This project aims at experimentally validating the commercial viability of a new silicon photonics design which provides state of the art monolithic integration of various CMOS control electronics and BiCMOS high speed RF drive electronics which may be combined with high bandwidth non-hermetic SMT packages capable of withstanding standard high volume solder reflow processes.
This research helps to automate ontology development in order to support semi-supervised and active learning chatbot as much as possible, so that the overhead of chatbot training that requires human supervision is minimized, while relevant knowledge management activities become more efficient. The research objectives are both to refine the quality of the chatbot interactions and to automate its development and training as much as possible, to implement and test its practical and cost saving capabilities in tourism industry.
The World Wide Web Consortium (W3C) has published a set of guidelines to assist designers in the task of designing digital content which will be accessible to users with a wide range of abilities. These guidelines are known as the Web Content Accessibility Guidelines (WCAG), version 2.1. Students in the Inclusive Design program learn to think about WCAG as a good starting point for how to design inclusively in a variety of contexts, but also about ways in which they fall short of providing ideal accessibility.
As the digital landscape continues to evolve, it becomes increasingly more important to understand how technology affects cognition, emotion, and behaviour. This rapid growth of technology has also led to the development of empathetic computing programs with the goal of augmenting people’s lives. It is crucial to discover the practical implications of these systems. This project will focus on analyzing Maslo’s (and the industry’s) empathetic computing technologies through the lenses of cognitive psychology, neuroscience, and evolutionary psychology.
The objective of this project is to develop techniques and tools that leverage artificial intelligence to automate the process of handling system crashes at Ericsson, one of the largest telecom and software companies in the world, and where the handling of crash reports (CRs) and continuous monitoring of key infrastructures tend to be particularly complex due to the large client base the company serves. In this project, we will explore the use of deep learning algorithms to classify CRs based on a variety of features including crash traces, CR descriptions, and a combination of both.
The goal of this project is to develop machine learning and data mining algorithms relying on non-intrusive common sensor data to estimate and predict smart buildings’ occupancy and activities. Efficient feedbacks are automatically supplied to the end user to involve occupants and increase their awareness about energy systems. This consists of generating reports helping the occupant to understand his/her energy management system and thus to be involved in the decision-making process.
Penetration testing is a key security tactic, where defenders thinks like an attacker to predict the latter’s actions and develop effective defense. However, for large-scale cyber-physical infrastructures like the smart grid, traditional penetration tests on individual devices or networks are insufficient to exhaust all potential exploits or to reveal infrastructure-level vulnerabilities invisible to the local system.
Edge computing is expected to play a transformative role for future AI applications in 5G networks by bringing cloud-style resource provisioning closer to the devices that have the data. Instead of running resource-intensive AI applications at the end devices, we can consolidate their execution at the edge, which brings many benefits, such as eliminating the redundant task processing, running machine learning (ML) tasks with sizeable data sets and running ML tasks in a spatial context that is shared by many devices.
The proposed Mitacs cluster project aims to apply advanced artificial intelligence (AI) technologies to attack challenging video quality-of-experience (QoE) assessment and quality assurance problems that are critical in real-world large-scale video distribution systems. Six internship students will work closely with the technical staff members at SSIMWAVE INC, a deep-tech startup company based in Waterloo Ontario, to develop AI-based automated video QoE assessment and video anomaly detection algorithms and software prototypes.
This research will focus on the design of 5G networks to provide for future wireless services include the use cases of Enhanced Mobile Broadband (eMBB), Massive Machine Type Communication (mMTC), and Ultra-reliable and Low Latency Communications (URLLC), and application area use cases such as Smart City, Smart Home/In-building, Augmented Reality, Self-Driving Cars, etc. 5G Technology has been standardized according to a broad framework in terms of the format of the transmitted wireless signals and the basic protocols including its compatibility with LTE networks.