![]() ![]() Moreover, we conduct explorative experiments to certify the robustness and effectiveness of the mm-SNN. Second, we introduce general and straightforward attention-based improvements into the mm-SNN to enhance the data representation, helping promote performance. Specifically, we first present the overall design of mm-SNN, which is adaptive and easily expanded for multi-sensor systems. In this paper, we propose a general neuromorphic framework, termed mm-SNN, to process mmWave radar data with spiking neural networks (SNNs), leveraging the intrinsic advantages of SNNs in processing noisy and sparse data. However, due to some inevitable obstacles, e.g., noise and sparsity issues in data, the existing approaches are generally customized for specific scenarios. To be effective, instead of using conventional approaches, recent works have employed modern neural network models to process mmWave radar data. We can coordinate transferring the exact datafiles used.Emerging usages for millimeter wave (mmWave) radar have drawn extensive attention and inspired the exploration of learning mmWave radar data. Please see the GHRC website () if you wish to download the radar and in-situ data or contact me. The data for the analysis on the observations are not provided here because of the size of the radar data. We showed this with the 3rd case in the paper: Run_Chase2021_NN.ipynbħ) New in this version - APR data used to show how to run the neural network retrieval: Chase_2021_NN_APR03Dec2015.nc You will need these to scale your data if you wish to run the retrieval.Ħ) New in this version - Example notebook of how to run the trained neural network on Ku- Ka- band observations. ![]() You will need this to run the retrieval.ĥ) Scalers needed to apply the neural network: scaler_X_V2.pkl, scaler_y_V2.pkl These are the sklearn scalers used in training the neural network. This is the notebook used to train the neural network.Ĥ)Trained tensorflow neural network: NN_6by8.h5 This is the hdf5 tensorflow model that resulted from the training. This was the result of combining the PSDs and DDA/GMM particles randomly to build the training and test dataset.ģ) Notebook for training the network using the synthetic database and Google Colab (tensorflow): Train_Neural_Network_Chase2020.ipynb The first column is just an index column.Ģ) Synthetic Data used to train and test the neural network: Unrimed_simulation_wholespecturm_train_V2.nc, Unrimed_simulation_wholespecturm_test_V2.nc The column names are D: Maximum dimension in meters, M: particle mass in grams kg, sigma_ku: backscatter cross-section at ku in m^2, sigma_ka: backscatter cross-section at ka in m^2, sigma_w: backscatter cross-section at w in m^2. This is the combined dataset from the following papers: Leinonen & Moisseev, 2015 Leinonen & Szyrmer, 2015 Lu et al., 2016 Kuo et al., 2016 Eriksson et al., 2018. Please email me if you have specific questions about units etc.ġ) DDA/GMM database of scattering properties: base_df_DDA.csv Chase we have the data used in the manuscript. McFarquhar Corresponding author: Randy J. Please see the github for the most up-to-date data after the revision process: Īuthors: Randy J. This is the dataset that accompanies the paper titled "A Dual-Frequency Radar Retrieval of Snowfall Properties Using a Neural Network", submitted for peer review in August 2020. ![]()
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