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AuthorKerdjidj, Oussama
AuthorHimeur, Yassine
AuthorAtalla, Shadi
AuthorCopiac, Abigail
AuthorSohail, Shahab Saquib
AuthorFadli, Fodil
AuthorAmira, Abbes
AuthorMansoor, Wathiq
AuthorGawanmeh, Amjad
Available date2024-03-18T08:44:40Z
Publication Date2023-11
Publication Name2023 6th International Conference on Signal Processing and Information Security, ICSPIS 2023
Identifierhttp://dx.doi.org/10.1109/ICSPIS60075.2023.10343825
CitationKerdjidj, O., Himeur, Y., Atalla, S., Copiac, A., Sohail, S. S., Fadli, F., ... & Gawanmeh, A. (2023, November). Exploring 2D Representation and Transfer Learning Techniques for People Identification in Indoor Localization. In 2023 6th International Conference on Signal Processing and Information Security (ICSPIS) (pp. 173-177). IEEE.
ISBN979-835032959-9
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85182277014&origin=inward
URIhttp://hdl.handle.net/10576/53146
AbstractIndoor localization is a crucial aspect of various disciplines in our daily lives. It enables efficient administration tasks and improves safety by identifying the position of items or people inside spaces, making it useful for activities like interior navigation, asset tracking, people rescue, and building security. However, traditional systems have limited performance due to various phenomena. In this paper, a novel system is proposed to identify users inside a building using a transfer learning algorithm and a received signal strength indicator signal as an image. The system utilizes pre-trained models and the scalogram technique to increase the performance of localizing the converted data RSSI to an image. The results demonstrate that the two models can recognize users with 90% accuracy for GoogleNet and 86% accuracy for SqueezNet model.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc. (IEEE)
Subject2D conversion
Indoor localization
RSSI
Scalogram
Security
Transfer Learning
TitleExploring 2D Representation and Transfer Learning Techniques for People Identification in Indoor Localization
TypeConference Paper
Pagination173-177
dc.accessType Full Text


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