Improving road safety has a direct impact on the lives of drivers as well as the costs incurred by companies operating commercial vehicles. One important aspect of road safety is timely and effective vehicle maintenance. By forecasting vehicle maintenance needs and predicting breakdowns before they occur, valuable insights can be provided to drivers and fleet managers ahead of time. This information allows them to make informed decisions on when to perform vehicle maintenance and avoid accidents arising from unexpected vehicle breakdowns while on the road.
The proposed project will develop a system that combines ambient environmental sensors with sleep detection methods to measure sleep quality and allow the user to improve their focus during activities of their daily lives. Observing sleep patterns through all the stages of sleep to model the users body clock and quality of sleep. In addition, using environmental sensors to help users identify optimal sleeping conditions. This will be done by developing an algorithm that will estimate sleeping patterns with environmental sensors and produce a report to optimize their sleep.
Currently, the service provided by GRAD4 allows online storing and sharing of computer-aided design (CAD) models. However, there is no interface developed for visualization of CAD models in the service in a fast and comprehensible way to the users: both manufacturers and buyers. The main challenge of such implementations is in relatively high computational cost of such visualizations via tools used for web-development: while a regular PC handles such task efficiently, web-based tools have means insufficient for similar performance and, thus, mostly involve cloud computing.
Machine learning algorithms are being used in a wide range of applications. It is a branch of computer science where the system can learn from the data and make decisions. Financial fraud is an increasing hazard in the financial industry, and it is important to detect a fraudulent transaction. Machine learning algorithms can be used to decide whether the transaction is fraud or not. After the system makes its prediction, it is important for users to understand the reason behind the prediction in such cases.
The main objective of the project is to upgrade the existing system at Cheetah Networks to make use of Canadian cellular CAT M1 monitored network data to develop innovative QoE analytics that can be used to provide actionable insights. The system will explore applying new techniques to capture in real-time QoE visibility into experiences locally, regionally and nationally. The primary methodologies that we will be employing are based on machine learning and deep learning techniques for data classification, clustering and analysis.
BlueNode is a SaaS company focused on the sanitation and analysis of marine shipping data. The research project is focused on increasing the precision and accuracy of shipped goods processed through Canadian ports. Should the research prove the be successful, the technical methods used with be directly incorporated into the BlueNode system.
The project investigates how collaborative tasks can be enhanced in AR environments. The intern will develop three approaches to present shared information in a co-located AR setting and conduct usability studies comparing these approaches.
Fraudulent activities are hard to detect, but they cost financial institutions millions of dollars in monetary losses and legal costs every year. Millions of dollars are being lost in credit transactions as criminals are finding new, more sophisticated ways to conduct financial crime. This research project examines novel ways of detecting fraudulent behavior using powerful tools such as Recurrent Neural Networks, a type of machine learning model that is well suited for sequence or historical data.
45 Drives—a Nova Scotia based company—offers a high-density, low-cost data storage solution called the Storinator. While this product has been very successful, clients have indicated they would like a clustered solution which offers similar performance and redundancy, without sacrificing security or drastically increasing the cost. Researchers at the University of New Brunswick have been identified as a good fit for creating a clustered software-architecture in tandem with 45 Drives’ hardware-architecture.
The goal of this project is to design and develop a wireless network testbed at the University of British Columbia (UBC) for Rogers Communications Canada Inc. to support different use cases for the fifth generation (5G) wireless networks. We will study the concept of self-organizing network (SON) and design a deep learning-based algorithm for our testbed to determine the optimal network parameters based on network traffic data and key performance indicator (KPI) statistics. We will also design a network traffic forecasting algorithm by capturing the mobility patterns of users.
