Merge pull request #17 from haihabi/release_0_3

Prepare for release 0.3

README.md

@@ -61,62 +61,23 @@ Please cite one of following paper if you found our neural network model useful.
 
 [1] 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}
-} 
 
-```
 
 [2] 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}
-} 
 
-```
 
 [3] 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}
-}
-
-```
 
 [4] Habi, Hai Victor, and Hagit Messer. "Recurrent neural network for rain estimation using commercial microwave links." IEEE Transactions on Geoscience and Remote Sensing 59.5 (2020): 3672-3681.
-```
-@article{habi2020recurrent,
-  title={Recurrent neural network for rain estimation using commercial microwave links},
-  author={Habi, Hai Victor and Messer, Hagit},
-  journal={IEEE Transactions on Geoscience and Remote Sensing},
-  volume={59},
-  number={5},
-  pages={3672--3681},
-  year={2020},
-  publisher={IEEE}
-}
-```
+
 
 Also, this package contains the implementations of the following papers:
 
 [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
+[6] M. Schleiss and A. Berne, “Identification of dry and rainy periods using telecommunication  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. <br>
@@ -126,9 +87,12 @@ 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.
 
+[9] Goldshtein, Oren, Hagit Messer, and Artem Zinevich. "Rain rate estimation using measurements from commercial telecommunications links." IEEE Transactions on signal processing 57.4 (2009): 1616-1625.
+
+
 And include PyTorch implementation of the OpenMRG dataset:
 
-[9] van de Beek, Remco CZ, et al. OpenMRG: Open data from Microwave links, Radar, and Gauges for rainfall quantification in Gothenburg, Sweden. No. EGU23-14295. Copernicus Meetings, 2023.
+[10] van de Beek, Remco CZ, et al. OpenMRG: Open data from Microwave links, Radar, and Gauges for rainfall quantification in Gothenburg, Sweden. No. EGU23-14295. Copernicus Meetings, 2023.
 
 
 If you found one of those methods usefully please cite.

pynncml/__init__.py

@@ -9,4 +9,4 @@
 from pynncml import simulation
 from pynncml.plot_common import change_x_axis_time_format
 
-__version__ = "0.2.0"
+__version__ = "0.3.0"