Projets novateurs réalisés

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

31 620 projets complétés

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856
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Projets par catégorie

Design and optimization of low-frequency piezoelectric energy harvesters

Portable electronic applications are typically powered by batteries, which have limited lifespan and size constraints. Energy harvesting from the spatial environment is a promising solution to sustainable power supplies for low-power portable devices and sensor networks. Vibration-based energy harvesting has received much attention due to the recent advances in microfabrication of piezoceramic materials. These smart materials can convert mechanical parasitic vibrations to electric charge through the direct piezoelectric effect. The resulting energy can be extracted after using an interface circuit. We propose a novel wideband piezoelectric energy harvester that can be used as a long-term reliable electrical power supply for small electronic devices based on the low-frequency nature of environmental vibrations (e.g., wind, ocean waves). Therefore, the proposed energy harvester in this research has a lot of potential to be commercialized and deployed in inaccessible regions, like offshore and deep marine environments.

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Superviseur du corps professoral :

Lihong Zhang;Mohammad Al Janaideh

Étudiant :

Partenaire :

Springboard Atlantic Inc.

Discipline :

Engineering

Secteur :

Environmental Science and Technology; Clean Technology; Ocean Tech

Université :

Memorial University of Newfoundland

Programme :

Accelerate

lnfluence of odour presentation on panelist response variability

Olfactometry is the science of using human subjects to measure odours without bias, as no instrument yet can replace the human nose. Recently, the method presenting odours to panellists has been identified as a potential element affecting response and measurement variability. Furthermore, the sensitivity of electronic noses also developed to measure odours remains an issue. The objective of the research is to use 3 odours and present them in 3 different ways to 6 panellists, and compare panelists’ response to that of two widely used commercial noses. The experimental work will beconducted in the olfactory laboratory of Consumaj Inc. in St-Hyacinthe. TOBECONT’D

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Superviseur du corps professoral :

Catherine Mulligan

Étudiant :

Partenaire :

Consumaj Inc

Discipline :

Engineering

Secteur :

Technology

Université :

Concordia University

Programme :

Accelerate

Sustainable functionalized magnetic particles for efficient treatment of marine oil pollution

Oily wastewater production and discharge from different sources such as industries and daily human activities are the main sources of marine oil pollution. More importantly, accidental oil spills occurred in oil extraction/production, refining, and transportation stages can cause detrimental impacts on the aquatic ecosystems and marine environments. In this Lab2Market project, we apply a special coating solution provide a proper dispersibility aqueous/continuous in various environments to optimize the adsorption performance by considering all affective physiochemical variables to be improved for on-site purposes.

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Superviseur du corps professoral :

Sohrab Zendehboudi

Étudiant :

Partenaire :

Springboard Atlantic Inc.

Discipline :

Engineering

Secteur :

Ocean Tech; Water; Environmental Science and Technology

Université :

Memorial University of Newfoundland

Programme :

Accelerate

Approche autonome pour la cartographie des réseaux urbains souterrains

Le géoradar (GPR) est une technique électromagnétique (EM) pour l’imagerie non invasive de supports opaques, souvent utilisé pour les levés souterrains. Le traitement des données GPR, surtout dans des zones urbaines souterraines, repose sur des opérateurs qualifiés et implique de nombreuses étapes coûteuses. Les résultats sont souvent basés sur de larges hypothèses et impliquent une intervention humaine, source parfois d’incohérence et de biais subjectif, ce qui rend les résultats peu fiables.
Ce projet justifie et vise à développer, un nouveau classifieur basé sur un réseau de neurones, pour détecter et interpréter automatiquement les signaux en vue de la localisation et les orientations des infrastructures souterrains. La causalité entre les services publics souterrains et leurs signatures GPR sera étudiée. En plus, il permettra de développer une expertise dans le domaine de cartographie des réseaux souterrains. Ceci permettra à Groupe ABS de développer son avantage concurrentiel.

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Superviseur du corps professoral :

Gabriel J Assaf

Étudiant :

Partenaire :

Groupe ABS inc.

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

École de technologie supérieure

Programme :

Accelerate

AK Platform – Soft skills learning as a key resource for faster job placement

Canada, a country composed presently of 20 percentage of immigrants, aims to be a world leader in global problem-solving. To do so, and to draw from the insights and ingenuity of the immigrant population, requires that Canadian organizations develop faster and more effective ways of adapting newcomers into the labor market. Especially, as extant research has found, that skilled immigrant workers are presently constrained within the labor market and this restricts Canadian organizational performance and curtails Canada’s competitiveness. Identifying the key soft skills needed in the Canadian labor market and finding innovative ways to pass this knowledge on is fundamental not only to reduce the length of time for immigrants and international students to adapt to Canadian culture but to also improve Canadian economic performance. This research project aims to review Canadian literature and academic publications containing soft skills analysis focusing on professional experiences in the Canadian Labor market for the last decade to identify the most coveted soft skills. The research will be conducted by a secondary research analysis systematic methodology with procedural and evaluative steps.

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Superviseur du corps professoral :

Barry Wright

Étudiant :

Partenaire :

Loonie AK

Discipline :

Sociology

Secteur :

Education

Université :

Brock University

Programme :

Accelerate

Improved Deep Learning-based Sea-ice Monitoring System

This project will develop a robust software package that can be embedded with a camera system to provide an onboard sea-ice monitoring system. The software package consists of two main components: (1) Deep learning classification model, which involves a deep learning network trained to identify and classify sea-ice; (2) Lens artifact removal method, which is a set of morphological operations that remove any lens artifacts, which can be water droplets, particles, or objects on the camera lens obstructing the studied scene. This software package obtains the scene information in-situ, which is important for real-time sea-ice monitoring systems and enables the vessels to safely maneuver in such harsh icy water environments.

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Superviseur du corps professoral :

Octavia Dobre

Étudiant :

Partenaire :

Springboard Atlantic Inc.

Discipline :

Computer science

Secteur :

Artificial Intelligence; Ocean Tech; Technology

Université :

Memorial University of Newfoundland

Programme :

Accelerate

Use of Artificial Neural Networks for Predicting the Offshore Wind Turbine Power Curve Using Various Training Algorithms in Newfoundland

Low predictive accuracy of energy output is one of the weakest points in wind power generation. Power curve of a wind turbine provides assistance in energy assessment, warranty formulations, and performance monitoring of the wind turbines. Wind turbines are being installed in offshore diverse climatic conditions causing substantial departure of these power curves from the warranted values. As industrial organizations being managed by enterprise-wide systems, a software like solution for the prediction of wind farm power output is desirable. The proposed wind farm performance prediction models should be able to forecast the amount of energy produced on different time scales such as 10 min, 1 hour and a day. Such prediction models would transform a wind farm into a wind power plant. Artificial Neural Network is such model that has been used in this study to accurately forecast the power output of a proposed offshore wind farm.

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Superviseur du corps professoral :

Jianming James Yang

Étudiant :

Partenaire :

Springboard Atlantic Inc.

Discipline :

Engineering

Secteur :

Green/Alternative Energy; Clean Technology; Artificial Intelligence

Université :

Memorial University of Newfoundland

Programme :

Accelerate

Development and commercialization of bio-sensor using cholesteric structure of magnetoplasmonic nanoparticle chain

Recently, bio-sensing R&D using plasmonic has been actively carried out in Korea and overseas. The global bio-sensing market is projected to grow rapidly by an average of 11.6% per year, with a high potential for future development and increased demand in the field. Helical nanostructure using magneto-plasmonic nanoparticles exhibit signals in visible light regions such as DNA and RNA, which are very easy to apply to biomedical fields. Signals represented by the structure of self-assembled helical nano chains are influenced by electron cloud signals of organic matter, resulting in wavelength shifts and these databases can be an indicator of the presence and qualitative determination of organic chiral moieties handedness. Therefore, micro-and miniaturization development, which implements the chiral three-dimensional structure of magneto-plasmonic nanoparticles on the substrate, is very necessary. The development of new micro-size bio-sensing devices provides a breakthrough technology that enables the early diagnosis of many intractable diseases.

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Superviseur du corps professoral :

Simon Park

Étudiant :

Partenaire :

Chungnam National University

Discipline :

Physics

Secteur :

Nanotechnology; Biotechnology; Health and Related Sciences & Technology

Université :

University of Calgary

Programme :

Globalink Research Award

Artificial Intelligence to Support Autonomous Seabed Mapping Operations

Presently the Ocean Mapping Community is spending a lot of time manually cleaning the raw sonar survey files. If this cleaning process is done by an Artificial Intelligence system as clean as a human would do, this could be transforming the entire Ocean Mapping community. Therefore, we propose a novel solution of using an Artificial Intelligence Algorithm – Reinforcement Learning to clean the sonar survey files. The proposed algorithm was used by Google’s DeepMind to beat GO Grandmasters. We believe that by tapping this algorithm we would be able to clean the raw sonar survey files as efficient as the manual cleaning of the raw sonar survey files.

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Superviseur du corps professoral :

Ian Church

Étudiant :

Partenaire :

Springboard Atlantic Inc.

Discipline :

Engineering

Secteur :

Ocean Tech; Artificial Intelligence; Environmental Science and Technology

Université :

University of New Brunswick

Programme :

Accelerate

UofT-JMIR Paper-Peer Reviewer Recommender System

Rapid and open dissemination of research is critical for the advancement of science. Preprint servers (such as MedRxiv, BioRxiv, PsyRxiv, and aRxiv) are becoming increasingly popular in health and medicine to share early research results, particularly in the context of the current COVID-19 pandemic. Given the need for rapid peer review, we need to innovate in rapid prioritization, classification, assignment, and evaluation of research papers (with a focus on medicine). A proposed AI-based method to match STM papers/submissions with suitable reviewers will significantly speed up the review process and improve the quality of feedback provided to the researchers. The proposed approach will also help in identifying the reviewers with similar expertise and interests to build peer-reviewer communities.

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Superviseur du corps professoral :

Eldan Cohen;Michael Guerzhoy

Étudiant :

Partenaire :

JMIR Publications Inc

Discipline :

Computer science

Secteur :

Information and cultural industries

Université :

University of Toronto

Programme :

Accelerate

An AI Approach to VLT Games Modeling

Video Lottery Terminals (VLT) are common gaming machines for money usually found in various venues. These machines are anonymous and stateless and do not record any information about player’s identity. The data obtained from these machines is very complex and hard to analyse. The gaming industry is keen to understand how a game or a possible change to a game will be perceived in different environments by different types of customers. This understanding will provide insights into both gaming performance and responsible gaming related behaviours. The most common current approach is to release a game, monitor the data and the analyze the results along with player interviews. This is expensive and inefficient, and many players choose to abstain from participating in these experiments. One goal in this project is to identify the most relevant VLT data features to predict positive user experiences and train models that imitate the behaviour of players to get a good estimation of a machine learning model as a reliable substitute for real world players. TO BE CONT’.

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Superviseur du corps professoral :

Vlado Keselj;Colin Conrad

Étudiant :

Partenaire :

IGT

Discipline :

Computer science

Secteur :

Arts, entertainment and recreation; Information and cultural industries

Université :

Dalhousie University

Programme :

Accelerate

Gait and Balance Parameters Inferred with the PROTXX Head Accelerometer (Phase 2)

The vestibular organs detect head movement and are involved in the coordination of standing balance. With balance problems being a common and expensive healthcare cost internationally, there is a growing need for new diagnostic and therapeutic medical devices that target vestibular balance function. In particular, a wearable device that could be used outside the clinic could provide a convenient, low-cost alternative. Here we explore the feasibility of integrating Electrical Vestibular Stimulation (EVS) – a relatively new technique for probing vestibular- specific balance function – with the commercially available head motion sensor from PROTXX. The wearable technology we are co-developing will enable frequent, accurate, and mobile assessments of vestibular function, as well as provide a novel therapeutic approach for enhancing balance control in patients at risk of falling (analogous to a vestibular prosthetic).

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Superviseur du corps professoral :

Ryan Peters

Étudiant :

Partenaire :

PROTXX

Discipline :

Life Sciences

Secteur :

Professional, scientific and technical services

Université :

University of Calgary

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.