Grant: Discovery Grants Program - Individual
Université du Québec en Outaouais
$18,000.00
Design of adaptive spatiotemporal information and control systems
EF 2012-13
Résumé
Steep increases in the data gathering, processing and networking capabilities of today's remote sensing systems have put remote sensing firmly on the path to become a pervasive technology, affecting society in a variety of ways. What is now critically needed are powerful object detection and scene interpretation methods amenable to automated processing and interpretation of this wealth of information. This project builds on the synergy of learning control and pattern recognition/image processing to propose innovative techniques for modeling, monitoring, and control of complex, dynamic spatiotemporal systems as encountered mainly in environmental applications. The overall objective is to develop an integrated design methodology that addresses issues relating to the adaptive processing of large quantities of multi-dimensional, heterogeneous sensor data. More specific objectives include research on theoretical and methodological foundations for the design and optimization of biologically-inspired attentive vision algorithms for spatiotemporal saliency in heterogeneous, sparse remote sensing imagery, and the development of novel iterative learning methods to solve ill-posed object detection and classification problems. The methodological approach has been defined in the context of the scarcity of ground truth reference information, which can be difficult and costly to acquire. It involves the development of hybrid technologies integrating kernel-based methods and pertinent machine learning paradigms (deterministic learning and iterative learning) for time-variable, uncertain system monitoring and control applications. This interdisciplinary project will help develop research personnel at all levels, with its training program significantly enhanced by the opportunities arising from the collaboration with industrial partners. Apart from the contribution to new scientific knowledge, the results of this research will directly benefit Canadian society, helping to meet the need for sophisticated information technologies to efficiently monitor and manage natural resources in Canada's vast territory.
Programme
Discovery Grants Program - Individual
Bénéficiaire
Emplacement
Quebec, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
9227-2012
Name nom
Zaremba, Marek
Department département
Informatique et d'ingénierie
Organization id
54
Institution établissement
Université du Québec en Outaouais
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2012
Competition year année de concours
2012
Award amount
18000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1510
Committee name en
Electrical and Computer Engineering
Committee name fr
Génie électrique et informatique
Area of application code
805
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Communications technologies (satellites, radar, etc.)
Area of application fr
Technologie des télécommunications (satellites, radar,etc.)
Research subject code
2510
Research subject group en
Electrical and electronic engineering
Research subject group fr
Génie électrique et électronique
Research subject en
Adaptive, learning and evolutionary systems
Research subject fr
Systèmes d'apprentissage adaptatifs et évolutifs
Partie
1
Nb partie
5
Application title
Design of adaptive spatiotemporal information and control systems
Keyword
NSERC
Application summary
Steep increases in the data gathering, processing and networking capabilities of today's remote sensing systems have put remote sensing firmly on the path to become a pervasive technology, affecting society in a variety of ways. What is now critically needed are powerful object detection and scene interpretation methods amenable to automated processing and interpretation of this wealth of information. This project builds on the synergy of learning control and pattern recognition/image processing to propose innovative techniques for modeling, monitoring, and control of complex, dynamic spatiotemporal systems as encountered mainly in environmental applications. The overall objective is to develop an integrated design methodology that addresses issues relating to the adaptive processing of large quantities of multi-dimensional, heterogeneous sensor data. More specific objectives include research on theoretical and methodological foundations for the design and optimization of biologically-inspired attentive vision algorithms for spatiotemporal saliency in heterogeneous, sparse remote sensing imagery, and the development of novel iterative learning methods to solve ill-posed object detection and classification problems. The methodological approach has been defined in the context of the scarcity of ground truth reference information, which can be difficult and costly to acquire. It involves the development of hybrid technologies integrating kernel-based methods and pertinent machine learning paradigms (deterministic learning and iterative learning) for time-variable, uncertain system monitoring and control applications. This interdisciplinary project will help develop research personnel at all levels, with its training program significantly enhanced by the opportunities arising from the collaboration with industrial partners. Apart from the contribution to new scientific knowledge, the results of this research will directly benefit Canadian society, helping to meet the need for sophisticated information technologies to efficiently monitor and manage natural resources in Canada's vast territory.