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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Zainab Mohammed Resan
University of Technology-Iraq
Computer Science & IT

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Software engineering model based smart indoor localization system using deep-learning Zainab Mohammed Resan; Muayad Sadik Croock
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.14318

Abstract

During the last few years, the allocation of objects or persons inside a specific building is highly required. It is well known that the global positioning system (GPS) cannot be adopted in indoor environment due to the lack of signals. Therefore, it is important to discover a new way that works inside. The proposed system uses the deep learning techniques to classify places based on capturing images. The proposed system contains two parts: software part and hardware part. The software part is built based on software engineering model to increase the reliability, flexibility, and scalability. In addition, this part, the dataset is collected using the Raspberry Pi III camera as training and validating data set. This dataset is used as an input to the proposed deep learning model. In the hardware part, Raspberry Pi III is used for loading the proposed model and producing prediction results and a camera that is used to collect the images dataset. Two wheels’ car is adopted as an object for introducing indoor localization project. The obtained training accuracy is 99.6% for training dataset and 100% for validating dataset.
Co-Authors Muayad Sadik Croock