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

Preliminary Fall Detection/Prediction Data Science Project

Fall detection systems, are targeted to older adults living alone to identify fall events and mitigate prolonged wait times to treat injuries typically. These systems use a wearable, such as a Ffitbit or Apple watch, paired with an algorithm to detect falls and alert caregivers or emergency services. However, large variability in type and circumstances of falls (e.g., from a small height with low impact) are a problem when trying to accurately detect and predict falls. The proposed approach aims to explore and exploit data gathered from NurtureWatch alarm to improve fall detection algorithms through the use of machine learning techniques. With the addition of machine learning Jabber Monkey can provide increased service and support to its clients in the event of a serious injury from a fall.

View Full Project Description
Faculty Supervisor:

James Tung

Student:

Mina Nouredanesh

Partner:

MoviWear

Discipline:

Engineering - mechanical

Sector:

Life sciences

University:

University of Waterloo

Program:

Accelerate

Measuring the effectiveness of a novel treatment of Chronic Lateral Epicondylitis: the ArmLock sleeve.

Lateral epicondylitis is a common source of lateral elbow pain and causes restrictions in performance during daily activities as the pain increases with wrist and hand movements. It is necessary to explore new treatments that decrease the symptoms of lateral epicondylitis. We aim to investigate the effects of a new non-surgical treatment (the ArmLock Sleeve) on pain, movement, and performance in daily activities in adults diagnosed with lateral epicondylitis. We also want to investigate the acceptance of the ArmLock Sleeve by the study participants. The partner organization will benefit by having its product (the ArmLock Sleeve) validated for use by its clients. The feedback will also help the partner organization to scale up its product by marketing it to a wider range of users and/or industries.

View Full Project Description
Faculty Supervisor:

Adriana Rios Rincon;Antonio Miguel Cruz;Christine Guptill

Student:

Ann Tran

Partner:

Tennis Elbow R & D Ltd.

Discipline:

Other

Sector:

Life sciences

University:

University of Alberta

Program:

Accelerate

Intelligent Cyber-Physical Situational Awareness for Smart Infrastructures

The availability of big data in smart infrastructures have become a strategical asset for operators to understand the situation of the infrastructure and monitor potential threats. However, most of the data still have not circulated beyond traditional corporation and technological boundaries, which have limited the visibility that could have been provided by the abundant data. To break the barriers and raise the awareness of situations for the smart infrastructure, the project will develop an effective framework based on networked microgrids, which employs artificial intelligence to collect, align and analyze the cyber-physical data to provide a clear understanding of the environment and events in networked regional power grids. The advanced situational awareness technique developed by the project will allow more accurate evaluation of the risks and more effective mitigations against them, so that the networked systems and infrastructures can be better protected in the incoming era of Internet-of-Things and 5G communications.

View Full Project Description
Faculty Supervisor:

Jun Yan

Student:

William Lardier;Moshfeka Rahman

Partner:

Ericsson Canada

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Concordia University

Program:

Accelerate

Transparent and Trustworthy Deep Feature Learning for Cyber-Physical System Security

The latest artificial intelligence (AI) technologies have effectively leveraged the wealth of data from cyber-physical systems (CPSs) to automate intelligent decisions. However, for safety-critical CPS like smart grids and smart cities, the conversion of massive data into actionable information by the AI must be not only effective but also reliable. To this end, this project will develop innovative feature learning methods that can distill raw spatiotemporal data, integrate with establish expert knowledge and system models, and present decision-supporting information with transparency and trustworthiness. With a focus on security monitoring applications in the safety-critical CPS, new scientific tools and practice guides developed by the project will benefit the research and development of AI-based & 5G-enabled CPS products and solutions for Ericsson while enhancing the smart infrastructure security for the general public of Canada.

View Full Project Description
Faculty Supervisor:

Jun Yan

Student:

Yongxuan Zhang

Partner:

Ericsson Canada

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Concordia University

Program:

Accelerate

Rapid assessment of decision biases using reach-decision tasks in web-based applications

Imagine being asked two questions during a job interview: 1) Are you more collaborative or more individual? 2) Would you prefer working from home or working in the office? Now imagine that you feel strongly that you are collaborative, and slightly prefer working from home. An interviewer might look at those two responses and feel they are contradictory. However, if they knew that you were more indecisive about working at home, it would make more sense. Here, we propose to use movement dynamics recoded via mobile apps to provide this more detailed decision information. For Paradigm, our partner organization that specializes in assessment, this new assessment platform will mean they can more easily access this rich decision information, resulting in better information collected from more people in less time.

View Full Project Description
Faculty Supervisor:

Craig Chapman

Student:

Alexandra Ouellette Zuk

Partner:

University of Exeter

Discipline:

Kinesiology

Sector:

University:

University of Alberta

Program:

Accelerate International

Sodium Manganese Oxide Coated with Polymers as Zinc and Sodium Dual Ions Battery Cathode

Rechargeable aqueous zinc sodium dual-ions batteries are considered as alternatives of lithium ion batteries because of their safety and low-cost. As an available cathode for the zinc sodium dual-ions batteries, sodium manganese oxide (NMO) shows relatively high specific discharge capacity. Polyaniline (PANI) is promising for coating NMO to stabilize the NMO system because it can supress the crystalline structure collapse. Metanilic acid is promising for doping PANI to increase the conductivity of PANI. In this research, we will modify different parameters such as the molar ratio of Na and Mn, the weight percentage of PANI coated on NMO to obtain better cycling performance than bare N

View Full Project Description
Faculty Supervisor:

Pu Chen

Student:

Yan Wu

Partner:

Enerclean Technology Ltd

Discipline:

Engineering - chemical / biological

Sector:

Manufacturing

University:

University of Waterloo

Program:

Accelerate

Bond Pricing AI Improvement

The fixed-income market consists of government and corporate bonds and other debt instruments which are used to finance operations and capital investments. The bond market remains heavily reliant on exchanges of information between counterparties and as a result information on prices is decentralized and market participants operate with different levels of information. The objective of this research project is to create improved Artificial Intelligence models which will allow market participants to better manage trading activities, manage risk, or make portfolio funding allocations. Improving the incorporation of relevant data to allow for more accurate bond price forecasting will contribute to the efficiency, stability, and competitiveness of Canada’s financial system.

View Full Project Description
Faculty Supervisor:

Cody Hyndman;Frédéric Godin;Geneviève Gauthier

Student:

Xiang Gao;Martha Zaverdinos

Partner:

Overbond

Discipline:

Statistics / Actuarial sciences

Sector:

University:

Program:

Accelerate

Variational methods for pipeline safety and data analysis

This project will explore the non-invasive ways to find potential leaks in buried gas distribution pipelines using sound propagation. When there is a sound source at one point of the pipeline, the nature of the sound coming to another point of the pipeline will depend on the properties of the surrounding soil, properties of the pipe and its integrity. We will study the mechanics of sound propagation in a buried pipeline surrounded by soil, using methods of modern mechanics. We will also use similar methods to formulate best practices of data analysis. Both of these topics are important to the competitiveness of Canadian industry. Preserving the integrity of gas pipelines is also important to limit GHG emissions in the atmosphere.

View Full Project Description
Faculty Supervisor:

Arturo Pianzola

Student:

Tagir Farkhutdinov

Partner:

ATCO Ltd

Discipline:

Statistics / Actuarial sciences

Sector:

Energy

University:

University of Alberta

Program:

Accelerate

Research, analysis and development of business processes for innovation service delivery in the aerospace market

The research activity is to collect, compile, and analyze relevant information from external open sources about specific topics impacting the aerospace market. Additionally, research into management and marketing theory related to processes, frameworks, and best practices for market analysis and investment decision-making will also be part of the project. Findings from the initial market research will used for further research, analysis and development of the new business processes to support the introduction of new services and innovation projects by PAL Aerospace into the global aerospace market.

View Full Project Description
Faculty Supervisor:

Tom Cooper

Student:

Ismael Golmohammadi

Partner:

PAL Aerospace Ltd

Discipline:

Resources and environmental management

Sector:

Aerospace and defense

University:

Memorial University of Newfoundland

Program:

Accelerate

Acceptability of Multimedia animations as preoperative multi-language guides for surgical patients in Montreal

Effective patient communication is challenging and obstacles such as health literacy, culture and language play a vital role in comprehension and can influence treatment outcomes. Tools to help healthcare professionals educate patients present limitations, particularly for patients with barriers to traditional teaching methods. Preoperative education helps patients to experience less anxiety, take an active role in decision-making process, and increase self-efficacy. Multimedia tools for patient education are effective at all ages and the use of audiovisual resources can improve learning and optimize clinical outcomes. This study aims to assess acceptability of creation and application of multimedia animation as preoperative multi-language guides for surgical patients in Montreal, as well as analyze patient satisfaction with the animations. Short animations will be created focusing on the holistic education of patients about preoperative information, offering an overview of the treatment, the surgical procedure and the postoperative phase.

View Full Project Description
Faculty Supervisor:

Dan Deckelbaum

Student:

Gabriel Schnitman

Partner:

Precare

Discipline:

Medicine

Sector:

Health care and social assistance

University:

McGill University

Program:

Accelerate

Development of Low Smoke Zero Halogen Smart Polymer Compounds/Nanocomposites for Wire and Cable Covering Material

Currently halogen-based flame retardant is widely used as cable covering material in various industries including oil and gas. Studies have concluded that these materials will produce toxic gases and acidic fumes, and also persistent against degradation in landfill. These negative consequences resulted in a global ban on halogen-based flame retardant. This project is a direct response to the global ban by developing new low smoke zero halogen (LSZH) flame retardant without the aforementioned problems. New LSZH additives developed by Shawcor will first be characterized to determine its flame retardancy and other properties. Next, innovative nano-additives will be studied and characterized. Possible interactions between multiple additives will then be investigated in order to further improve performance. Finally, the fabrication process will be optimized to make the new composite suitable for mass production. The outcome of this project will offer Shawcor a solution to replace halogen-based flame retardant in its product lineup in response to the global ban.

View Full Project Description
Faculty Supervisor:

Hani Naguib

Student:

Terek Li

Partner:

Shawcor

Discipline:

Engineering - mechanical

Sector:

Manufacturing

University:

University of Toronto

Program:

Accelerate

Conveyor throughput optimization at a distribution centre

FedEx Supply Chain, the 3PL provider for the Canadian Tire distribution centre located at Coteau-du-Lac, Quebec intends to improve its delivery performance to its customers’ retail stores, especially during the high-volume periods of the year. The focus is on improving the throughput of the conveyor system, as it is considered to be a critical part of the outbound process. This applied research project targets to develop a simulation model to identify bottlenecks and to predict the effects of different control levers that may be used to optimize the conveyor throughput. Managerial insights and recommendations based on the results will be provided to the Company.

View Full Project Description
Faculty Supervisor:

Satyaveer Chauhan

Student:

Alexandru Vana

Partner:

FedEx Supply Chain

Discipline:

Business

Sector:

Transportation and warehousing

University:

Concordia University

Program:

Accelerate

Previous
1…905906907908909910911…1,106
Next

Join a thriving innovation ecosystem.

Subscribe to our newsletter now!

Subscribe