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

Low-shear, scalable microfluidic sorting of platelets and leukocytes for pooled platelet lysate

Extem supplies research grade mesenchymal stem cells (MSCs) and is looking to differentiate themselves by providing a proprietary human-derived growth media to replace the cow-derived fetal bovine serum (FBS) typically used. Pooled platelet lysate (PPL) may be a viable alternative. However, existing PPL methods lack quality control, standardization, and can result in up to 50% cell viability loss. Extem would like to develop a more economical solution that integrates beneficial quality control and standardization. By collaborating with Dr. Gray at Simon Fraser University (SFU), the team proposes to develop a scalable microfluidic solution with high-throughput and low cell loss. The development of such an isolation technology would allow Extem to remain leaders in the stem cell market and open up new markets such as the platelet rich plasma therapy market.

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Faculty Supervisor:

Bonnie Gray

Student:

Sean Romanuik

Partner:

Extem Biosciences Corp

Discipline:

Engineering

Sector:

Medical devices

University:

Program:

Accelerate

Water Monitoring: Instrumentation and Software Research Project (2)

The Sustainable Water Governance and Indigenous Law Project (SWGIL) is funded by a SSHRC Partnership Grant. A key goal of the project is to create a prototype of an Indigenous-led, community-based water monitoring program. .By synthetizing modern technologies, with traditional stewardship practices, the project will empower Indigenous individuals and communities to actively engage in monitoring, protecting and conserving fresh water resources.

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Faculty Supervisor:

Mark Johnson

Student:

Theodore Eyster

Partner:

Brinkman and Associates Reforestation Ltd.

Discipline:

Environmental sciences

Sector:

Aboriginal affairs

University:

Program:

Accelerate

Three-dimensional simulation of Mississippi Lake for water quality management

Water quality concerns, such as algae blooms, are common in many aquatic systems across the country. Increasing development along waterfront properties and climate change are leading stressors causing poor water quality. In this project, an intern will apply a three-dimensional computer model to help understand how these stressors are contributing to poor water in Mississippi Lake, through a partnership between Queen’s University, the Mississippi Valley Conservation Authority and the Mississippi Lake Association. This investigation is designed to improve the understanding of nutrient movements within the lake and its tributaries, identify important sources of nutrient input to the system and to provide a predictive and exportable tool to guide future lake management strategies.

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Faculty Supervisor:

Leon Boegman

Student:

Nader Nakhaei

Partner:

Mississippi Valley Conservation Authority

Discipline:

Engineering - civil

Sector:

Natural resources

University:

Program:

Accelerate

High-fidelity robotic simulation framework for artificially intelligent medical robot

There is a great need and benefit to integrate artificial intelligence, robotic simulation, and robotics technology into Canada’s medical industry, hospital environment, and healthcare system. Robotic simulation technology, which has been successfully used for space rover missions such as NASA’s Curiosity rover and the Mars Pathfinder and Sojourner mission, provides the ability to create and test different types of robots with different hardware configurations, and develop their associated artificial intelligence systems. This project will develop a novel highly-accurate simulation framework specifically for medical applications in order to help realize and create real-world artificially intelligent medical robots. The project being proposed will use special algorithms and technology to advance the current state of robotic simulation research to new levels of detail, accuracy, and fidelity which ultimately will improve the healthcare of Canadians. TO BE CONT’D

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Faculty Supervisor:

Jinjun Shan

Student:

Scott Judzentis

Partner:

Microsat Systems Canada Inc

Discipline:

Engineering

Sector:

Medical devices

University:

Program:

Accelerate

Using Machine Learning to Optimize a Workflow Management System.

Workflow management frameworks support the creation of task dependencies and make efficient use of resources while running those workloads. Typically, these tasks can be long running processes like machine learning algorithms or access data from databases. Workflow management consists of mapping tasks to suitable resources and the management of workflow execution in a cloud environment. The goal of this project is to optimize the job scheduling algorithm using machine learning techniques in a workflow orchestration framework that manage workloads across a heterogeneous system. Our proposed approach applies a machine learning algorithm to workflow event logs to learn properties of resources required to perform a task. When a new process is initiated, the trained classifier can suggest a suitable resource to undertake the specified task. Adding these features to Rubikloud’s machine learning pipeline would improve the efficiency and scalability of the existing machine learning infrastructure. TO BE CONT’D

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Faculty Supervisor:

Eyal de Lara

Student:

Nisal Perera

Partner:

Rubikloud Technologies Inc.

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Towards a Universal Cloud-based IoT Platform for Smart Applications

With the Internet of Things technology, a multitude of small devices and sensors are connected to the cloud and social networks using the Internet. These devices generate a huge volume of data, which can be used to discover trends and profiles. This enables building diverse useful applications for our modern society. This project is a collaboration with the industrial partner ACME Engineering Products, which manufactures sensing technologies. Our objective is to design and test a universal, autonomic and adaptable IoT system that can be managed remotely by the end-user and which can be used in a variety of smart applications. Three Master students carry out this project in an industrial context and help them acquire industrial experience and make them valuable future employees in the embedded and IoT systems field.

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Faculty Supervisor:

Abdelouahed Gherbi

Student:

Vigninou Horace Horace Gandji, Mahjoub Ghoudi, Ilyass Arbaoui

Partner:

ACME Engineering Products Ltd

Discipline:

Engineering - civil

Sector:

Information and communications technologies

University:

Program:

Accelerate

Direct Recycling Process of Spent Lithium Iron-phosphate Batteries

Lithium batteries, developed during 80’s, are used more and more as energy sources for electronic devices, hybrid or electric vehicles or other uses. Another application currently in development is the use of lithium batteries to stabilize the energy grid system and energy fluctuations from renewable energy sources such as solar and wind power. Hydro-Québec is developing such large-scale energy storage system based on a lithium battery technology called “Lithium iron-phosphate”, developed at Hydro-Quebec. However, the use of such storage system will eventually generate, after about 10 years of usage, a significant amount of spent batteries. Their recycling will reduce our ecological footprint and embrace the principles of sustainable development. The current research project is aiming to develop a process to recycle the spent lithium to produce fresh material to build new batteries. 

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Faculty Supervisor:

George Demopoulos

Student:

Direct Recycling Process of Spent Lithium Iron-phosphate Batteries

Partner:

Institut de Recherche Hydro-Québec - Laboratoire des Technologies de l'Énergie

Discipline:

Engineering

Sector:

Environmental industry

University:

Program:

Accelerate

Validation of novel balance assessment software using Microsoft Kinect v2.0

Objective and reliable assessment of balance and postural sway has the ability to drastically improve the screening process for risk of falls among elderly populations. Standard assessment protocols and questionnaires are in place, however these do not provide objective, reliable fall prediction. Standard concussion assessment protocols determine the presence and severity of a concussion, as well as the athlete’s return to play fitness by measuring balance and postural sway. However, these tests lack objectivity, and exhibit poor inter-examiner reliability. With the commercially available Microsoft Kinect v2.0 camera, Kinetisense, Inc. uses marker-less motion capture to measure postural sway in all three planes of motion objectively, and reliably. The Kinetisense software will be validated against gold standard Vicon motion capture and force plate data. Validation of the software is a critical step towards providing clinicians and researchers with the means to better understand balance and postural sway for a variety of populations.

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Faculty Supervisor:

Reed Ferber

Student:

A.J. Macaulay

Partner:

Kinetisense Inc

Discipline:

Kinesiology

Sector:

Medical devices

University:

Program:

Accelerate

3-D imaging of plants and vegetables

The agriculture industry is a labour intensive industry. Using a reliable method of plant monitoring can greatly help farmers to reduce their labours and consequently their production costs. Creating an accurate 3-D model of each plant or vegetable provides farmers with more information about the growth stage of the plant which helps them to make smarter decisions for irrigation and harvesting. There are various 3-D technologies available in the market. The aim of the current project is to explore a variety of 3-D image processing techniques to create the accurate 3-D model of selected plants and vegetables. The partner organization would benefit from this project by having a reliable 3-D model of plants for automated irrigation.

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Faculty Supervisor:

Kenneth McIsaac

Student:

Leila Bahman

Partner:

Vineland Research and Innovation Centre

Discipline:

Engineering - computer / electrical

Sector:

Agriculture

University:

Program:

Accelerate

Linguistic Data Science for the Development of a Business Corpus

This project is dedicated to the development of a new business corpus as a novel data for the company’s business intelligence. It focuses on linguistic pre-processing for the business domain using two types of collected corpora: text and speech. An automatic annotation of the pre-processed business corpus will be completed using labels related to sentiment analysis and emotion mining technologies. Specific rules will be used to strengthen these labels. Last, a cognitive social analysis on human behaviors and team dynamics will be completed within a business meeting.

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Faculty Supervisor:

Fatiha Sadat

Student:

Alexandro Fernandes da Fonseca

Partner:

Winning Acuity Ltd

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Development of low-cost feeding strategies for group-housed gestating sows – Year 2

Feed restriction in gestating sows is required to prevent excessive body weight gain and the associated negative consequences on lactation, locomotion, farrowing, and feed intake during lactation. Aggression and stereotypies associated with restricted feeding become a welfare and production concern when the sows are housed in groups. Delayed gastric emptying, increased swelling of contents in the stomach, and/or fermentation metabolites associated with bulky or high fibre diets during gestation may increase feelings of satiety and ameliorate behavioural problems associated with restricted feed intake. In addition, feeding high-fibre diets to sows may result in an increase in litter size and growth performance of offspring. The proposed project will seek to examine the effect of feeding processed straw on metabolic indicators of satiety and behavioural measures in group-housed gestating sows and growth indicators of litter performance.

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Faculty Supervisor:

Daniel Columbus

Student:

Atta Kofi Agyekum

Partner:

Saskatchewan Pork Development Board

Discipline:

Animal science

Sector:

Agriculture

University:

Program:

Elevate

Development of Graphene Modified Cathode for Next Generation Aqueous Rechargeable Lithium Batteries – Year 2

Aqueous rechargeable lithium battery has received great attention recently due to the less toxicity, lower cost and higher safety compared to the non-aqueous systems. When using the commercially available lithium manganese oxide as active materials, there are demands in suppressing manganese dissolution and graphite consumption in the cathode. As a potential solution to achieve these goals, in this proposal, two dimensional graphene materials are integrated on the surface of the cathode, forming a hybrid cathode aqueous battery. Attributing to the unique physical and chemical properties of graphene, such rechargeable hybrid aqueous battery (ReHAB) is expected to show significantly improved electrochemical performance over traditional aqueous batteries, constituting a viable alternative to large scale energy storage application. The progress made by this research will be directly transferred to the industrial partner, POSITEC Group Canada, based in Toronto, to further optimize large-scale manufacturing and design start-stop energy suppliers in electric vehicles.

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Faculty Supervisor:

Pu Chen

Student:

Jian Zhi

Partner:

University of Waterloo

Discipline:

Engineering - chemical / biological

Sector:

Energy

University:

Program:

Elevate

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