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

IoT Big-data-based network performance analytics

The main objective of the project is to upgrade the existing system at Cheetah Networks to make use of Canadian cellular CAT M1 monitored network data to develop innovative QoE analytics that can be used to provide actionable insights. The system will explore applying new techniques to capture in real-time QoE visibility into experiences locally, regionally and nationally. The primary methodologies that we will be employing are based on machine learning and deep learning techniques for data classification, clustering and analysis.

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Faculty Supervisor:

Azzedine Boukerche

Student:

Safa Otoum

Partner:

Cheetah Networks

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

University of Ottawa

Program:

Accelerate

A Photovoice Study of Settlement Experiences and Needs of Recent Immigrant Men in Central Alberta

Red Deer College, together with the Red Deer Local Immigration Partnership (RDLIP), received Mitacs Accelerate funding to research the settlement experiences of recent immigrant men to Central Alberta. There have been concerted efforts from the federal, provincial, and municipal governments to attract and retain newcomers in mid-size cities and rural communities in Canada; nevertheless, the overall settlement experiences and needs of newcomers in these communities have not received much empirical investigation; this is especially the case among immigrant men. This research will examine settlement experiences of recent immigrant men in Central Alberta to address the knowledge gap. The research team will share research findings with community members, settlement services provider organizations, and policymakers through Photovoice exhibition, conference presentation, and publication in an academic journal.

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Faculty Supervisor:

Choon-Lee Chai;Jones Adjei

Student:

Sarah Orcutt

Partner:

Red Deer Local Immigration Partnership

Discipline:

Sociology

Sector:

Fisheries and wildlife

University:

Red Deer College

Program:

Accelerate

Comparative assessment of Machine Learning methods for fraud detection and improving the interpretability of the best model

Machine learning algorithms are being used in a wide range of applications. It is a branch of computer science where the system can learn from the data and make decisions. Financial fraud is an increasing hazard in the financial industry, and it is important to detect a fraudulent transaction. Machine learning algorithms can be used to decide whether the transaction is fraud or not. After the system makes its prediction, it is important for users to understand the reason behind the prediction in such cases. This research project presents a machine learning classifier for fraud detection that will predict if the transaction is fraudulent or not, and also the interpretation of the predictions made by the model for them to be understood by humans.

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Faculty Supervisor:

Lourdes Peña-Castillo

Student:

Kratika Naskulwar

Partner:

Verafin Inc.

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Memorial University of Newfoundland

Program:

Accelerate

Visual Inspection UAV in Harsh Environments and Confined Spaces

Preforming regular visual inspections is essential in up keeping structures. These inspections are used to identify defects at an early stage before they pose a major threat. Unfortunately, these inspections require scaffolding or hiring a boom to access certain areas. Other areas are tight and put the worker at risk. The use of visual inspection drones, specifically tailored for confined space, provide an excellent tool to perform these inspections. Throughout this research project we will be testing and analyzing how these drones perform/withstand different harsh environments. These environments include: extreme temperatures, high moisture, acidic vapour. Initially, we will test the subcomponents individually, then proceeding to testing the drone as a whole. Ultimately, we will provide our partner will a fully functional confined space visual inspection drone able to operate in harsh environments.

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Faculty Supervisor:

Hadi Mohammadi

Student:

Keaton Roch

Partner:

Redwood Engineering

Discipline:

Engineering - other

Sector:

Construction and infrastructure

University:

Program:

Accelerate

Visualization of manufacturing complexity highlights on CAD file

Currently, the service provided by GRAD4 allows online storing and sharing of computer-aided design (CAD) models. However, there is no interface developed for visualization of CAD models in the service in a fast and comprehensible way to the users: both manufacturers and buyers. The main challenge of such implementations is in relatively high computational cost of such visualizations via tools used for web-development: while a regular PC handles such task efficiently, web-based tools have means insufficient for similar performance and, thus, mostly involve cloud computing. Based on this, it is proposed 1) to investigate various approaches for geometric representations of CAD models on a web-page; 2) to develop a framework and an approach for visual representation of CAD models; and 3) to implement the approach either in a software prototype or as a plug-in for an existing online geometric modeling tool.

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Faculty Supervisor:

Yaoyao Fiona Zhao

Student:

Nikita Letov

Partner:

GRAD4 Inc

Discipline:

Engineering - mechanical

Sector:

Information and communications technologies

University:

McGill University

Program:

Accelerate

Experimental investigation and validation of flow control device (FCD) performance and design in thermal oil production

Oil recovery processes use flow control devices (FCDs) to ensure uniforms flow of fluids with minimized potential for well failure. These devices operate by restricting the flow through nozzles causing its velocity and pressure to significantly change. For the flow to keep its momentum, its pressure has to drop which unfortunately increases the likelihood of local well failure to occur. In this research, the performance of various nozzle types will be tested to investigate the effect of geometry on the pressure drop. The results obtained will be analysed to identify improvements that can be made to existing nozzle designs. A test facility will be commissioned whereby testing procedures and experimental protocols will also be established to use the setup as the primary tool for testing and validation of flow control devices.

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Faculty Supervisor:

David Nobes

Student:

Yishak Yusuf

Partner:

RGL Reservoir Management Inc.

Discipline:

Engineering - mechanical

Sector:

Oil and gas

University:

University of Alberta

Program:

Accelerate

Low Dose Computed Tomography Denoising Using Deep Learning

CT (Computed Tomography) scans are widely used medical images used to diagnose disease such as cancer. CT Scanners pass x-rays through the body in order to generate cross-sectional images. Unfortunately pro-longed exposure to radiation (via x-rays) can damage the body, and thus one aims to minimize the x-ray dose they receive. However, modern CT scanners produce lower quality images when using low x-ray dose which defeats their purpose as a diagnostic tool. We propose a post-processing algorithm to enhance the quality of CT images produced at low radiation dose. The industry and partner organization will benefit from this by integrating this algorithm into products that can be marketed towards radiologists.

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Faculty Supervisor:

Javad Alirezaie

Student:

Sepehr Ataei

Partner:

Dr. Paul Babyn Professional Medical Corporation

Discipline:

Engineering - computer / electrical

Sector:

Medical devices

University:

Ryerson University

Program:

Accelerate

Thermal design of electric wheel integrated with hybrid battery pack

This project will integrate normal temperature battery (NTB) that is cheap but only can discharge above-20?, and low temperature battery (LTB) that is expensive but can work at -40? into an insulated housing with a smart hybrid battery management system. And a heat pipe with design trigger temperature will be integrated to the housing to prevent overheating of NTB, which will ensure the hybrid battery can work properly in both hot and cold environment. The partner, a lithium ion battery supplier, will increase its battery application in Canada by widening the working temperature range with a acceptable cost.

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Faculty Supervisor:

Hossam Gaber

Student:

Yoka Cho

Partner:

Wina North American Technology

Discipline:

Physics / Astronomy

Sector:

Energy

University:

Ontario Tech University

Program:

Accelerate

Optimization of rice and other protein based extrusion encapsulation

The proposed project deals with the extrusion encapsulation of bioactive components using rice and corn protein based food matrix. It is well recognized that the performance properties of natural lake pigment encapsulated products are influenced by the structural properties of the matrix protein used for the extrusion encapsulation. The type, mixture and nature of proteins can have a significant effect on the extrusion encapsulated product as well as its functionality. Significant changes in the product properties can be expected due to the difference in the manufacturing steps. Preliminary attempts by the company with enzymatic rice protein in extrusion encapsulation of natural pigments have not been successful. This project explores alternative procedures and optimization concepts to obtain a successful encapsulated product

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Faculty Supervisor:

Hosahalli Ramaswamy

Student:

Ghaidaa Alharaty

Partner:

Capol Inc

Discipline:

Food science

Sector:

Agriculture

University:

McGill University

Program:

Accelerate

Fast and Accurate Computation of Wasserstein Adversarial Examples

Machine learning (ML) has recently achieved impressive success in many applications. As ML starts to penetrate into safety-critical domains, security/robustness concerns on ML systems have received lots of attention lately. Very surprisingly, recent work has shown that current ML models are vulnerable to adversarial attacks, e.g. by perturbing the input slightly ML models can be manipulated to output completely unexpected results. Many attack and defence algorithms have been developed in the field under the convenient but questionable Lp attack model. We study an alternative attack model based on the Wasserstein distance, which has rich geometric meaning and is better aligned with human perceptron. Existing algorithm for computing Wasserstein adversarial example is very time-consuming. The goal of this project is to significantly speed up the generation process for Wasserstein adversarial examples by carefully reformulating the problem and by exploiting better optimization techniques.

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Faculty Supervisor:

Yaoliang Yu

Student:

Kaiwen Wu

Partner:

Borealis AI

Discipline:

Computer science

Sector:

Finance, insurance and business

University:

University of Waterloo

Program:

Accelerate

Developing niosome-based vehicles to deliver plant immune aids

Innovative approaches that ensure food security in light of the increasing world population, increasing variety of crop pests and microbes, and accelerating climate change are urgently needed. Suncor has developed a novel plant immune aid that can effectively enhance the disease resistance of crops to enhance agricultural yields. Through this collaboration with Dr. Todd Hoare’s lab at McMaster, these potent compounds will be formulated into a new type of nanoparticle formulation that can achieve long term stability under sunlight, offer good rainfastness to the leaf, facilitate controlled release for longer-term efficacy, and promote enhanced uptake and transportation of plant immune aids into plants, all of which are essential for the commercial efficacy of these plant bioactive compounds. Based on the collaboration, we aim to generate 3-5 effective formulations to take forward into more detailed commercialization studies. We anticipate these developments will facilitate the translation of Suncor’s plant bioactive compounds, providing both economic impact for Canada as well as enhancements in agricultural yields.

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Faculty Supervisor:

Todd Ryan Hoare

Student:

Lisha Zhao

Partner:

Suncor Energy Inc.

Discipline:

Engineering - chemical / biological

Sector:

Agriculture

University:

McMaster University

Program:

Accelerate

Disentangling Effects of Multiple Stressors on Nuisance Benthic Algae (Cladophora) in Nearshore Regions of the Great Lakes

We will analyze long-term monitoring data that were sampled over ten years from nearshore regions of the Great Lakes to find out key factors that cause the proliferation of nuisance benthic algae and fouling of shorelines of Lake Ontario in the Toronto–Durham region and throughout the Great Lakes. Additionally, we will test whether environmental DNA in water and sediment samples can be used to track the dispersal of nuisance benthic algae. Our project will contribute directly to the ongoing monitoring programs in the Great Lakes and will be relevant for management of nuisance benthic algae. We will explore how nuisance benthic algal biomass is influenced by human-factors (wastewater inputs and urban runoff), water quality (e.g., light and nutrients), lake circulation, aquatic invasive species, and climate change. We will also develop models that will inform management strategies to control shoreline fouling by nuisance algae.

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Faculty Supervisor:

Paul Weidman;Ken Droulliard

Student:

Zhuoyan Song

Partner:

Toronto and Region Conservation Authority

Discipline:

Environmental sciences

Sector:

Fisheries and wildlife

University:

University of Windsor

Program:

Accelerate

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