Projets novateurs réalisés

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

31 133 projets complétés

2940
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5159
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837
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685
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882
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9292
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601
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Projets par catégorie

Exploration of an executive digital twin application

The workplace is evolving at the frontier, people and machines work side by side. In factories and industries, the trend for the past two-hundred years has been to provide mechanical devices to assist in manual labour. The benefit for people is that workplaces are more equitable and accommodating to people with disabilities. The future will see people and machines work side-by-side on problem-solving tasks of increasing complexity. This project prepares for that future by showing how camera vision and digital twins can augment the senses available to people engaged in intrinsically human enterprise.

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Superviseur du corps professoral :

Stephen Czarnuch

Étudiant :

Partenaire :

nOw Innovation Inc.

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

Memorial University of Newfoundland

Programme :

Accelerate

Visual Analytics for Financial Risk

Project 1: The objective of this project is to design a Visual Systemic ‘Risk Map’ as one possible prototype to address some of the issues of risk identification and analysis in the context of global financial systems. We propose the design concept of the ‘Risk Map’ using principles of Cognitive Systems Engineering (instrumental papers in this field are Rasmussen et al. 1994; Vicente and Rasmussen, 1990; Woods and Roth, 1988), which is suited to engineering for complex systems. Cognitive systems engineering addresses the issue of analyzing and designing process control systems from a human factors perspective. It diverges from the more common engineering approaches to designing process control systems by distinguishing between the physical system, sub-systems and their components, from what the operator or organization needs to do with the system to accomplish the purpose of the system; but at the same time creating user interface designs that integrate information that enables the operator to observe how failure in achieving desired system goals are propagated through the system to sub-system to components.

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Superviseur du corps professoral :

Victoria Lemieux

Étudiant :

Partenaire :

Boeing Vancouver (Richmond, BC);University of British Columbia

Discipline :

Computer science

Secteur :

Aerospace

Université :

The University of British Columbia

Programme :

Elevate

Porous noble metal nanosponge plasmonic photocatalysts

Plasmonic catalysis is an emerging technology which holds the promise of utilizing light to facilitate a chemical transformation. Plasmonic materials interact with incident light through the excitation of localized surface plasmon resonance producing highly energetic electron-hole pairs. These hot electrons and holes are very active and get adsorbed to the materials in their surroundings through a series of chemical reactions, providing desired products. The optical properties of these plasmonic metal nanoparticles (Au, Ag, etc.,) varies with their shape, size, arrangement, and surrounding dielectric medium. Since the materials interact with light, investigation of how these materials interact with light is crucial. The overall goal of this project is to investigate the interaction of light with the different plasmonic sponge structures (Au, Ag) with various surrounding media (TiO2, NiO) in the first step and investigation of the ability of these materials for solar energy conversion, and production of clean and green fuels such as hydrogen via water splitting, and carbon dioxide mitigation and water treatment in the second step.

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Superviseur du corps professoral :

Karthik Shankar

Étudiant :

Partenaire :

Technical University of Munich

Discipline :

Engineering

Secteur :

Nanotechnology; Green/Alternative Energy; Environmental Science and Technology

Université :

University of Alberta

Programme :

Globalink Research Award

An EDA tool for layout retargeting of analog integrated circuits in advanced technologies

With the advances in the technology and shrinkage of the device sizes, many new challenges have been introduce in the design process of electronic devices. Therefore, the designers are required to spend so much time and effort and go through several cycles of design and test to tackle these challenges. This is even more serious in analog IC designs, where even small deviations from device parameters may lead to huge performance loss. Electronic design automation (EDA) tools can provide great support to the designers by addressing the new technology challenges. However, most commercially available tools are only suitable for digital circuits. Therefore, in this project we aim to develop an EDA tool that can provide great support to analog designers by addressing the new technology challenges and speeding up the design and test process. This product has a lot of potential to be commercialized and deployed by any designer who need to design an analog device in advanced technology nodes.

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Superviseur du corps professoral :

Lihong Zhang

Étudiant :

Partenaire :

Springboard Atlantic Inc.

Discipline :

Engineering

Secteur :

Technology; Advanced Manufacturing

Université :

Memorial University of Newfoundland

Programme :

Accelerate

Network Analysis and Mathematical Modeling of Neuron-Astrocyte Multiplex Net

Understanding and predicting brain activity is the main objective of computational neuroscience. Neuronal cells are the building blocks of the brain and responsible for electrical signaling and cognitive ability. Recent studies indicate that another cell type, called astrocyte, plays an important role as well. These cells have been proposed to interact with neurons and modulate properties of neurological networks although the biochemical processes remain unknown. Further research is necessary since astrocytes are likely involved in numerous neurological disorders, including Alzheimer’s disease. In our project, we will analyze underlying astrocyte-astrocyte and neuron-astrocyte interaction networks based on high-quality data sets and mathematically model the dynamic behavior. We hypothesize that interactions between astrocytes and neurons are relevant and that astrocytes actively participate in neuronal signaling. With our mathematical model, we expect to identify essential aspects of astrocyte-astrocyte and neuron-astrocyte interaction, which would close a critical knowledge gap in neuroscience.

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Superviseur du corps professoral :

Joern Davidsen

Étudiant :

Partenaire :

Goethe University Frankfurt

Discipline :

Life Sciences

Secteur :

Life Sciences (not health); Information and Communications Technology; Health and Related Sciences & Technology

Université :

University of Calgary

Programme :

Globalink Research Award

Investigating continuous miners’ performance at Goderich salt mine to minimize the generation of fine particles

Continuous miner (CM) optimal performance depends upon various factors which controls the rock-bit (cutter) interaction, including the type and geometry of the bits (cutters), operational parameters (drum speed, advance rate and etc.) and geological setting of the target rock. According to the conducted investigation on CMs performance at Goderich mine, although, these machines have improved the production rate significantly, fine particle (<28 mesh) generation has been increased drastically as well. Increasing fines has several drawbacks including growth of respirable dust generation which is extremely hazardous for workers, material handling difficulties (flowability) and costumer dissatisfaction. Implementing numerical simulation such as discrete element method (DEM) is the key approach to simulate the rock-bit interaction process quantitatively which is essential to understand the performance of the CM. This simulation will reveal the role of the CMs bit geometry and type along with the operational parameters on rock fragmentation and size distribution of the generated particles considering the geological features of the target mine.

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Superviseur du corps professoral :

Kamran Esmaeili

Étudiant :

Partenaire :

Compass Minerals Canada Corp.

Discipline :

Engineering

Secteur :

Manufacturing; Mining

Université :

University of Toronto

Programme :

Accelerate

Assessing the ovarian cancer microenvironment with optical coherence tomography and artificial intelligence

Ovarian cancer is one of the deadliest gynaecological conditions in the developed world, as it is often detected late into progression. Efforts to improve surgical and chemotherapeutic approaches have only made marginal improvements to patient outcomes over the past 30 years, but there remains potential within immunotherapeutic approaches. Understanding the tumor microenvironment, and particularly the immune response to cancers and pre-cancers, is critical to selecting appropriate therapies. A novel dataset of volumetric structural imaging of ex vivo fallopian tubes, the origin of the most prevalent ovarian cancers, has been collected with corresponding histopathology. This work explores whether there are imaging biomarkers associated with immune response such as t-cell infiltration in the tumor microenvironment that can be distinguished using deep learning vision models.

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Superviseur du corps professoral :

Pierre Lane;Shuo Tang

Étudiant :

Partenaire :

BC Cancer

Discipline :

Engineering

Secteur :

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

Université :

The University of British Columbia

Programme :

Accelerate

Efficient Learning-based Parameter Configuration of Cellular Networks

Network parameter configuration is crucial for optimizing performance in a cellular network. Often, such parameters are too numerous and their interdependence too complicated for them to be efficiently configured by human experts. Therefore, it is of great interest to study network parameter configuration as a machine learning problem. We aim to expand on the machine learning approaches in recent work from industry leaders such as Huawei. Recent solutions make use of “transfer learning”, that is, sharing information learned between cells in the cellular network to improve the overall performance. In this project, we will consider additional constraints for each cell that must be satisfied and may conflict with the primary goal of network performance optimization.

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Superviseur du corps professoral :

Ning Lu

Étudiant :

Partenaire :

Pennsylvania State University

Discipline :

Computer science

Secteur :

Education

Université :

Queen's University

Programme :

Globalink Research Award

Hospital Optimizer: Horaire récurrent avec perturbations

Thales développe le Hospital Optimizer, un outil pour gérer l’utilisation des salles d’opérations en les affectant à différentes équipes médicales. Cette allocation tient compte de la demande, des disponibilités médecins, de la disponibilité des salles et des types de chirurgies pouvant être effectuées dans chaque salle. Si la version courante du Hospital Optimizer prend bien en compte ces éléments du problème (et même plus), il lui manque toutefois une fonctionnalité cruciale afin de permettre l’acceptabilité de cet outil dans le milieu de travail : la production d’horaires récurrents. Toutefois, dû à de trop nombreuses perturbations dans les disponibilités des médecins et des salles, il est impossible de garantir un horaire qui sera identique de semaine en semaine sans compromette une utilisation maximale des salles d’opération. Nous proposons donc de créer des horaires qui varient le moins possible d’une semaine à l’autre tout en maintenant une utilisation maximale des salles d’opération.

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Superviseur du corps professoral :

Claude-Guy Quimper

Étudiant :

Partenaire :

Thales Canada Inc

Discipline :

Computer science

Secteur :

Management of companies and enterprises; Manufacturing; Professional, scientific and technical services

Université :

Université Laval

Programme :

Accelerate

Optimizing medical marijuana: developing genetic and chemical resources to improve the quality and quantity of cannabinoid metabolites in Cannabis sativa L strains

Cannabinoids are synthesized and accumulate in Cannabis plant hairs called glandular trichomes, which are particularly abundant on female flower buds. Cannabinoid content and composition vary dramatically among Cannabis strains, with two general groupings distinguished: marijuana strains, defined by high THC and low CBD levels, and hemp strains containing low THC and high CBD (or low total cannabinoid content). Marijuana breeding efforts have increased the cannabinoid potency and profiles of cannabis strains, and have produced marijuana strains with highly variable chemical phenotypes (chemotypes). While these breeding efforts have been successful in dramatically increasing THC levels, they commonly have reduced the content of other pharmaceutically valuable cannabinoids that compete for overlapping starter substrates (4). Many cannabinoids are known to have important pharmacological properties, and the relative abundance of the major cannabinoids (THC, CBD and CBC), minor cannabinoids and terpenoids can alter the therapeutic effects of marijuana strains. For instance, the presence of CBC augments some of the pharmacological effects of THC, while CBD has the ability to reduce THC side effects (5, 6). Metabolic profiling efforts proposed here will determine which cannabinoids accumulate in the female flowers of select medical marijuana strains, and the relative abundance of these cannabinoids in each strain.

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Superviseur du corps professoral :

Anne Lacey Samuels

Étudiant :

Partenaire :

Anandia Therapeutics

Discipline :

Life Sciences

Secteur :

Professional, scientific and technical services

Université :

The University of British Columbia

Programme :

Elevate

Development of a biosensor to monitor food spoilage in real time

Food freshness is an essential consideration for food quality control and consumer acceptance, especially for fish and related products which are highly perishable due to their special biological and physicochemical characteristics. Conventional methods to detect fish spoilage are generally not suitable for on-site application due to the complexity of procedures and high testing requirements. Thus, innovative biosensors with good portability for on-line measurements or in-situ testing are of great interest to satisfy the current requirement for rapid food quality control. The objective of this project is to develop a new inexpensive, portable, quick biosensor with good sensitivity to detect one of the fish degradation products to monitor food spoilage in real-time. A highly selective chromogen will be used as a visible indicator immobilized on the paper-based biosensor to indicate the freshness of fish product. The developed biosensor will be applied to several fishery products to certify its performance. This research will provide a new possibility of one-step simple detection in food spoilage control field.

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Superviseur du corps professoral :

Benjamin Simpson

Étudiant :

Partenaire :

Complutense University of Madrid

Discipline :

Engineering

Secteur :

Education

Université :

McGill University

Programme :

Globalink Research Award

Developing a mitochondrial gene risk for patients with neurodegenerative and neuropsychiatric diseases.

Neurodegenerative diseases represent one of the largest healthcare burdens in Canada. However, there is limited understanding of how the complex biological factors affect the development of neurodegenerative diseases, thus the era of precision medicine has not to come to treat them yet. Here, we take focus on bioenergetic disruption as a shared risk factor of neurodegenerative diseases and common conditions as type 2 diabetes, psychiatric disorders, and cardiovascular diseases. Hence, we apply machine learning techniques to analyze sets of biological measurements related to mitochondria – the powerhouse of the cell controlling bioenergetics – to identify biological factors that contribute to risk of developing neurodegenerative diseases and related conditions. NetraMark’s novel AI will analyze how diseases can be stratified based on biological markers, and the results will be added to their analytic depository for further algorithmic development. MitoCanada Foundation will deliver novel findings to patients, clinicians, and scientists in approachable medium to accelerate mitochondrial research and education.

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Superviseur du corps professoral :

Ana Andreazza

Étudiant :

Partenaire :

NetraMark Corp;MitoCanada Foundation

Discipline :

Life Sciences

Secteur :

Professional, scientific and technical services

Université :

University of Toronto

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.