PYTHONPATH should be: CellEnMon-Research First Longditude Second Latitude

First Transmit Second Receive

Suggested presets:

• download pycharm
• install Ansible Vault plugin

Overleaf Thesis https://www.overleaf.com/project/60e0745b27bb63de45f01822

WandB https://wandb.ai/sagitiminsky/CellEnMon_CycleGan

Documentation: - Please look in the secrets.yaml required creds. You can find the master password in our cellenmon whatsapp description

https://app.swimm.io/workspaces/jllXRmECUZBMawQBnXtN

Medium: https://medium.com/me/stories/public

Cross Domain Environmental Monitoring Using Generative Adeversarial Netwoeks and Cycle Consistency Regularization

A python toolbox based on PyTorch which utilized neural network for rain estimation and classification from commercial microwave link (CMLs) data. This toolbox provides 4 main tools:

1. API gateway to the Israeli Metereological servoce
2. Scrapping tool for the Daily measurement explorer: A TAU hosted site which saves all the cellular data
3. Visualization of the positioning of gauges and CML stations across Israel
4. A cyclce consistency GAN frame work which allows us to map the rain domain to attenuation romain and vise versa.

Projects Structure

1. DME scrapping
2. Utilization of IMS API
3. Preprocessing
4. Visualization
5. Metrics
6. Robustness

Dataset

The collection of datasets that were used for training the model can be found at: Datasets Please note that the dataset are not publicly available. To gain access to the bucket please open an issue.

Usage

The following examples:

Model Zoo

In this project we supply a set of trained networks in our Model Zoo.

Contributing

If you find a bug or have a question, please create a GitHub issue.

Publications

Please cite one of following paper if you found our neural network model useful. Thanks!

[1] S. Timinsky, J. Ostrometzky and H. Messer""

@inproceedings{timinsky2022,
  title={Cross Domain Environmental Monitoring Using Generative Adeversarial Netwoeks and Cycle Consistency Regularization},
  author={Sergey Timinsky},
  journal={M.Sc. Thesis, Tel Aviv University},
  year={2022},
} 

Also this package contains the is based on the following papers:

[2] Habi, Hai Victor and Messer, Hagit. "Wet-Dry Classification Using LSTM and Commercial Microwave Links"

@inproceedings{habi2018wet,
  title={Wet-Dry Classification Using LSTM and Commercial Microwave Links},
  author={Habi, Hai Victor and Messer, Hagit},
  booktitle={2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM)},
  pages={149--153},
  year={2018},
  organization={IEEE}
} 

[3] Habi, Hai Victor and Messer, Hagit. "RNN MODELS FOR RAIN DETECTION"

@inproceedings{habi2019rnn,
  title={RNN MODELS FOR RAIN DETECTION},
  author={Habi, Hai Victor and Messer, Hagit},
  booktitle={2019 IEEE International Workshop on Signal Processing Systems  (SiPS)},
  year={2019},
  organization={IEEE}
} 

[4] Habi, Hai Victor. "Rain Detection and Estimation Using Recurrent Neural Network and Commercial Microwave Links"

@article{habi2020,
  title={Rain Detection and Estimation Using Recurrent Neural Network and Commercial Microwave Links},
  author={Habi, Hai Victor},
  journal={M.Sc. Thesis, Tel Aviv University},
  year={2019}
}

[5] J. Ostrometzky and H. Messer, “Dynamic determination of the baselinelevel in microwave links for rain monitoring from minimum attenuationvalues,”IEEE Journal of Selected Topics in Applied Earth Observationsand Remote Sensing, vol. 11, no. 1, pp. 24–33, Jan 2018.

[6] M. Schleiss and A. Berne, “Identification of dry and rainy periods usingtelecommunication microwave links,”IEEE Geoscience and RemoteSensing Letters, vol. 7, no. 3, pp. 611–615, 2010

[7] Jonatan Ostrometzky, Adam Eshel, Pinhas Alpert, and Hagit Messer. Induced bias in attenuation measurements taken from commercial microwave links. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3744–3748. IEEE,2017.

[8] Jonatan Ostrometzky, Roi Raich, Adam Eshel, and Hagit Messer. Calibration of the attenuation-rain rate power-law parameters using measurements from commercial microwave networks. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3736–3740. IEEE, 2016.