Querying databases without a layer of privacy protection might lead to serious privacy issues. Such issues include access patterns and communication volume patterns. By combining the state-of-the-art privacy standard (differential privacy) and encryption in provides resilience to a host of attacks on remote databases, including data reconstruction attacks. However, there is still research work needed in building a private access system on top of an encrypted database.

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.

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.

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.

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.

The common method of training and warming-up in baseball is by using weighted disks that are added to the player’s baseball bats. The current scientific evidences showed that the use of these devices doesn’t improve the performances of the players and actually slows down their swing speed and batted ball speed. Our partner developed a new device that focuses on increasing the swing speed in rotational sports such as baseball. The prototype uses elastics to allow a variable resistance and some timing aspect to the player’s training.

The evolution of aerospace technologies and automated systems has been accompanied by the phenomenon of “de-crewing”. A large body of current research focuses on how to move to single-pilot operations (SPO), but a major barrier to the implementation of SPO and other autonomous commercial aircraft operations is that advances in human-machine interactions and human factors have not kept pace with technological change. The objective of the research project that is the subject of this proposal is to develop a methodology to simulate autonomous flight in a real-time, virtual environment.

FedEx Supply Chain, the 3PL provider for the Canadian Tire distribution centre located at Coteau-du-Lac, Quebec intends to improve its delivery performance to its customers’ retail stores, especially during the high-volume periods of the year. The focus is on improving the throughput of the conveyor system, as it is considered to be a critical part of the outbound process. This applied research project targets to develop a simulation model to identify bottlenecks and to predict the effects of different control levers that may be used to optimize the conveyor throughput.