Thales Canada develops control systems for avionics applications, which operate in harsh environments that may compromise the functionality of very high density chips. The company needs to develop a generic power interface for different avionics applications with a high level of criticality. However, such circuitry requires a lot of space on printed circuit boards when implemented as discrete components.
Just like for the automotive industry, there is growing interest in the development of fully autonomous trains. One of the key steps in the creation of a fully autonomous solution is optaining an accurate estimate of the train position and velocity. Accurate estimates are critical component of the train safety during operation and better estimates allow more trains to operate safely on the same track. The current project deals with trains operating in areas without GPS coverage, such as subways, and so accurate position measurements cannot be obtained as frequently.
The proposed internships aim at investigating the relevance of deep learning (DL) techniques for target detection in radar data processing. More specifically, we are looking to demonstrate the feasibility of DL techniques to deal with unusual types of data (i.e., radar data) in situations where an well performing processing with classical techniques is a challenge (e.g., detection of objects in noisy scenes from a maritime environment caused by the interference produced by the reflection of the radar waves on the sea).
This projects objective is to develop a capability to detect and describe anomalous situations in ground vehicle traffic. Anomalous situations are described as substantial/important changes from the traffic frequently observed for a particular route and/or time. In this sense, anomaly can be quantitatively measured by the degree of predictability of current traffic given historical observations. In the use case of interest, information from traffic will be captured from a GMTI sensor performing recurrent surveillances (1-3 hours per day, multiple days per week) over the same area.
This project aims at creating a robust, efficient and reliable tool for Named Entities Recognition (NER) from vast amounts of textual data related to the customer service.
Named entities recognition, a subtask of information extraction, seeks to locate and classify elements in text into pre-defined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.
Moreover, those extracted named entities will be mapped to existing concepts of an ontology.
The development of such tool will enable easier and quicker decision-ma
NLP techniques have been used and tested for several years in different environments and for different applications/domains. The performances of the Natural Language Understanding (NLU) toolbox are closely related to the quality of the text but also on the specific knowledge-domain. Social Media content typically use short sentences with simple grammar and tend to include specific jargon and abbreviations. Grammatical rules are not always respected and spelling errors are common. These characteristics are also common to the human generated military intelligence/tactical reports.
The aeronautic and aerospace industries are exploring new approached to reduce the mass of cables, bulky electronic systems. This rationally leads onto aircraft weight reduction as well as the amount of CO2 and greenhouse gas emitted by aircrafts. To reduce the mass of cables, merging/embedding different electronic systems in a single chip is an alternative. In this approach, massive electronic modules are miniaturized in a so-called SoC. Different SoCs can be embedded in a single package called SiP.
The aeronautic and aerospace industries are exploring new approached to reduce the mass of cables, bulky electronic systems. This rationally leads onto aircraft weight reduction as well as the amount of CO2 and greenhouse gas emitted by aircrafts. To reduce the mass of cables, power harvesting technique could be utilized. In this approach, the energy needed to power on electronic systems can be harvested from available and reliable sources such as vibration, passenger’s seat heat, data line idle states etc.
The development of test means for aircraft flight control systems for is a complex, multidisciplinary and time consuming task. In this project a Master student will develop a reusable model-based development framework, based on an open source tool and methodology. This model-based systems engineering approach will allow the formalization of the generic aspects of the flight control system test means as well as the variability and specific aspects.
This project targets the design of a highly accurate proximity sensing system that is capable of operating in a wide distance range under wide variations in temperature and for different sensor characteristics. The system is based on passive inductive proximity sensors that can withstand harsh environments, and, therefore, are widely used in avionic applications. Our design methodology consists of implementing a sensor excitation logic and a low-complexity response processing logic in FPGA.