Grant: Discovery Grants Program - Individual
École de technologie supérieure
$55,000.00
Next-generations of flexible hybrid sensing platforms for edge-computing: fabrication, integration and deployment
FY 2022-23
Summary
The primary objective of this ambitious Discovery program is to enable high-performance fully-integrated flexible hybrid electronic (FHE) sensing platforms for edge-computing. This fundamental research into the fabrication, integration and deployment of ultra low-cost materials and devices is essential to provide new and better ubiquitous sensing and data processing platforms. This program builds on my team's unique expertise and track record in the fabrication and integration of unique optoelectronic device architectures using new functional materials, cutting-edge digital inkjet printing and emerging post-processing & assembly techniques. Leveraging this fundamental research remains absolutely essential in order to eventually see those exciting new ultra low-cost materials and devices spawn viable products. At the same time, we also wish to explore a different approach to further enhance the physical performance of our printed sensor devices using recent breakthroughs in machine learning. Indeed, very preliminary results acquired in the last few months already suggest that dramatic accuracy improvements can be possible using low-cost printed sensors together with existing machine learning techniques. In turn, we firmly believe that combining more conventional sensor designs with on-chip machine learning capabilities to run inferences locally can also provide much better datasets, while alleviating some of the latency and security concerns associated with cloud computing. As such, the outcome of this ambitious multifaceted research program will be to provide the scientific community and the Canadian industry with the new knowledge and necessary tools to meet the exponentially-growing demand for sensing devices targeting a wide range of applications including wearables & health monitoring, advanced manufacturing, IoT & smart building platforms. In turn, these new sensing platforms will help provide more complete and higher-quality datasets in order to exploit new machine learning and artificial intelligence tools to their fullest capabilities.
Program
Discovery Grants Program - Individual
Recipient
Location
Quebec, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
RGPIN-2022-03083
Name nom
Cloutier, Sylvain
Department département
Génie électrique - Génie électrique
Organization id
53
Institution établissement
École de technologie supérieure
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2022
Award amount
55000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1510
Committee name en
Electrical and Computer Engineering
Committee name fr
Génie électrique et informatique
Area of application code
500
Area of application group en
Manufacturing processes and products
Area of application group fr
Procédés et produits industriels
Area of application en
Manufacturing processes and products
Area of application fr
Procédés et produits industriels
Research subject code
2500
Research subject group en
Electrical and electronic engineering
Research subject group fr
Génie électrique et électronique
Research subject en
Electrical and electronic engineering
Research subject fr
Génie électrique et électronique
Installment
inst:1
Partie
1
Nb partie
5
Application title
Next-generations of flexible hybrid sensing platforms for edge-computing: fabrication, integration and deployment
Application summary
The primary objective of this ambitious Discovery program is to enable high-performance fully-integrated flexible hybrid electronic (FHE) sensing platforms for edge-computing. This fundamental research into the fabrication, integration and deployment of ultra low-cost materials and devices is essential to provide new and better ubiquitous sensing and data processing platforms. This program builds on my team's unique expertise and track record in the fabrication and integration of unique optoelectronic device architectures using new functional materials, cutting-edge digital inkjet printing and emerging post-processing & assembly techniques. Leveraging this fundamental research remains absolutely essential in order to eventually see those exciting new ultra low-cost materials and devices spawn viable products. At the same time, we also wish to explore a different approach to further enhance the physical performance of our printed sensor devices using recent breakthroughs in machine learning. Indeed, very preliminary results acquired in the last few months already suggest that dramatic accuracy improvements can be possible using low-cost printed sensors together with existing machine learning techniques. In turn, we firmly believe that combining more conventional sensor designs with on-chip machine learning capabilities to run inferences locally can also provide much better datasets, while alleviating some of the latency and security concerns associated with cloud computing. As such, the outcome of this ambitious multifaceted research program will be to provide the scientific community and the Canadian industry with the new knowledge and necessary tools to meet the exponentially-growing demand for sensing devices targeting a wide range of applications including wearables & health monitoring, advanced manufacturing, IoT & smart building platforms. In turn, these new sensing platforms will help provide more complete and higher-quality datasets in order to exploit new machine learning and artificial intelligence tools to their fullest capabilities.