- To explore neural networks training using various data types (images from camera, sensor measurements, maps, etc.) and to analyze methods of obtaining selected data in indoor environment.
- To propose and to implement a neural network bound to a specific building with focus on its applicability in particular indoor navigation and positioning problems.
- To evaluate the performance of trained neural network in particular problems and the practical applicability in indoor navigation application.
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Mendoza-Silva, G.M., Torres-Sospedra, J. and Huerta, J., 2019. A meta-review of indoor positioning systems. Sensors, 19(20), p.4507.
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Noroozi, M. and Favaro, P., 2016, October. Unsupervised learning of visual representations by solving jigsaw puzzles. In European conference on computer vision (pp. 69-84). Springer, Cham.
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Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N. and Huang, X., 2020. Pre-trained models for natural language processing: A survey. Science China Technological Sciences, pp.1-26.