowpack models by developing a dashboard that allows Avalanche Canada forecasters to visualize spatial snowpack patterns, alarm them of critical changes, and provide an assessment of the model’s accuracy. Novel methods of comparing model output with snow observations will be investigated and spatial clustering methods will offer a new dynamic view of regional snowpack patterns. The project will improve the accuracy and quality of Avalanche Canada’s public safety products and warnings in data-sparse areas.

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Faculty Supervisor:

Pascal Haegeli

Student:

Simon Horton

Partner:

Avalanche Canada

Discipline:

Environmental sciences

Sector:

University:

Simon Fraser University

Program:

Elevate

Wavefield Imaging for Stored Grain Monitoring and Biomedical Applications

Electromagnetic and/or ultrasound imaging technology uses a hardware system to measure a target’s response to interrogating energy. Using sophisticated computer algorithms, these measurements can be converted into an image of the interior properties of the imaging target. Herein, targets of interest lie in the areas of biomedical imaging and stored agricultural crop monitoring.
Major advances in imaging technology over the past decade have made it possible to pursue these applications commercially, but there remains significant opportunity for continued improvements. This project broadly aims to achieve advances in electromagnetic and ultrasound imaging, as well as other complementary monitoring technologies.

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Faculty Supervisor:

Ian Jeffrey;Colin Gilmore;Jitendra Paliwal

Student:

Max Hughson;Ryan Kruk;Amir Ghasemi;Hannah Fogel;Forouz Mahdinezhad Saraskanroud;Vahab Khosdel;Pedram Mojabi

Partner:

151 Research Inc.

Discipline:

Engineering - computer / electrical

Sector:

Manufacturing

University:

University of Manitoba

Program:

Accelerate

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