Integration of Safety Analysis into Model-Based Systems Engineering

In recent years, there has been a rapid advancement of technology and an increased use of computer models to develop aircraft and aircraft systems. Unfortunately, safety assessment procedures have remained largely document-based, and thus have struggled to keep up with such rapid design changes. The objective of this project is to develop a solution that will enable safety assessment to be conducted more efficiently and reliably, and hence decreasing development time and cost.

Symbolic Model-Based Design of a Semi-Autonomous Vehicle Prototype Implementing Independent Wheel Torque Vectoring for Training an Advanced Driver Assistance System

There is a strong belief that autonomous vehicles will play a vital role in the future of the global transportation economy. There, however, exists many open challenges which need to be overcome to realize this future vision. One such challenge is the acceptance from the driver to relinquish full control of a vehicle and ultimately putting one’s safety in the hands of a computer.

Open Data Guidelines and Policy

The intent of this project is to provide the policy and guidelines in support of the Districts Open Data program. The Open Data Guidelines and Policy will provide a framework for data governance across the organization, in order to improve access to and use of data to empower community decision making. Our objective is to develop Open Data Policy and Guidelines designed for the unique needs of the District of Squamish.

Estimation of Disease Severity in Rice with Deep Learning Neural Networks

NIRS is a popular secondary analytical method that is being used for non-destructive quantification of compounds and mixtures in the agriculture and agri-food sector. The study aims to estimate the starch content (amylose and amylopectin) in rice samples with NIRS. A dataset is being established by obtaining NIRS spectra (400 to 2500 nm, 0.5 nm resolution) on over 400 milled and ground rice samples. Iodine-binding and spectrophotometric techniques will be used for acquiring the ground-truth.

Evaluation of the mechanical properties of the bone-implant interface in dental implants

Skin-penetrating bone-anchored implants are used in a variety of applications to provide tremendous functional benefits to patients. Globally, the dental implant industry has been valued at 5.08 billion USD where implants are used for replacing single teeth, for larger prostheses, and for full dental arches. The success of these implants relies on a structural integration between the implant and the living bone. Evaluation of the integrity of the bone-implant interface is important to prescribe loading, to identify the risk of failure, and to monitor the long-term health of the implant.

In Vitro Fundamental Dispersion Studies of Allergens and COVID-19 Sized Particles

Red Maple Trials (Ottawa, ON) created a facility for the research of allergy, in which patients can be exposed (challenged) to airborne allergens and symptoms can be monitored in a controlled manner. The primary clientele of Red Maple Trials are pharmaceutical companies testing allergy medications.

Closed loop cementous mixing and feeding system development

The main problem being addressed is the current lack of control and quality in the deposition of mixed mortar in the application of large-scale 3D printing. This is a significant issue because the properties of the mixes used depend heavily on the material ratios and mixing/pumping time. This means that slight variations can lead to blockages in the system or even collapse of the intended printed items.
Current 3D Concrete printing system use many different mix/pumping systems. Two system which are very popular are the MTEC Duo Mix 2000 Connect from MTEC, MAI®MULTIMIX-3D.

Comprehensive Waste Management Plan and Assessment

Durham Region is committed to responding to the climate emergency by embedding climate change considerations across all elements of Regional business. This research will involve an examination of the Region’s entire waste management system and develop a full life cycle analysis model for GHG emissions which can be used to identify opportunities and support strategies to reduce GHG emissions caused by waste management activities.

Sectoral use of ammonia as a clean solution

This project consists of three subprojects. In the first subproject, an ammonia-fueled a power generator will be developed and experimentally tested. Thus, the emission released from the generator such as CO2, NOX, and SOX, will be reduced substantially. The second subproject of the project is to investigate the ammonia economy starting from production to last use in various sectors. Evaluating a microgrid system and compatible electrical vehicle, and their economic benefits, advantages or disadvantages will be researched extensively.

DARSA (Deep-learning Assisted Radiological Software Application):Innovative Machine Learning approaches for Detecting Pathology inImages

Many aspects of healthcare are time consuming and error prone. Recently there has been great progress in using artificial intelligence to solve a number of problems. One of the best examples of this is image labelling using a type of neural network approach called deep learning. Recent research has shown that deep learning approaches can outperform expert human radiologists when diagnosing disease in chest x-rays, in some situations. In this project we use a large set of chest x-rays as a test bed and develop a new method for software based radiological diagnosis using deep learning models.

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