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

Investigating the efficacy of GelDerm* in detection of wound infection in a rat model

Burn injuries and wounds caused by burns are big health problems and in Canada alone cost nearly $290 million. Additionally, these wounds usually persist and become infected and subsequently drastically compromise patients’ health, result in significantly longer hospitalization, delayed wound healing, higher costs and higher risk of death. Therefore, prevention and management of wound infections have priority in treatment of burn patients. In order to early diagnose microbial infections in wounds and accelerate wound healing to such injuries, 4M Biotech under leadership of Dr. Akbari has developed a smart dressing in a form of a gel patch referred to as GelDerm* and confirmed it efficacy using in vitro and ex vivo models. The objective of the proposed study is to test the efficacy of GelDerm* in an animal model to evaluate the efficacy of GelDerm* to detect wound infection by sensing the variations in wound pH. We anticipate that the application of GelDerm would enhance the early detection of wound infection and thus provide the opportunity for timely interventions to treat wound infection.

View Full Project Description
Faculty Supervisor:

Aziz Ghahary

Student:

Navid Karimi

Partner:

4M Biotech

Discipline:

Medicine

Sector:

Life sciences

University:

University of British Columbia

Program:

Accelerate

XFEM-based fatigue crack growth simulation and surrogate model development for probabilistic remaining fatigue life prediction of pipelines

Pipelines have significantly contributed to the Canadian energy industry and overall economy. Specifically, nearly 60% of energy consumed in Canada comprises of oil and gas delivered through pipelines. However, in pipeline steel, many failures were caused by cracks during pipeline operation. The proposed research project aims at developing a reliable and effective tool to predict fatigue crack growth under cyclic fatigue loading. Specifically, this project will explore the use of the newly developed extended finite element method (XFEM) for fatigue crack growth modeling in conjunction with well-established fatigue crack growth laws. The XFEM models will be validated and further used to develop efficient data-driven models. The surrogate models will eventually be used with the data from real-world pipelines, including SCADA (supervisory control and data acquisition) data and ILI (in-line inspection) data to predict the remaining fatigue life of pipelines.

View Full Project Description
Faculty Supervisor:

Yong Li;Samer Adeeb

Student:

Durlabh Bartaula

Partner:

C-FER Technologies

Discipline:

Engineering - civil

Sector:

Oil and gas

University:

University of Alberta

Program:

Accelerate

Closed-loop flow control for microfluidic 3D bioprinting

Aspect Biosystems is developing a novel microfluidic 3D bioprinting technology that has the potential to fundamentally change the way many diseases are treated through the creation of functional human tissue. The technology manages highly complex fluid handling operations and requires sophisticated control systems to deliver reliable and repeatable results. This project is focused on developing such a control system specifically for fluid flow control through the microfluidic printhead. A successful, sophisticated flow control system comprises critical technology that will be essential for realizing clinical applications and is an important step toward realizing the full potential of the technology.

View Full Project Description
Faculty Supervisor:

Jason Hein

Student:

Sebastian Steiner

Partner:

Aspect Biosystems Ltd

Discipline:

Chemistry

Sector:

Medical devices

University:

University of British Columbia

Program:

Accelerate

Development and integration of feature detection algorithms for metal-based direct deposition processes

Metal-based direct energy deposition processes, such as robotic welding and laser powder fed additive manufacturing, ideally require feedback sensing of the deposition quality using vision detectors. Image processing algorithms are challenging to develop due to changing process operating conditions. Despite challenges, implementing in-process image processing algorithms is beneficial for traceability and quality assurance, for calibrating process models, and for developing closed loop control algorithms which are able to maintain deposition quality within acceptable quality margins. The objective of this research is to develop and integrate feature detection algorithms which are adaptive to the changing operating conditions typically present in metal-based direct energy deposition processes. Such algorithms are directly applicable to low-cost and industrially relevant high dynamic vision detectors. The outcomes will apply directly to future in-process vision-based sensing of features such as, but not limited to, process signatures (melt-pool size and shape, plasma plume characteristics, intensity map, particle ejections) and/or deposition qualities (geometry, continuity).

View Full Project Description
Faculty Supervisor:

Mihaela Luminita Vlasea

Student:

Gijs van Houtum

Partner:

Xiris Automation Inc.

Discipline:

Engineering - mechanical

Sector:

Manufacturing

University:

University of Waterloo

Program:

Accelerate

Biogeocemenation of a Coal Mine Tailings Pond

A decommissioned mine facility in Canada is looking for a new and innovative way to handle mine waste and reclaim the mine site. The potential solution to this problem is the use of microorganisms which are capable of producing calcite, or cement, as part of their natural biological process. These microorganisms will be combined with the mine tailings in test cells in the lab to produce a cement, which will then be tested for milestones like strength and moisture content. BGC Engineering Inc. is involved in this project and will provide access to their geotechnical testing facilities and assist in establishing meaningful protocols for sample collection and testing for the pilot scale experiments which can then be used in larger scales in mines across Canada.

View Full Project Description
Faculty Supervisor:

Pascale Champagne

Student:

Nicole Woodcock

Partner:

BGC Engineering

Discipline:

Engineering - civil

Sector:

Mining and quarrying

University:

Queen's University

Program:

Accelerate

Full-scale testing of a liquid cooling system for electric vehicle inverters

It is critical that on-board power electronic components of electric vehicle inverters operate within optimal temperature ranges. Failure to accomplish this results in overheating, oversizing and degradation of electronic components. Moreover, reduced efficiency and motor drive performance will have significant economical impacts on global automakers. This research will further contribute to developing a new thermal management system incorporating impinging-jet-based technology with liquid cooling, for improved heat transfer capabilities; a current prototype had been tested. This investigation will continue with a full-scale model and improved testing facility. A new method of supplying power, emulating electrical switches, will be implemented with the application of cartridge heaters and heater blocks. The objective is to fabricate the full-scale model and testing platform, conduct a comprehensive full-scale test and evaluate performance of the impinging-jet liquid cooling technology at various driving conditions. Favourable outcomes may lead to commercialization, enabling electric vehicles to operate with improved efficiency.

View Full Project Description
Faculty Supervisor:

Ram Balachandar

Student:

Corey Klinkhamer

Partner:

Magna

Discipline:

Engineering - mechanical

Sector:

Automotive and transportation

University:

University of Windsor

Program:

Accelerate

Investigation of the relationship between firefighting water additive formulation and environmental fate and toxicity

Firefighting water additives are a mixture of chemicals that are mixed with water to more effectively extinguish fires (i.e., residential, industrial, forest fires). The use of these additives is likely to increase in fighting forest fires due to the projected increase in forest fire occurrence and intensity due to climate change. Ingredients of firefighting water additives used in the past were found to be persistent and detrimental to the environment. The Canadian company FireRein is working to design new firefighting water additives that are effective at fire suppression but also pose a relatively low risk to the environment. This project will investigate the toxicity of individual ingredients used by FireRein and new formulations water additives designed by FireRein on aquatic and terrestrial organisms. The objective of this project is to provide data that will allow FireRein to optimize the formulation of their new water additives so that they are as effective at fire suppression as additives currently on the market while also pose a relatively low risk to the environment.

View Full Project Description
Faculty Supervisor:

Ryan Prosser

Student:

Jenna Anderson

Partner:

FireRein

Discipline:

Environmental sciences

Sector:

Manufacturing

University:

University of Guelph

Program:

Accelerate

Deep Fraud Detection

Financial fraud is a serious issue that is taking place globally and causing considerable damage at great expense. Statistical analysis and machine learning tools can help financial institutions detect different types of fraud. In some cases however, mislabeling and the cost of classification may actually increase the volume of ‘false positives’ for supervised methods. As the number of normal transactions in financial domains far outweigh the number of anomalous transactions, it is challenging to classify the anomaly labels. In this research project, a combination of semi-supervised and unsupervised Deep Learning methods will be applied to detect outliers from different perspectives.

View Full Project Description
Faculty Supervisor:

Hamid Usefi

Student:

Majid Afshar

Partner:

Verafin Inc.

Discipline:

Computer science

Sector:

Finance, insurance and business

University:

Memorial University of Newfoundland

Program:

Accelerate

Geosynthetic drainage for improved stability of fine-grained materials in slopes & embankments

Innovative geosynthetic drainage products have been developed that have the potential to significantly benefit the stability of constructed embankments or reconstructed slopes especially where these are constructed from soil (or soil-like materials such as tailings) that are finer and less permeable (and thus weaker) then free-draining coarse-grained granular fills. Applications include reconstruction and stabilisation of natural slopes, embankments or dams constructed of (or at least partly from) mine tailings or other finer-grained materials. The project will consist of testing the DrainTube® material to determine its ability to convey water in saturated and unsaturated conditions. A computer numerical model will be developed using the properties of the DrainTube®, and a small physical model will be constructed to verify the numerical model. Parameters including spacing of tubes, slope of the ground, soil permeability, infiltration, and anticipated rainfall will be analyzed. The numerical simulation will then be optimized based on these parameters, and design guidelines will be recommended. The project partners will benefit from results that allow the DrainTube® product to be used confidently in engineering design.

View Full Project Description
Faculty Supervisor:

Ian Fleming

Student:

Michael Gregory Andree

Partner:

Groupe CTT

Discipline:

Environmental sciences

Sector:

Professional, scientific and technical services

University:

University of Saskatchewan

Program:

Accelerate

High Performance Computing (HPC) of Full Waveform Inversion and Reverse Time Migration (FWI/RTM)

We will develop advanced software toolkits for seismic inversion and imaging. These method are called Full Waveform Inversion and Reverse Time Migration (FWIIRTM). The FWIIRTM will be used to obtain accurate 30 images and elastic properties of subsurface complex structures.

View Full Project Description
Faculty Supervisor:

Wenyuan Liao

Student:

Hassan Khaniani

Partner:

Absolute Imaging

Discipline:

Geography / Geology / Earth science

Sector:

Information and communications technologies

University:

University of Calgary

Program:

Accelerate

Application of Different Machine Learning and Data Mining Algorithms in the Detection of Financial Fraud

Detection of financial fraud is a priority for financial institutions. There are a variety of techniques and models that can be used to address the problem of financial fraud. However, as fraudsters are becoming more inventive and adaptive, they have been able to penetrate the conventional protective methods. This is one of the main reasons for the growth in financial fraud activity, regardless of the efforts of financial institutions and government and law enforcement agencies. This project investigates the use of artificial intelligence and machine learning algorithms to detect financial fraud.

View Full Project Description
Faculty Supervisor:

Sohrab Zendehboudi

Student:

Mohammad Mahdi Ghiasi

Partner:

Verafin Inc.

Discipline:

Engineering - computer / electrical

Sector:

Finance, insurance and business

University:

Memorial University of Newfoundland

Program:

Accelerate

A Dynamic Predictive Lead Scoring System for Inside Sales

Lead scoring is essential for lead management. The result of lead scoring is a list consists of leads with scores assigned indicating how likely each lead can be converted into the next stage of sales process. The Lamb or Spam and the Rule-Based are the two lead scoring methods that have been discussed in the literature. As various machine learning algorithms and artificial intelligence started to reemerge, predictive lead scoring models seem to be the next promising solution for lead scoring activity. This research project aims to develop a dynamic predictive lead scoring system that leverages on predictive analytics to automate lead scoring process based on historical customer data for a more accurate and reliable result. The outcome of this research project will demonstrate the value of application of data-driven predictive analytics in inside sales by offering business practitioners a model that can help optimize resource allocation and ultimately improve company success.

View Full Project Description
Faculty Supervisor:

Morad Benyoucef;Pavel Andreev

Student:

Migao Wu

Partner:

VanillaSoft

Discipline:

Engineering - computer / electrical

Sector:

Mining and quarrying

University:

University of Ottawa

Program:

Accelerate

Previous
1…929930931932933934935…1,106
Next

Join a thriving innovation ecosystem.

Subscribe to our newsletter now!

Subscribe