Reference number : GC-2017-Q3-00600, amendment number : , agreement type : G, recipient legal name : Braidy, Nadi (Université de Sherbrooke), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : The recent technological improvements in microscopy now allows the collection of extensive information of_x000D_
samples at the nanoscale. However, in many cases, the amount of data collected surpasses the current_x000D_
visualization capacity making their interpretation and analysis impractical. While two and three physical_x000D_
dimensions visualization is quite intuitive, the interpretation of datasets with a higher number of dimensions_x000D_
such as with chemical tomography, is less intuitive. Nevertheless, the information provided by extra_x000D_
dimensions is essential when attempting to uncover similarities or correlations essential for materials_x000D_
development (e.g. distinguish phases in a material, identify and map impurities)._x000D_
The research group of Pr. Nadi Braidy have been successful in developing a classification algorithm to simplify_x000D_
the interpretation of multi-spectral datasets. However, the efficiency of the algorithm and the code is not_x000D_
adapted to large datasets (i.e. several giga/terabytes). The goal of this project is to develop and implement_x000D_
scientific and software improvements to the existing algorithm to improve its computational efficiency. The_x000D_
revamped algorithm will be implemented into Object Research System (ORS) Inc. visualization software,_x000D_
Dragonfly._x000D_
In the framework of this Engage, the collaboration between Pr. Braidy and ORS will provide ORS with a tool_x000D_
to handle multi-modal datasets in its software while providing support in improving its efficiency., program purpose french : The recent technological improvements in microscopy now allows the collection of extensive information of_x000D_
samples at the nanoscale. However, in many cases, the amount of data collected surpasses the current_x000D_
visualization capacity making their interpretation and analysis impractical. While two and three physical_x000D_
dimensions visualization is quite intuitive, the interpretation of datasets with a higher number of dimensions_x000D_
such as with chemical tomography, is less intuitive. Nevertheless, the information provided by extra_x000D_
dimensions is essential when attempting to uncover similarities or correlations essential for materials_x000D_
development (e.g. distinguish phases in a material, identify and map impurities)._x000D_
The research group of Pr. Nadi Braidy have been successful in developing a classification algorithm to simplify_x000D_
the interpretation of multi-spectral datasets. However, the efficiency of the algorithm and the code is not_x000D_
adapted to large datasets (i.e. several giga/terabytes). The goal of this project is to develop and implement_x000D_
scientific and software improvements to the existing algorithm to improve its computational efficiency. The_x000D_
revamped algorithm will be implemented into Object Research System (ORS) Inc. visualization software,_x000D_
Dragonfly._x000D_
In the framework of this Engage, the collaboration between Pr. Braidy and ORS will provide ORS with a tool_x000D_
to handle multi-modal datasets in its software while providing support in improving its efficiency., agreement title english : Novel algorithms for image classification and visualisation of multispectral datasets, agreement title french : Novel algorithms for image classification and visualisation of multispectral datasets, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-11-08, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada