Supplementary material "Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification"

This package contains all the functions used to run the experiments in the paper cited below. Please, if you want to use this software, don't forget to cite the source. * D. Quezada-Gaibor, J. Torres-Sospedra, J. Nurmi, Y. Koucheryavy and J. Huerta, "Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification," 2022 International Conference on Localization and GNSS (ICL-GNSS), 2022, pp. 01-06, doi: 10.1109/ICL-GNSS54081.2022.9797021. If you would like to run the experiments, please follow the instructions in the README file. Note: This package is based on the software provided by R. Dogaru, et al. (https://github.com/radu-dogaru/LightWeight_Binary_CNN_and_ELM_Keras/blob/master/BCONV-ELM.ipynb) * R. Dogaru and I. Dogaru, "BCONV - ELM: Binary Weights Convolutional Neural Network Simulator based on Keras/Tensorflow, for Low Complexity Implementations," 2019 6th International Symposium on Electrical and Electronics Engineering (ISEEE), 2019, pp. 1-6, doi: 10.1109/ISEEE48094.2019.9136102. If you would like to re-use the databases included in this paper, please cite the corresponding sources as indicated in the readme file in the folder 'datasets'. Don't hesitate to contact me if you have any questions ([email protected])