Creating smart indoor environments that detect and understand human daily life activities is attracting significant amount of attention, due to their huge potential impact on context-aware technologies. Aerial Technologies, our partner organization, is developing products that provide device-free indoor localization and activity recognition Wi-Fi signals generated by off-the-shelf Wi-Fi enabled devices. This project will use state of the art machine learning and time-series analysis algorithms to improve the detection and classification capabilities of these systems.
The goal of this project is to improve the processing on hearing aids for music. Currently hearing aids are designed for speech, but for some hearing aid users music is an important part of their lives. A concert where the room acoustics are changed will be used to collect audio samples for testing changes to the hearing aids to improve the quality of live music for hearing aid users.
The goal of this project is to explore the use of log analytics and machine/deep learning techniques to improve Ubisoft operational intelligence. Logs contain a wealth of information, but often hindered by the lack of best practices, tools, and processes. Despite the importance of logging, the area has not evolved much over the years. At Ubisoft, logs are used extensively for various system diagnosis tasks. The analysis of logs, however, is usually performed manually, limiting the full potential of the information contained in logs.
Continuous blood pressure (BP) monitoring is highly of assistance for detection of the major public health issue which is cardiovascular problems. Nowadays the BP monitoring technique which are commonly used are mostly using an uncomfortable cuff that makes continuous monitoring impossible since it slows the blood flow. However, measuring continues blood pressures are necessarily and crucial in some cases such as diagnostic of hypertension, for CVD patients, Sleep apnea and etc.
As a result of the advancement of renewable energy and power electronic (PE) converter technologies, renewable energy sources are increasingly interfaced to the grid through PE based interconnections such as Voltage Sourced Converters (VSC) and Modular Multi-level Converters (MMC). It is essential to model and predict the behavior and effects of these components in the power system for safe and reliable operation. This proposed research project will focus on how renewable sources connected to the grid through PE converters, affect the stability power transmission system.
IMAX is a Canadian-born invention that has had little scholarly investigation despite its continued commercial success. The importance of this research project stems from the lack of scholarly research about the beginnings of IMAX as a Canadian-born invention and its place within the history media technology.
This project involves the research and development of a new skin examination system for Elucid Labs Inc. (Elucid) to add to its imaging and diagnostic capabilities in the area of skin cancer, disease, and conditions. Elucid is building a comprehensive system that identifies and tracks a patients’ moles to perform screening and risk assessment. Moreover, Elucid will be developing a multimodal microscopy system that may prove to be useful in streamlining the histopathology process pertaining to skin biopsies.
Due to increasing miniaturization, future systems will be made of components that are more energy efficient and at the same time more sensitive to external radiation. To ensure that future systems remain protected against cosmic radiation and single events, aircraft and flight systems manufacturers must collect in-flight data for cosmic radiations and develop a global strategy for real-time processing of this data to provide pilots, crew and aircraft operations, with appropriate information to help them make the right decisions in case of unusually high cosmic radiation exposure.
Drone technology has recently gained wide-scale acceptance in multiple military and commercial applications, including security surveillance and medicine delivery. In 2017 alone, the market revenue from drones is estimated to be around $6 billion. With this, there is also an emerging need for sophisticated anti-drone technology to detect rogue drones entering secure territories, such as military bases and prisons.
In this project, a vehicle occupancy detection system will be developed for automatic monitoring of HOV (High Occupancy Vehicle) and HOT (High Occupancy Toll) lanes. The system makes use of machine vision along with artificial intelligent algorithms, developed based on deep learning, to detect number of passengers in a vehicle in real-time. The developed technology will enable the industry partner to commercialize this solution in Canada and globally for efficient and enforceable monitoring of HOV and HOT lanes.