A methodological approach to the use of data-supported environmental factors in support of the introduction of autonomous air vehicles in a shared airspace

The evolution of aerospace technologies and automated systems has been accompanied by the phenomenon of “de-crewing”. A large body of current research focuses on how to move to single-pilot operations (SPO), but a major barrier to the implementation of SPO and other autonomous commercial aircraft operations is that advances in human-machine interactions and human factors have not kept pace with technological change. The objective of the research project that is the subject of this proposal is to develop a methodology to simulate autonomous flight in a real-time, virtual environment.

Transparent and Trustworthy Deep Feature Learning for Cyber-Physical System Security

The latest artificial intelligence (AI) technologies have effectively leveraged the wealth of data from cyber-physical systems (CPSs) to automate intelligent decisions. However, for safety-critical CPS like smart grids and smart cities, the conversion of massive data into actionable information by the AI must be not only effective but also reliable.

Intelligent Cyber-Physical Situational Awareness for Smart Infrastructures

The availability of big data in smart infrastructures have become a strategical asset for operators to understand the situation of the infrastructure and monitor potential threats. However, most of the data still have not circulated beyond traditional corporation and technological boundaries, which have limited the visibility that could have been provided by the abundant data.

OFDM radio receiver with Deep Learning

This project involves research in applied artificial intelligence in the field of communications. Using AI, complex building blocks in communication systems are to be simplified and designed in a highly cost-effective manner. The use of AI will allow communications systems become more cognitive in nature and give access to affordable software defined radios. This program would provide the means for the intern to innovate and execute a technology that would not have been possible otherwise.

Chipless RF Sensing for E-smart Composite Pipeline Integrity

Recent pipeline projects in Canada and the US have attracted lots of attention due to their importance for our future economy and environment. In the proposed project University of Alberta and Shawcor propose to work together towards developing E-smart pipelines and creating defect free system. We will utilize the vast amount of emerging and cutting-edge technical know-how in wireless technologies and apply that for the benefit of our energy and environmental sectors. Such information provides the opportunity to intelligently develop defect free pipeline.

Sub Synchronous Oscillations in Power Systems

With increased levels of series compensation of transmission lines (Which is the most economical solution for bulk power transmission over long distances) and with more power electronic controllers such as HVDC, FACTS and converter based distributed generation in the power network, Sub Synchronous Interaction (SSI) problems arise. It is necessary to identify different types of SSI that could occur in the power network through proper means and to prevent such at the design stage or to take counter measures if required.

Ultrasonic Power Transmission and Data Communication through Metallic Barriers for Non-Destructive Testing in Hazardous Industrial Spaces

The need to inspect harsh environments and confined spaces is present in virtually all industries. Inspecting these industrial facilities are key to optimize operation and maintenance costs. Hazardous industrial spaces (e.g., confined spaces) are among the most challenging and costly areas to inspect. In BC alone, over the last decade, WorksafeBC has reported about 18 fatalities per year as the result of operation in confined spaces. To mitigate the risks, remote monitoring and inspection is an attractive alternative to conventional methods.

Uptime Energy Control Procedure Using Machine Learning

In a time where energy use awareness is more and more prevalent due to its significance in global warming, all sectors of society are putting effort to participate in finding new ways to reduce energy consumption. The industrial sector in Canada consumes nearly 1/3 of total energy. A challenge for industrial organizations is to define improvement actions that will significantly reduce the energy consumption of their production facilities, resulting in significant energy savings, while maintaining an adequate level of production for its customers.

Automatic Classification of Security Events

IBM QRadar needs to be able to categorize events generated by hundreds of different network devices in order to function as a Security Information and Event Management (SIEM). This categorization is currently a manual process and our aim is to automate this task. We have a database of over 579,000 events coming from over 300 devices that have been manually classified over the years. We also have the classification categories: 18 high level categories, broken down into 500+ subcategories; these categories broadly correspond to security threats.

Towards Developing Trust in Online Third-Party Reporting Systems for Survivors of Sexual Assault

Over 600,000 sexual assault happen in Canada every year, however, 95% of sexual assault go unreported. We are working towards building a research-based online third-party reporting system that will aid the reporting of sexual assault. Survivors will now have the opportunity to document their evidence and experiences and submit the report at their own convenience. VESTA is committed to building research backed technology that draws on the principles of building technology for good.