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

3D Reconstruction of sub-sea assets using Simultaneous Localization and Mapping (SLAM)

In this project our goal is to facilitate the 3D reconstruction of assets that are found in the ocean bed for the purpose of monitoring the state of the asset, especially to assess whether there is corrosion or cracks in the equipment. Through the use of high-quality subsea imaging, we can obtain images of assets that can be visually inspected for corrosion and cracks. A 3D reconstruction of the asset would be a valuable tool to communicate the location of the detected regions of interest, as the 3D model can be tagged with positional markers that indicate the exact location of the damaged regions. In this project, we will produce a prototype of such a 3D reconstruction system. The partner organization will benefit from the system in that it will allow them to accelerate the discovery and documentation of the places where the assets exhibit corrosion or cracks.

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Faculty Supervisor:

Oscar Meruvia-Pastor;Andrew Vardy

Student:

Mohammed Abdullhak

Partner:

qualiTEAS Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Memorial University of Newfoundland

Program:

Accelerate

Autonomous Motion Planning for a Safe and Efficient Last Mile Delivery Robot

In recent years, the North American population has become increasingly dependent on food and consumer product delivery. As a result of the current COVID-19 pandemic there have been surges in delivery demand. There are several active driving-based delivery methods, such as Uber Eats, however drivers are required to navigate through traffic, park, turn off their vehicle, exit and walk to the customer doorstep to drop products off. This cumbersome and inefficient final step of the service is known as the last-mile delivery problem. The last mile is time consuming, expensive, and environmentally unfriendly, especially in densely populated cities. Tinymile.ai is a company developing tele-operated wheeled mobile robots (WMR) to address the last-mile delivery problem and perform contact-less delivery amidst the current pandemic. These robots are semi-autonomous as operators control their movements remotely. The research objective is to develop an optimal, controlled motion planning approach to enhance functionality and controllability of WMRs when performing deliveries.

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Faculty Supervisor:

Jonathan Kelly

Student:

Ioakeim Norihisa Kaltsidis

Partner:

Tinymile.ai

Discipline:

Engineering - mechanical

Sector:

Transportation and warehousing

University:

University of Toronto

Program:

Accelerate

Kinetics study of Recycling Process of Spent Lithium Iron-phosphate Batteries

Lithium-ion batteries (LIBs) are powering a myriad of electronic and electric devices. There are many types of LIBs used for various applications including electric vehicles, electronics, and stationary energy storage. They are mostly based on lithiumcobalt cathodic compounds. Conversely, lithium iron phosphate (LFP) is a cobalt free cathodic material that is preferred over cobalt based LIBs for powering electrical and hybrid buses because of its relatively good energy density, its highly safe operation, its low cost, and its lower environmental impact. Nevertheless, the current recycling methods are not adapted for this material. Indeed, after being processed in current pyrometallurgical or hydrometallurgical recycling centres, the lithium, the iron, and the phosphorous composing LFP end-up in a solid waste. Therefore, a new recycling process based on selective hydrometallurgy was developed by Hydro-Québec to regenerate spent LFP as a new battery material. The current project is aiming to understand the reaction mechanisms and kinetics in order to optimize the process.

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Faculty Supervisor:

George Demopoulos

Student:

François Larouche

Partner:

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

Discipline:

Engineering

Sector:

University:

McGill University

Program:

Accelerate

Pickup and Delivery with Smart Service Times

The research problem to be addressed is the pickup and delivery problem (last mile) with precedence constraints for the pickup/delivery operations, and time windows. The objective is: (i) the design of a new algorithm that will use the algorithm of Curtois et al. [6] as a starting point, and to generalize it with precedence constraints, time windows and smart service times constraints and, the implementation and the testing of that algorithm with data sets coming from regular customers of Clear Destination, (ii) the design of a machine learning model and algorithm for the computation of the size of the time windows based on the type of goods and the characteristics of the pickup/delivery addresses, using the current available data, its implementation and testing , and (iii) a set of recommendations for the collection or the acquisition of more data related to the characteristics of the pickup/delivery addresses.
Benefit for the partner organization will be a more flexible and robust software for the planning of last mile pickup and delivery, with shorter and more accurate time windows. This will in turn increase the satisfaction of the destined clients of the pickup and delivery operations.

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Faculty Supervisor:

Brigitte Jaumard

Student:

Jean Lucas de Sozua Toniolli

Partner:

Clear Destination

Discipline:

Engineering - computer / electrical

Sector:

Professional, scientific and technical services

University:

Concordia University

Program:

Accelerate

Whales from Space: Surveying Baleen Whales in the Gulf of St. Lawrence using VHR Satellite Imagery

Ship strikes and fishing gear entanglement in particular have put right whales at risk of extinction with approximately 400 of this species remaining. Monitoring the remaining whale requires reliable detection of whales over large spatial scales. Traditionally, whale population size and distribution are estimated using trained experts on survey boats and planes. This project will develop an automated methods to visually detect endangered North Atlantic right whales from aerial and satellite imagery. The outcome of this project will allow for improved identification of whale location, improving the information available to ocean transportation by more easily identifying the location of right whales and allowing shipping companies and government partners to make more informed decisions about shipping lanes, vessel speed, and other movement criteria that may impact the reliability of shipping logistics with balancing the safety of the whales.

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Faculty Supervisor:

Sageev Oore;Boris Worm

Student:

Mirerfan Gheibi

Partner:

Global Spatial Technology Solutions Inc.

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Dalhousie University

Program:

Accelerate

Development of mathematical models/applied AI for estimation of compressive strength of concrete using non-destructive testing methods

The proposed project aims to apply artificial intelligence methods to augment in-place non-destructive testing technologies in order to reduce or eliminate the need for intrusive methods (i.e. concrete core extraction) for concrete strength estimation. The proposed approach is based on the SonReb method, which combines two non-destructive testing technologies, namely ultrasonic pulse velocity and rebound hammer, for assessing subsurface and near-surface concrete properties. The project will involve the development of a rich regional database that will be used to train an artificial neural network, which in turn will be used to develop algorithms/mathematical models that can be programmed into user-friendly software for rapid implementation in the field. It is expected that the project will ultimately result in the development of both a new software tool and a dual-purpose testing device that will result in new revenue streams and expanded market share for the project partner.

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Faculty Supervisor:

Martin Noel

Student:

Seyed Alireza Alavi;Aws Hasak

Partner:

FPrimeC Solutions Inc

Discipline:

Engineering - civil

Sector:

Professional, scientific and technical services

University:

University of Ottawa

Program:

Accelerate

Single-view 3D tracking and trajectory classification of road users at intersections

The goal of the project is to research and develop computer vision algorithms, software, and specialized hardware for the analysis of mixed traffic at intersections. Road users will be detected and classified as motor vehicles, pedestrians and bicycles. Road users will be geo-located within a 3D model of the intersection, tracked and classified according to trajectory. Our partner TransPlan will benefit in that they will be the Canadian receptor for the algorithms and software that Shahab generates. They will use this to automatic their current manual traffic analysis process, which will make them more efficient and improve their competitiveness in the market. With TransPlan, we will also explore wider licensing or sales of the technology as a product globally. As a leading technology-oriented transportation engineering company in the GTA with a long list of clients, TransPlan is well-placed to take advantage of this technology.

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Faculty Supervisor:

James Elder

Student:

Shahab Nabavi

Partner:

TransPlan Inc.

Discipline:

Other

Sector:

Transportation and warehousing

University:

York University

Program:

Accelerate

HI-DSR: Hyperspectral Images enhancement via Deep Sparse Representation model

The current non-destructive and fast method of hyperspectral imaging technology are used for different application from remote sensing to medical imaging and food processing. Due to the nature of acquired data which are massive as well as physical consideration depend on the type of application, the careful, fast and accurate data analysis is mandatory to accelerate the usage of HSI technology. To cope with the type of HSI data in which the sparsity assumption is applicable, this project aims to address the HSI data representation though the sparse problem under deep learning approach. Eventually, the research project will provide possible unique feature extraction spatially/spectrally to give more accurate/realistic results but provable in the sense of sparsity.

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Faculty Supervisor:

Saeed Gazor

Student:

Ahmed Faid Alrashidy;Mohammadkia Zamiri-Jafarian

Partner:

MatrixSpec Solutions

Discipline:

Engineering - computer / electrical

Sector:

Agriculture

University:

Queen's University

Program:

Accelerate

Architectural and Design Modelling for Blockchain-Intensive Systems

Blockchain technologies are increasingly becoming integral parts of information systems in domains that exhibit an increased need for resilience and can make no assumption of trust between parties. However, properly adopting blockchain in an information system design remains difficult, unsystematic and requires thorough understanding of the technology. In this project we explore ways by which traditional model-driven architecting and design techniques can be augmented to support incorporation of blockchain components. We do so by iteratively devising designs for a real world case in the crop supply chain domain and focus on the aspects of the process and the artifacts that are most affected by the inclusion of a blockchain component. This way we hope to acquire knowledge that can form the basis for the development of design patterns, language extensions and architectural decision support tools useful for systematic incorporation of blockchain technologies to information systems.

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Faculty Supervisor:

Sotirios Liaskos

Student:

Eli Yakubov

Partner:

Grain Discovery

Discipline:

Other

Sector:

Finance, insurance and business

University:

York University

Program:

Accelerate

Security Analysis of POLARIS

Blockchain and distributed ledger technologies provide networks a method to ensure trust in the network while not relying on a central authority, or even any individual node in the network. Moreover, these technologies enable secure transaction and recording keeping within the network by using distributed consensus to verify the actions of users and cryptographically linking blocks of transaction with one another. A crucial component of the security of these technologies is the use of a secure commitment scheme for users to submit transaction to the network before execution, thus keeping users honest. Additionally, it is important for the details of verified transaction to remain secret until the appropriate time to complete the transaction. While traditional blockchains and distributed ledgers attempt to achieve the previous security needs they are still unable to address a core limitation of the protocols themselves. Traditional blockchains and distributed ledgers are unable to provide a fair ordering of transactions. This project aims to prove the security and functionality of the POLARIS protocol which addresses the two listed security needs and provide a consensus on the correct order of transactions for the network.

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Faculty Supervisor:

Atefeh Mashatan

Student:

Dawson Brown;Brian Goncalves

Partner:

TrustWave

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Ryerson University

Program:

Accelerate

Development of an Oral Reovirus-Based Vaccination Platform for COVID-19

It is essential to develop a vaccine against SARS-CoV-2, the virus causing the global COVID-19 pandemic. The most efficient vaccines are built on attenuated live viruses, which can be engineered to display specific antigens and, once administered in humans, can safely induce an immune response and immunity to the disease of interest. Fast, reliable, and safe platforms are needed to develop a COVID-19 vaccine and move promising candidates to clinical trials. To support global vaccination campaigns, the vaccine should be easily produced, stored, and administered. Identifying the best possible vaccine candidates early will avoid costly delays in clinical trials. We have identified a novel viral platform based on an anti-cancer virus called Reovirus. We will use this platform to develop safe, reliable, and stable COVID-19 vaccine candidates that can be orally administered and efficiently produced. Our goal is to develop up to 25 vaccine candidates within one year. We will test promising vaccine candidates in cell and animal models to deliver the evidence needed to move the best candidates to clinical trial.

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Faculty Supervisor:

Tommy Alain

Student:

Xiao Xiang

Partner:

Genvira

Discipline:

Biochemistry / Molecular biology

Sector:

Professional, scientific and technical services

University:

University of Ottawa

Program:

Accelerate

Anion Exchange Membranes for use in Industrial Electrolysis Systems for Salt-Splitting

Salt splitting is a technology in which an electrochemical cell containing 2 membranes to transport positive and negative ions, is used to produce sulfuric acid and caustic soda from sodium sulfate, a compound found commonly from industrial brine streams. Salt splitting electrolysis is a sustainable solution for the expanding markets of acid and caustic recovery and treatment of neutralization waste products, which would otherwise be disposed. One of the major challenges of this technology is to produce a relatively concentrated sulfuric acid product with high efficiency and low power consumption, due to leakage through the anion exchange membrane. Therefore, developing an anion exchange membrane with the capability of effectively blocking protons is key to the success of this technology.

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Faculty Supervisor:

David Dreisinger

Student:

Zizheng (Jackie) Zhou

Partner:

Ionomr Innovations Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of British Columbia

Program:

Accelerate

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