Projets novateurs réalisés

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

29 670 projets achevés

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4990
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801
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663
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825
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8841
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9197
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95
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568
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1088
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Projets par catégorie

Novaxe: Novel Online System for Guitar Music Score Recognition, Retrieval, Presentation, and Animation

This project is going to digitalize a patented method of presenting the guitar tablature and build an online system to assist guitar teaching. The new method invented by Mr. Mark Vandendool can present the guitar tablature with 8 building clocks. People can easily learn the chores of thousands of songs, which is impossible with the other methods. The system is called Novaxe. Novaxe is going to have a graph screen to display the fretboard with the information about the chord progression, rhythm pattern, tempo, key, `position`, song form, chord form, title, artist, year, and genre of a song. The fretboard contains the map of the scale notes as diamonds, and temporary chord tones as circles underneath. There are several difficulties in this project. First, the search function needs some sophisticated algorithm to search for songs in the library. Detecting of the music patterns is not an easy task and there is no 100% accuracy as there are different interpretations of music patterns. Second, the animation required on the user interface is complicated. And all the animation needs to be implemented by JavaScript. Third, the storage of the songs is unique than the business logic we are used to in database-design

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Superviseur du corps professoral :

Yuhong Yan;Mohammad Zulkernine

Étudiant :

Partenaire :

OMP Music

Discipline :

Engineering

Secteur :

Arts, entertainment and recreation

Université :

Concordia University; Queen's University

Programme :

Accelerate

Evaluate the impact of green infrastructure on reducing the risk of contamination during contact recreational activities in dense urban areas using a QMRA model

The project aims to investigate whether implementing Blue-Green Infrastructure (BGI) can provide extra protection to bathing and water recreation areas from Combined Sewer Overflow (CSO) risks. By utilizing the Storm Water Management Model (SWMM) software and Quantitative Microbial Risk Assessment (QMRA), this internship abroad offers a unique chance to assess such risks comprehensively. It also involves studying the impact of BGI on urban water recreation zones, particularly in reducing microbiological contamination, crucial amidst increasing overflows due to climate change. Moreover, the internship enriches my academic and professional journey by providing exposure to international expertise, fostering skill development, and facilitating collaboration across institutions, ultimately advancing research in microbiological risk assessment and urban water management. The participating institutions can expect benefits such as enhanced research insights, expanded networks, and potential contributions to policy development aimed at addressing pressing environmental and public health challenges.

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Superviseur du corps professoral :

Françoise Bichai

Étudiant :

Partenaire :

Technische Universität Wien

Discipline :

Engineering

Secteur :

Environmental Science and Technology; Health and Related Sciences & Technology; Sustainability & the Environment

Université :

Polytechnique Montréal

Programme :

Globalink Research Award

Développement d’outil d’aide décision pour l’automatisation manufacturière dans le contexte des produits récréatifs

L’introduction de nouvelles technologies dans la production des produits récréatifs s’est considérablement accélérée chez BRP, Leader mondial dans la conception et la fabrication de produits récréatifs. La personnalisation des produits et l’augmentation des systèmes embarqués sur les véhicules contribuent à alourdir les tâches de reconfigurations qu’il faut apporter à la ligne d’assemblage. Le département de stratégie manufacturière est présentement à la recherche d’une méthodologie adaptée à la réalité de BRP qui servira comme outil d’aide à la décision pour l’automatisation à déployer sur un nouveau produit.
Dans un contexte manufacturier multisite, multiproduit à haute variété, à cadences très variables entre les différents produits, la sélection des différents niveaux d’automatisation des procédés d’assemblage et de fabrication est basée sur la rentabilité des investissements (ROI) basée sur le coût de la main d’œuvre. Le projet a pour objectifs de : (1) Définir les critères et les barèmes de décision permettant de rationaliser la décision d’automatiser ou non un procédé d’assemblage ou de fabrication selon le type de procédé dans un contexte de coûts totaux; (2) Fournir une méthodologie et les outils basés sur des calculs en fonction du type de procédé et des critères identifiés; (3) De définir les critères et les barèmes de décision permettant de sélectionner les procédés potentiels à robotiser en phase préliminaire de développement de produit.

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Superviseur du corps professoral :

Antoine Tahan;Souheil-Antoine Tahan;Lucas Hof

Étudiant :

Partenaire :

Bombardier Produits Recreatifs

Discipline :

Engineering

Secteur :

Manufacturing

Université :

École de technologie supérieure

Programme :

Accelerate

Global Talent Streamlining: Leveraging AI for Strategic Immigration Solutions in the Canadian Labour Market

Our project addresses the critical challenge in the Canadian labour market: the mismatch between the demand for talent and the availability of skilled individuals to fulfill economic priorities. With Canada facing prolonged labour shortages across various sectors due to demographic shifts, this initiative seeks to bridge the gap by leveraging AI to analyze and match foreign talent with Canadian employment opportunities. By meticulously examining data sets of potential candidates and job vacancies, we aim to identify the most suitable immigration pathways and align skills, experiences, and qualifications with the needs of the Canadian economy. This comprehensive approach not only promises to mitigate the looming national crisis in industries like healthcare, construction, and technology but also sets a precedent for applying innovative solutions to global labour market challenges.

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Superviseur du corps professoral :

Salimur Choudhury

Étudiant :

Partenaire :

Greenberg Hameed PC

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

Queen's University

Programme :

Accelerate

Scaling laws of GPBO algorithms

Déterminer les lois de scalabilité du cadre algorithmique GPBO pour l’appliquer à la résolution de n’importe quelle disponibilité de ressource physique en termes de mémoire et capacité computationnelle, à partir des microcontrôleurs jusqu’au clusters de computation. En particulier, évaluer :
1) le contraintes physiques et mathématiques qui déterminent la fonction multivariée entre la disponibilité de ressources de computation et la dimension du problème maximal faisable,
2) les lois d’implémentation optimale du système d’optimisation à travers plateformes de dimensionnalité variée et la valeur relative des choix impliqués dans les compromis d’implémentation – tel que une réduction du problème ou des limites à la mémoire d’optimisation immagasinée.

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Superviseur du corps professoral :

Marco Bonizzato

Étudiant :

Partenaire :

Université Gustave Eiffel

Discipline :

Engineering

Secteur :

Education

Université :

Université de Montréal

Programme :

Globalink Research Award

Streamlining Medical Administration: Developing an AI Solution for Automating Medical Form Processing in Canada

Physicians in Canada spend significant time on administrative tasks, including completing paperwork that requires filling out various forms in different formats. It is estimated that most physicians spend about 19 hours per week on paperwork. Such demands not only lead to burnout but also detract from patient care. Moreover, this additional workload is also an impediment to caring for physicians’ patients. To reduce the administrative workload of physicians, this project aims to investigate the use of artificial intelligence (AI) to automatically fill medical forms by extracting data from unstructured and unlabelled medical documents. The project will start by conducting structured interviews and surveys with physicians to understand the specific requirements and experiences with administrative tasks. Subsequently, machine learning and natural language processing techniques will be used to develop a prototype solution for analyzing medical records and filling out forms automatically. This project will use publicly available datasets and/or synthetic data for training and validation. Finally, the project aims to explore future directions for developing a complete AI-based solution that has the potential to fill out a variety of forms in different formats.

Voir la description complète du projet
Superviseur du corps professoral :

Salimur Choudhury

Étudiant :

Partenaire :

WaiveTheWait Inc.

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

Queen's University

Programme :

Accelerate

Developing Climate Action Policy Exemplars for School Districts of British Columbia

Climate change is a significant challenge we’re facing today, affecting ecosystems, economies, and societies globally. In Canada, there’s a strong push to achieve net-zero emissions by 2050, with British Columbia leading the charge through initiatives like CleanBC, aiming to reduce greenhouse gas emissions by 40% by 2030. In order to support these goals, the British Columbia School Trustees Association (BCSTA) is collaborating with relevant ministries to develop a strategy for a 50% reduction in emissions. This involves investigating how school districts across BC are addressing climate action and creating exemplars to help improve climate efforts in schools. The project includes three objectives: first, to assess existing climate action policies in BC school boards; second, to develop practical examples based on successful approaches identified during the research; and third, to benchmark climate action exemplars with climate action champions. These exemplars will cover various strategies, policies, and programs tailored to different types of school districts. By adopting these examples, BC school districts can enhance their efforts to tackle climate change.

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Superviseur du corps professoral :

Rajeev Ruparathna

Étudiant :

Partenaire :

British Columbia School Trustees Association

Discipline :

Engineering

Secteur :

Education

Université :

University of Windsor

Programme :

Accelerate

Lignin-Sourced Radical Initiators towards Visible-Light-Promoted cycloaddition reactions

This research initiative focuses on the development of sustainable and eco-friendly methods for harnessing natural resources, particularly wood, to produce valuable chemical compounds with applications in materials, pharmaceuticals, and agriculture. Through a collaborative effort involving experts from Canada and Italy, we aim to pioneer chemical reactions that leverage visible light and wood-derived compounds as catalysts to efficiently transform substrates into high-value products. This innovative approach not only addresses the pressing need for sustainable resource management but also mitigates potential health and environmental risks associated with traditional chemical processes. The project’s emphasis on green chemistry principles aligns with global efforts to reduce the environmental footprint of chemical industries. By working at the intersection of chemistry and sustainability, this research contributes to advancing eco-friendly practices and strengthening Canada’s position as a leader in innovative and environmentally conscious chemistry.

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Superviseur du corps professoral :

Shawn Collins

Étudiant :

Partenaire :

University of Parma

Discipline :

Life Sciences

Secteur :

Life Sciences (not health); Health and Related Sciences & Technology; Environmental Science and Technology

Université :

Université de Montréal

Programme :

Globalink Research Award

Measurement of carrier mobility and lifetime in blocking layers of a-Se X-ray detectors

The amorphous selenium (a-Se) based active matrix flat-panel imager (AMFPI) has demonstrated promising performance in breast imaging, exhibiting high image quality and dynamic range. However, sensitivity variation due to previous x-ray exposures, known as ghosting, is a common phenomenon in direct conversion detectors, which can lead to the incorrect interpretation of breast images for diagnosis and screening. The trapped charge carriers in the multi-layer a-Se detector are believed to be responsible for this ghosting phenomenon. In this project, the charge carrier lifetime in the different layers of the detector will be investigated and its effects on the ghosting phenomenon of the AMFPI will be determined.

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Superviseur du corps professoral :

M. Zahangir Kabir

Étudiant :

Partenaire :

Analogic Canada Corporation

Discipline :

Physics

Secteur :

Wholesale trade

Université :

Concordia University

Programme :

Accelerate

Development of an industrial process for colour removal of plasticizers

The projects consist to apply commercially known colour removal technology to a used plasticizers blend produce in Tarkett vinyl sheet flooring plant. Using a continuous packed bed column, the intern will screen the performance and efficiency of several colour and metals removal resins. Following the colour removal experiments, the effect of adding the treated material in Tarkett formulation will be evaluated through different quality control tests developed by the company. In the instance that economically relevant results are obtained, a unit design will have to be carried out.

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Superviseur du corps professoral :

Milan Maric

Étudiant :

Partenaire :

Tarkett

Discipline :

Engineering

Secteur :

Manufacturing

Université :

McGill University

Programme :

Accelerate

Analyzing the effects of wearable ring scanners on sustainability and ergonomics in warehouse operations

This project aims to evaluate the effectiveness of a wearable ring scanner in improving environmental, economic, and social sustainability specifically in a warehouse setting. By addressing these objectives, this research aims to provide valuable insights into the suitability and sustainability of the ring scanner as a potential means of enhancing warehouse operations. A life cycle assessment, activity-based cost analysis, and comfort rating scale will be used as research methods. The results of this research will immediately benefit the Canadian warehouse and distribution industries and its relevant stakeholders (e.g., policy makers, marketers and sustainability practitioners) in Canada.

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Superviseur du corps professoral :

Jay Jonghun Park

Étudiant :

Partenaire :

Lotwork Inc.

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

Toronto Metropolitan University

Programme :

Accelerate

Abnormality Detection Using Variational Autoencoder: An Application in Banking System

This internship project focuses on Variational Autoencoders (VAEs) for the precise identification of fraudulent activities within the banking sector, notably on check fraud detection. The literature shows that VAEs are very powerful in extracting principal features and components of a given dataset. This project meticulously outlines a comprehensive methodology where VAE is utilized for analyzing check images and extracting critical features such as signature styles, printed patterns, and unique identifiers to transform complex, high-dimensional data into an encoded, simplified representation. This project proposes an innovative integration of machine learning techniques to address challenges in financial fraud detection. By utilizing VAEs, we aim to extract and decode complex patterns, optimizing the detection of anomalies in the banking system. The methodology involves the application of VAEs for feature extraction from high-dimensional data, subsequently clustering these features to identify irregularities and predict long-term performance. The project leverages publicly available datasets, such as RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) and NIST Special Database 19, for checks to refine models capable of handling diverse scenarios.

Voir la description complète du projet
Superviseur du corps professoral :

Jahrul Alam;Kevin Pope

Étudiant :

Partenaire :

NASDAQ Canada Inc

Discipline :

Mathematics

Secteur :

Finance and Insurance; Artificial Intelligence

Université :

Memorial University of Newfoundland

Programme :

Accelerate

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