This project will demonstrate the cause and effect of industrial material failures in the harsh Newfoundland climate under the general umbrella of corrosion. Our main focus is materials used in the marine and oil & gas industries.
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).
Character motion in games and animations often have high requirements of realism, aesthetics, and interactivity. For instance, in soccer simulation games, users control the players to move in different directions and perform actions such as passing and shooting. Modern data-driven approaches like motion fields provide convenient ways to synthesizing natural motions from a given database of motion capture data. In this work, we look to improve motion fields by leveraging deep reinforcement learning.
Personalized health is increasingly gaining public attention in the media as the future of healthcare. Personalized health is the idea that medical treatment will be tailored to the individual based on their predicted response or risks of disease.
The focus of the project is to develop an packet-optical network resource optimization model that minimizes the total network cost across IP-optical platform while meeting the following requirements: (i) Offers full protection from any network node and link level failure. (ii) Ability to handle large scale networks and traffic demand (i.e., network scalability). (iii) Meets end-to-end latency requirement. (iv) Provides efficient link utilization across the packet-optical networks. (v) Ability to forecast network capacity augment requirement.
The interns will develop a quantum communication network built on RBCs optical fiber infrastructure and perform secure commercial transactions using quantum-generated secure keys in the integrated classical communication network. The quantum communication network will be based on the measurement-device-independent quantum key distribution technology, which is developed by Prof. Hoi-Kwong Los and Prof. Li Qians groups at the University of Toronto. It will be an important milestone towards cybersecurity in the financial sector in Canada.
Data centers (DCs) in network softwarization and 5G eras are significantly different from those operated nowadays by public cloud providers. They are massively distributed, closer to end-users, heterogeneous (e.g., multi-access edge, central office as a data center, etc.) and rely on much more complex technologies (e.g., Network Functions Virtualization [NFV] and Software-Defined Networking [SDN]). This makes their Operation and Management (O&M) much more challenging. Much more intelligence is required for automating the various tasks.
Ericsson is an industry leader in offering telecommunication solutions and products. As an important step on the path towards the automatic and autonomous management of next generation networks, Ericsson is developing technology in machine learning and artificial intelligence that will benefit operators around the world, including in Canada where Ericsson supplies technology to most of the major telecommunication network operators.
Le projet consiste à bâtir un outil permettant d’évaluer les économies que les clients peuvent espérer en mettant en place le procédé et le logiciel proposé par Merinio. Le logiciel est un outil permettant de faire la gestion de la main d’œuvre de façon plus efficace en automatisant des tâches, en éliminant des tâches répétitives et réduisant les erreurs. Le stagiaire devra faire une analyse des facteurs qui peuvent permettre de réaliser des économies monétaires, en temps, en qualité ou en satisfaction dans un projet.