Innovative Projects Realized

Explore thousands of successful projects resulting from collaboration between organizations and post-secondary talent.

29670 Completed Projects

2811
AB
4990
BC
801
MB
663
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825
SK
8841
ON
9197
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95
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568
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1088
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Projects by Category

Intelligent Orchards: Redefining the Production and Management of Tree Fruits

One of the main challenges in tree fruit orchards is to accurately predict apple yield and identify the health of individual trees (e.g., healthy foliage, fruit development, detecting and identifying diseased trees). Manual performance of these tasks is labour intensive and costly. Therefore, automated processes provide novel solutions with enhanced accuracy, efficiency, and productivity. The proposed solution is to develop an automated system that utilizes machine vision and artificial intelligence strategies to accurately count flower blooms and apples, detect and identify diseased fruit and trees, and improve yield estimates for producers. Automating this process will reduce labour costs and improve apple yields for producers. The system will provide truly meaningful and easy-to-digest information to the farmer about the orchard, including the predicted productivity, suggested trimming, and possible growing issues (such as unhealthy trees or areas that require more attention).

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Faculty Supervisor:

John Cline;Mohammad Biglarbegian

Student:

Partner:

George Weston;Dr Robot Inc

Discipline:

Engineering

Sector:

Manufacturing; Retail trade

University:

University of Guelph

Program:

Accelerate

Visual-haptic Representation for Zero-shot Learning

Humans recognise objects in the world leveraging multi-modal sensory inputs beyond visual aspects (images and videos). Touch based information (Haptics) possesses rich information about structure, shape and other objetness properties. In this work, we will study and learn cross-modal representations between vision and touch. To connect vision and touch, we plan to introduce a zero shot classification task of recognising unseen object categories from shapenet dataset using haptics signals. We will train our model to encode the haptics information to a view agnostic embedding space that captures the geometrical aspects of the object. To support our claims, we will use shapenet dataset, a repository consisting of CAD models of various categories of objects that can be rendered from different views and the Johns Hopkins Modular Prosthetic Limb for haptics data. Our hypothesis is that our learnt representation can help transfer representations across modalities, for zero shot classification and object retrieval.

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Faculty Supervisor:

Aaron Courville

Student:

Partner:

ServiceNow Canada

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

Quantifying the oxidization of polysaccharides and optimizing dextran-bovine serum albumin glycation conditions for development of a new pneumococcal vaccine

Pneumonia remains the single leading cause of childhood death under age 5 worldwide. The price per dose of current vaccines is high and supply is limited due to a complex manufacturing process and low yield, significantly reducing its distribution in developing nations. A newly patented vacuo dry-glycation process promises much higher efficacy than the conjugation chemistry used currently, paving the way towards a much lower dosage cost. and its vaccine is a kind of polysaccharide-protein conjugate system. However, the process conditions required for activation of the polysaccharide by vacuo dry-glycation have not been optimized, which is linking with the properties such as molecular weight and oxidation ratio of activated polysaccharides and coupling ratio of conjugate products. This research addresses these deficiencies, enabling PnuVax Inc. to further the development of a more affordable vaccine that can be used in Canada and around the world to reduce childhood death due to pneumonia.

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Faculty Supervisor:

Robin Hutchinson

Student:

Partner:

PnuVax Inc (Kingston, ON)

Discipline:

Engineering

Sector:

Manufacturing

University:

Queen's University

Program:

Accelerate

Deep Unsupervised Anomaly Detection in Options Markets

In the last few years, a high increase in the interest of traders and investors towards financial instruments directly lead to an important augmentation of the information received daily by exchanges. Exchanges regulators, who constantly monitor markets to unveil potential infractions, traditionally perform their investigation manually and the notable growth in market activity represents an important risk of fraudulent events going unnoticed. In response to that new reality, exchanges around the globe are establishing automated surveillance systems that track markets activity. In this project, we set to design a new artificial intelligence algorithm that will detect anything in the Montreal exchange’s market that seems abnormal or fraudulent, so that analysts can focus on these alerts. Such a system could potentially detect fraudulent cases that are currently going unnoticed, while drastically reducing human costs and validation time.

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Faculty Supervisor:

Manuel Morales;Gilles Caporossi;Thomas Hurtut

Student:

Partner:

Bourse de Montréal

Discipline:

Mathematics

Sector:

Finance and Insurance

University:

HEC Montréal; Polytechnique Montréal; Université de Montréal

Program:

Accelerate

AI for delivering product recommendation in retail consumer categories

E-commerce has evolved rapidly in recent decades resulted from globalization and international trade. The demand of online shopping is increasing every day, which has opened business opportunities to attract more costumers locally and globally. However, achieving satisfactory user experience in online shopping remains challenging compared to in-person walk-in shopping. Currently, customers have to input static text and images, or use webcam. Engaging interaction and automatic products recommendation is missing. In this project, we use eyeglasses as a use case to demonstrate how an AI-driven recommendation system can be designed and implemented for retail consumer categories. We will integrate image processing, computer vision and machine learning techniques to address the current issues of poor product recommendations. We will also create a basic deployment interface, enabling the trained model to integrate easily into a production environment.

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Faculty Supervisor:

Irene Cheng

Student:

Partner:

Eyevious Style

Discipline:

Computer science

Sector:

Professional, scientific and technical services; Retail trade

University:

University of Alberta

Program:

Accelerate

Using AI to generate mining algorithms

Hard-rock mining of uranium in Canada’s north is challenging and often difficult. Operating risk exposures are heightened when mining in high-grade uranium ore bodies that are exposed to possible flooding from water above the mine. To succeed in this environment Cameco has successfully mechanized their operations and relies on Jet Boring technology. This proposed project is planned to advance the visibility and automation systems for Jet Boring by: 1) creating large volumes of data created by ongoing measurement of the process; 2) collecting, analyzing, and synthesizing that data in order to form conclusions; and 3) confirming the effectiveness of using those conclusions to inform, control and direct operational decisions and automation of the mechanized Jet Boring systems. At this early stage, proof of concept level work will identify further opportunities that can point to or suggest a roadmap for further automation and optimization possibilities within mining.

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Faculty Supervisor:

Donna Beneteau;Terry Peckham;Douglas Milne;Cyril Coupal

Student:

Partner:

International Minerals Innovation Institute;Cameco Corporation (Saskatoon, SK)

Discipline:

Computer science

Sector:

Mining; Professional, scientific and technical services

University:

Saskatchewan Polytechnic; University of Saskatchewan

Program:

Accelerate

Knowledge Management Systems – Identification of best practicesregarding design & maintenance

SmartSimple is a growing Toronto based software company with operations in UK, Canada
and US. One core means of business generation is the response to RFPs. Because
responding to RFPs is such a central part of SmartSimple’s sales operations, the company is
looking to streamline the processes by using its platform to create a Knowledge Management
System (KMS). This research aims to identify not only the benefits and pitfalls involved in the
implementation of Knowledge Management System but also best practices to design such a
system. By-product of this research would be the creation of a marketable product for use by
existing and potential clients of SmartSimple. The academic output would ideally be a
research paper on KMS designs and applications. The practical value would be hands on
experience in designing and configuring a knowledge management system using
SmartSimple platform, which accompanied by extensive market research/analysis would help
SmartSimple to enter a new unexplored market of KMS

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Faculty Supervisor:

Wendy Cukier

Student:

Partner:

SmartSimple Software Inc

Discipline:

Business

Sector:

Professional, scientific and technical services

University:

Toronto Metropolitan University

Program:

Accelerate

Rural community pandemic research response at the Applied Research and Innovation Center – Selkirk College

This proposed rural community pandemic research response at the Applied Research and Innovation Center (ARIC) aims to collectively address and support rural issues related to the pandemic through research. The main themes support sustainable economic development and diversification, community resiliency, and sustainable community indicators for development and prosperity. Economic development and diversification will entail collaboration between different scales of human organization and stakeholders concerning economic projections related the pandemic, and models to help build resiliency and combat future rural community issues. In addition, applied rural pandemic adaptation and response may include smaller projects that define community-based strategies, expectations, and required supports in adapting to climate change. Throughout all of these themes, the need for a collective approach and collaborative research in the rural region of the Columbia Basin can be identified. The aim of this research initiative is to create a research response that provides the tools, skills, individuals, and resources required to support these themes for resilient local communities.

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Faculty Supervisor:

Lauren Rethoret;Ines Schrottenbaum;Jonathan Doyle;Sarah-Patricia Breen

Student:

Partner:

Community Futures Central Kootenay;Kootenay Association For Science and Technology;Kootenay Rockies Tourism;City of Nelson

Discipline:

Sociology

Sector:

Other services (except public administration); Professional, scientific and technical services; Public administration

University:

Selkirk College

Program:

Accelerate

Indoor Mapping based on recorded videos

Knowledge of indoor spatial information is vital to stores, warehouses, industries, and homes alike. It is used to optimize layouts to achieve easier navigation for humans, machines, and autonomous robots. Maps provide limited data about the specific placement of objects in the environment and inferring information about the physical space can be impossible. The objective of the study is: (i) to design a comprehensive indoor mapping solution based on the processing of previously recorded videos, (ii) experiment with sensor data to enrich the indoor mapping solution, and (iii) research spatial recognition patterns and optimal navigation to enable efficient spatial discovery and mapping. After a successful research, the partner organization will have a decent architecture for spatial recognition with recorded videos, and this can either be used to be integrated into an individual product or to assist the development of other diversify techniques.

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Faculty Supervisor:

Anthony Bonner;Sven Dickinson;Kiriakos Neoklis Kutulakos

Student:

Partner:

SOTI Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Quantifying the Effects of Stress and Mycorrhizal Relations on the Production of Phytocannabinoids in Rhododendron dauricum and its Fungal Symbiont Albatrellus ovinus

Rhododendron dauricum produces medicinal compounds that are similar to cannabinoids (e.g. CBD). Cannabis plants are exposed to stress to increase their production of cannabinoids. The effect of stress on the production of medicinal compounds in R. dauricum is unknown. R. dauricum has a symbiotic relationship with a fungus, Albatrel/us ovinus, that produces similar medicinal compounds. The effect of A. ovinus on R. dauricum’s medicinal compound concentration is unknown. This project aims to discover growing conditions to increase the yield of these compounds. Additionally, this project will create analytical methods that can be used to identify new cannabinoid-like medicinal compounds. Because these compounds are similar to cannabinoids they can be used as a reference material to identify and quantitate cannabinoids. Supra Research and Development is interested in producing reference materials to help screen natural health products for medicinally important compounds.

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Faculty Supervisor:

Michael Deyholos

Student:

Partner:

Supra Research and Development

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

The University of British Columbia - Okanagan

Program:

Accelerate

Powder Functionality in Laser Powder-Bed Fusion of Ti-6Al-4V

This project aims at studying the powder characteristics in the laser powder-bed fusion of Ti-6Al-4V. As an additive manufacturing technique, the laser powder-bed fusion process produces metal objects layer-by-layer using a laser source. Ti-6Al-4V is being used in the aerospace industry because it offers high strength-to-weight ratio and outstanding corrosion resistance. This collaborative work between McMaster University and AP&C investigates the influence of the oxygen content in Ti-6Al-4V powders on the microstructure and mechanical properties of parts produced using the laser powder-bed fusion process. The study includes the initial oxygen content of Ti-6Al-4V powders, as well as the oxygen pick up during manufacturing. AP&C, a subdivision of GE Additive, aims to produce high-quality metal powders as reliable feedstock for the L-PBF process. Therefore, greater understanding for the characterization of additive manufacturing feedstock powders is needed.

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Faculty Supervisor:

Mo Elbestawi

Student:

Partner:

AP&C

Discipline:

Engineering

Sector:

Manufacturing

University:

McMaster University

Program:

Accelerate

Assessing and Identifying Indoor environmental Quality Gaps in Commercial Buildings using Wireless sensors and Big Data Analysis Tools

This project’s objective is to create a proprietary digital platform which will allow for a faster, more accurate diagnosis of a building’s indoor environmental quality (IEQ) – at a fraction of the cost of today’s industry testing rates. The project aims to ensure that data being collected can be properly categorized and analyzed, creating a fully automated diagnostic tool. This novel analysis method requires being able to identify deficiencies in a commercial building that can be remedied, as well as proposing an actionable resolution plan for each identified deficiency.

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Faculty Supervisor:

Saman Muthukumarana;Yang Wang

Student:

Partner:

ioAirFlow

Discipline:

Mathematics

Sector:

Professional, scientific and technical services

University:

University of Manitoba

Program:

Accelerate

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