Augmented Reality can help in simplifying the filmmaking process by intelligently suggesting shot composition angles for amateur filmmakers to take better shots. In partnership with Rubber Match Productions, researchers will investigate how actors within an AR filmmaking environment can be dynamically tracked to provide real-time guidance to the filmmaker for shot composition. The project will utilize latest advances in artificial intelligence and computer vision to track actors in real-time through videos.
Our research will focus on ways VUCAVU.com can meet the increasing demand for online access to Canadian independent film and video for use in educational contexts. The purpose is twofold; first, in-class testing of our new private page online streaming functions and secondly, to gather feedback from the instructors on their online class processes and platform user experiences. This research will help guide our future development and give us insight into what educators and students need from a service like ours.
In the past few years, a number of research groups around the world have demonstrated small scale, photonic quantum information processing using approaches like Gaussian Boson Sampling, which can function with the current “Noisy, Intermediate Scale” (NISQ) quantum devices. At the same time, the industry partner and other groups have proposed a variety of large-scale photonic quantum computing architectures. These large-scale architectures have yet to be realized in practice and will require large investments to bring to fruition.
One of the major challenges today is reducing greenhouse gas emissions (GHG) into the atmosphere and the increasing demand in the hydrogen energy sector. Currently, Steam Methane Reforming (SMR) is the industry standard in producing H2. Unfortunately, the H2 produced here is classified as “grey hydrogen” as the reaction between methane and water also produces carbon dioxide (CO2). Methane pyrolysis (MP) offers an alternative approach to H2 production as it decomposes methane molecules into H2 and solid carbon only, making the process significantly cleaner than SMR.
The increasing frequency of flooding has driven research to improve near real-time flood mapping from remote-sensing data. In Quebec, in
the spring of 2017, several regions experienced severe flooding caused by consecutive record-setting rain events during snowsmelt from early
April to mi0-d-May. The current project aims to provide real-time monitoring tools not only for flooding but for drought as well, i.e.,
visualization and simulation tools using both remote sensing data, but also data collected from an Internet of Things network.
Using simplified language understandable to a layperson; provide a general, one-paragraph description of the proposed research project to be undertaken by the intern(s) as well as the expected benefit to the partner organization. (100 - 150 words) This project aims to increase image datasets by not doing experiments or collecting physical checks. Instead, the image data augmentation is implemented by generative adversarial networks (GANs), generating new images from original images using different algorithms. GANs have a generator and a discriminator.
Gnowit is an Ottawa-based information services company that employs artificial intelligence and machine learning to automate the process of monitoring web sources at scale to provide real-time briefings and notifications for the purposes of competitive intelligence, evidence-based policy research and media monitoring. The company currently monitors more than 40 thousand web sources and generates atleast 1.2 million fully analysed documents daily.
As the power processing density in new technologies such as 5G and 6G increases, reduction of ripples caused by switching power converter operation becomes a gradually more difficult design problem to solve. Analysis of these ripples and exploring various methods for the ripple reduction in the RF power amplifier circuits are the main topics for this project.
Several de-icing systems have been developed to reduce economic losses and safety hazards due to ice and snow accretion on home pathways. These de-icing systems need to be energy-efficient, and therefore, must be accompanied by high-performance sensors capable of accurately detecting ice and snow to control the de-icing system. In addition to this application, recent industrial developments have shifted the paradigm of ice sensors towards extreme environment applications requiring sensors be operated in harsh conditions.
Winnipeg’s inner city is home to low-income Indigenous, Black and people of colour communities that have long struggled with homelessness, poverty and the ongoing impacts of colonialism (CCPA-MB and CCEDNet, 2015; Silver, 2016). These challenges are now compounded by high COVID-19 rates and COVID-19-related barriers to accessing basic needs (CCPA-MB, 2020). Community-based organizations in the inner city have identified the inclusion of the needs and priorities of inner-city residents and communities as central to the social and economic recovery from the pandemic.