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Completely Automated CNN Architecture Design Based on VGG Blocks for Fingerprinting Localisation
Sinha, Shreya (2021) Completely Automated CNN Architecture Design Based on VGG Blocks for Fingerprinting Localisation.
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| Abstract: | Abstract—WiFi fingerprinting using Convolutional Neural Net-works (CNN) is one of the most promising techniques for indoor localisation due to the extraordinary performance of CNN in image classification. However, the performance of CNN is architecture dependant, and thus an architecture that works well in one case may not work in another, especially for the WiFi-based localisation problems. Most of the solutions use an existing hand-crafted architecture or a semi-automated CNN design for fingerprinting, which requires significant CNN expertise and time. Therefore, a satisfactory solution may not be guaranteed as it is challenging to design numerous possible architectures. In this work, this challenge is addressed by developing a framework that completely automates the CNN architecture design process. The automated architectures based on VGG blocks have shown superior performance compared to standard architectures such as VGG-16. Further, three heuristics are explored for automation: Bayesian optimisation, Hyperband, and Random Search, which demonstrate their importance towards the automated CNN architecture development for WiFi fingerprinting. Experiments are conducted on publicly available datasets and, a comparative study between the automated architectures and other models is presented. This work would, therefore, facilitate the CNN design process for WiFi indoor localisation. |
| Item Type: | Essay (Master) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Embedded Systems MSc (60331) |
| Link to this item: | https://purl.utwente.nl/essays/88551 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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