Reference number : GC-2017-Q4-01810, amendment number : , agreement type : G, recipient legal name : Ukwatta, Eranga (Carleton University), recipient country : CA, recipient province : nan, recipient city : Ontario, 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 objective of this proposal is to develop a software tool consisting of image analysis techniques to automatically convert pixel based digitalized Floor Plans (FPs) into a structured data. Our proposed software will automatically extract the information present into the FPs and represent it in structures like walls, windows, and doors. This information can be used to create a digital model of the building (DMB) represented by the FP. With the grow of the Internet of Things the number of wireless devices tends to increase. A reliable Internet connection can be achieved by correctly placing the wireless access points (APs). The optimal APs distribution can be obtained by computer simulations of the radio waves propagation within a building. To generate the model necessary to the simulation, the FP of the building can be used. Ericsson is currently developing a software to plan the APs distribution within a building, but the FP conversion into a DMB is performing poorly, generating a model with missing features andmisidentifications. In this project, we will determine the feasibility of incorporating state-of-the-art image segmentation methods based on machine learning to address some key technical challenges in converting FPs into DMBs. In conjunction with our industrial partner, Ericsson Canada, we intend to increase the accuracy and speed of this model creation by developing new image segmentation methods. These methods will ensure improved accuracy and speed in conversion of FPs into DMBs that would enable Ericson to be in the edge in this highly competitive field., program purpose french : The objective of this proposal is to develop a software tool consisting of image analysis techniques to automatically convert pixel based digitalized Floor Plans (FPs) into a structured data. Our proposed software will automatically extract the information present into the FPs and represent it in structures like walls, windows, and doors. This information can be used to create a digital model of the building (DMB) represented by the FP. With the grow of the Internet of Things the number of wireless devices tends to increase. A reliable Internet connection can be achieved by correctly placing the wireless access points (APs). The optimal APs distribution can be obtained by computer simulations of the radio waves propagation within a building. To generate the model necessary to the simulation, the FP of the building can be used. Ericsson is currently developing a software to plan the APs distribution within a building, but the FP conversion into a DMB is performing poorly, generating a model with missing features andmisidentifications. In this project, we will determine the feasibility of incorporating state-of-the-art image segmentation methods based on machine learning to address some key technical challenges in converting FPs into DMBs. In conjunction with our industrial partner, Ericsson Canada, we intend to increase the accuracy and speed of this model creation by developing new image segmentation methods. These methods will ensure improved accuracy and speed in conversion of FPs into DMBs that would enable Ericson to be in the edge in this highly competitive field., agreement title english : Development of an Automated Image Processing Framework for Segmenting Floor Plans for Optimal_x000D_
Placement of Internet Access Points, agreement title french : Development of an Automated Image Processing Framework for Segmenting Floor Plans for Optimal_x000D_
Placement of Internet Access Points, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-02-07, 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