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Projets par catégorie

Mental illness after exposure to medical cannabis

Although most cannabis use is recreational, cannabis is approved for various medical indications. One of most major safety concerns regarding the use of cannabis is its association with mental illness, in particular, induction or exacerbation of psychosis and schizophrenia, suicidal attempts and depression. That said, current available data in the field is based mostly upon studies of recreational users of cannabis and is therefore subject to inherent limitations and weaknesses. This study’s main objective is to study the association between exposure to medical cannabis and the risk for mental illness, including the risk for psychotic episodes, anxiety and suicidal behavior in meticulously designed studies. The data will be retrieved from three Israeli databases
Expected results: We expect this study will enable us to learn the association between medical cannabis and mental illness. This study will either confirm or refute the current concern about the mental effects of medical cannabis. The findings from the study will provide crucially needed data that could be immediately implemented in clinical practice, will contribute to the ongoing debate about legalization of recreational cannabis and may lay the groundwork for additional research.

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Superviseur du corps professoral :

Laurent Azoulay

Étudiant :

Partenaire :

L’Université hébraïque de Jérusalem

Discipline :

Sciences de la vie

Secteur :

Sciences de la santé et technologies connexes; Pharmaceutique; Biotechnologie

Université :

Université McGill

Programme :

Bourse de recherche Globalink

Deep Learning Based Approaches to Synthetic Data Generation

Synthetic population generation is the process of combining multiple socioeonomic and demographic datasets from various sources and at different granularity, and downscaling them to an individual level. Although it is a fundamental step for many data science tasks, an efficient and standard framework is absent. In this project, we propose a multi-stage framework called SynC (Synthetic Population via Gaussian Copula) that is novel and scalable to address this research problem. We aim to make both theoretical advances by developing new algorithms, as well as release easy-to-use implements for others addressing similar challenges.

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Superviseur du corps professoral :

Periklis Andritsos

Étudiant :

Partenaire :

Arima Inc

Discipline :

Informatique

Secteur :

Services professionnels, scientifiques et techniques

Université :

Université de Toronto

Programme :

Accélération

The affect of the perceptions of social workers and policymakers regarding people living in poverty on policies and their implementation

The research examines how the perceptions of social workers and policymakers regarding people living in poverty affect policies and their implementation. The study will focus on exploring ideas about the perceived values of people in economic need, the conceptual values associated with policy decisions concerning the needy, the way such ideas affect the construction of social constructions related to the needy and how they are influenced by these ideas. Additionally, a comparison will be made with Canada to examine imagination and variation between the countries and impact of international social learning on social policy.

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Superviseur du corps professoral :

Daniel Béland

Étudiant :

Partenaire :

L’Université hébraïque de Jérusalem

Discipline :

Sociologie

Secteur :

Service public, politiques et gouvernance; Autres

Université :

Université McGill

Programme :

Bourse de recherche Globalink

Improving the First Pass Yield of an industrial electroplating line through a combined Design of Experiments and Causal Inference integrated with Deep Learning algorithms (DLs)

The performance of an industrial electroplating line is evaluated using the First Pass Yield (FPY).
Improving the FPY of an industrial electroplating line is complex and depends on lots of environmental
and technical parameters. These parameters have different nature that makes it hard to assess the
interaction between them and consequently to detect the causes of plating defects. In a bid to tackle this
problem, we will apply a casual reasoning model equipped with deep learning to find the underlying
causes of process failures. Moreover, we will take advantage of Design of Experiments (DoE) techniques
to learn more about the weight of each parameter in the result of the plating process, leading to a lowvalue
FPY.

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Superviseur du corps professoral :

Usef Faghihi;Laurent Cormier

Étudiant :

Partenaire :

Héroux Devtek Inc

Discipline :

Génie

Secteur :

Fabrication

Université :

Université du Québec à Trois-Rivières

Programme :

Accélération

Diagnostics and Prediction of Mobile Device Behavior using Data Analytics and Machine Learning

SOTI has accumulated a large number of devices in the past ten years, and how to diagnose and predict the status of these devices has become a very important topic. Through this research we hope to find various parameters and patterns contributing to particular negative impacts. Additionally we would like to be able to Create demonstrable visualizations of such factors, patterns, that affect device performance or behaviour over time. Finally, we will build prediction models to quantify and visualize progression of negative trends up to terminal failures

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Superviseur du corps professoral :

Bo Wang

Étudiant :

Partenaire :

SOTI Inc

Discipline :

Informatique

Secteur :

les industries de l’information et de la culture; Services professionnels, scientifiques et techniques

Université :

Université de Toronto

Programme :

Accélération

Mobile Electroencephalography and Mobility in Parkinson’s Disease

Attention, an important aspect of human cognition, is needed for safe mobility and navigation through the environment. With age, the ability to move and navigate through the world requires greater cognitive resources. Previous brain imaging research has shown that mobility impairments are associated with reduced attention. However, previous work was limited to assessing attention while participants were stationary and/or in a laboratory environment, which does not necessarily translate to what would occur in the real-world. My research will use mobile neuroimaging to observe and compare brain activity in a real-world environment across younger adults, older adults (with and without a history of falls), and adults diagnosed with Parkinson’s disease. Participants will be required to pay attention to naturally occurring hazards while walking outside. These findings have the potential to expand current understandings of brain function in Parkinson’s disease, human mobility, and fall risk using real-world methods and technology.

Voir la description complète du projet
Superviseur du corps professoral :

Lindsay Nagamatsu

Étudiant :

Partenaire :

Société Parkinson Sud-Ouest de l’Ontario

Discipline :

Sciences de la vie

Secteur :

Autres services (sauf administration publique)

Université :

The University of Western Ontario; University of Western Ontario.

Programme :

Accélération

Modular Magnetorheological Actuators Controls

Since its foundation in 2013, Exonetik has been developing and manufacturing magnetorheological (MR) actuator systems that enable novel functionalities to satisfy unmet customer needs. Exonetik’s patented actuators offer a unique combination of torque-density, high bandwidth, superior back-drivability, low-cost and aerospace-level reliability. To reach its ambitious goals, Exonetik has built several strategic relationships with major OEMs in the aerospace and automotive sectors and now wishes to repeat the process in the robotics’ field.
In 2018, Exonetik started exploring collaborative robotics, where it saw a unique opportunity for its MR actuators. In this field, Exonetik initially investigated the potential of remote actuator systems through the development of various prototypes (e.g.: 3 degree-of-freedom – DOF – wearable hydraulic robotic manipulator and teleoperated cable-driven arms). While the performance of these prototypes was excellent, this approach leads to unnecessary complexities (e.g.: routing) for 6 DOF robotic arms. In 2020, Exonetik thus developed its first modular MR robot actuator, and in 2021, its first 6 DOF robot for human-robot interaction. The modular MR robot’s promising performance has led compagnies to sign agreements with Exonetik to develop robotic arms for various robotics tasks.

Voir la description complète du projet
Superviseur du corps professoral :

Alexis Lussier Desbiens

Étudiant :

Partenaire :

Exonetik Inc

Discipline :

Génie

Secteur :

Services professionnels, scientifiques et techniques

Université :

Université de Sherbrooke

Programme :

Accélération

Automatic Machine Learning for Recommender Systems

Crossings Minds provides machine learning-based recommendation systems that allow companies to integrate their data and easily obtain on-demand personalized recommendations for their users. Currently, onboarding a new customer requires significant work on the behalf of Crossing Minds engineers to create and polish a machine learning model for that specific customer. The research project will explore the field of auto-machine learning, which studies the way that machine learning models themselves learn. The goal would be to provide tools that make the machine learning decision-making process easier for Crossing Minds engineers and will explore potential opportunities in each step of the machine learning pipeline. Such tools will reduce the turnaround time for onboarding of new customers and will therefore improve the company’s scalability.

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Superviseur du corps professoral :

Nisarg Shah;Scott Sanner;Eldan Cohen

Étudiant :

Partenaire :

Crossing Minds Canada Inc.

Discipline :

Informatique

Secteur :

Services professionnels, scientifiques et techniques

Université :

Université de Toronto

Programme :

Accélération

Vers le développement d’un outil de prise de décision pour les propriétaires, les concepteurs et les municipalités : étude des technologies et outils actuels de conservation de l’eau domestique pour les habitations unifamiliales

Le projet proposé portera en revue les technologies et approches décentralisées de conservation de l’eau « de premier ordre » telles que les systèmes de collecte des eaux de pluie et les systèmes de recyclage des eaux grises destinés aux habitations unifamiliales au Canada. Le projet proposé évaluera également tous les outils et ressources fournis par les autorités de conservation de l’eau, telles que le CMHC, aux propriétaires, aux concepteurs de bâtiments et aux municipalités. Le projet proposé travaillera avec S2E pour élaborer un rapport final complet qui permettra d’appliquer les résultats de la recherche au projet Smart Community de S2E. Ce projet sert de base à des comparaisons entre les technologies de conservation de l’eau disponibles et servira de base pour le développement d’un outil décisionnel qui pourrait être utilisé par l’industrie du bâtiment.

Voir la description complète du projet
Superviseur du corps professoral :

Miljana Horvat

Étudiant :

Partenaire :

S2E Technologies Inc

Discipline :

Génie

Secteur :

Construction et infrastructures; Finance et assurance; Services professionnels, scientifiques et techniques

Université :

Université métropolitaine de Toronto

Programme :

Accélération

Multi-modal machine learning for business-critical insights in video conversations

The team is building a machine learning platform and solution to extract meeting insights from online meetings. Meeting insights denote moments from these meetings that may impact the company’s future product features, revenue, and customer satisfaction. This platform is driven by the market created by the widespread adoption of online virtual meetings as the main means of reaching clients in recent years. The internship will focus on developing a multi-modal solution that takes audio, visual, and its transcriptions as features and outputs moments of key insights. Specifically, the detection task is to detect interruptions, which identifies video and audio snippets where individuals are talking over another happening in business meetings.

Voir la description complète du projet
Superviseur du corps professoral :

Scott Sanner;Anthony Bonner

Étudiant :

Partenaire :

Talka AI Canada

Discipline :

Informatique

Secteur :

Services professionnels, scientifiques et techniques

Université :

Université de Toronto

Programme :

Accélération

Visual Semantic SLAM using Bottlenose Camera System

Simultaneous Localization and Mapping (SLAM) is useful in multiple applications such as autonomous driving, augmented reality, and surveillance. Visual SLAM is a popular and widely used SLAM technique due to simplicity of the sensor network. Semantics and machine learning are used widely in recent research to achieve better robust real time SLAM performance. Hardware acceleration can be used to perform repetitive and compute intensive parts of the image processing in SLAM. In this research we plan to use the Bottlenose camera system from Labforge Inc which has these features of semantic segmentation, deep learning-based hardware accelerated object detection to achieve improved SLAM performance. The additional processing power from Bottlenose
camera processing will help to offload some work from the central processor and result in better real time performance. The research will take existing SLAM solutions and analyze how the Bottlenose camera system can be integrated with them to provide better and improved SLAM solutions

Voir la description complète du projet
Superviseur du corps professoral :

Mohamed Atia

Étudiant :

Partenaire :

Labforge Inc.

Discipline :

Génie

Secteur :

Fabrication

Université :

Université Carleton

Programme :

Accélération

Conception intégrée et fabrication d’une électrode composite flexible sèche d’électroencéphalographie et d’un système de support à pression variable pour utilisation mobile de longue durée in-situ

Durant les dernières années, de nouvelles méthodes d’analyse par ordinateur impliquant l’intelligence artificielle ont permis de grandement améliorer notre capacité à interpréter les signaux obtenus par électroencéphalographie pour des applications allant de la santé à la défense en passant par les sports et l’amélioration du bien-être mentale. Toutefois, les avancées dans ce domaine sont aujourd’hui limitées par les systèmes d’acquisition des signaux d’électroencéphalographie. Les électrodes et casques employés ne peuvent être portés pour de longues durées dû à l’asséchement graduels des électrodes humides ou l’inconfort provoqué par des méthodes sèches métalliques. Ce projet propose de développer un système casque/électrode à polymère composite flexible qui permet de contrôler la pression appliquée tout en étant confortable pour l’utilisateur pour de longues durées.

Voir la description complète du projet
Superviseur du corps professoral :

Ilyass Tabiai;Sofiane Achiche;Fabio Cicoira;Eric David;Gelareh Momen;Martine Dube

Étudiant :

Partenaire :

Intelligence augmentée de BMU

Discipline :

Génie

Secteur :

Services professionnels, scientifiques et techniques

Université :

École de technologie supérieure; Polytechnique Montréal

Programme :

Accélération

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