Design of a Multi-modal Electronic Stethoscope for the Digital Acquisition and Automatic Diagnosis of Auscultation Signals

The proposed research project aims to develop a multi-modal stethoscope, containing superior digitized heart and lung sounds, telemedicine capabilities and assistive diagnostics. This is achieved by leveraging new advancements in piezo, microphone, wireless and machine learning technologies. The project will investigate these technologies and integrate them into custom made electronics and mechanical designs to achieve an optimal digitized sound that provides superior auscultation capabilities to medical professionals for lung and heart sound diagnosis.

Development of signal processing techniques for animal movement data

In the past decade, the development of sophisticated sensors attached to animals (tags) have researchers to infer of horizontal and vertical movement of marine animals across time and space. The amount of data collected from these tags along with the analytical challenges surrounding the extraction of behavioural patterns has presented a significant barrier for researchers to adopt this technology.

Detection of Fights in Crowd Video

Detection of fights and anomalous behavior of individuals in a crowd is a common problem in computer vision. Some tools that currently exist rely on optical flow of tracked features is a sequence of video frames. These motion algorithms are sensitive to independently moving objects in the frame. What constitutes an “anomaly” is context (eg. location) specific, thereby adding to the complexity.

Feature selection for Deep Learning applied to the identification of impaired drivers

DriveABLE Inc uses a set of simple video tasks to identify the impaired drivers. Video tasks come in the form of simple games and measure cognitive ability. The test results are analysed by AI powered algorithm that predicts the impairment level of the driver. Our project’s main objective is to redesign the AI in such a way that it can cover more use cases with fewer tasks. In particular we will redesign the algorithm so that it will accept incomplete tests. We will also identify redundant games in order to make overall test shorter.

Advanced pricing methods for property and casualty isurance

Pricing risks is of pivotal importance for the insurer’s well-being. Indeed, inappropriately determined prices, whether too high or too low, may result in insolvency of insurance policies, failure of business lines, and even bankruptcy of entire insurance enterprises. This project will help Wawanesa Insurance to develop sophisticated pricing techniques that will take into account (a) exogenous pricing factors, and (b) interdependencies among risks. Wawanesa Insurance will therefore benefit from the resulting competitive advantage.

Text Recognition Software Development for Legal Services

The rapid advancement in the areas of machine learning and artificial intelligence has led to many breakthroughs in industries. As an online medium legal intermediary platform, Right Legal aims to connect clients and lawyers by providing them with a secure, convenient, and efficient platform. In order to accomplish this, we take into account of (1) the resourceful text information generated from the platform (e.g., the request and feedback from clients), (2) the lawyers’ profiles, and (3) the service quality offered by the lawyers.

Mathematical Foundations of Hybrid Quantum Technologies and Quantum Leap Africa

We are on the doorstep of a quantum revolution in modern science, perhaps most significantly in the development of new types of information and communication technologies, and Canada has positioned itself as a world leader in these efforts. This proposal includes an expansion of research into quantum information and communication by the University of Guelph’s David Kribs, in collaboration with the African Institute for Mathematical Sciences (AIMS), which has identified quantum information as an area of fundamental importance to its network.

Mapping the surface flow velocity of Minas Passage using RADAR data

This project will investigate the use of RADAR data to estimate the ocean surface velocity in regions of interest, specifically where tidal turbines will be deployed in Minas Passage, Bay of Fundy. The Fundy Ocean Research Center for Energy (FORCE) currently owns a single RADAR on the North side of the Minas Passage. Initial investigations have been done with this single RADAR; however, more intensive analysis must be done to reach the long-term goal of having a network of RADARs in the area.

Prediction of Insurance Coverage and Wait Times

The purpose of this project will be to develop a flexible and statistically sound methodology for leveraging BPI’s database of current and historical publicly available coverage information to model coverage trends in the U.S. health insurance industry. The project will also include the development of an R Markdown template for future predictive analytics reports. The templates and methodologies developed for this project will be integrated into BPI, Inc.'s custom consulting division.

Optimization tools for short-term hydropower generation management

Short-term hydropower optimization models are used on a daily basis to dispatch the available water for production between the turbines of the power plants that compose an hydropower system. Rio Tinto owns and operates power plants in the Saguenay Lac-St-Jean region of the province of Quebec and is currently lacking efficient tools to help the engineers in the daily decision making for the management of their hydropower system. The objective of this project is to develop tools to solve the short-term optimization model and therefore improve the water productivity of the hydropower system.