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.
Brushless permanent magnet synchronous motors (PMSMs) are widely used in many applications including automations, instrumentation, propulsion, vehicular systems, etc. This project is focused on research and development of a modified drive system and novel control algorithms for PMSMs that could improve efficiency and torque performance compared to conventional methods.
Micro-Electro-Mechanical Systems (MEMS) are complex systems with sizes in the range of few microns (human hair has thickness of 150-200 microns) which have both mechanical and electronic components. MEMS technology has entered in many industries such as optical technology, point of care diagnostics, telecommunications, automotive, and military. Today, there are hundreds of MEMS devices, e.g. microscale gyroscopes and accelerometers, used in cars to control different components, including wheels, brakes, steering, and air bags.
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.
A seizure is a medical emergency. 1 in 10 people will be hit by at least one seizure in their lifetime. 1 in 26 people continue to be hit by seizures recurrently: this is epilepsy. When medications do not work, surgery is needed to cut out seizing brain tissue. Unfortunately, many people cannot presently benefit from epilepsy surgery. Our research will harness the power of dream sleep (rapid eye movement or REM sleep) to help locate where the seizures are coming from. Empowered with this information, we can help guide the surgeon on where to perform life-changing epilepsy surgery.
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.
Non-invasive breath analysis has substantial potential for monitoring of a wide range of medical conditions and observation of overall health status. Breath testing is easy and painless; it can be done quickly and inexpensively, and can be repeated as often as needed, making it an attractive approach for screening or clinical diagnosis. In this work, we aim to leverage machine-learning principles to improve and validate the performance of a novel breath-based cancer screening tool.
Globally, industries are seeking to develop new products and services from the large-scale data sets they hold. As these systems move from prototypes into fully operational 24/7/265 commercial solutions additional services must be provided to detect and address system faults and failure as they arise. Within classical engineering plants, e.g., those of the telecommunications, petrochemical, transportation, etc. industries, these tasks are performed by fault detection and diagnosis (FDD) and situation awareness solutions.
The main goal of this project is to develop machine learning and natural language processing approaches to help customers to communicate their preferred brands and/or retailers via Heyday solutions. These approaches will automate answers and help to humanely engage with customers. In order to reach these objectives, some challenges will be tackled such as automatically recognizing the users intent and replying to frequently asked questions. Recognizing ambiguous words is another challenging task to provide accurate answers.