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

Automated Land Use and Land Cover (LULC) Classification for Hydrological Modelling and Physically-Based Inflow Forecasting

The problem considered in this work is how to produce highly accurate and consistent land-use/land-cover (LULC) maps significantly faster than current semi?automated methods for use by Manitoba Hydro. The goal is to improve the ability to produce maps quickly and efficiently as priority needs arise. This project will use an approach for automated LULC mapping from satellite images using deep learning methods pioneered by the applicants. By classifying each pixel in a satellite image into LULC categories using neural networks, rapid and accurate LULC maps can be successfully produced. These LULC maps can then be included in improving hydrological modelling and inflow forecasting as an additive layer to improve overall modelling processes. Secondly, this research will also develop a solution for higher resolution satellite data. Lastly, the final objective incorporates seasonal water levels into the LULC products that can contribute to the mapping of hydrological connectivity and disconnectivity.

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Faculty Supervisor:

Christopher D Storie;Christopher Henry;Joni Storie

Student:

Rostyslav-Mykola Tsenov

Partner:

Manitoba Hydro

Discipline:

Computer science

Sector:

Energy

University:

University of Winnipeg

Program:

Accelerate

The making of a woman feature filmmaker: Gender and cultural production in a Montreal-based film school – Year two

In Canada, women have made significant inroads in television, web series, documentaries, and experimental films. But few women directors and screenwriters participate in big-budget feature film production. This study explores the marginalization of women in the feature film industry through the lens of film production training. As previous studies have shown, film education can shape student filmmakers? professional identity and aesthetic repertoires. Situated in a Montreal-based film education center, my project will analyze socio-structural arrangements that influence women student filmmakers? subject formation, career choices, and artistic approaches. Also, this study will explore the strategies that women students employ to succeed in the film school and in the job market. Using qualitative research methods, such as participatory photography, interview, focus group, and on-site observation, this study will generate new insights to promote a gender-sensitive approach to film education. 

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Faculty Supervisor:

Student:

Partner:

Femmes du cinéma, de la télévision et des médias numériques

Discipline:

Visual arts

Sector:

Other services (except public administration)

University:

Concordia University

Program:

Elevate

An Integrated Co-Simulation-Data Analytics Platform for Smart Grid Modeling and Cybersecurity Analysis

The smart grid represents a marriage between power systems and information technology to provide increased and reliable access to power. The greater dependence on information systems however makes it more vulnerable to cyberattack. Modeling these systems accurately is a significant challenge due to their complexity and connected nature. In this work, we focus on the open research problem of developing a modeling platform that combines co-simulation, real equipment and data analytics. We demonstrate how for cybersecurity applications the platform provides an advanced tool for simulating cyberattacks, investigating their impact on the power grid, developing attack detection and mitigation and validating the performance of mitigation response. The co-simulator models developed will enable Hydro-Québec to investigate cybersecurity issues of timely interest as well as performance enhancement in general for power grid automation processes and information infrastructure with the goal of application to real systems.

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Faculty Supervisor:

Student:

Amir Abiri Jahromi

Partner:

Institut de recherche d’Hydro‐Québec

Discipline:

Engineering - computer / electrical

Sector:

Energy

University:

University of Toronto

Program:

Accelerate

Assessing the effectiveness of customer management efforts on profitability in the insurance industry

To have a strategic advantage over competitors, companies have been encouraged to adopt customer-centric, value added processes and capabilities.  Firms allocate resources to train their employees in the necessary skills to build and maintain healthy relationships with their customers, yet little is understood on how investments in training impacts a firm?s performance.   The objective of the proposed research is to investigate (1) Which customer management training activities have a positive impact on profitability? (2) How frequently should companies offer training to their employees? and (3) Who will benefit from more (less) training activities? To answer these questions, we will develop a model to estimate growth in profitability as a function of training efforts while controlling for economic factors. This research will help understand how investments in customer management training will enhance the firm?s overall performance and competitiveness in business markets.

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Faculty Supervisor:

Tanya Mark

Student:

Shaheer Kamran

Partner:

Cooperators

Discipline:

Business

Sector:

Finance, insurance and business

University:

University of Guelph

Program:

Accelerate

Simulation of the foamy oil flow during the solution gas drive production of heavy oils – Year two

Foamy oil behavior is a unique phenomenon associated with cold production of heavy crude oils. It is believed that the foaming mechanism has a significant impact on the abnormally high production rate of viscous crude oils observed in many heavy oil producing reservoirs through solution gas drive.
Due to the non-equilibrium nature of the foamy oil flow, the mathematical modeling of this process involves few challenges. The main non-equilibrium process exist between solution gas and free gas that leads to a significant supersaturation of dissolved gas in the oil phase. Even though different models on foamy-oil behavior with a diverse experimental data are available in literature, there is scarcity of published experimental data for a heavy oil reservoir with a Canadian origin. This research will be focused on developing a kinetic model which is developed and tuned for a heavy oil reservoir with a Canadian origin.TO BE CONT’D

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Faculty Supervisor:

Sudarshan (Raj) Mehta

Student:

Jafar Modaresghazani

Partner:

Canadian Natural Resources Ltd.

Discipline:

Engineering - chemical / biological

Sector:

Mining and quarrying

University:

University of Calgary

Program:

Elevate

Making Stronger and Lighter Wallboard

The ultimate goal of this project is to produce stronger and lighter gypsum wallboards through more sustainable production procedure. The wallboard production plant is divided in three sections: upstream where the raw gypsum is received, midstream where the raw gypsum is processed to produce Calcium Sulfate Hemihydrate (stucco), and downstream where the final wallboard product is produced. The main focus of this internship will be on the downstream section. We will try to improve the wallboards through controlling their chemical composition and physical structure. Gypsum Lab at University of Alberta has been performing research on this topic in last three years and this internship will build up on out current knowledge. Both Continental Building Product Inc. and Gypsum Lab at University of Alberta will benefit from outcomes of this research.

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Faculty Supervisor:

Qingxia (Chad) Liu

Student:

Mohammad Khalkhali

Partner:

Continental Building Products

Discipline:

Engineering - chemical / biological

Sector:

Manufacturing

University:

University of Alberta

Program:

Accelerate International

Assessing Harvest and Post Harvest Fruit Quality in Blueberry

Blueberry is a soft fruit species for which consumption has increased drastically in the last two decades. The rapid increase in consumption is mainly driven by the health benefits and pleasant flavour of the fruit. However, consumers assess fruit quality in terms of flavour, texture and aroma and use these features as the main criteria for purchasing product. Nonetheless, there is a lack of study for these quality traits for blueberry at harvest and post harvest stages. Therefore, this project aims to develop a method for assessing fruit quality using analytical platforms, comparing new potential varieties (i.e., breeding selections) with established commercial varieties and developing novel technologies to improve postharvest blueberry quality.

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Faculty Supervisor:

Simone Diego Castellarin;Anubhav Pratap Singh

Student:

Maryam Shojaei

Partner:

British Columbia Blueberry Council

Discipline:

Food science

Sector:

Agriculture

University:

University of British Columbia

Program:

Accelerate

Designing Student Success: Building a Mobile Application to Improve Student Retention and Persistence

Ipse offers self-help to students transitioning to college or university to achieve their goals in a way that suits their personality. It uses machine-learning and crowdsourcing to recommend action plans to the students. The proposed research in collaboration with Ipse is aimed at furthering our understanding of personality traits and identification of suitable action plans based on those traits. Specifically we will survey a target population to identify common student traits and the associated action plans. We will propose advanced machine learning techniques to recommend an action plan based on a student’s personality. We will also explore various visualisation approaches to improve student participation. The research will allow Ipse to further develop/improve their product that would ultimately result in an engaged student population.

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Faculty Supervisor:

Yasushi Akiyama;Pawan Lingras;Steven Smith;Meghan Norris

Student:

Jonny White;Siddhartha Lahkar;Rishi Karki;Pratikkumar Gadhiya

Partner:

Ipse Media

Discipline:

Computer science

Sector:

Education

University:

Program:

Accelerate

Production Data Analysis From Mechanised Forest Operations

The aim of the study is to look at the available research on forest machinery production data and collect further data on forest machinery working in conditions that have not been studied before. In so doing we hope to determine what value the production data collected in British Columbia provides as a management tool when it is used in the conventional way and compare that to systems around the world that have access to the same or more advanced production data. By doing this we hope to create a better understanding of the unique challenges to management of forest machinery in British Columbia and how the production data system can be improved.

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Faculty Supervisor:

Dominik Roeser

Student:

Jacques Malan

Partner:

FPInnovations

Discipline:

Forestry

Sector:

Forestry

University:

University of British Columbia

Program:

Accelerate International

Automatic Understanding of the Semantics of Source Code For IdentifyingSensitive Code Fragments

Source code is what programmers write as instructions to the computer to execute to complete a desired task. All operating systems and applications on a computer or a mobile device is a runnable version of a compiled source code. Experienced programmers can easily browse and understand source code in different programming languages because they have the necessary technical background that is not available for every-day users. Those experienced programmers can identify parts of source code that are of interest (e.g., can make the program run 10x faster if improved) or pose a threat (e.g., if reverse-engineered can expose cleints’ personal information). With billions of lines of code available within private companies or in public code repositories, it is not scalable to ask experienced programmers to identify these parts of code. This project targets finding a way to automatically detect such parts using machine learning.

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Faculty Supervisor:

Ahmed El-Roby

Student:

Jeffery Zhang;Mariama Drame

Partner:

Irdeto Canada

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Carleton University

Program:

Accelerate

Design and Prototype Validation of a Bioabsorbable Flow Diverting Stent

An aneurysm is a balloon off a blood vessel in the brain, that could potentially bleed resulting in devastating consequences for the patient. Brain aneurysms a common, and are present in up to 7% of the general population. Traditional treatment of complex brain aneurysms involves placing a metal “flow-diverting” stent across the neck of the aneurysm, leading to redirection of blood flow away from the aneurysm dome. Bioabsorbable flow-diverting stents have only recently been developed by Fluid Biotech Inc. as a novel way to treat brain aneurysms. These stents are made from polymeric materials that are designed to dissolve after the stent is placed and the aneurysm heals. Little is known, however, about their absorption pattern and whether these stents would be prone to breaking apart as they dissolve, which could potentially lead to strokes and other complications. TO BE CONT’D

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Faculty Supervisor:

Alim Mitha;Andrew P Braun

Student:

Mehdi Jamshidi

Partner:

Fluid Biotech Inc

Discipline:

Medicine

Sector:

Life sciences

University:

University of Calgary

Program:

Accelerate

Develop data analysis software for improving operation management in making drinking water for small and rural communities

The project is to develop a middleware system for improving drinking water management system. The middleware integrates multiple data sources in addition to the real-time network data, including information of weather from satellite/ radar and water quality of surface water from remote sensing and then analyze them. It’s smart algorithms will predict and prioritizes events depending on the severity of the problem.

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Faculty Supervisor:

Alexander Rutherford

Student:

Reza Rezaei

Partner:

Aqua Intelligent Technology

Discipline:

Engineering - chemical / biological

Sector:

Information and communications technologies

University:

Simon Fraser University

Program:

Accelerate

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