Decreasing operational costs is a key criterion for organizations that manage compute clusters, such as Amazon, Microsoft, Google, Alibaba, etc. One way to decrease costs it to improve resource utilization in the cluster [13, 14]. Yet, high resource utilization can negatively affect workload performance and thus user satisfaction. Performance degradation happens when workloads running on […]

NonDestructive Testing (NDT) is a key discipline in major industrial sectors to ensure quality and safety. Several methods are regularly employed in areas ranging from x-ray or ultrasound testing of metallic or composite components in the automotive and aerospace industries, to the inspection of petrochemical ducts using eddy currents or acoustical emissions. The present proposal […]

Modern power systems wherein renewable energy sources are prevalent will exhibit larger frequency deviations than conventional power systems due to the diluted share of conventional generation based upon large electric machines with massive spinning rotors. To combat this, power-electronic converters that are used to interface renewable sources need to provide ancillary, such as frequency support […]

One in every five people will develop liver disease in their lifetime. Few people think about liver disease until the disease has progressed and has permanently damaged the liver. Fatty liver disease falls into this category with obesity as the most common cause. Given more than 50% of Canadians are overweight and 75% of obese […]

The proposed internships aim at investigating the relevance of deep learning (DL) techniques for target detection in radar data processing. More specifically, we are looking to demonstrate the feasibility of DL techniques to deal with unusual types of data (i.e., radar data) in situations where an well performing processing with classical techniques is a challenge […]

The proposed project represents a critical effort towards developing the enabling communication technology for the future of subsea connectivity where conventional communications technologies such as Wi-Fi and GPS cannot be used. The intern will work to completely overhaul traditional underwater communications methodologies and advance acoustic communications towards the higher reliability and data rates needed for […]

Autonomous vehicular applications require the distribution of high volume of information-rich and safety-critical data among heterogeneous players. Autonomous vehicles (AV) communicate with each other and the world around them in high mobility manner under poor connectivity and tough signal propagation. Attacking AVs are applicable business and cyber-attacks can affect the AV industry and cause severe […]

In this research, a new approach to model Frequency Dependant Network Equivalent (FDNE) will be introduced and implemented in PSCAD/EMTDC. FDNEs are used to accelerate and reduce the size of unnecessary part of the network under simulation. The new approach utilizes Brune’s network synthesis and Tellegen’s extension to create a multiport network whose impedance is […]

It is difficult to perform the dynamic analysis on large scale power systems within a desirable time frame. Most utilities therefore resort to reduce the scale of power system by representing the external system using an equivalent network. This project proposal in conjunction with Manitoba HVDC Research Centre aims to develop simulation based methods complemented […]

With the ever-increasing growth of the consumer Electric Vehicle (EV) market and environmental awareness of federal and provincial governments, electrification of public transit systems has come under the spotlight in recent years. Currently, there is limited practical knowledge on how to efficiently deploy EV buses across different Canadian regions, which results in a wide gap […]

Across industries, many engineering documents and drawings have accumulated over the past few decades. However, they are mostly archived in paper or rudimentary electronic form (typically in an image or PDF format), rendering information retrieval highly inconvenient. As such, a lot of valuable engineering data have been left unutilized or at very least, difficult to […]

The proposed project aims to develop a systematic approach for improving deep-learning-based computer vision systems by augmenting the local pixel data with the global shape data (more specifically, Jordan curves) and by adjusting system architectures to accommodate the augmented input. Three canonical computer vision problems will be investigated in this project. They are respectively image […]