The specific aim of this project is to design and manufacture affordable, high fidelity, and high quality synthetic surgical training models of diseased valves and arteries to simulate the possible surgical options such as prolapse mitral valve reconstruction surgeries, aortic valve repair for aortic insufficiencies (novel idea) and coronary and radial arteries [side to side and end to side] anastomosis surgeries. These synthetic models can be used frequently by training surgeons in order to achieve a higher level of skill, confidence and expertise to help improve patient care worldwide.

The environmental noise pollution is continuously increasing and has multiple adverse effects on human health, quality of life and wildlife ecosystem. The noise levels are quite high as compared to the standard proposed by World Health Organization for residential neighborhoods. This kind of pollution is generally not taken care of and there exists the need to establish public awareness and create regulations to counter this issue.

Data centers are now growing and expanding massively. They are large scale and heterogeneous. In addition, they rely more and more on emerging technologies such as Software Defined Networking (SDN) and Network Functions Virtualization (NFV) with “network softwarization” as their key feature. Moreover, they are now being augmented with edge data centers rooted in concepts such as cloudlets, ETSI Mobile Edge Computing (MEC), and fog computing. Such data centers bring a host of new challenges when it comes to the automation of configuration management and deployment.

Teck Resources Limited is searching for a method to characterize and quantify the heterogeneity of ore based on numerous parameters. Naturally, when characterizing an ore body’s heterogeneity, the variability in the deposit can contribute towards the sortability of the deposit.
The main objective of this research is to investigate a method to quantify the sortability and ore heterogeneity in a systematic manner with clear ranking criteria.

The proposed research work will be a breakthrough in the emerging data engineering field, especially in satellite data management, Machine learning algorithms, quality and quantitative analytics. The machine learning platform quickly scans vast archives of satellite images and delivers usable insights to decision makers.

Heat and electricity generation from biomass combustion in power boilers and co-generation plants produces large quantities of ash residues in British Columbia (BC) each year. In 2013, approximately two thirds of the produced ash were landfilled in Canada and only the remaining one third beneficially utilized. On the other hand, high-quality construction materials are rare in many parts of the world, and most often engineers are forced to seek alternatives to reach the stipulated requirements.

The Mercedes-Benz Fuel Cell Division (MBFC) in Burnaby, Canada develops and runs the manufacturing processes required for the assembly of Fuel Cell Stacks prototypes. MBFC uses the Manufacturing Execution System (MES) to collect and analyse data from the manufacturing lines to the database system. However, because the size of the collected data is very large, MBFC is not able to detect certain fuel cell defects in a timely manner and sometimes not at all.

The Ladle Metallurgy Furnace is used for adjustment of chemical composition and temperature, and control of tiny particles called “inclusions”. Controlling inclusions is carried out by adding calcium to modify the solid alumina or magnesium aluminate inclusions to less harmful liquid inclusions.
During ladle process, reaction of top slag, steel and inclusions occur simultaneously. Therefore, establishing a model to describe ladle process is indeed a challenge.

This project attempts to improve on a Ground Penetrating Radar (GPR) based assessment system that is used to evaluate the thickness of the salt layer in the roof of Potash mining rooms to enhance mine safety. The goal is to improve the operation of the algorithm by studying the GPR signatures of known geological structures which can affect the operation of the algorithm and hence the evaluation of the roof thickness in order to adjust for these structures.

Deep neural networks have demonstrated state-of-the-art modeling accuracy on a wide range of real-life problems, with some cases surpassing human performance. Despite the promise of deep neural networks as an enabling technology for a large number of industries and fields, there are two particular key challenges in the design of deep neural networks in real-world, operational scenarios. First, the design of deep neural networks is a very time consuming process for a machine learning expert, and often results in complex, non-optimal deep neural networks.