This project aims at developing an automated system for water sampling along the coast of Nova Scotia using a swarm of flying robots. Currently, water is sampled by hand by teams of 2-5 people each on a number of boats, that must regularly travel to different sites, collect samples, and bring them to land for analysis. This task is slow and costly and exposes people to the inevitable dangers of the ocean.
With the development of advance telecommunication systems, new opportunities for real-time public transport monitoring has been created. Traffic congestion in the vehicular ad-hoc network can be typically caused by an accident, construction zones, special events, and adverse weather. This research presents a cognitive framework to address real-time routing problem and and arrival time prediction for bus system using a machine learning method.
With the increasing demand for data rates in modern high-speed links come new requirements for the simulation environments that are used for their design. With optical modulator now achieving beyond-100-GHz large-signal modulation bandwidth in hybrid silicon photonics, the main challenges that such systems are currently facing is the lack of efficient interconnects to interface with the outside world. These interconnects are designed and optimized using full-wave simulations.
The project aims at developing a new type of Reinforcement Learning algorithm that would allow to retain more control over the artificial agent once its training is completed. This framework would combine modern unsupervised modeling techniques to capture the variability of a set of demonstrations and user-defined programmatic functions that can characterise particularly important factors of variations. Ultimately, the user would be able to specify to the learned agent what type of behavior should be executed at test-time.
New generation of civil transport aircraft presents interaction between flight mechanics and structural dynamics. Innovative CLaws have been developed to address this issue. They need to be verified thoroughly by high-fidelity simulations. For a research and development project, the traditional industrial verification process is too demanding and would be too time consuming. Indeed, each high-fidelity simulation is very slow.
The computing solutions of tomorrow must be more energy efficient than those of today, which requires combined efforts to be conducted on multiple research areas: from new transistor technologies to innovative software algorithms by way of original processor architectures. This project enters into this last research area by revisiting the vector processing model, which provides a highly efficient way of exploiting data parallelism in scientific computations, sensor processing, and machine learning algorithms.
L’intérêt pour la gestion intelligente de la consommation d’énergie résidentielle prend une force importante grâce à l’avancement de la domotique et la dissémination des technologies connectées facilitant la configuration et la gestion à distance des appareils. Ces progrès sont aussi supportés par le développement des nouvelles techniques de l’intelligence artificielle, la miniaturisation des systèmes embarqués et la réduction des coûts d’acquisition et d’installation des capteurs intelligents.
The project will identify and test combinations of AI (machine learning, neural network) algorithms for detection of suspicious activity (anomaly detection) on the IT network of a large bank or company. The project will benchmark which algorithms work best to identify instances of insider fraud based on high-volume continuous streaming event and content data from all endpoints (computers, laptops, tablets, phones) on a corporate IT network.
Next generation (5G) mobile networks address diverging requirements from different industry segments by sharing and isolating resources over the same physical infrastructure. This paradigm led to the objective of Fixed Mobile Convergence (FMC), an attempt to integrate fixed-line access to the 5G core system. The Access Gateway Function (AGF) is presently being considered as a mean to achieve FMC.
The COVID-19 crisis has developed, and not unjustifiably, a strong data aspect, requiring to handle, store and exchange large amounts of data in an efficient and secure way, not only among hospitals and clinics, but also between government services globally. In this project, we propose the development of an extendible prototype of a big data platform for COVID-19 data. We focus on the scalability and the security of this platform, by employing novel technologies, such as NoSQL databases, MapReduce analytics and Blockchain.