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Poster
		    MATERIAL IDENTIFICATION USING RF SENSORS AND CONVOLUTIONAL NEURAL NETWORKS

- Citation Author(s):
 - Submitted by:
 - Gianluca Agresti
 - Last updated:
 - 8 May 2019 - 9:34am
 - Document Type:
 - Poster
 - Document Year:
 - 2019
 - Event:
 - Presenters:
 - Simone MIlani
 - Paper Code:
 - 4700
 
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Recent years have assisted a widespreading of  Radio-Frequency-based tracking and mapping algorithms for a wide range of applications, ranging from environment surveillance to human-computer interface.
This work presents a material identification system based on a portable 3D imaging radar-based system, the Walabot sensor by Vayyar Technologies; the acquired three-dimensional radiance map of the analyzed object is processed by a Convolutional Neural Network in order to identify which material the object is made of. Experimental results show that processing the three-dimensional radiance volume proves to be more efficient thas processing the raw signals from antennas. Moreover,  the proposed solution presents a higher accuracy with respect to some previous state-of-the-art solutions.