Today buildings, which account for 40% of the total global energy consumption, constitute large glazing surfaces given that windows provide the necessary spacious feel and direct daylighting for occupants. However, 60% of the heat loss through its exterior surface is attributable to glazed surfaces (windows). Therefore, it is not surprising that advanced fenestration products have enormous potential to realize large energy savings and contribute toward the vision of net/near-zero energy buildings.
Recently, due to the widespread effects of “fake news”, a form of propaganda that is intentionally designed to mislead the reader, there has been a significant research effort to automate the process of detection of misinformation in social media. Although existing methods for automatic fake news detection are promising, distinguishing between true and false news is a hard task even for a human, and there is considerable scope for performance improvement.
The intern will be part of the ATCO Electricity Innovation Team and will support in delivering novel next generation prototypes that will define the future of the electricity grid in Alberta, broader Canada and globally. Some of the current projects in the portfolio includes smart EV charging, artificial intelligence based microgrids, home of the future and power systems technologies for making the electricity grid autonomous and smart. The student will be part of a horizontal team structure and will report directly to the Innovation Director.
Trust lies at the very foundations of computer and information security, and is the basis for real-world schemes that require security properties, such as those that underlie consumer banking. Under this research project, we will investigate models for delegation of trust that meet desirable properties, for example, that guarantee that no security compromises occur unless certain trust assumptions are violated.
“Biometric-enabled Sensing Technologies for Smart Cities” is a 4-year project dedicated to the research and development of smart sensing and monitoring devices and systems for applications in smart cities. The project is a collaboration between the Biometric Technologies Laboratory from the University of Calgary and Oasis Technologies Inc.. Stage one of the project consists of algorithm development for the smart detection of various human events (such as slips, trips, and falls in public areas) and vehicle events (such as collisions and vehicle rollovers).
The project aims to develop the deep learning-based algorithm that translate the image style of specific object to the reference style. Firstly, the proposed research focuses on identifying the accurate region in image for style transfer, and then translating the image style in that region. Current techniques about image style transfer are struggling to focus on translating the desired objects while keeping the rest of regions in the image unchanged.
The objective of the research is to develop a system leveraging data captured for commercial building management systems (BMS) to take decisions in to reduce energy consumption without affecting comfort. The idea is to showcase how intelligent control can be implemented in existing BMS to optimize energy consumption. The project is divided in three parts: data visualization and insight (discovery of potential avenues for the improvement of energy optimization), time-series prediction (prediction future energy consumption), and control (acting on said predictions).
Modern software organizations use continuous integration (CI) practices to build and test their products after each code change in order to detect quality issues as soon as possible. Unfortunately, the number of builds scales super-linearly with the number of hardware and feature configurations that should be supported. In order to avoid running out of build resources, organizations are no longer able to build individual code changes, but instead need to build groups of successive code changes. Worse, certain ?flaky?
Medtronic is a global leader in medical technology and biomedical engineering, working towards non-contact vital signs detection. This technology would be especially helpful in the case of high-risk or premature newborns. Continuous or at least frequent monitoring of newborns outside of clinical environments improves quality of life for parents and newborns recently discharged from the Neonatal Intensive Care Unit (NICU). Feasibility of such monitoring depends in part on the monitors being non-invasive and non-obtrusive.
The goal of this research project is to identify ways to apply machine learning technology to help communication network operators cope with the vast amounts of data they must process to understand the health of their networks and to quickly resolve problems.