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

Detecting noise and artifact in CW ultrasound signal processing using machine learning and cloud-based tools

Continuous wave (CW) ultrasound systems are extremely sensitive to movement, noise, and artifacts of reflective tissues within the body that return doppler ultrasound signals to the receiver. In the application of CW ultrasound to clinical applications, classifying and handling noise/artifacts is essential for broad clinical adoption. A machine learning (ML) algorithm is commonly used for pattern recognition of large sets of data, such as physiological signals, and it has been used recently for biomedical applications. Moreover, cloud computing allows the execution of large and complex calculations without the need for expensive or dedicated hardware. The aim of this project is to develop cloud-based computing tools in the Python programming language that will annotate, classify, and label physiological signals. These annotations and labeled signals will then inform and feed digital and ML signal processing methods. Furthermore, process automation for new incoming data and quality assurance checks for the remotely acquired signals will also be developed as cloud-based tool.

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Faculty Supervisor:

Jeremy Brown

Student:

Alejandro Ivan Villalba Euan

Partner:

Flosonics Medical

Discipline:

Engineering - computer / electrical

Sector:

Manufacturing

University:

Dalhousie University

Program:

Accelerate

Mobile energy efficient tissue culture laboratory for northern agriculture development and crop improvements

Food production and agriculture in Canada’s far North is met with many challenges. Short growing seasons, poor soil, extreme weather all impact the ability to effectively and efficiently produce food locally. In addition, Northern communities are at the end of a South to North supply chain, suspectable to multiple disruptions, both physical and economic. Our mobile lab allows for exponential plant cloning through sustainable processes. It will provide access to big agriculture technology for farmers and food producers of all sizes for rural and remote communities, minimizing the impacts of supply chain disruption, producing resilient and hearty plants, and enable food sovereignty amidst times of crisis.
The partners, OUTFRNT and Cold Acres, will benefit through the commercialization and deployment of the lab. This initial research will allow for the deployment of a live feasibility pilot as this project is a first in Canada innovation (mobilization of micropropagation).

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Faculty Supervisor:

Pankaj Bhowmik;Zheng Liu;Michael Deyholos

Student:

Dustin MacLean;Brian McFadden

Partner:

ColdAcre

Discipline:

Biology

Sector:

Other

University:

University of British Columbia Okanagan

Program:

Accelerate

Development of MXene/Graphene composite electrodes for high performance capbattery

Burning fuels such as gasoline and diesel in our vehicles causes green house gas emissions and it is a major cause of global warming. If electric vehicles replace gasoline powered vehicles, the emission will be much lower. The peak power demand of an electric vehicle during acceleration is the key factor for its battery size. The large battery size causes less optimal use of energy and finally ends up with lower mileage in electric vehicles. These limitations could be solved by combination of battery system with supercapacitors. The project goal is to develop a high-performance capbattery device that can successfully integrate with batteries in partner organization’s electric vehicles and thereby greatly reduce the maximum power requirement of the battery. This will allow for a smaller battery size and faster charging and also reduce the cost of electric vehicles.

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Faculty Supervisor:

Venkataraman Thangadurai

Student:

Sanoop Palakkathodi Kammampata

Partner:

Nissan Motor Company

Discipline:

Chemistry

Sector:

Other

University:

University of Calgary

Program:

Prediction of AL Amyloidosis Using Machine Learning

AL amyloidosis is a rare protein disorder that can be often fatal if it is not diagnosed and managed early. This disorder is caused by misfolding of proteins that clump together and form amyloid fibril deposits in major body organs. Diagnosis of AL amyloidosis is often not easy as the signs and symptoms can be mistaken for common diseases. The median survival rate after diagnosis is less than six months when the underlying plasma cell dyscrasia is left untreated in AL amyloidosis patients. Hence, it is paramount to develop novel diagnostic methodologies that are not based on the signs and symptoms of AL amyloidosis, but are based on the underlying molecular mechanisms of amyloidogenic clone, which can provide evidences for predisposition much long before the disease sets its course on the body. We propose to develop a machine learning technique to predict whether a light chain amino acid sequence will form proteins that misfold and produce amyloid fibrils, leading to AL amyloidosis.

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Faculty Supervisor:

Bin Ma;Lila Kari

Student:

Anupa Murali

Partner:

Rapid Novor Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Waterloo

Program:

Accelerate

Digital Readiness: An Evaluation of Rural Broadband Models in British Columbia

Connectivity is a critical service, a foundational need to actively participate in the economy and society. However, rural communities in British Columbia (BC) continue to face challenges with connectivity. The COVID-19 pandemic has put a spotlight on this digital divide, demonstrating the inequities resulting from connectivity challenges.
Across rural BC, local governments have been increasingly taking ownership over connectivity. The result is a wide range of innovative approaches relating to infrastructure, network operation, and internet service provision. Little research has been done to investigate or understand these approaches, leaving communities with no information to demonstrate what enables models to be successful, or to help communities to understand what models may be appropriate to use.
The goal of this research is to identify, understand, and evaluate existing models of rural connectivity in rural BC.

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Faculty Supervisor:

Sarah-Patricia Breen

Student:

Mckenna Dubois;Ishith Nigam

Partner:

City West

Discipline:

Other

Sector:

Energy

University:

Selkirk College

Program:

Accelerate

Smart Dashboard for Sustainable Destination Decision Making Part 2

Destinations have quickly become victims of their own success. Destination Management Organisations (DMO’s) worldwide are making a much needed shift towards the inclusion of management alongside their marketing priorities for destination management, but are often ill equipped. There is a current gap in the marketplace for useful, comprehensive and user-friendly tools to assist them. This project will create a tool to access the information Ottawa Tourism needs, creating a tool that will be the first data driven tool to combat overtourism, improve resident sentiment, improve efficiency of resources and planning, increase destination revenue and overhead and improve overall destination health.

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Faculty Supervisor:

Phil Walsh

Student:

Michelle Novotny

Partner:

Ottawa Tourism

Discipline:

Other

Sector:

Professional, scientific and technical services

University:

Ryerson University

Program:

Accelerate

Cold active Enzyme Booster Technology (EnBooT®) for decontamination of petroleum hydrocarbons

Petroleum hydrocarbons (comprise crude oil, gasoline, and diesel) are an important energy resource used by industries and human. At the same time, contamination by oil and oil products has caused serious harm to humans and the environment and increasing attention has been paid to the development and implementation of innovative technologies for the removal of these contaminants. Bioremediation, which is based on certain species of microorganisms to metabolize petroleum hydrocarbons completely or partially, is considered a cheap and effective method to clean up contaminated water and soil. However, low temperatures can adversely affect the clean time in cold climate regions. Some bacteria are known as cold-active oil-eating strains that can use petroleum hydrocarbons as sources of energy for growth at low temperatures (<15C). These bacteria can produce biological agents (enzymes) in the presence of petroleum hydrocarbons which can be useful for the removal of petroleum hydrocarbon spills in a short treatment time. we proposed Enzyme Booster Technology (EnBooT) as a novel, green, and effective advanced bioremediation method. In this regard, the combination of cold-active enzymes will be used for petroleum hydrocarbons for soil clean-up in Canadian cold sites.

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Faculty Supervisor:

Satinder Kaur Brar

Student:

Saba Miri

Partner:

Incubate Innovate Network of Canada

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

York University

Program:

Digitization of supply chain routing decisions for freight transportation

Identifying an optimal routing, that ensures a delivery passage between suppliers and vendors, with a minimum cost while respecting the various constraints (including shortest delivery, availability of fleets and routes, traffic, etc.), could be a challenging task. This is mainly due to the need to solve a combinatorial optimization problem with discrete choices of pathways and routes that could become an NP-hard problem. Researchers and practitioners have adopted heuristic approaches to simplify the solution process. With the availability of large datasets, practitioners are increasingly looking into developing intelligent routing algorithms that learn from the data and extract rules for optimal routing decisions. The aim of this R&D project is to conduct research on digitization of routing practices in order to identify opportunities for upgrading a routing decision-making platform, previously developed by Hitek Logistics,

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Faculty Supervisor:

Fereshteh Mafakheri

Student:

Foad Esmaeili

Partner:

Hitek Logistic Inc.

Discipline:

Engineering - civil

Sector:

Transportation and warehousing

University:

Concordia University

Program:

Accelerate

Exploring the role of an innovative lean visual management technology in enhancing the efficiency of construction sites in Quebec

There is a need for a standardisation of lean visual management practices on construction sites to improve transparency of the work process, incorporating information into various workplace activities, ensuring predictability in micro-decision making and maximising collective intelligence. In this study our main aim is to identify existing VM conditions and the role of an innovative VM Rapid Check™ technology in adapting visual workplace framework on Quebec construction sites. The intern will be collecting data in the form of interviews and site observation to assess the status of existing VM conditions and study the impact of the Rapid Check™ technology on the site. This data would allow the partner organisation to convince the different stakeholders in the construction industry to use their innovative technology for a better workplace with less stress and better safety measures.

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Faculty Supervisor:

Mohamed Meguid

Student:

Antoneta Teresa Joseph

Partner:

Rapid Check Solution

Discipline:

Engineering - civil

Sector:

Manufacturing

University:

McGill University

Program:

Accelerate

Conditional Flow-based Speech-Driven Gesture Synthesis

Speech-driven gesture synthesis is the process of automatically generating relatable and realistic gestures given speech and high-level attributes such as speaker’s style. It is an active research area with applications in video games, animated movies, communicative agents, and human-computer interaction. Commonly, a database of gestures is manually created which are then triggered at different times by markup in dialog. This is a significantly time consuming and tedious step in animation pipelines. Recently, with the power of machine learning approaches, character animation has been pushed forward towards new boundaries. Yet, modelling speech-driven gesture synthesis using machine learning architectures has been proved to be difficult due to the particular characteristics and the nature of the human gesture. To this end, this project aims at pushing forward state-of-the-art speech-driven gesture synthesis performance through: (1) proposing a novel generative machine learning approach that can model natural variations of human motion and can modulate speaker’s style into the generated gestures, (2) capturing a new data set containing a large variation of gestures and styles, and (3) a qualitative evaluation of the proposed approach by comparing it with other baselines.

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Faculty Supervisor:

Nikolaus F Troje

Student:

Saeed Ghorbani

Partner:

Ubisoft

Discipline:

Engineering - computer / electrical

Sector:

Information and cultural industries

University:

York University

Program:

Accelerate

Development of a lymphatic in-vitro model to study drug uptake

The lymphatic system serves an integral role in fluid homeostasis, lipid metabolism and immune control of the human body. Lipid based nano-carriers employing lymphatic voyage offer multitude of advantages like enhanced bioavailability, selective targeting to localized as well as metastatic conditions, controlled delivery and others. Lipid-based nano-carriers have been tested on various experimental various in-vivo, in-vitro, ex-vivo and in-silico models. The in-vivo models provide the best drug transport estimation, but they are both invasive and irreversible. However, presently there is no in vitro model that can be used to accurately investigate the lymphotropic lipid-based nano-formulations. In this study, we aim at developing an in-vitro model for studying intestinal lymph targeted drug delivery including simulated lymphatic fluid and cellular environment and to establish a 3D model for lymphotropic drug transport. Attaining these objectives would open a wide door for the development of drugs targeting the lymphatic system for various indications and vaccinations.

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Faculty Supervisor:

Neal Davies

Student:

Malaz Yousef;Jieyu Zuo

Partner:

RS Therapeutics Inc

Discipline:

Pharmacy / Pharmacology

Sector:

Manufacturing

University:

University of Alberta

Program:

Accelerate

Evaluation of the antimicrobial activity of Hop aqueous extracts

This project intends to test the antimicrobial potential of hop extracts. Using different kinds of hops we will test our own extraction procedure (currently not in use industrially) against different types of bacteria and fungi to see if they can be used as an antimicrobial treatment. This antimicrobial treatment can be for both human use as well as agriculture, with emphasis on grapes. We will try to find the active ingredients in extracts that show promise to adapt for future antimicrobial research. The partner company will gain the data necessary to patent the extraction procedure and allow for commercial sale of extracts for their antimicrobial properties.

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Faculty Supervisor:

Silvia Cardona;John Sorensen

Student:

Anna Motnenko

Partner:

Nature ReCombined Sciences Inc

Discipline:

Biology

Sector:

Manufacturing

University:

University of Manitoba

Program:

Accelerate

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