Innovations 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

A data-driven framework for integrating visual inspection into injection moulding pipeline

Recent advances in machine vision has led to new opportunities for automating that entire manufacturing pipeline. Consider, for example, the situation where an unattended computer vision system inspects the widget and decides whether or not to discard it. Even this little amount of automation can save many hundreds of person-hours on a typical factory floor. While for simple designs, we now have automated inspection methods relying upon lasers, 3D scanning or other imaging modalities that can decide if a widget has any defect. For complex designs, this ability remains elusive. More importantly, however, automated inspection schemes can only decide if a widget deviates from its intended design, say available in the form of a CAD drawing, it cannot decide what changes should be made down the manufacturing pipeline to prevent similar defects in the future. This project aims to explore machine learning techniques that integrate automated inspection with manufacturing process. Specifically, we will focus on injection moulding process in this project. We will develop new theory and methods for characterizing the injection moulding process in terms of quantities measured via an automated inspection system.

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Faculty Supervisor:

Faisal Qureshi

Student:

Babak Delavarpour;Mohammad Shakirul Islam

Partner:

Axiom Plastics Inc

Discipline:

Other

Sector:

Professional, scientific and technical services

University:

University of Ontario Institute of Technology

Program:

Accelerate

Evaluation of Clustering Methods on Game Play Data

The goal of the project is to evaluate several clustering algorithms on players’ styles data in the context of Video
Lottery Terminals (VLTs). The previous work has shown that by segmenting anonymous player data by
sessions, and then clustering the sessions using the simple k-means algorithm, we can get a descriptive
statistic on player styles, including problem gambling behavior, recreational player styles, and similar. An open
question is whether the preprocessing techniques were optimal for this purpose and whether the k-means
algorithm is the most appropriate algorithm. In this project, of a number of clustering algorithms, such as
partition-based (e.g., k-means), hierarchical-based (e.g., hierarchical k-means), density-based (e.g.,
DBSCAN), model-based (e.g., statistical model based such as EM), and grid-based (e.g., STING) algorithms
will be evaluated and their performance on game play data will be analyzed.

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Faculty Supervisor:

Vlado Keselj

Student:

Soheil Latifi

Partner:

IGT

Discipline:

Computer science

Sector:

Arts, entertainment and recreation

University:

Dalhousie University

Program:

Accelerate

Production of renewable fuels from biomass pyrolysis using a dual spinning-disc reactor

Biomass is a key feedstock for the production of renewable fuels and chemicals with potential zero carbon emissions and at low cost. State of the art conversion of biomass to bio-fuels focuses on the pyrolysis of the feedstock at high temperature in conventional reactors. However, current technologies face many challenges to achieve lower costs than fossil fuels, higher yields, improved energy efficiency and product quality. This project aims to evaluate the production of renewable fuels from biomass using a dual spinning-disc reactor. With this technology, a combination of frictional pyrolysis and vortex mixing/separation offers the possibility to achieve high temperatures, shorter reactor residence and controlled operating conditions resulting in higher yield and selectivity towards the desired products. This project will build from proprietary knowledge of Vorsana Environmental Inc. and will set the grounds for use of novel reactor configurations for production of renewable fuels with higher efficiency and lower costs than conventional technologies.

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Faculty Supervisor:

Nader Mahinpey

Student:

Azhar Uddin;Yohannis Mitiku Tobo

Partner:

Vorsana

Discipline:

Engineering - chemical / biological

Sector:

Manufacturing

University:

University of Calgary

Program:

Accelerate

Automatic Verification of Comparators and Hash Functions

The implementation of data structures usually requires checking for certain mathematical properties such as equality. Those properties are usually implemented in methods that reason about the objects stored in these data structures. However, the implementation of such methods is fairly complex, and may exhibit software bugs that may not necessarily lead to program crashes. Therefore, it is often hard to reproduce such bugs. This project aims at developing an automatic method that verifies the correctness of the implementation of such methods, without the need to reproduce the bugs that may result from incorrect implementations. Our focus will be comparators and hash functions as prime examples of such methods that check for mathematical properties.

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Faculty Supervisor:

Karim Ali

Student:

Jaehyung Jeff Cho

Partner:

Synopsys Canada ULC

Discipline:

Computer science

Sector:

Information and cultural industries

University:

University of Alberta

Program:

Accelerate

Integration of Machine Learning and AI Based Optimization from IoT Datastreams and Business Information Systems

Internet of things (IoT) includes of multitude of sensors from a wide variety of applications. These sensors produce high volume and high velocity data. Recently there has been much interest in application of such technologies to improve agricultural practices. The sensors that are installed in the field transmit real time data regarding numerous environmental variables of interest. This data is then used to forecast a future state and to make a well informed business/operation decision according to an expected future state. One of the challenges in application of such technology is to improve prediction accuracy of the forecast. This project will design a generalized framework based on machine learning and artificial intelligence methodologies to improve prediction accuracy that will ultimately result in reduced operating costs and higher yields in agriculture sector.

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Faculty Supervisor:

Pawan Lingras

Student:

Anshul Hardat;Abhijith Santhosh Jaya

Partner:

Perennia Food and Agriculture Inc

Discipline:

Computer science

Sector:

Other

University:

Saint Mary's University

Program:

Accelerate

Non-destructive Testing of Backfill Strength in Support of Continuous Mining

Underground mines generate significant volumes of crushed waste rock, called tailings, but almost half of these can be returned underground and used as value-added backfill. This research uses non-destructive sensors that monitor the backfill’s strength in real time and provides operators with information needed to safely place backfill as quickly as possible. The sensors will be developed and deployed at operating mines so that they are validated under real-world operating conditions. Extensive laboratory testing conducted on the backfill materials will calibrate the sensors to backfill strength. Safely achieving continuous backfill operations will be key to the mining industry’s ambition of realizing continuous mining processes and improving the sector’s commercial and environmental sustainability.

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Faculty Supervisor:

Murray Grabinsky

Student:

Mohammadamin Jafari

Partner:

Kirkland Lake Gold Ltd

Discipline:

Engineering - civil

Sector:

Mining and quarrying

University:

University of Toronto

Program:

Surface water motility due to an imposed air flow for vehicle drying applications

Customer satisfaction with an automatic car wash is highly dependent on the quality of the final drying phase, with residual water leading to spots and thus poor perception of performance. As such, Suncor Energy is committed to improving the drying phase of their next generation car wash facilities through principled design guidelines based upon scientific investigation of water droplet movement on a surface. This project extends a previous study of the dynamics of individual droplets on an aluminum substrate to consider the aerodynamic interaction between multiple droplets as well as surface water films towards identifying air blower configurations and operational parameters that lead to maximal surface water movement. Specifically, the motion of arrays of droplets, droplet agglomerates, and surface water films are investigated using optical diagnostic tools towards developing predictive models of surface water motility and design guidelines for next-generation air drying modules for car wash facilities.

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Faculty Supervisor:

Sean D Peterson;Serhiy Yarusevych

Student:

Xueqing Zhang

Partner:

Suncor Energy Inc

Discipline:

Engineering - mechanical

Sector:

Mining and quarrying

University:

University of Waterloo

Program:

Accelerate

The nexus of high frequency, big, and long-term data – catalysing new opportunities to support drinking water treatment

Within the prairies, water treatment brings unique challenges. Source waters are often nutrient rich, and within lakes, this can lead to enhanced cyanobacterial bloom risk, and elevated dissolved organic matter leading to production of disinfection by-products. There are myriad challenges, which can be supported by improved understanding of source waters, and improved technology supporting decisions for water treatment, and water resource management. This work integrates proactive-long-term monitoring to develop systems to understand long-term changes in a key drinking water resource, combined with new sensor-based tools, and analytical approaches to genomics and toxin analyses to help inform water treatment. First steps include formalizing decision support for managing incidents of rising floc associated with cyanobacterial blooms and integrating data management systems to help support plant operations. Our partner organization is on the cusp of a ~$250million upgrade to support safe drinking water for the next 3-4 decades for 25% of the population of the province of Saskatchewan. This work will help bridge our best knowledge of changes in source water to changes in treatment minimizing costs, maximizing reliability and helping support their core mission of provision of safe drinking water.

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Faculty Supervisor:

Jason Venkiteswaran;Helen Baulch

Student:

Megan L Larsen

Partner:

Buffalo Pound Water Treatment Plant

Discipline:

Environmental sciences

Sector:

Other

University:

Program:

Development of Smart Analytics Software for Remote Water Quality Assessment

The management of quality of drinking water systems, river and lakes is a significant environmental challenge. In this research project, we plan to develop low-cost real-time water quality monitoring and analytics software, which can be used to analyze and predict water quality in remote lakes, rivers, drinking water plants and other water bodies. The Aquahive remote water monitoring system developed by the Aquatic Life Ltd., a Canadian company, will be deployed to capture the physical, chemical, and biological characteristics of the water quality in real-time. The primary objective of this research is to develop algorithms for performing anomaly detection and predictive analytics in real-time for assessing the water quality. The second objective of this research is to automate the process of analyzing and monitoring water quality by developing the software. The software will contain in-built functions for cleaning the collected data, visualizing the data, performing anomaly detection, and predicting the future values of the water quality variables in real-time.

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Faculty Supervisor:

Saman Muthukumarana;Miguel Uyaguari;Wouter Deconinck

Student:

Shruti Kaushik

Partner:

Aquatic Life Ltd

Discipline:

Statistics / Actuarial sciences

Sector:

Mining and quarrying

University:

University of Manitoba

Program:

Optimizing the prebiotic profile of donor human milk for preterm infants:feasibility of a new donor milk matching strategy based on maternal secretorstatus

Breastmilk is the best nutrition for a premature infant. When a mother’s milk is not available, the best alternative is donor human milk (DHM). Currently, DHM is pooled together from different mothers and there is no matching process based on the unique genetics or needs of the infant. This project will examine the possibility of developing a rapid test to match DHM to be more like the milk of each preterm infant’s mother, based on a genetic marker. We think that by doing this, we can help the infant to have a healthier gut microbiome. The research that we are doing could help the NorthernStar Mothers’ Milk Bank to change their processes to better match the needs of the infants who they serve. Also, our findings will be shared with the Weston Foundation to enhance understanding and evidence of the benefits of providing human milk to enhance the gut microbiome.

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Faculty Supervisor:

Meghan Azad

Student:

Meredith Brockway;Sarah M Reyes Paredes

Partner:

Weston Family Microbiome Initiative

Discipline:

Medicine

Sector:

Other services (except public administration)

University:

University of Manitoba

Program:

Design and implementation of plasmonic-enhanced ultra-high frequency terahertz transmitters and receivers

Terahertz spectroscopy for material imaging/sensing and characterization has received a great deal of attention over the past decade. Terahertz (THz) electromagnetic waves have frequencies in the range of 1012 Hz. Terahertz spectroscopy and imaging has many applications ranging from security, communication, food production, quality control for pharmaceutical industries, and cancer diagnosis. In the heart of every terahertz spectroscopy imaging system, there is terahertz transmitter and receiver pair antennas. In essence, the overall performance of terahertz spectroscopic systems will be enhanced by any improvement in characteristics of transmitter and receiver antennas. This project aims to improve THz generation and detection in transmitter/receiver devices, and produce ultra-high frequency detectors through utilization of nanostructures for plasmatic-resonance enhanced absorption.

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Faculty Supervisor:

Bo Cui

Student:

Navid Mohammad Sadeghi Jahed

Partner:

TeTechs Inc.

Discipline:

Engineering - computer / electrical

Sector:

Manufacturing

University:

University of Waterloo

Program:

Pollinator Steward Certification Program

The role of the intern will be assisting with the development, implementation, and evaluation of the Pollinator Steward Certification program in the Province of Ontario. In addition, research undertaken by the intern will seek to assess alignment of stewardship training with expected certification outcomes and current evidence of good practice as well as develop a ‘report card’ that brings together existing measures of socio-ecological well-being to assist conservationists and stewards to benchmark and assess their conservation efforts. Through the evaluative process that will include surveys, interviews, focus groups and site visits, this research will help provide an understanding of conservation engagement behaviour and the challenges faced by pollinator stewards, and how to better align stakeholder (participant) needs, collaborative conservation efforts, and evidence-based practice. This will benefit P2C by providing feedback on program delivery and outcomes, as well as metric development for ongoing evaluation and improvement.

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Faculty Supervisor:

Alison Blay-Palmer

Student:

Jennifer Marshman

Partner:

Pollinator Partnership Canada

Discipline:

Environmental sciences

Sector:

Professional, scientific and technical services

University:

Wilfrid Laurier University

Program:

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