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

Improved process modeling and optimization of the Birla Carbon Process

This project is a collaborative relationship between the Combustion Research Laboratory (CRL) at the University of Toronto and Birla Carbon. Birla Carbon is one of the largest manufacturers and suppliers of high quality carbon black additives globally. Carbon black has usage potential across an array of diverse application segments including rubber products, black pigment, UV protection and conductivity in plastics. Carbon black performance is determined by its fundamental properties including its particle and aggregate size, surface activity and physical form.
The objectives of this project are to improve and validate a model to predict the carbon black particle size distribution during the production process. This will increase our understanding of how the particles evolve during the process and will be used to better understand ways to control the process to produce the desired final product.

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Faculty Supervisor:

Murray Thomson

Student:

Carson Chu;Ali Naseri;Neil Juan

Partner:

Birla Carbon

Discipline:

Engineering - mechanical

Sector:

Manufacturing

University:

University of Toronto

Program:

Accelerate

Optimum landmark placement for improving accuracy of pedestriandead reckoning in indoor localization

Localization is a technical to find the location of a target, such as a worker walking in a plant. For outdoor localization, satellite-based global positioning system (GPS) is commonly used. GPS does not provide good accuracy for indoor localization due to the complicated indoor environment that affects the GPS signal propagation. Currently, there are two types of indoor localization methods, one is based on existing indoor wireless infrastructure (such as WiFi), and another is based on data collected by motion sensors that are attached to the target object. Both methods do not work well in many industrial environments. The WiFi coverage is either incomplete or unstable due to physical limitation or strong interference caused by machines; while algorithms using data from motion sensors can cumulate errors that exceed a tolerable level within short distances. Combining both methods helps improve the localization accuracy, but still requires sufficient WiFi coverage. Placing landmarks at fixed and known locations helps recalibrate the latter method by resetting the error to zero or near zero. In this research, we will study how to minimize the cost to place the landmarks, while achieving the required localization accuracy.

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Faculty Supervisor:

Dongmei Zhao

Student:

YiQiong Miao

Partner:

Muldi Vision Ltd.

Discipline:

Engineering - computer / electrical

Sector:

Professional, scientific and technical services

University:

McMaster University

Program:

Accelerate

Detection of enumeration attacks in cloud environments using infrastructure log data

Most computer services nowadays are provided in cloud environments. Inevitably, every individual needs to use these environments when they have to use computer services. Considering cyber threats in the cloud infrastructure, security and privacy conservation of one is really challenging. Out of date techniques are no more executable in these infrastructures. However, machine learning algorithms due to capable of handling massive data, are effective on this theme. In this project, we proposed machine learning algorithms to detect threats in the cloud environment. A basic user-friendly dashboard is developed for security analysts to conveniently monitor detected threats by this system. eSentire could benefit from this project by protecting its own customers owing to having state-of-the-art security solutions.

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Faculty Supervisor:

Ali Dehghantanha

Student:

Samira Eisaloo Gharghasheh

Partner:

eSentire Inc.

Discipline:

Computer science

Sector:

Information and cultural industries

University:

University of Guelph

Program:

Accelerate

Affine Multivariate GARCH Models

The objective of the proposed research program is to develop a flexible and unified multivariate framework for modeling the returns of financial assets. The program is innovative since it establishes closed-form formulas for an efficient and reliable calculation of risk measures and derivative prices. For financial institutions and government regulators, who are performing pricing and risk management calculations very frequently with thousands of assets, closed form solutions are of immense importance. The proposed research program is applied to the stock market since this is the centerpiece of financial markets and the only market for which the necessary comprehensive data are currently available, though generalizations to other markets are obvious.

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Faculty Supervisor:

Marcos Escobar Anel;Lars Stentoft

Student:

Javad Rastegari

Partner:

Canadian Derivatives Institute

Discipline:

Statistics / Actuarial sciences

Sector:

Finance, insurance and business

University:

Western University

Program:

Accelerate

Few-shot Generative Adversarial Networks

The most successful computer vision approaches are based on deep learning architectures, which typically require a large amount of labeled data. This can be impractical or expensive to acquire. Therefore, few-shot learning techniques were proposed to learn new concepts with just one or few annotated examples. However, unsupervised methods such as generative adversarial networks (GANs) still require a huge amount of data to be trained. As such, this project will focus on few-shot learning for GANs. This means that at inference time, the user can input a few images of a class never seen before by the model and the model can generate new images from that class. The proposed project will use a standard

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Faculty Supervisor:

Derek Nowrouzezahrai

Student:

Matthew Tesfaldet

Partner:

Element AI

Discipline:

Engineering - computer / electrical

Sector:

Professional, scientific and technical services

University:

McGill University

Program:

Accelerate

Numerical modeling and optimization of the coagulation-flocculation process for seawater pre-treatment

The result of this project can be used by engineers to design a multivariable control system that will optimize the dosing of chemicals in seawater pretreatment. Besides, the plant operators and technicians will be able to perform ‘what if’ scenarios based on computer modeling results to ensure that the outlet water quality is not decreased. The model will also provide an understanding of the effective parameters that determine the optimum coagulant/ flocculant dosage required for an effective water treatment process. This project is a part of a bigger project which is desalination of sea water, which can positively affect the Canadian community in case of water quality and availability. The results of the modeling can be directly used in that main project at Pani-energy.

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Faculty Supervisor:

Madjid Mohseni

Student:

Morteza Jafarikojour

Partner:

Pani Energy

Discipline:

Engineering - chemical / biological

Sector:

Energy

University:

University of British Columbia

Program:

Accelerate

Key standards, Industry Best Practices, Future Development for Cyber Security Strategy on BC Hydro’s Industrial Control Systems

Cyber security is fundamental to guarantee the reliable operation of the electric power systems. As the energy industry migrates to the digital space with information and communication technology (ICT), managing the electricity delivery is becoming complex and increasingly dependent on industrial control systems (ICS). The heavy reliance on ICT and the rapid penetration of ICS devices, however, have exposed the power systems to new cyber security challenges. In this project, we aim to investigate the key standards and industry best practices for BC Hydro to develop its cyber security strategy that can continuously improve its ability to detect and respond to cyber security threats to ICS. Based on the risk assessments and security requirements of BC Hydro’s ICS, we aim to provide customized solutions for BC Hydro to implement cyber security technologies and processes that comply with existing and future legal requirements to enhance the cyber resilience of its ICS.

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Faculty Supervisor:

Lutz Lampe;Vincent Wong

Student:

Yanan Sun

Partner:

British Columbia Hydro and Power Authority

Discipline:

Engineering - computer / electrical

Sector:

Energy

University:

University of British Columbia

Program:

Accelerate

Humic Land, a biological promoter of crop growth and the soil microbiome

Humic Land is a multi-purpose, 100% organic fertilizer that was produced from black peat using innovative technology that protects live soil microorganisms. It contains a microbial consortia that may produce plant-growth promoting substances, thereby acting as biological promotor of crops growing in stressful conditions. This research will evaluate three mechanisms that could explain Humic Land’s benefits to crops: (1) Humic Land promotes nutrient availablity and uptake by corn; (2) Humic Land contributes auxin-like substances that increase corn growth; (3) Humic Land improves osmoregulation in corn. These possible mode of action of Humic Land on corn and the soil microbiome will be evaluated in a controlled growth bench study by the intern, Naseer Hussain. The project will demonstrate the commercial potential of Humic Land for corn production in Canada.

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Faculty Supervisor:

Joanne Whalen

Student:

Naseer Hussain

Partner:

Rogitex International Inc

Discipline:

Resources and environmental management

Sector:

Transportation and warehousing

University:

McGill University

Program:

Accelerate

Evaluation of self-sovereign identity and ethics of data in public safety

Three in five persons with dementia will wander. This statistic however was derived from the USA, and it is unknown as to how this number was generated and what it specifically entails. The collection of Canadian-based data involving missing persons with dementia in Canada is limited. Secours.io’s initiative of collecting missing persons data generated from partners such as Project Lifesaver, could assist in filling this gap. Due to the sensitivity that arises from vulnerable persons data, this project will focus on identifying balanced, effective and ethical approaches for Secours.io to collect this data. This project will involve a literature review and interviews among key stakeholders across Canada and the USA. Seocurs.io mission is to transform public safety through the collection of data. A partnership with researchers, such as the intern, will provide credibility regarding effective and ethical data collection methods to inform and contribute to decisions about self-sovereign identity.

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Faculty Supervisor:

Lili Liu

Student:

Noelannah Neubauer

Partner:

Secours.io Ltd

Discipline:

Other

Sector:

University:

University of Waterloo

Program:

Accelerate

Understanding factors limiting Bull Trout (Salvelinus confluentus) abundance in the foothills of Alberta

Bull Trout (Salvenlinus confluentus) is a large fish species in the Salmonidae family, that is undergoing dramatic declines. This study will focus on stream reaches with critical Bull Trout habitat features in the foothills in Alberta, Canada. The objectives of this study are to: 1) determine temporal trends in Bull Trout abundance, and 2) assess competition between Bull Trout and invasive salmonids (e.g. Brown Trout, Brook Trout) and potential hybrids. Data will be collected and analyzed, comparing Bull Trout habitat and adult presence in 6 watercourses having different stressors (e.g. land use impacts and invasive species) for each watershed. The results of this study will help understand factors limiting Bull Trout in the foothills of Alberta and to develop appropriate management and recovery actions.

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Faculty Supervisor:

Mark Poesch

Student:

Jacqueline Pallard

Partner:

West Fraser Mills Ltd.

Discipline:

Resources and environmental management

Sector:

Agriculture

University:

University of Alberta

Program:

Accelerate

Development and application of a field method that evaluates propulsive force generation and transfer in Canoe Kayak Sprint

Canoe Kayak Sprint is a highly technical sport, where small changes in athlete stroke technique and/or boat movement can have large implications on race performance. Due to the complexity of these movements it has been difficult in the past to obtain equipment that is precise enough to measure kayak sprint technique accurately. With continual advancements in kinematics and kinetics measurement equipment/technology this problem can now be solved. The objective of this project is to create an instrumented kayak system (i.e. boat, paddle) that can be used to accurately measure kayak sprint technique in the athletes’ daily training environment. The researchers will develop and validate a kayak foot board, seat, and paddle which will measure the forces and moments (i.e. kinetics) being transferred from the water to the boat by the athlete. In addition, full-body and boat movements (i.e. kinematics) will be collected to better understand the temporal and spatial relationships of kinetics and kinematics during the kayak stroke.

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Faculty Supervisor:

Michel Ladouceur

Student:

Joshua Goreham;Kayla Bugeya Miller

Partner:

Canoe Kayak Canada

Discipline:

Other

Sector:

Arts, entertainment and recreation

University:

Dalhousie University

Program:

Accelerate

NetRepAIr: Making networks reliable for next-generation applications using AI/ML techniques

Networks have grown from small topologies connecting a dozen of devices to large, shared infrastructures supporting primary needs of our society. Today, we count on networked services for trading, commuting, monitoring weather conditions, meeting people. In order to provide reliable services, network operators need to cope with the daunting challenge of ensuring millions of flows from heterogeneous devices arrive at their destination on time and showing a reasonable throughput. Despite the significant advances recent Software Defined Networks (SDNs) provided towards managing large scale network infrastructures, they still fall short to enable fault-tolerant, performance-guaranteed data transmissions to the level next-generation applications such as 5G, smart cities, augmented reality and the Tactile Internet demand. In this project, we propose a new view to the problem of network reliability. Through Artificial Intelligence (AI) and Machine Learning (ML) techniques, we look for building a smart, highly scalable and robust network repair system. Our design will combine state-of-the-art machine learning techniques such as deep reinforcement learning and graph neural networks with high-performance and flexible network devices (e.g., P4 switches, NetFPGAs, and SmartNICs) to detect and correct network faults with high accuracy and in extremely short timescales.

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Faculty Supervisor:

Israat Haque

Student:

Miguel Neves

Partner:

Discipline:

Computer science

Sector:

University:

Dalhousie University

Program:

Accelerate

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