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Université de Sherbrooke

Country: Canada

Université de Sherbrooke

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8 Projects, page 1 of 2
  • Funder: EC Project Code: 220583
  • Funder: EC Project Code: 951477
    Overall Budget: 9,559,650 EURFunder Contribution: 9,559,650 EUR

    The goals of this project are to decipher how the interplay between central and peripheral mechanisms controls locomotion in four legged animals (tetrapods) and to the delineate the reorganization of motor circuits linked to functional regeneration after spinal cord lesion. We will take advantage of the evolutionarily conserved traits of neural structures in vertebrates to address these two fundamental questions by using salamanders as model organisms. Salamanders are best suited to these aims for two main reasons: First, because they have an anatomically simplified nervous system, which yet possesses the main features of all tetrapods; second, because they have unique regeneration abilities among vertebrates and can functionally repair their spinal cord after full transection. Taking an interdisciplinary approach, we will investigate the dynamic interactions between the nervous system, the body, and its environment before and after spinal cord lesion. We will combine numerical models of locomotor neural circuits, robotics, and advanced functional analyses in genetically modified salamanders in a way that will allow us to test biological data in neuromechanical models (simulations and robots) and, conversely, to validate model-based predictions in animals. Through the concerted and tightly collaborative activities in our laboratories, implementing state of the art assays ranging from the molecular to the organism level, we expect to create a blueprint of tetrapod locomotion control: how appropriate movements are generated in response to various environmental or intrinsic stimuli, and how such function can be recovered after injury. The synergy between our groups of complementary expertise will boost scientific research at multiple levels, not only in the field of neuroscience but also in regeneration research, robotics, and numerical modeling.

  • Funder: CHIST-ERA Project Code: CHIST-ERA-18-ACAI-005

    Edge computing (EC) and the development of portable devices such as cell phones, autonomous robot or health tracking systems represent one of the big challenges for artificial intelligence (AI) deployment. These hardware systems present very tight constraints in terms of energy consumption and computing power that today’s AI strategies cannot cope with. While high power GPU are well adapted to deep neural network implementations that should strongly benefit to AI development, ultra-low power and robust computing with limited resources need to be proposed for EC applications. To this end, we propose to explore the hardware implementation of small-scale neural networks with limited complexity that could satisfy EC requirements. Notably, spiking neural networks present a real opportunity to this end since, they can combine low power operation and non-trivial computing functions as biological neural networks do. In fact, spiking neural networks (SNNs) of moderate size can reproduce important aspects that are not considered in state-of-the-art machine learning approaches: i) non-linear dynamical regime (i.e. synchronized, critic, driven by attractor dynamics, sequences of spikes) that might explain basic mechanisms in perception and ii) the fast computing that occurs in the brain even if neurons are slow. The UNICO project proposes to address the material implementation of such SNNs by integrating in a dedicated hardware, the key ingredients at work in such SNNs. In fact, we can anticipate that the physical implementation of such highly parallel systems will encounter strong limitations with conventional technologies. A real breakthrough for Information and Communication Technologies would be to capitalize on emerging nanotechnologies to implement efficiently these SNNs on an ultra-low power hardware. Here, state of the art analog resistive memory technologies, or memristive devices, will be developed and integrated in the Back End Of Line of CMOS for implementing analog SNNs. By gathering competences from material sciences, device engineering, neuromorphic engineering and machine learning, we will explore how such SNNs can be deployed on various computing tasks of interest for EC applications. The expected innovations at both the hardware and computing levels could benefit to a wide range of AI applications in the future.

  • Funder: CHIST-ERA Project Code: IGLU

    Language is an ability that develops in young children through joint interaction with their caretakers and their physical environment. At this level, human language understanding could be referred as interpreting and expressing semantic concepts (e.g. objects, actions and relations) through what can be perceived (or inferred) from current context in the environment. Previous work in the field of artificial intelligence has failed to address the acquisition of such perceptually-grounded knowledge in virtual agents (avatars), mainly because of the lack of physical embodiment (ability to interact physically) and dialogue, communication skills (ability to interact verbally). We believe that robotic agents are more appropriate for this task, and that interaction is a so important aspect of human language learning and understanding that pragmatic knowledge (identifying or conveying intention) must be present to complement semantic knowledge. Through a developmental approach where knowledge grows in complexity while driven by multimodal experience and language interaction with a human, we propose an agent that will incorporate models of dialogues, human emotions and intentions as part of its decision-making process. This will lead anticipation and reaction not only based on its internal state (own goal and intention, perception of the environment), but also on the perceived state and intention of the human interactant. This will be possible through the development of advanced machine learning methods (combining developmental, deep and reinforcement learning) to handle large-scale multimodal inputs, besides leveraging state-of-the-art technological components involved in a language-based dialog system available within the consortium. Evaluations of learned skills and knowledge will be performed using an integrated architecture in a culinary use-case, and novel databases enabling research in grounded human language understanding will be released. IGLU will gather an interdisciplinary consortium composed of committed and experienced researchers in machine learning, neurosciences and cognitive sciences, developmental robotics, speech and language technologies, and multimodal/multimedia signal processing. We expect to have key impacts in the development of more interactive and adaptable systems sharing our environment in everyday life.

  • Funder: EC Project Code: 730038
    Overall Budget: 1,604,250 EURFunder Contribution: 1,544,160 EUR

    Satellite contractors are permanently looking for cost and performance improvements. This cascades to the PPU, a subsystem having a very high impact on the cost and performance of EP systems. Hence, we propose to focus on the PPU “heart” studying a disruptive power converter, with major innovations complementary to the incremental improvements, beyond the state of the art. We will demonstrate and combine in a synergistic way innovative technologies (such as GaN, digital control, adaptive filtering and embedded packaging), thus resulting in a radical breakthrough applicable to advanced EP architectures based on such PPU designs. The consortium plans to demonstrate the selected technologies by means of a 7.5 kW power converter to be tested in electrical propulsion existing test facilities, thus providing measurable validation, and specification definition, within the 2016 Phase 1 time frame. This will lead to dramatic improvements in cost, mass and volume targeting part list reduction (by 3), converter efficiency (98%) and optimized thermal characteristics (200°C), translating into system optimization and increased power requirements. Being at the forefront of technological developments, the consortium members are able to anticipate emerging technologies and medium to long term performance requirements consistent with existing and planned space programs at national, commercial and ESA levels. GaNOMIC will constitute a solid technical basis for future Direct Drive configurations, and further down the line, to “distributed” configurations where the PPU can be eliminated altogether. In addition to promoting and accelerating the development of breakthrough EP-related concepts, the consortium members have identified other markets, e.g. aeronautics and automotive, which could benefit from these innovating high performance power converter and related technologies under consideration. The consortium is committed to continue this study in future calls of the SRC.


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