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

LGBTQI2S Seniors’ Safety in Public Services

This project will result in a national environmental scan on LGBTQI2S seniors’ safety in health care, social care and municipal public services. It aims to identify promising policies and practices as well as systemic and structural barriers. It will also include the experiences of LGBTQI2S workers who serve seniors, a largely unexplored area. Egale Canada Human Rights Trust (Egale) and the Canadian Union of Public Employees (CUPE) are national organizations with common interests and distinct positions from which to influence change. They have identified this environmental scan as an important step in mapping out challenges and opportunities for collaboration and member engagement in education and advocacy. The results will be shared in a discussion paper, a fact sheet and lists of non/government service providers and champions, offering resources to improve safety for LGBTQI2S seniors and workers across the country.

View Full Project Description
Faculty Supervisor:

Susan Braedley

Student:

Christine Streeter

Partner:

Egale Canada Human Rights Trust

Discipline:

Social work

Sector:

Other services (except public administration)

University:

Program:

Accelerate

The Leisure, Sport, And Recreation Labor Market In Alberta: History And Current Trends

The project looks to explore the trends and directions of the labor market in the recreation, sport, and leisure industry for the Province of Alberta. Within this 6-month project, a review of the current literature in the human resource area of the field will be undertaken along with look at the current and past trends of the labor market within the province. These activities will lead to two chapters written for the partner organization which will help guide their future policies and directions as it relates to both awareness and advocacy of the industry to the general public and the various key stakeholder groups throughout the province.

View Full Project Description
Faculty Supervisor:

Brian Soebbing

Student:

Nanxi Yan

Partner:

Alberta Recreation & Parks Association

Discipline:

Kinesiology

Sector:

University:

Program:

Accelerate

Automated Fatty Liver Diagnosis

Up to 30% of the population has a Fatty Liver Disease (FLD, a condition in which fat builds up in your liver). Non-invasive ultrasound assessment of this liver condition is an increasing demand in healthcare service due to its high risks leads to advanced liver diseases. However, an ultrasound-based examination has made the manual inspection a lengthy and tedious task and observer dependent. The proposed research aims at a computer-aided liver ultrasound assessment software toolkit facilitating the diagnosis of FLD. In particular, this proposal will design computational methods, such as machine learning and recent deep learning, to automatically extract related features in ultrasound data, and to detect and grade the levels of FLD. This project is a pioneering attempt. In both industry and academia, there is limited prior work. It will provide a strategic base towards a full automatic liver ultrasound diagnosis system for the partner organization.

View Full Project Description
Faculty Supervisor:

Shuo Li

Student:

Zhifan Gao

Partner:

Medo

Discipline:

Medicine

Sector:

University:

Program:

Accelerate

Problem detection and aerial mapping for construction site management

On time discovery of problems and constant monitoring of construction sites have great economical benefit. It requires the capability of highly efficient and accurate object detection and segmentation algorithms that can work with coarsely labelled training samples. The project is aimed to develop new learning-based object detection and segmentation algorithms for problem detection and mapping of construction sites with high accuracy and efficiency. This project will improve operation efficiency for construction related projects. This project is also able to advance the application and research of advanced AI technologies in industries, which can increase the competitive advantage of Canadian companies in international market.

View Full Project Description
Faculty Supervisor:

Steven Waslander

Student:

Lei Wang

Partner:

SiteVue Incorporated

Discipline:

Aerospace studies

Sector:

Information and cultural industries

University:

Program:

Accelerate

Floatability and washability characterization of difficult to process coal seams at Teck Greenhills coal mine

Teck Coal produces metallurgical quality coal from the coalfields in southeastern British Columbia and it is the second largest exporter of metallurgical coal in the world. It operates four coal mines in SE BC. One of the Teck Coal operations has been experiencing difficulty in processing fine coal for several years. Overall, this situation has led to less than expected plant performance, resulting in some losses in production. The research undertaken in this project will develop a deeper understanding of the root causes of these processing problems and will work towards finding viable solutions at this processing plant.
The research performed by the student will advance Teck’s understanding of the problems and find solutions to the processing of fine coal from a planning, mining and processing perspective. TO BE CONt’D

View Full Project Description
Faculty Supervisor:

Maria Holuszko

Student:

Zijiang Yang

Partner:

Teck Coal Limited

Discipline:

Engineering - other

Sector:

Mining and quarrying

University:

Program:

Accelerate

Facial expression identification over a time series of images

Facial expression is a universal language to convey emotions and significantly affects social interactions. While psychologists have investigated facial expressions for decades, they have recently found their way into human-computer interactions and the gaming industry. A lot of research has been published on automatic detection of human emotions given either a single image or a series of images. In this project, we propose a new method for facial expression interpretation over a time series of images. We will first identify key facial expressions utilizing convolutional neural networks (CNN) and long short-term memory (LSTM) methods. Then, we will interpolate between these key facial expressions utilizing artificial intelligence while tracking head, eye, mouth, and eyelid. Finally, we will classify the emotions conveyed by the facial expressions over the entire time series of images. We expect to improve the state-of-the-art performance with this approach. Furthermore, we think that this approach would be more easily conveyed to the animation and gaming industries.

View Full Project Description
Faculty Supervisor:

W. Robert J. Funnell

Student:

Majid Soleimani

Partner:

SeekShift

Discipline:

Engineering - biomedical

Sector:

Information and cultural industries

University:

Program:

Accelerate

Quality and degradation assessments of polymer-lined thrust bearings by indentation and tribological testing

Current needs for renewable and emission-free technologies imposes hydroelectric power plants to generate power in a predictable and reliable fashion. Replacing metallic to polymeric coatings in thrust bearings allows hydroelectric turbines to operate at a wider range of operation parameters. However, the sensibility of polymeric materials to the manufacturing method imposes important uncertainties on the performance and longevity these materials can have in service conditions. In this project lab-bench tests will be used to critically evaluate the results of portable non-destructive tools that can be used to assess the quality of polymeric coatings used in thrust bearings. High-pressure and high-temperature immersion tests will force the material into an accelerated aging process. The effects of aging on the polymer performance will be tested into an environment simulating the turbine operation conditions. The research outcomes will enable Hydro-Québec to determine whether a polymer lining is suitable for continuous operation or not.

View Full Project Description
Faculty Supervisor:

Richard Chromik

Student:

Alexandre Nascimento

Partner:

Hydro-Québec

Discipline:

Engineering - other

Sector:

Energy

University:

Program:

Accelerate

Development and application of physiological markers of Grizzly Bear health

Grizzly bears reside on changing landscapes across Alberta, Canada. The goal of this study is to determine how disturbances in the landscape affect the health of grizzly bears. This will be monitored by analyzing the (1) expression of proteins in skin that are associated with energetics, reproduction, and stress and (2) concentrations of hormones in hair that are associated with reproductive status and long-term stress. In collaboration with the Foothills Research Institute (FRI), skin and hair samples will be collected from free-ranging grizzly bears in Alberta, Canada. Laboratory techniques have been developed at the University of Saskatchewan (UofS) to isolate protein from skin biopsies and measure hormone concentrations in hair. FRI’s expert knowledge of landscape changes and bear behavior will be combined with the physiological assessments by UofS to create a tool to detect compromised health in individuals and identify drivers of stress in threatened species managed by FRI.

View Full Project Description
Faculty Supervisor:

David Janz

Student:

Abbey Wilson

Partner:

Foothills Research Institute

Discipline:

Animal science

Sector:

Agriculture

University:

Program:

Accelerate

Detecting Company-Specific Purchase Evidence from Twitter Posts

Delphia’s business model revolves around using proprietary data sets and data extraction techniques to inform its active trading strategies on the financial markets.  It has been shown that detecting when Twitter users post about recent or future purchases has the potential to increase the accuracy of company sales forecasts, which in turn can inform stock trading strategies. This internship project aims to develop automated means to detect and quantify purchase related posts on Twitter.

View Full Project Description
Faculty Supervisor:

Yang Xu

Student:

So Hyun Park

Partner:

Delphia Inc.

Discipline:

Computer science

Sector:

University:

Program:

Accelerate

Intelligent Character Recognition (ICR), Optical Character Recognition (OCR) and machine learning based corrections of data transcription from scanned business documents

SS&C processes more than 80% of financial scanned and faxed documents in the US and requires large amount of manual labor in order to map information from a document into another form. Advances in neural networks applied to computer vision have produced text detection and recognition that nears human performance. This project will be leveraging these approaches to address the main challenge of applying image segmentation and character recognition techniques to large volumes of documents, namely the sensitivity of the process to phenomena like the variability of text, document formats and imaging conditions. The expected benefits of the project to the industrial partner are (i) reduction of human error in the document workflow, and (ii) faster turn-around times for customers of SS&C.

View Full Project Description
Faculty Supervisor:

Joseph Jay Williams

Student:

Chen Chen

Partner:

SS&C

Discipline:

Computer science

Sector:

Other

University:

Program:

Accelerate

Advanced Analytics in Multiple Sclerosis Research

The multiple sclerosis (MS) clinic at St. Michael’s Hospital (SMH) is among the largest in the world. While considerable data is collected from the MS clinic in both structured and unstructured form, the ability to glean this information to assess quality of care and conduct advanced analytics such as predictive modeling is limited. In this project, a quality improvement dashboard will be developed based on automation of clinical information extraction process. Predictive models will be used on existing clinical data to optimize treatment strategies and predict patient outcomes such as relapse rates, disability progression, and treatment failure. These models could then be used in clinical practice to identify high risk patients in a timely manner for appropriate follow-up and treatment optimization.

View Full Project Description
Faculty Supervisor:

Marzyeh Ghassemi;Muhammad Mamdani;Chloé Pou-Prom;Josh Murray

Student:

Zhen Yang

Partner:

Hoffmann-La Roche Limited

Discipline:

Computer science

Sector:

Manufacturing

University:

Program:

Accelerate

Design of the next-generation of content-based, context-aware product recommender systems

We are in the process of creating and growing a team of researchers expert in the field of machine learning and data-mining. Ultimately, our aim is to create solutions to eliminate the need to manually define personalization strategies. We are working with more than 1000 retail locations across North America and collecting large-scale datasets of customer behaviour. Through a data-sharing/consulting partnership we plan to perform research on the design of recommender systems and predictive models customized for the datasets available to retailers. These methods can be used in their physical and online marketing programs as well as in their dynamical promotions/pricing strategies.

View Full Project Description
Faculty Supervisor:

Jiannan Wang

Student:

Tommy Betz

Partner:

FIND Innovation Labs Inc.

Discipline:

Computer science

Sector:

University:

Program:

Accelerate

Previous
1…877878879880881882883…1,106
Next

Join a thriving innovation ecosystem.

Subscribe to our newsletter now!

Subscribe