Projets novateurs réalisés

Explorez des milliers de projets réussis issus de la collaboration entre organisations et talents postsecondaires.

31 620 projets complétés

2978
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5221
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856
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696
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899
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9419
ON
9858
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98
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619
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1192
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Projets par catégorie

Leveraging native species restoration and remote sensing techniques for large-scale dust mitigation enhancement

This proposal aims to advance large-scale dust mitigation strategies in hydroelectric reservoir sites through improved revegetation outcomes, and through leveraging revegetation efforts to additionally enhance local biodiversity and cultural values. Our team has developed a strategic partnership between university researchers, BC Hydro, environmental industry partners, and the local First Nations community to provide an interdisciplinary and stakeholder-informed approach for decision-making that builds on existing reservoir mitigation programs and knowledge. Focusing on the Williston Reservoir in British Columbia, we seek to determine which plant species are able to provide the best within-year dust control and between-year soil stability, with species selection processes that prioritize native species with potential cultural value to local communities. We will use lab and greenhouse trials to test growth rates and success of a range of potential plant species, with and without soil amendments that may enhance the growing conditions of the harsh reservoir drawdown zone. Lab results will guide large-scale field trials at Williston Reservoir. These trials will cross the complex topographic landscape of the drawdown zone, allowing continuous assessment of plant performance changes based on location and local conditions.

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Superviseur du corps professoral :

Nancy Shackelford;Christopher Bone

Étudiant :

Partenaire :

Chu Cho Industries

Discipline :

Earth science

Secteur :

Professional, scientific and technical services

Université :

University of Victoria

Programme :

Accelerate

Supply chain sustainability and risk management in the post-Covid context: Assessing and deploying industry 4.0 solutions

The COVID-19 pandemic has exacerbated the challenges Canadian manufacturers face in managing increasingly complex, multi-tiered global supply chains, including a wide rang of sustainability issues ranging from forced labor to carbon emission management. Industry 4.0 technologies – including the internet of things (IoT), blockchain, and machine learning – can potentially address these issues via enhanced transparency, predictability, and reliability. There remains, however, considerable uncertainty as to how these technologies can be deployed, integrated, and managed. This project internship will support the “Accelerating the economic recovery by building resilience in manufacturing” Open Innovation Challenge, hosted by Bonjour Startup Montréal in collaboration with Desjardins bank. Drawing on a variety of research resources and networks, the Intern will ensure that tech start-up proposals are successfully assessed and matched to Canadian manufacturers seeking solutions to their SSR challenges, helping to foster valuable partnerships between these companies.

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Superviseur du corps professoral :

Jose Carlos Marques

Étudiant :

Partenaire :

Bonjour Startup Montreal

Discipline :

Business

Secteur :

Other services (except public administration)

Université :

University of Ottawa

Programme :

Business Strategy Internship

Hybrid modeling framework for flood prediction

A hybrid computational framework for short-term flood prediction in urban watersheds (characterized by overland runoff) will be developed to improve prediction accuracy. The framework aims to accurately predict an event, e.g. flood or no-flood, as opposed to traditional methods which estimate water flow characteristics, e.g. 6 feet above flood stage. Successful early prediction of these events can help authorities to take appropriate mitigation measures and to minimize losses from the flooding. The framework requires only current and historical data on water levels and precipitation in the area of interest (such as those collected by TRCA’s flood monitoring gauge network). The development of the new framework will complement existing hydrologic simulation systems to improve and enhance services provided by TRCA to local agencies and public.

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Superviseur du corps professoral :

Marina Erechtchoukova

Étudiant :

Partenaire :

Toronto and Region Conservation Authority (Toronto, ON)

Discipline :

Computer science

Secteur :

Arts, entertainment and recreation; Professional, scientific and technical services; Public administration

Université :

York University

Programme :

Accelerate

Assessment of current vision-based machine learning modelling efforts in directed energy deposition on defect prediction

Metal-based direct energy deposition processes ideally require feedback sensing of the deposition quality using camera detectors as they provide spatial and temporal state signatures of the process. Image processing algorithms are challenging to develop due to changing process operating conditions. Machine learning models have recently gained in popularity due to their ability to predict process defects using various monitoring technologies, and in particular vision-based cameras. The existing machine learning model architectures in literature are however trained on image-based datasets acquired through in-process recording of a single AM machine, a single camera setup and a limited set of process influencing parameter combinations. Comparison of the existing models is therefore challenging. Furthermore, the generalizability of these models is questionable, since none of the models evaluate model performance on unseen data, acquired from a different DED machine setup. A thorough investigation and comparison of the current state-of-the-art machine learning architectures on a large diverse dataset, acquired through vision camera based monitoring of several different DED machines and camera setups is however currently missing. The objective of this research is to compare the state-of-the-art machine learning architectures which use vision-based cameras to predict defects during metal-based material deposition using DED machines.

Voir la description complète du projet
Superviseur du corps professoral :

Mihaela Luminita Vlasea

Étudiant :

Partenaire :

I-INC Foundation for Business Development

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

University of Waterloo

Programme :

Accelerate

La créativité numérique au service des arts de la scène: Le cas du Quartier des spectacles de Montréal

La ville de Montréal est reconnue pour sa dynamique culturelle et festive. Aujourd’hui et afin d’appuyer cette réputation, de nombreuses opportunités voient le jours et de nouveaux modes de création et de diffusions émergent. Le but de cette recherche est de rendre compte de la place accordée aux arts numériques au sein des arts de la scène comme forte valeur ajoutée touristique pour la ville de Montréal. Cette étude se réalisera conjointement avec le Partenariat du quartier des spectacles, acteur majeur du développement culturel et ayant pour mandat de favoriser la création, l’innovation, la production et la diffusion culturelle au sein de ce quartier du centre-ville.

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Superviseur du corps professoral :

Paul Arseneault

Étudiant :

Partenaire :

Quartier des Spectacles

Discipline :

Sociology

Secteur :

Arts, entertainment and recreation

Université :

Université du Québec à Montréal

Programme :

Accelerate

Understanding shortcut learning in training deep neural networks for computer vision

Recent years have seen a great amount of success in using deep learning in publicly available and standardized datasets such as ImageNet. However, deep learning methods fail to perform as well in a lot of real-world applications such as medical imaging datasets. One potential reason for this challenge is that deep learning models tend to learn superficial features (aka spurious features) such as texture, color, etc, which merely correlate with the labels instead of discriminative features that can explain the labels. In medical imaging in particular, shortcut learning could potentially have fatal implications. Therefore, understanding this phenomenon and taking measures to prevent them can improve neural networks in terms of performance, generalizability to unseen demographics and interpretability. In this project, we will explore representation learning techniques for learning generalizable features. We will study the efficacy of these techniques on benchmarks that better reflect properties of real-world medical imaging problems.

Voir la description complète du projet
Superviseur du corps professoral :

Samira E Kahou

Étudiant :

Partenaire :

Imagia

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

École de technologie supérieure

Programme :

Accelerate

Bio-Aggregate Sustainable Concrete panels for Farm Buildings

This research aims in utilizing agriculture wastes in production of sustainable eco-friendly concrete panels for floors and walls in farms building. The mechanical and durability performance of the concrete panels exposed to the aggressive agriculture environment will be evaluated. The research will investigate the effects of different factors mainly related to physical properties of used wastes and binding materials. This leading-edge research on reusing/recycling agro-wastes in construction materials will allow IRDA to economically and sustainably transform such wastes into a high-value product. IRDA will lead the research in this area focusing on developing and innovating sustainable building materials for farm buildings and various construction applications, with an anticipated measurable impact on the Canadian specifications for concrete.

Voir la description complète du projet
Superviseur du corps professoral :

Ahmed Soliman

Étudiant :

Partenaire :

Institut de Recherche et de Développement en Agroenvironnement

Discipline :

Engineering

Secteur :

Agriculture; Education; Professional, scientific and technical services

Université :

Concordia University

Programme :

Accelerate

Exploring the Utility of TVISFD with MLSE LaunchPad in Moss Park

MLSE LaunchPad is a sport for development (SFD) organization in Moss Park, Toronto that uses sport and physical activity to build healthy communities. A trauma- and violence- informed approach is a treatment practice used to provide safe and inclusive spaces for traumatized youth, however, it is rarely incorporated in SFD programs. In collaboration with MLSE Launchpad, this research will explore how trauma- and violence- informed approaches are used in SFD to prevent gender-based violence and intersecting health inequities. Research participants will engage in arts-based methods and interviews to discuss their experiences in sports programming, including how their gender identities have impacted their experiences, and how these programs have impacted their health and well-being.

Voir la description complète du projet
Superviseur du corps professoral :

Lyndsay Hayhurst

Étudiant :

Partenaire :

MLSE LaunchPad

Discipline :

Sociology

Secteur :

Arts, entertainment and recreation

Université :

York University

Programme :

Accelerate

Utilisation de l’habitat naturel pendant la saison de reproduction d’une population nordique de martinets ramoneurs (Chaetura pelagica)

La mission du Zoo de Granby est orientée vers la préservation du monde animal. En partenariat avec un étudiant à la maîtrise à l’Université Laval, l’équipe de chercheurs du Zoo de Granby participe au projet sur l’utilisation de l’habitat naturel pendant la saison de reproduction du Martinet ramoneur (Chaetura pelagica). Cet insectivore aérien possède le statut d’espèce menacée au Canada. Ce petit oiseau nichant dans les cheminées de maisons a vu sa population diminuer de 90% depuis les années 1970 (COSEPAC, 2018). Une des principales causes de ce déclin est la perte d’habitat de nidification et repos. De plus, l’utilisation de l’habitat naturel pour la reproduction de l’espèce est très méconnue à ce jour. Cette recherche vise à documenter l’utilisation du milieu forestier en période de reproduction par une population de Martinets se reproduisant dans la région du Lac-Édouard.

Voir la description complète du projet
Superviseur du corps professoral :

André Desrochers;Junior A. Tremblay

Étudiant :

Partenaire :

Zoo de Granby

Discipline :

Life Sciences

Secteur :

Environmental Science and Technology; Forestry; Life Sciences (not health)

Université :

Université Laval

Programme :

Accelerate

Using administrative data to predict health outcomes: a machine learning approach

Adverse events due to benzodiazepine use and falls are important clinical outcomes in older adults. As well, high-cost users of the health care system, although small in number, are a substantial burden with regards to health costs. Continued use of BZRAs in older adults is concerning from a public health standpoint; 1 in 3 older adults experience a fall in the community; 5% of health care users consume about 60% of hospital and home care spending. Being able to identify these individuals would add to the current efforts to reduce health care burden. Currently, there are no risk assessment tools using administrative data to predict risk of these outcomes. Machine Learning offers a framework to use administrative data to identify high risk individuals and provide targets for intervention.

Voir la description complète du projet
Superviseur du corps professoral :

Dean Eurich

Étudiant :

Partenaire :

OKAKI

Discipline :

Life Sciences

Secteur :

Health and Related Sciences & Technology; Information and cultural industries; Professional, scientific and technical services

Université :

University of Alberta

Programme :

Accelerate

Insole-based sensor fusion for ambulatory gait analysis for occupational health & safety

MEGA InTech aims to maintain healthy workers and prevent injuries. This research will advance a smart insole to deliver analyses of ambulatory gait and posture. Specifically, signal processing and sensor fusion algorithms to deliver detailed metrics of gait and occupational ergonomics will be developed. The proposed methodology will leverage the partner’s prototype insole sensor hardware to generate sensor fusion algorithms to estimating the target metrics related to occupational health & safety (e.g., falls, fatigue, soft tissue injury). To develop and test new algorithms, research-grade biomechanics equipment available at the University of Waterloo will be used as criterion-standard measurements to compare algorithm outputs derived from prototype insole sensor signals. The deliverables of this project are: 1) algorithms delivering gait and posture analytics, 2) reports detailing methods and test results, and 3) acquired datasets. The proposed research will advance products and tools towards maintaining occupational health and safety.

Voir la description complète du projet
Superviseur du corps professoral :

James Tung;Arash Arami

Étudiant :

Partenaire :

MEGA InTech

Discipline :

Engineering

Secteur :

Wholesale trade

Université :

University of Waterloo

Programme :

Accelerate

The Discovery of Potential New Antidepressant Compounds from Mushrooms Native to British Columbia

Mental illnesses, particularly depression, is one of the leading causes of global disease burden. In addition to reducing the quality of life of patients and their relatives, it costs billions of dollars annually to the Canadian economy. Unfortunately, current antidepressant drugs are barely satisfactory and have numerous side-effects. The goal of this project is to discover potential new antidepressant drugs from wild mushrooms native to British Columbia. This is in line with Translational Life Sciences (TLS) Inc., a drug discovery biotechnology company in Vancouver that is interested in finding new psychedelic/antidepressant compounds. TLS will have the proprietary rights to any discovered compounds and is therefore expected to benefit financially in the near future.

Voir la description complète du projet
Superviseur du corps professoral :

Chow Lee

Étudiant :

Partenaire :

Translational Life Sciences Inc

Discipline :

Life Sciences

Secteur :

Professional, scientific and technical services

Université :

University of Northern British Columbia

Programme :

Accelerate

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Mitacs — Retour sur innovation

Mitacs est financé par le gouvernement du Canada, le gouvernement de l’Alberta, le gouvernement de la Colombie-Britannique, Research Manitoba, le gouvernement du Nouveau-Brunswick, le gouvernement de la Nouvelle-Écosse, le gouvernement de l’Ontario, Innovation PEI, le gouvernement du Québec, le gouvernement de la Saskatchewan, le gouvernement de Terre-Neuve-et-Labrador et le gouvernement du Yukon.