Innovative Projects Realized

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

29670 Completed Projects

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Projects by Category

Developing biomarker identification tools for neurodegenerative diseases

Neurodegenerative diseases have caused massive economic burden on the healthcare system as well as reduced patient and caregiver’s quality of life. Identifying biomarkers for early clinical diagnosis, tracking of disease progression, and evaluation of treatment therapies is critical. However, developing such tools remains a significant challenge. The goal of this project is to manage and analyze complex data sets collected from a large sample of patients with neurodegenerative diseases from multi-modal assessment platforms to investigate decline of speed of information processing in the brain. A central neuroinformatics platform, developed by the partner company, InDoc Research is utilized to integrate the large volumes of data across multiple diseases and different data types. Processing speed is a core and basic ability reflected in many brain functions, and deficits in speed of processing are common to many neurodegenerative disorders. TO BE CONT’D

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Faculty Supervisor:

Doug Munoz

Student:

Partner:

Indoc Research

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

Queen's University

Program:

Accelerate

Evaluation of low-lignin alfalfa in mixtures with grasses

Forage nutritive value is a key component of profitable dairy production with forage fiber concentration and digestibility being critical, as high quality forages can reduce requirements for high-cost concentrated feeds. One approach and new technology developed to increase forage quality has been the release of high digestibility/lowlignin alfalfa cultivars, as alfalfa is the main forage legume used in eastern Canada. These cultivars overall have a lower lignin concentration and/or greater digestibility compared to standard cultivars and thus be harvested at a later stage of development, resulting in greater yields and potentially greater persistence. There is, however, currently to our knowledge very limited information on the benefit of using low-lignin/highly digestible alfalfa cultivars in mixtures with grasses. TO BE CONT’D

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Faculty Supervisor:

Philippe Seguin;Arif Mustafa;Raj Duggavathi

Student:

Partner:

Bélisle Solution Nutrition Inc

Discipline:

Earth science

Sector:

Agriculture

University:

McGill University

Program:

Accelerate

Assessment of deep learning for analyzing radar signals in maritime environment

The proposed internships aim at investigating the relevance of deep learning (DL) techniques for target detection in radar data processing. More specifically, we are looking to demonstrate the feasibility of DL techniques to deal with unusual types of data (i.e., radar data) in situations where an well performing processing with classical techniques is a challenge (e.g., detection of objects in noisy scenes from a maritime environment caused by the interference produced by the reflection of the radar waves on the sea). The project is broken down into three main sub-goals: (1) review of relevant literature on radar detection in a maritime environment, (2) exploitation of existing datasets (e.g., IPIX, CSIR), and the state-of-the-art on radar simulation, and (3) application of DL, in particular convolutional neural networks (CNN), for suppressing sea clutter and detecting targets in radar image in maritime environment. TO BE CONT’D

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Faculty Supervisor:

Christian Gagné

Student:

Partner:

Thales Canada Inc

Discipline:

Engineering

Sector:

Technology; Information and Communications Technology; Ocean Tech

University:

Université Laval

Program:

Accelerate

Full characterization of Drug-Drug interactions using deep learning methods

Better understanding Drug-Drug interactions (DDIs) is crucial for planning therapies and drugs co-administration. While, considerable efforts are spent in labor-intensive in vivo experiments and time-consuming clinical trials, understanding the pharmacological implications and adverse side-effects for some drug combinations is challenging. The majority of interactions remains undetected until therapies are prescribed to patients. We propose to use computational tools for predicting interactions in order to reduce experimental costs and improve safety. To achieve this, we will use data about the drugs and information about their biological target that are available at the beginning the drug R&D process. Our hypothesis is that artificial intelligence will improve DDI characterization and provide information earlier in the drug development process. Creating such comprehensive tool will help to reduce the risks associated to drug interactions.

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Faculty Supervisor:

François Laviolette

Student:

Partner:

Valence Discovery Inc

Discipline:

Computer science

Sector:

Pharmaceuticals; Health and Related Sciences & Technology; Information and Communications Technology

University:

Université Laval

Program:

Accelerate

Endogenous quality turnover, macroeconomic dynamics and the informal economy

To what extent do changes in the size of the informal sector affect changes in the quality of goods consumed in developing countries? It is well known that a poor assessment of the quantity and quality of goods consumed by households could lead economic models to underestimate the fluctuations of most macroeconomic variables. However, in developing countries, nearly 40% of economic activity is informal and is therefore hidden from the public authorities in order to avoid taxes and compliance (Schneider et al., 2010). This hidden production gives very little information on the quality as well as the number of varieties of goods consumed by households. We therefore propose in this research project to examine the role of the informal sector in the volume and quality of demand made by households. Our results will gain insight into the fluctuations of economic variables in order to recommend effective stabilization policies.

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Faculty Supervisor:

Jonathan Goyette

Student:

Partner:

Waseda University

Discipline:

Sociology

Sector:

Education

University:

Université de Sherbrooke

Program:

Globalink Research Award

(In)visibilité de la contamination radioactive dans la photographie japonaise post-Fukushima : expositions et médiation

Mon projet doctoral examine la création artistique contemporaine japonaise suite à l’accident nucléaire de Fukushima, survenu au Japon en mars 2011. J’étudie plus particulièrement le travail photographique de trois artistes japonais qui permettent de rendre visible la contamination radioactive et observe leur réception. Mes recherches au Japon seront composées de séries d’entrevues avec les artistes composant mon corpus, de spécialistes et de professionnels du domaine, de recherches archivistiques, de visites d’expositions, d’une enquête de terrain dans l’ancienne zone d’évacuation et, pour finir, de recherches à l’Institut international de recherche sur la paix (Université de Meiji Gakuin) avec le Professeur Shintaro Namioka. Les résultats attendus pour ce séjour de recherche au Japon concernent principalement la récolte de données pour mener à bien ma thèse doctorale et mes projets dans le domaine de l’art japonais. TO BE CONT’D

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Faculty Supervisor:

Suzanne Paquet

Student:

Partner:

Meiji Gakuin University

Discipline:

Sociology

Sector:

New and Digital Media; Entertainment and Media; Sustainability & the Environment

University:

Université de Montréal

Program:

Globalink Research Award

Position sensitive detector density well logging tool development

In well-logging industry, gamma-ray density logging is an indispensable method to determine formations’ lithology and porosity. This research proposal under the support of MITACS and Rimpac Advanced Technology Development Ltd. aims at developing a high-resolution density well logging tool which is expected to achieve higher precision and better vertical resolution of formation density measurement. This work is based on a previous MITACS project. In this project, we will study the performance of this novel density logging tool in some complex geologic models.

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Faculty Supervisor:

Dugan O’Neil

Student:

Partner:

Rimpac Advanced Technology Development Ltd.

Discipline:

Physics

Sector:

Finance and Insurance

University:

Simon Fraser University

Program:

Accelerate

Cellular regulation of human inducible nitric oxide synthase

The main aim of the project is to find new mechanisms by which Inducible nitric oxide synthase (iNOS) expression is regulated in human cells. Regulation of iNOS through post-translational mechanism stage can give better results. That is why in this project iNOS expression is controlled at the post-translational level.
High concentration of NO can cause tissue damage, and iNOS is the enzyme responsible for the continuous synthesis of NO. The expression of iNOS is tightly regulated by complex molecular mechanisms, involving both translational and post-translational levels. It was found that NO acts in a positive feedback manner to amplify iNOS protein levels. Reduction in iNOS protein degradation is done by mTOR, a kinase that plays a role in the regulation of cell growth, proliferation, survival, and metabolism. Here, we attempt to find how this kinase affects iNOS protein expression. TO BE CONT’D

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Faculty Supervisor:

Jonathan Choy

Student:

Partner:

Indian Institute of Technology Kharagpur

Discipline:

Life Sciences

Sector:

Education

University:

Simon Fraser University

Program:

Globalink Research Award

Chudnovsky-Ramanujan Type Formulae

In a well-known 1914 paper, Ramanujan gave a number of rapidly converging series for 1/pi which are derived using modular functions of higher level. The Chudnovskys derived an analogous series representation 1/pi, often used in practice for record breaking computations of pi. These formulae have recently been explained in the context of elliptic curves, modular curves, and the Picard-Fuchs differential equation. In particular, these series representations for 1/pi arise from relations between periods of families of elliptic curves and hypergeometric solutions to their associated Picard-Fuch differential equations; we refer to such relations as Chudnovsky-Ramanujan type formulae. The aim of this project is to apply this framework to derive a complete list of Chudnovsky-Ramanujan type formulae. This will be done by identifying the families of elliptic curves suitable for the method and then applying the method on each such family to produce the resulting list of Chudnovsky-Ramanujan type formulae.

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Faculty Supervisor:

Imin Chen

Student:

Partner:

Indian Institute of Technology Bombay

Discipline:

Mathematics

Sector:

Education

University:

Simon Fraser University

Program:

Globalink Research Award

Development of algorithms and methods for information fusion enabled decisionsupport

In every situation humans make observations and analyse the observed information, by combining and evaluating the relationships between the observation to understand the situation. Often these observations can be uncertain, redundant, from unknown sources, contradictory, and too many for a human to be able to analyse fast enough and correctly, leading to incorrect assessment of the situation. The proposed research is based on the hypothesis that through application of various probablistic, possibilistic, and other applied mathematics methods it is possile to enhance humans ability to analyse large amounts of redundant, uncertain, sometimes contradictory information, enhancing his/her capability to correctly understant situations. Te proposed research will extend the currently ongoing research on the application of imperfect information representation theories (including belief functions with their link to probability) to enhance situation understanding in a context where information, coming from a broad variety of sources (internet, police, military, coast guard, etc.) is uncertain…….

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Faculty Supervisor:

Jiri Patera

Student:

Partner:

OODA Technologies Inc

Discipline:

Mathematics

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

Design and optimization of new peroxidase ribozymes based on RNA Mango constructs

DNA and RNA oligonucleotides that fold to form four-stranded structures (G-quadruplexes) are known to bind heme and such complexes to catalyze 1-electron (peroxidase-like) and 2-electron (peroxygenase-like) oxidation reactions. While successful in competing with protein peroxidase enzymes in this respect, G-quadruplex•heme complexes are currently not competitive with protein enzymes with regard to the enantioselectivity of the products generated. We will try to study, mutate, and develop existing complex RNA G-quadruplexes, the so-called “RNA Mango” molecules, which bind heme but provide a more complex, “enzyme active site-like” environment around the heme. These may thus be more promising reagents for giving enantioselective oxidation products from chosen substrates.

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Faculty Supervisor:

Dipankar Sen

Student:

Partner:

Indian Institute of Technology Madras

Discipline:

Physics

Sector:

Health and Related Sciences & Technology

University:

Simon Fraser University

Program:

Globalink Research Award

Inter-Operability Standards in Information Technology Sector: The Role of Coopetition

1. development of a simulation mode of the information Technology Sector
2. Understanding and modelling the simultaneous Competition and Cooperation (“co-opetition”) among firms where they jointly form interoperability standards by consensus under the ambit of their chosen Standards Setting Organizations (SSOs).
3. Conduct Analysis using an appropriate simulation method, which will be implemented using either an available Graphical User Interface tool or programming tool such as Netlogo

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Faculty Supervisor:

Nilesh Saraf

Student:

Partner:

Indian Institute of Technology Madras

Discipline:

Business

Sector:

Information and Communications Technology; Commercial Services; Other

University:

Simon Fraser University

Program:

Globalink Research Award

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