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What is ReMI

Resilient Machine Learning Institute

L’École de technologie supérieure (ÉTS) and multinational Ultra Electronics have joined forces to create the first institute of distributed artificial intelligence in Montreal : The ÉTS-Ultra TCS Resilient Machine learning Institute, dubbed the ReMI (Resilient Machine learning Institute).

Reputed for working in tight collaboration with the technological ecosystem, ETS has allied itself with Ultra Electronics TCS, a world leader in critical communications systems, to adapt artificial-intelligence techniques for use in systems functioning in extreme conditions.

Created in 2019, ReMI will apply its innovations when communication problems arise in the wake of major incidents around the world, such as floods, tsunamis or earthquakes and even terrorist activity.


The goals of the institute


To adapt artificial intelligence techniques for use in distributed systems functioning in extreme conditions


Efficient implementations of such techniques in embedded and real-time systems


Train high-level students in the most advanced artificial intelligence techniques

Explore more about ReMI

Development of Spectrum Sensing Cooperation and Fusion Strategies for Tactical Heterogeneous Networks in Mobile Environments

Development of Channel Sensing Cooperation and Fusion Strategies for Wireless Propagation Analysis

Machine Learning for Mobile Radio Localization in Contested Environment

Machine Learning for MIMO Link Adaptation in Contested and Mobile Environments

Adaptive Context-Aware RAT (Radio Access Technology) PHY/MAC Parameters Optimization for Mission Critical Applications

Hybrid Random/Synchronized Medium Access Control for Contested Environments

Data-Driven Wireless Channel Scheduling Optimization for Multicast Traffic

Optimal Gaming Strategies for MARL (Multi-Agent Reinforcement Learning) Heterogeneous Communication Systems Facing Reactive Jammers

Distributed Reinforcement Learning Strategies Applied to Multi-RAT (Radio Access Technologies) in Dense Networks

Machine Learning Strategy for Optimal Cooperation in Symbiotic Networks

Data-Driven Approach for Joint Routing and Wireless Channel Scheduling

A Docitive Machine Learning Strategy for Ultra-Reliable and Low-Latency Communications

Machine Learning for Software Defined Networks Applied to Mission Critical Applications

Neural Adaptive Content-Aware Traffic Compression for Tactical Applications

A Wireless Network Emulation Platform for Learning Algorithms

Real-time Machine Learning Techniques for HetNets Using Field-Programmable Gate Arrays

Real-time Machine Learning Techniques for HetNets Using Embedded Network Processors

Enhanced ECCM Communication Network Prototyping Based on Multi-Channel Commercial-Off-The-Shelf (COTS) Heterogeneous Communication Systems

LTE/5G eNB/UE Fast Mode Switching for Resilient Self-Organizing Networks

L2.5 Routing Capability for 802.11ax WIFI chipsets