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

Automatic Estimation of Walking Speed in Older Adults Using a Smartwatch

Walking speed is a fundamental indicator of health status in older adults that can be used for early detection of several chronic illnesses and smartwatches are promising tools for ambulatory measurement of walking speed. To address the problem of walking speed estimation in older adult using a smartwatch, arm swing motion during walking will be measured from older adults living in a long-term care facility. Mathematical models will be developed to automatically map arm swing motion to walking speed. The industry partner, Bigmotion Technologies is an ICT startup company which develops an Android-based medical grade wearable for elder care is an excellent candidate to bring the proposed technology into market. In a nutshell, the proposed research can significantly benefit the society of older adults by providing them with a cost-effectives solution for early detection and timely intervention of their neurodegenerative disorders and will improve their quality of life.

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Faculty Supervisor:

Edward Park

Student:

Shaghayegh Zihajehzadeh

Partner:

Bigmotion Technologies Inc.

Discipline:

Engineering - mechanical

Sector:

Medical devices

University:

Program:

Accelerate

Evaluating the impact of an educational arts program on adolescent socio-emotional and academic growth among inner-city, high needs schools

Capturing the impact of program performance on adolescent outcomes is an important way to understand the ways in which a program has best provided its services for optimal outcome success. However, there is limited literature on valid measurement of program success among arts-based educational programs. The project will undertake an outcome evaluation, which focuses on using evidence-based methods that can be validly and reliably used to capture adolescent outcomes that align with the program’s objectives. In other words, the goal is to align program goals and latent concepts, to measurable activities, ultimately informing observable measures that would indicate that a program’s outcomes are being achieved. Additionally, data will be gathered and analyzed throughout program sessions in order to measure program activities, outputs, and outcomes.

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Faculty Supervisor:

Kelly McShane

Student:

Sofia Puente-Duran

Partner:

Lakeshore Arts Committee

Discipline:

Psychology

Sector:

Education

University:

Program:

Accelerate

Enhancing Lateness Management in Cross-docking

Today’s marketplace is moving faster than ever, and companies are challenged to distribute their products more quickly, efficiently and cost-effectively. This has led to the rise of cross-docking in the global supply chain to help keep pace with customer demand. Cross-docking refers to the practice of unloading goods or materials from an incoming vehicle (e.g., train car, truck, vessel container) and then loading them directly onto outbound vehicles with no storage in between. A common form of cross-docking operations corresponds to single or multi-item pallets, which are unloaded, sorted based on their destination, and placed directly onto outbound trucks. This strategy allows transportation companies to move towards more proactive, agile and flexible supply chains, with shorter product cycles and easier product customization.
The objectives of the project are to improve the existing software tools that plans the scheduling of the incoming/outgoing vehicles of a crossdocking facility in order to reduce the lateness (tardiness/earliness) of the goods deliveries. In addition, we will explore the integration of machine learning tools in order to enhance those software tools.

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Faculty Supervisor:

Brigitte Jaumard

Student:

Mahdis Bayani

Partner:

Clear Destination

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Program:

Accelerate

Development and validation of training load metrics and models for predicting athletic performance

This series of projects will provide coaches and sport scientists with a greater understanding of the relationship between training and performance. While there are several methods for monitoring how much and how hard athletes train, how these can be best used to predict future performance is still in question. The sports of rowing and middle-distance running involve similar race demands, that being a full effort over 5-10 minutes. That said, the impact an athlete endures training for each is quite different and this can result in a limitation to time spent training in running relative to rowing. We will both investigate current methods of monitoring training for their use in making these predictions, as well as develop new approaches with the aim of even better predicting athlete performance resulting from the different training approaches taken by a coach.

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Faculty Supervisor:

David Clarke

Student:

Ryan Brodie

Partner:

Own the Podium

Discipline:

Kinesiology

Sector:

Medical devices

University:

Program:

Accelerate

Modeling the Risks and Damages from a “Potential” Invasive Plant Species: Yellow Starthistle in British Columbia

The purpose of this research project is to forecast the timing and estimate the costs of yellow starthistle (Centaurea solstitialis) invasion into southern British Columbia. Yellow Starthistle is an invasive plant that has caused tens of millions of dollars of damages to agricultural production in the United States as well as millions of dollars of costs in the form of reduction of soil moisture, losses of biodiversity and tourism. YST has been detected in states immediately adjacent to the Canadian border (Washington & Idaho), and due to climate change, the spread of YST into Southern British Columbia and Alberta is not a question of if but of when and where. Based on an extensive literature review pertaining to invasive species prevention and control, and bio-economic modelling we will outline a set of policy recommendations that minimise the risk of invasion and therefore control costs. The above recommendations will be passed on to the invasive species societies whose mandate is public education, as well as prevention and control of biological invasions.

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Faculty Supervisor:

Duncan Knowler

Student:

Sergey Tsynkevych

Partner:

Cattle Industry Development Council

Discipline:

Environmental sciences

Sector:

Agriculture

University:

Program:

Accelerate

Life Cycle Analysis of Kakwa Derived LNG for Power Generation and DistrictHeating in China

Natural gas is one of the cleanest fuels for heat and power generation. But in China, coal is still the dominant fuel
but burning coal has caused severe and damaging air pollutions. The partner organization of this project, Seven
Generations Energy Ltd., is a significant Canadian producer of natural gas. This project will comprehensively
assess the overall environmental performance of natural gas production (by Seven Generations) and exporting it
to China to replace coal. The expected results include an environmental dataset of the natural gas from its
extraction to end use and quantifying the benefit of replacing coal for district heating in China. The identified intern
is a current Master’s student working on similar analysis for biofuels. His skills and interest match well with this
project. And this project is initiated by the partner organization therefore they will get the study results to answer
the research questions they set.

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Faculty Supervisor:

Xiaotao Bi

Student:

Yuhao Nie

Partner:

Seven Generations Energy Ltd

Discipline:

Engineering - chemical / biological

Sector:

Energy

University:

Program:

Accelerate

Hydrolytic de-polymerization of hydrolysis lignin using alkaline catalysts: effects of process parameters and optimization

Hydrolysis lignins (HL) are a byproduct from acid or enzymatic biomass pretreatment processes such as the ones employed in cellulosic sugar and/or ethanol plants. They are mainly composed of lignin , unreacted cellulose and mono and oligosaccharides. These lignins are, to a great extent, covalently bonded to cellulose and/or hemicellulose to form lignin carbohydrate complexes (LCCs) thereby making them insoluble in alkali and most common organic solvents – this, obviously, limits the range of applications in which they can be used, in particular, as a bio-substitute for aromatic chemicals for the synthesis lignin-based materials such as lignin-based phenol formaldehyde, polyurethane and epoxy resins. To address the above challenges in the valorization of HL as a chemical feedstock, this research targets hydrolytic depolymerization of HL in water in the presence of an alkaline catalyst to obtain de-polymerized hydrolysis lignin (DHL) with a much lower molecular weight, a higher solubility in various common solvents and, as a result, a higher chemical reactivity. In this proposal, different types of alkaline catalysts will be tested as catalysts for the process. TO BE CONT.

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Faculty Supervisor:

Charles Xu

Student:

Zaid Ahmad

Partner:

FPInnovations

Discipline:

Engineering - chemical / biological

Sector:

Forestry

University:

Program:

Accelerate

Applied Machine Learning for Malware and Network Intrusion Detection

Wedge Networks is a leading cybersecurity solution provider in Canada. In this project, we aim to investigate the application of statistical machine learning and deep learning to cyber threat detection, aiming to detect both network intrusions and malware binaries transmitted in the network. Based on the big data collected from Wedge’s system logs and anonymized domain-specific data gathered from the clients of Wedge Networks distributed worldwide, we will investigate: 1) Distributed Denial-of-Service prevention and network intrusion detection based on both supervised and unsupervised machine learning techniques, and 2) shallow and deep neural network models for malware detection and prevention. To scale up to the big data at Wedge Networks, we will implement the developed machine learning and deep learning algorithms on distributed processing platforms such as Spark and TensorFlow. We will also integrate the learning-based threat detection module in the WedgeARP product line.

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Faculty Supervisor:

Di Niu

Student:

Rui Zhu

Partner:

Wedge Networks Inc

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Program:

Accelerate

Resource potential of the Post Creek Property, Sudbury, Ontario

Sudbury represents the site of a meteorite impact structure originally greater than 200 km in diameter and that formed 1.85 billion years ago. Despite the proven and potential economic benefits of resource development at Sudbury, there are still major outstanding questions concerning our understanding of the structure and its ore deposits. A series of objectives have been composed concerning the origin of Sudbury Breccia, host to footwall vein deposits, and Offset Dykes at the Post Creek locality and their mineralization. Fieldwork forms the basis for this proposed research, coupled with sample investigation using a range of micro-analytical techniques. The results of the proposed research will address significant gaps in our current knowledge of the origin and emplacement of Offset Dykes and Sudbury Breccia and will enable the resource potential of the Post Creek Property to be determined.

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Faculty Supervisor:

Gordon Osinski

Student:

Thomas Baechler

Partner:

North American Nickel Inc

Discipline:

Geography / Geology / Earth science

Sector:

Natural resources

University:

Program:

Accelerate

Increasing Patient Engagement and Informing Marketing Decisions through the use of Patient Personas and Patient Journey Mapping

Mobile health (mHealth) apps allow patients to practice self-care and manage their chronic diseases. Common functions in mHealth tools allow users to monitor their symptoms and mood, keep a thought diary, track medication use and trend information; this provides data that can be used to better understand patient behaviour to ensure that patient needs are being met. By using a user-centered design approach for app design, the patient experience is captured through understanding their goals and challenges as well as their journey in living with or recovering from chronic disease(s). The Health Storylines platform, offered by the patient analytics company Self Care Catalysts, offers self-care tools which can provide these valuable insights in order to improve the app design and inform marketing strategies for specific patient groups, which will in turn drive higher engagement levels and improve patient empowerment.

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Faculty Supervisor:

Aviv Shachak

Student:

Linda Kaleis

Partner:

Self Care Catalysts Inc

Discipline:

Business

Sector:

Medical devices

University:

Program:

Accelerate

Prediction Improvement on User’s Consumption

The goal of the research is to implement different data mining algorithms in order to improve the prediction on a user’s electricity consumption. The research will be dedicated to improve the existing algorithms or implementing new algorithms for the improvement of the prediction accuracy. Besides application of the prediction algorithms, different data pre-processing methods will be used. Research will include supervised and unsupervised modelling of the dataset by using the R programming language. As well, the segmentation of the customers based on the similarity measures in order to increase the prediction accuracy will be investigated. This research will lead to the improvement of the prediction accuracy which will bring more customers to the company as well as help the existing customers to save more energy, therefore more money.

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Faculty Supervisor:

Sabine McConnell

Student:

Vazgen Minasyan

Partner:

Lowfoot Inc

Discipline:

Computer science

Sector:

Energy

University:

Program:

Accelerate

Automated CNC processing of complex and high-aspect-ratio microfluidic devices for biomedical applications

Disposable microfluidic devices, also known as labs-on-a-chip, made out of plastic materials have seen increasing applications in chemical and biomedical analysis. In most applications, microfluidic devices usually incorporate small channels and chambers for micro sized dimensions, using heights between a few hundred to a few micrometers. Currently, manufacturing processes have been established to create these sub-millimeter deep features. However, in other applications, higher (or deeper) features of a few millimeters may be needed. Using the traditional microfabrication methods for such millimeter range features could be inefficient, low-quality and very time consuming. As a result, the internship project aims to study existing computer-aided milling and laser technologies, applying such technologies to the fabrication of components that are too tall to be processed by microfabrication, and develop assembling processes to install individually microfabricated parts and milled or laser processed parts together for a complete microfluidic device.

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Faculty Supervisor:

Julie Audet

Student:

Nikola Andric

Partner:

FlowJEM Inc.

Discipline:

Engineering - biomedical

Sector:

Information and communications technologies

University:

Program:

Accelerate

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