MIMO indoor localization-based a Hybrid Neural Network approach transforming Tidy Data into Synthetic Images

This repository contains the source code used to obtain the results in "MIMO indoor localization-based a Hybrid Neural Network approach transforming Tidy Data into Synthetic Images".

• The "create_scenario" folder contains the code to convert the MIMO dataset into a tidy data format dataset to be used by TINTO.
• The "Results with HyNN" folder contains the Python code for the Hybrid Neural Network (HyNN) using synthetic images generated by TINTO.

This repository includes the primary files of the scientific paper. It should be noted that the TINTOlib library with the specified details was used for this article.

Citing this paper:

If you use this code or found it helpful for your work, please cite the following article: IEEE Journal of Selected Topics in Signal Processing:

```bib @ARTICLE{10946146, author={Castillo-Cara, Manuel and Martínez-Gómez, Jesus and Ballesteros-Jerez, Javier and García-Varea, Ismael and García-Castro, Raúl and Orozco-Barbosa, Luis}, journal={IEEE Journal of Selected Topics in Signal Processing}, title={MIMO-Based Indoor Localisation with Hybrid Neural Networks: Leveraging Synthetic Images from Tidy Data for Enhanced Deep Learning}, year={2025}, volume={}, number={}, pages={1-13}, keywords={Location awareness;Accuracy;Neural networks;Measurement;Deep learning;Complexity theory;Antennas;Antenna measurements;Base stations;Signal processing algorithms;Massive MIMO;Deep Learning;Hybrid Neural Network;Synthetic Images;Positioning;Indoor Localisation}, doi={10.1109/JSTSP.2025.3555067}}

```

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This repository has a dedicated space on DeepWiki, where you can explore semantic documentation, relevant links, bibliography, and answers to frequently asked questions about its use and application.

About TINTOlib

TINTOlib is a powerful Python library that transforms tidy tabular data into synthetic images, enabling the use of deep learning models like CNNs and Vision Transformers (ViTs). This bridges the gap between structured and image-based data for tasks such as classification and regression.

📺 VideoTutorial Course (English/Spanish)

🎥 Prefer not to register on Udemy or looking for the English version of the course? No worries — you can follow the full course directly on GitHub!

This hands-on tutorial includes bilingual videos (English/Spanish) and practical notebooks to help you learn how to use TINTOlib with deep learning models like CNNs, ViTs, and hybrid architectures.

💬 Learn More

• 📘 Documentation
• 🚀 GitHub
• 📹 Crash Course

🔧 Features

• Input formats: CSV or Pandas DataFrame
• Designed for tidy data (target column last)
• Output: grayscale images from reduction and transformation methods
• Compatible with Linux, Windows, macOS
• Requires Python 3.7+

Citing TINTOlib:

If you used TINTO in your work, please cite the SoftwareX:

bib @article{softwarex_TINTO, title = {TINTO: Converting Tidy Data into Image for Classification with 2-Dimensional Convolutional Neural Networks}, journal = {SoftwareX}, author = {Manuel Castillo-Cara and Reewos Talla-Chumpitaz and Raúl García-Castro and Luis Orozco-Barbosa}, volume={22}, pages={101391}, year = {2023}, issn = {2352-7110}, doi = {https://doi.org/10.1016/j.softx.2023.101391} }

And use-case developed in INFFUS Paper

bib @article{inffus_TINTO, title = {A novel deep learning approach using blurring image techniques for Bluetooth-based indoor localisation}, journal = {Information Fusion}, author = {Reewos Talla-Chumpitaz and Manuel Castillo-Cara and Luis Orozco-Barbosa and Raúl García-Castro}, volume = {91}, pages = {173-186}, year = {2023}, issn = {1566-2535}, doi = {https://doi.org/10.1016/j.inffus.2022.10.011} }

🧪 Supported Models

Supported image transformation models include:

| Models | Class | Hyperparameters | |:----------------------------------------------------------------:|:------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | TINTO | TINTO() | problem normalize verbose pixels algorithm blur submatrix amplification distance steps option times train_m zoom random_seed | | IGTD | IGTD() | problem normalize verbose scale fea_dist_method image_dist_method error max_step val_step switch_t min_gain zoom random_seed | | REFINED | REFINED() | problem normalize verbose hcIterations n_processors zoom random_seed | | BarGraph | BarGraph() | problem normalize verbose pixel_width gap zoom | | DistanceMatrix | DistanceMatrix() | problem normalize verbose zoom | | Combination | Combination() | problem normalize verbose zoom | | SuperTML | SuperTML() | problem normalize verbose pixels feature_importance font_size random_seed | | FeatureWrap | FeatureWrap() | problem normalize verbose size bins zoom | | BIE | BIE() | problem normalize verbose precision zoom |

Each model has its own hyperparameters and behaviors. See documentation for usage.

License

TINTOlib is available under the Apache License 2.0.

Authors

• Manuel Castillo-Cara
• Jesús Martínez Gómez
• Javier Ballesteros
• Ismael García-Varea
• Raúl García-Castro
• Luis Orozco-Barbosa

Contributors