Low Dose Computed Tomography Denoising Using Deep Learning

CT (Computed Tomography) scans are widely used medical images used to diagnose disease such as cancer. CT Scanners pass x-rays through the body in order to generate cross-sectional images. Unfortunately pro-longed exposure to radiation (via x-rays) can damage the body, and thus one aims to minimize the x-ray dose they receive. However, modern CT scanners produce lower quality images when using low x-ray dose which defeats their purpose as a diagnostic tool. We propose a post-processing algorithm to enhance the quality of CT images produced at low radiation dose.

Stories Incarnate: Using Body-tracking/Body-Sensing Technology to Create Interactive Narrative Experiences for Audiences

Ryerson’s Synaesthetic Media Lab is working with Cirque du Soleil Entertainment Group’s creative design studio, 4U2C, to develop several ways for audiences to meaningfully interact with live performance. This project is looking at how tracking sensors, computer vision, and digital displays can be used to track audience movements and/or emotions for audiences to be able to participate in the storytelling of a live show.

Near-Infrared Dyes for Next-Generation Motion detection technology

Currently, the traditional use of the dye-sensitized solar cell (DSSC) is well-known in the science community as an effective photovoltaic technology, where it works best in diffuse lighting conditions. With the insights brought from this research project, the DSSC can also be transformed into an optically sensing motion sensor based on the dye utilized within it. This project will focus on synthesizing a family of organic dyes that absorb in the near-infrared region, optimal for detecting movement. The second half of the project will utilize those dyes in device fabrication.

Smart Work Zone Management

Construction zones are one of the leading contributors to Toronto’s ever-growing congestion. The aim of this study is to develop an integrated construction zone traffic management framework to minimize disruption of the traffic and reduce the effect in terms of congestion. This study leverages historical and real data collected from on-board construction trucks provided by the partner organization to find an insight as to how far upstream and downstream of the work zone congestion propagates.

Exploring Consumer and Restaurant Employee Perspectives Towards Food Allergy Risk Communication Strategies

Our research studies aim to gain a better understanding of how restaurants communicate risk to people with food allergies from the perspectives of customers and restaurant staff. We seek to understand what methods are used to communicate risk, what methods are most effective, and what strategies restaurants take to make the public aware of the risks and risk-mitigation efforts at their restaurant.

Analysis of the cryptocurrency market microstructure: role of smart order routing

Over the recent years, cryptocurrencies have attracted tremendous amount of attention from both general public and professional investors as a new asset class. However, trading activities of cryptocurrencies are extremely fragmented and unregulated in most of countries around the world. The proposed research project aims to empirically study the microstructure of cryptocurrency exchanges in order to gain insight on what elements are needed to improve the market. In particular, the proposed research focuses on the potential role of smart order router (SOR).

A Reliable Lora based Tracking and Monitoring System for Underground Mines

The mining industry directly employs more than 426,000 workers across the Canada and contributed $97 billion to Canada’s GDP in 2017. However, mining workers are exposed to five-fold higher occupational hazards than the industrial average. Reliable underground communication is essential to alleviate incidents and escalate rescue operations. However, wireless communications in mines is a big challenge. Electromagnetic wave propagation is very poor in mines due to irregular confined shapes and rough walls.

Evaluation of Resiliency in Community Youth Empowerment Programs

Youths experience increased vulnerability to mental health challenges associated with their development and living contexts. Effective mental health promotion must consider the multidimensional determinants of resilience. To address these needs, Dr. Jenny Liu (Elevate applicant) has developed and validated an innovative model, Multi-System Model of Resilience (MSMR), which measures resilience capacities and needs at the individual, community, and structural levels. In collaboration with Hong Fook Mental Health Association (HFMHA) and with mentorship from Dr.

Machine learning classification for pump fault and failure detection

This project aims to develop an automated ability capable of detecting faults with pumps. This is referred as “Automated Fault Detection and Diagnosis” (AFDD). Equipment performance begins to worsen throughout time due to various reasons, where these reasons are referred to as “faults”. Generally, there is an understanding of the various faults and causes for equipment failure, but the challenge arises in development of a tool capable of accurately and automatically detecting these issues.

Automated Credit Risk Assessment Systems in Small Business Lending Decisions

Small businesses account for 89.6% of the total private labour force in Canada and, despite the vital role they play in the Canadian economy, fewer than half of small businesses will survive 10 years. One of the most commonly cited causes of small business failure is the inability to raise capital to finance its operations. This occurs, in part, because banks and lending agencies lack the tools necessary to draw valid conclusions from credit risk assessments.

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