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

Development of Broad Spectrum Hand Sanitizers and Disinfectants for the Fight Against COVID-19 Pathogens

COVID-19 first emerged in China in December 2019, resulting in one of the worst pandemics of the modern history. To this date (June 2020), about 6 million people have been diagnosed with COVID-19 and more than 360,000 people have lost their lives to this novel disease. The cleaning and disinfecting protocols developed by the World Health Organization (WHO) have proven to be highly efficient in breaking the chain of transmission of viral pandemic diseases. As such, alcohol-based and non-alcohol-based hand sanitizer and disinfectant formulations have been highly essential in containing this current COVID-19 pandemic. Due to the fragility of the global supply chains, most countries hit by the pandemic are currently facing severe shortage of sanitizers and disinfectants, and are looking into domestic production to meet the overwhelming market demand for these products. The outcome of this project will assist a major Canadian producer of surface cleaning products to shift production and deliver a remarkable volume of high quality and certified alcohol-based and non-alcohol-based sanitizing and disinfecting products into the Canadian market in the next few months.

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Faculty Supervisor:

Nariman Yousefi

Student:

Brett Lindenfield

Partner:

Walter Surface Technologies

Discipline:

Engineering - chemical / biological

Sector:

Manufacturing

University:

Ryerson University

Program:

Accelerate

Solid Fermentation of Barley

Canada is the fourth largest barley producer and the second largest malt exporter in the world, representing billions of dollars in domestic and international sales. Typically, barley is utilized as animal fodder, as a nutritional supplement for healthy foods and is also used in the production of alcoholic beverages and distilled spirits. The ethanol produced for alcoholic beverages can also be utilized in the production of petroleum products. Interestingly, ethanol production often results in significant amounts of thin stillage by-product. Analysis of thin stillage revealed the presence of the highly valuable cognitive nootropic, glycerophosphocholine (projected to surpass $10 billion in the next several years). Additional processing and enrichment of thin stillage can produce other valuable organic solutes and high-quality protein. Therefore, enhancing fermentation conditions, developing processing and purification methodologies, and demonstrating up-scale feasibility is of interest in contributing to the global supply of these coproducts, while maintaining current applications for ethanol production. Successful development of these processes will provide a means for the Canadian barley industry to adopt these supplemental technologies to produce additional sources significant revenue, without sacrificing current methodologies and processes of barley crop.

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Faculty Supervisor:

Martin Reaney

Student:

Yingxue (Cher) Hu

Partner:

Bioriginal Food and Science Corp

Discipline:

Forestry

Sector:

Agriculture

University:

University of Saskatchewan

Program:

Accelerate

Can deep learning algorithms be trained to automate the classification of information held in historical and repeat photographs?

Repeat photography is a valuable tool for evaluating long-term ecological change. Historical photographs used for repeat photography often predate conventional remote sensing data by decades, and the oblique perspective of the photographs capture details of the landscape absent in nadir imagery. To date, most approaches to quantifying landscape change using repeat photography have involved manual classification techniques (i.e. demarcating land cover categories by hand), which are time-consuming, expensive and difficult to reproduce consistently. The objective of this proposed research project is to develop a new approach that automates the classification process using deep learning models. Deep learning is a subset of machine learning in artificial intelligence, and has previously been used to dramatically improve the performance of tasks such as speech recognition, visual object recognition, and object detection. This project will configure and train a deep learning model to classify photographs, and assess the accuracy of these results using existing land cover data.

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Faculty Supervisor:

Eric Higgs

Student:

James Tricker

Partner:

Runtime Software Development

Discipline:

Environmental sciences

Sector:

Professional, scientific and technical services

University:

University of Victoria

Program:

Accelerate

Determining Impacts of Harvesting on Clam Beds in the Nanoose Bay Recreational Shellfish Reserve

The Nanoose Economic Development Corporation (NEDC) represents the Nanoose First Nation, who harvest clams from the Nanoose Bay Recreational Shellfish Reserve. Clams are a culturally significant food source for the Indigenous peoples in these traditional lands, and the potential overharvesting of clams in this area may impose irreversible negative effects.
By completion of a literature review, in-person surveys and observations, as well as observations from video footage of harvesting from the Shellfish Reserve, this project aims to determine the effects that potential overharvesting may have. The findings may be used to enhance conservation efforts for this culturally and ecologically significant species. This will not only benefit the partner organization, but will benefit the ecosystem in question, and the Indigenous peoples who rely on this species as a food source. If it is found that too much pressure is being placed on the clam beds, the Nanoose First Nation may propose installation of educational signage and advocate for stricter regulation or changes in policy to conserve the species.

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Faculty Supervisor:

Pamela Shaw

Student:

Celina Fletcher

Partner:

Nanoose Economic Development Corporation

Discipline:

Sociology

Sector:

Agriculture

University:

Vancouver Island University

Program:

Accelerate

Digital Storytelling for Social Justice: Impact assessment, Producer Approach, and Strategic Communication in an age of New Media

This study aims to evaluate and measure the impact of a Canadian produced documentary, I am Rohingya, A Genocide in Four Acts. By critically analysing the intended and unintended outcomes of this award-winning documentary, producers whose aim is to enhance their socially minded production process, can learn how to improve their techniques through feedback and by comparing their experiences to scholarly literature. Additionally, through a comparative analysis of Canada’s market of funding for social justice themed digital stories, producers and scholars can gain a better understanding of what requirements and accountability structures exist within a Canadian context that focus on the relationship between media practitioners and vulnerable populations. This study will be mindful of the importance of communication, networks, and the political spheres surrounding social causes in an effort to better understand if building ownership throughout the production process can increase distribution and change.

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Faculty Supervisor:

Wendy Rowe

Student:

Gary Hayes

Partner:

Innerspeak Digital Media Inc.

Discipline:

Sociology

Sector:

Information and cultural industries

University:

Royal Roads University

Program:

Accelerate

Data Visualization of GIS Data at Scale

Access to private or leased land is currently not a simple problem. Landowners would like to know who is on their land, when, and for what purpose. Getting permissions from landowners is often not a straightforward task which often leads to obtaining permission being bypassed. Some landowners do not want hunters, but do not mind artists or photographers accessing their land. Western Heritage is building the SaskLander Platform that provides an easy to use interface for landowners and those who would seek permission to access that land. Saskatchewan Polytechnic will be working with Western Heritage to provide a scalable visualization of GIS data that offers high level detail that can be drilled down to every piece of land across multiple provinces.

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Faculty Supervisor:

Terry Peckham;Susan Blum

Student:

Nathan Balaniuk

Partner:

Western Heritage Services Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Saskatchewan Polytechnic

Program:

Accelerate

Sustainable Management of Abandoned, Lost, or Discarded Fishing Gear in British Columbia: Through the Lens of Local Stakeholders

A review of current literature will be used to develop an enhanced record of knowledge on the topic of lost fishing gear in British Columbia; specifically looking at the socio-economic and ecological impacts, regionally applicable case study solutions, best practice framework recommendations, and current policies on environmental management of marine debris in Canada. Results will be utilized to develop a regional best management practices for BC ALDFG that can be applied by local stakeholders. The ideas will be developed on the basis of sustainability principles and regional community action, where an all-hands-on-deck approach will be required to tackle this problem and therefore, a recognition of different approaches to engagement needs to be understood. Shared knowledge and perspectives of local stakeholder groups (particularly fisheries and extended fisheries industries) should be valued and integrated into all subsequent planning for the reduction of regionally lost BC fishing gear.

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Faculty Supervisor:

Leslie King

Student:

Jenna Bright

Partner:

Archipelago Marine Research

Discipline:

Environmental sciences

Sector:

Professional, scientific and technical services

University:

Royal Roads University

Program:

Accelerate

Optimizing Machine Learning to Increase Relevance in Photo Selection for Private School-Based Social Media

Machine learning, specifically deep learning, for face recognition applications has advanced significantly over the past 20 years [ref: science direct survey on deep based facial recognition]. There are many deep learning concepts pertinent to face image analysis and facial recognition, and there is active research in outstanding problems ranging from effective algorithms to handle variations in pose, age, illumination, expression, and heterogeneous face matching. And research continues in data sampling, training and modeling to better understand and address issues related to bias. In addition to the advances in research and application, there is recent, and overdue, greater awareness of the impact machine learning and AI have on broad aspects of our society and personal privacy. Within the dynamics of technological advances and societal value, applications such as Vidigami’s private and secure school-based social media platform are seeking to evaluate and implement new functionality and policies to maintain the trust and provide relevance to its user community from students, parents, teachers and school staff. In this proposal research project, the intern(s) will review and evaluate the latest applied techniques to improve facial recognition (e.g. including use of non-facial characteristics such as height and context) and provide greater relevance to school communities.

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Faculty Supervisor:

Jiannan Wang

Student:

Sai Janaki Sathish

Partner:

Vidigami Media Inc.

Discipline:

Computer science

Sector:

Other

University:

Simon Fraser University

Program:

Accelerate

Evaluating Latency in Virtual Production Pipelines with Integrated Prediction Model for Motion Capture Data

Virtual Production (VP) is seeing a dramatic spike in interest and adaptation as the global film industry, particularly Hollywood, has been shutdown due to Covid-19. Virtual production is a broad term referring to a spectrum of computer-aided production and visualization filmmaking methods, and is also being used for broader applications from animation to industrial visualization. Of particular interest is the novel application of virtual production for live theatrical performance where machine learning is driving digital puppeteering from real-time motion capture for innovative and compelling storytelling. Machine learning for real-time motion mapping predictions is a key component of the live performance virtual production pipeline. However, serving predictions from trained machine learning models is emerging as a dominant challenge in production machine learning. These computationally intensive prediction pipelines must run continuously with a tight latency budget and in response to stochastic and often bursty query arrival processes. In this proposal research project, the intern(s) will review the latest applications and research related to latency issues in virtual production pipelines, with a focus on machine learning prediction.

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Faculty Supervisor:

Jiannan Wang

Student:

Zhou Jessie Cen;Sumukha Bharadwaj Balasubramanya

Partner:

AMPD

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Simon Fraser University

Program:

Accelerate

Design, Fabrication and Evaluation of a Small Scale and Low Cost Vertical Garden Hydroponic System

The main goal of this research project is to develop a sustainable fruit and vegetable production system for households and small communities through design, fabrication and evaluation a small scale and low cost vertical garden hydroponic system. One of the major agricultural challenges faced by Newfoundland and Labrador (NL) is the lack of locally produced fresh vegetables, primarily due to major barriers such climate, soil and lack of know how. This food insecurity can further be aggravated under sudden situations like COVID-19 where availability of food a real challenge with transportation and production restrictions. Hydroponic growing system could be a reasonable solution to address acidic and nutritionally poor soil as well as climatic limitations in NL. We expect to address complexity, high initial capital cost, and the high-energy consumption issues through development of a small scale and low cost hydroponic system.

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Faculty Supervisor:

Lakshman Galagedara;Raymond Thomas;Mumtaz Cheema;Mano Krishnapillai

Student:

Kamsika Jeyarasa;Elham Fathidarenijeh;Adelowokan Abiodun Abimbola

Partner:

Easy Grows Inc.

Discipline:

Environmental sciences

Sector:

Agriculture

University:

Memorial University of Newfoundland

Program:

Accelerate

Coda Continuous Delivery AI Platform

The Coda Continuous Delivery platform leverages advanced artificial intelligence techniques to accelerate innovation in software development and delivery. The project aims to improve the speed in which a software development team can achieve quality code and reduce the number of issues seen by customers in production.
Using a continuously learning platform, Coda catalogues historical issues and code updates and creates insights in near-real time that can be used by software professionals to serve customers better.

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Faculty Supervisor:

Jiannan Wang

Student:

Haoran Chen

Partner:

Striven Consulting Inc.

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Simon Fraser University

Program:

Accelerate

Advancing Climate-mediated Wetland Assessment and Change Detection using Earth Observation Data Fusion and Advanced Analytics

Quantifying the current extent of wetlands and how they have changed over the last number of years is important for understanding how wetland ecosystem services (including flood mitigation, food services, and migrating bird habitats) are sensitive to climate change. In this project, we will work with the partner organization to develop accurate methods for wetland identification and areal coverage using satellite and airborne remotely sensed data. The methods will be evaluated by the Mitacs Intern using ecological data collected at numerous boreal and parkland wetlands. In addition to this, the Mitacs Intern will quantify how wetlands are changing and the combined environmental characteristics that may make wetlands sensitive to climatic changes. The methods developed during this Mitacs internship will enable the partner organization to develop state-of-the-art methods for wetland characterization and monitoring, which are expected to increase revenue streams and continued collaboration with future industry partners.

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Faculty Supervisor:

Laura Chasmer

Student:

Nicholas Cuthbertson

Partner:

Hatfield Consultants

Discipline:

Environmental sciences

Sector:

Professional, scientific and technical services

University:

University of Lethbridge

Program:

Accelerate

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