Integrity Monitoring of Motion Estimation and Hazard Detection Algorithms in Environmentally-Impacted Scenarios

Imagine some of the difficult driving conditions experienced by vehicle operators. In these conditions, the sun might be blindingly bright, or the snow might obfuscate what is going on around the vehicle. Surprisingly, the sensors used by autonomous vehicles to understand the environment they are in suffer from similar effects. As a field, robotics has yet to tackle integrity monitoring of the sensors used in autonomous applications.

Automated Driver Drowsiness Control Technology Using Artificial Intelligence-based Decision Support System

The main purpose of this project is to develop the methodology to detect and predict driver drowsiness at the early stages using physical and physiological variables. A feasibility test is conducted to evaluate the accuracy and performance of the proposed methodology. The existing databases are leveraged to extract the required data. Signal processing, image processing, AI techniques and decision-making methods are utilized to analyze data for monitoring, detecting, predicting and controlling driver drowsiness.

Point-of-care breath analyzer for early-stage disease diagnosis

As the third documented emergence of an animal-to-human coronavirus during the past two decades (Severe Acute Respiratory Syndrome in 2002, Middle East Respiratory Syndrome in 2012), the current pandemic and near-certainty of future epidemics demands intensified surveillance and proactive screening. Definitive therapy for novel Coronavirus Disease 2019 (COVID-19) is likely at least half a year away. Current standard-of-care diagnostic testing with real-time Reverse Transcription Polymerase Chain Reaction (rRT-PCR) is resource intensive, costly and inaccurate.

Mobilizing public sector investments in vaccine R&D to address emerging viral threats

The COVID-19 pandemic reminds us that, first, global pandemics are significant threats to population health and material standard of living and, second, evidently not enough is being done to prevent pandemics. Future pandemics are likely if the system of pandemic preparedness is not improved. The first step to ensure we are ready for the next viral outbreak is to understand how the current system functions: who is funding this system and who is conducting the basic and applied R&D into diagnostics and vaccines? Which viruses are being targeted?

COVID-19 and ultraviolet-C disinfection of porous and non-porous surfaces: modeling, validation, and performance of new devices

The COVID-19 pandemic led to an increased demand for disinfection solutions, including ultraviolet C (UVC) light technologies. UVC works by inactivating microorganisms and show a strong potential to break the chain of infection in hospitals and public settings. CleanSlate and the University of Toronto are exploring this potential by characterizing how the virus that causes COVID-19 responds to UVC, and by evaluating the efficacy of new UVC devices for decontamination of high touch and other common surfaces.

Performance Based Design of Viscoelastic Coupling Dampers in Mass Timber Buildings

Viscoelastic Coupling Dampers (VCDs) have been developed over the past 15 years at the University of Toronto and by Kinetica for use in multi-storey buildings constructed with conventional construction techniques (steel and concrete). It has been shown the VCDs improve the wind and seismic performance of these buildings, leading to safer, higher performing and more resilient structures.
There has been a boom of mass timber construction due to the inherent sustainability, modularity and speed of construction using mass timber.

Enhanced Modelling of Exfiltration Events in Sun Life Cybersecurity Data

Theft or loss of sensitive data is a growing concern for companies who may suffer losses of consumer confidence, market valuation and intellectual property when large amounts of data are stolen. In this research project we will use an enhanced “screen and review” approach to combating exfiltration in a large data set of activity logs within a large corporate network.

Detargeting Protein-Protein Interactions For Cellular Design Applications, Using 3D Structure-Based Deep Learning Models

Rational protein design has had a tremendous impact on pharmaceutical, agriculture, and chemical industries over the past 30 years, by focusing exclusively on individual proteins and their intrinsic activities. The next generation of protein design tasks will seek to modify function inside living cells, competing and interacting directly with pre-existing cellular machinery. Modifying systems in living cells will open a new wave of biotechnology applications, such as living drug implants and diagnostic tools.

Chemistry, manufacturing, and control (CMC) assessment of Manganescan

To design effective and patient-specific cancer therapy, sensitive detection of relapse and distant metastases by non-invasive medical imaging is essential, for which MRI offers tremendous potential due to wide availability of the equipment in clinic and avoidance of ionizing radiation. Although gadolinium-based contrast agents are the most frequently used for MRI, they are associated with nephrogenic systemic fibrosis and brain deposition. Thus, less toxic manganese ions are exploited as an alternative for tumor detection using MRI.

PATH: Program to Accelerate Technologies for Homecare

Most people would like to continue living in their own homes as they age. A new ecosystem is needed to enable home health technologies to be developed, tested and successfully commercialized. This will require a program that provides a low-cost way for developers to test their products before introduction to the market. Therefore, in our Program to Accelerate Technologies for Homecare (PATH), the intern will develop a novel versatile connection protocol that will connect different devices and sensors to a single hub.