Human Motion Inverse Optimal Control Constraint Learning and Inertial Measurement Unit Sensor Design for Rehabilitation

During physiotherapy a continuous assessment and progress tracking of a patient’s performance is of clinical interest. In this project, based on the promising results from the initial prototype, we will redesign the wearable sensors to improve tracking accuracy, communication speed and robustness, incorporate onboard data storage and computation, and minimize cost and size. Furthermore, we will develop automated algorithms for the analysis of the measured data to help physiotherapists identify the causes of changes to the patients' movement profile.

Computer vision system for ice detection on power cables

In northern countries, ice storms can cause major power disruptions such as the one that occurred in the Toronto area on December 2013 that left more than 300,000 customers with no electricity immediately after the storm. Prediction of ice formation on power cables can help on taking actions for removing the ice before a major problem occurs. Currently Manitoba Hydro HVDC Research Centre has a vision based ice detection system that uses digital images taken from the overhead line conductors.

Modeling and HIL Simulation of Modular Multilevel Converters

The proposed internships will be aimed at developing advanced computer (software) and hardware platforms for simulation of modern power-electronic converter systems used in emerging electric power transmission systems. In particular, modular multi-level converter (MMC) simulations will be targeted. These converters are considered the primary candidates for dc systems used to integrate renewable energy sources into the existing grid. These two internships will develop functional models that can be readily used for the analysis and design of systems involving modular multi-level converters.

Exploring cloud computing paradigm for Telecommunication Applications

For economical and simplification purpose Operators in the Telecom market are looking to move as much as possible of their infrastructure from traditional deployment to Cloud deployment. However Cloud deployment of IMS still need to be defined and developed. This project aims at bringing further the knowledge for such a deployment and helping guide future development for Ericsson. This project focuses on providing future directions for the development of an IP Multimedia System (IMS) in a Cloud environment.

Deep Learning Analysis for Missing Tooth Detection in Mining Monitoring Systems

This project is aimed at using machine learning algorithms and techniques to enhance the current state of the art of missing tooth detection in mining monitoring systems. Unlike heuristic approaches that follow strictly static program instructions, machine learning techniques operate by building a model from example inputs in order to make data-driven predictions or decisions. We use machine learning techniques to identify the bucket and its teeth within the video frames taken by a camera located on the mining device.

Silicon Photonic Water Quality Sensor

Pesticide detection in water has become a high priority worldwide, from protecting the population from environmental contamination due to agricultural pesticides, and bio-threats from terrorist activities. An exhaustive characterization of water pollutants requires laboratory-based analyses which are inherently slow and expensive. Thus, regarding the above mentioned pesticide contaminations, developing a real-time and in-situ detection technique would be very valuable to rapidly protect the population and the environment, especially in remote places.

Training in Virtual Environments on Mobile Devices

New emerging Virtual Reality (VR) technologies and mobile devices are changing the way that we are interacting with computing technology. The partner organization has a multimedia product that is used to train technicians to perform various maintenance tasks or introduce them to the interconnected components of a machine. The goal of this project is to research and develop software designs to port this multimedia training framework to mobile platforms. The specific challenges include conducting multimedia training on standalone mobile devices.

Aiding Leak Detection in Pipelines Using System Identification - Year two

The ultimate goal of this project is to detect and localize leaks in pipelines in real time. Hifi Engineering has developed
distributed fiber optic sensors. Measurements are obtained at evenly spaced intervals along the pipeline (called
channels). This project aims to develop data processing tools to improve leak detection and localization. Many events
occur along a pipeline whose effects are registered by sensors (trucks driving by, compressors turning on, leaks).
Events are registered in many channels as sounds propagate down the pipeline.

Smart technology use with Public Safety and first responders

This project aims at identifying, analyzing, and documenting the operational requirements for a technological solution that will replace the currently used, time consuming, paper-based, building safety plans. Upon arriving at an incident scene, first responders rush to these on-site safety plans to know the ins and outs and the safety details of the incident scene. The few minutes spent doing this can be the difference between life and death and can be used to significantly reduce losses.

Switching frequency optimization and advanced current control techniques for an electric vehicle traction drive

The performance of an electric vehicle traction drive, comprised of an electronic power converter feeding power to an electric motor, is defined by its control stages and different system parameters- such as switching frequency of the semiconductor devices of the converter. The proposed internship aims to determine changes in conventional control strategies and selection of switching frequency to improve the power conversion efficiency and drive response time. First, selection of switching frequency to improve the power conversion efficiency and drive response time.