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- #Radar 10 homeopathic graphical representation install#
- #Radar 10 homeopathic graphical representation software#
- #Radar 10 homeopathic graphical representation code#
- #Radar 10 homeopathic graphical representation download#
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Intended to be used later with a Data Generator. To handle the large size of data, this method saves each array as a separate file and uses a key to store the labels and file name. 06_processed_dataset_creation_range_FFT.ipynbĬreates the range profiles dataset. This notebook excludes the corrupt data from the end of subject F's recordings. 05_processed_dataset_creation_doppler_spectrogram_without_corrupt.ipynbĬreates the final datasets for the CNN model from the micro-Doppler spectrogram images. This notebook uses all the data from all the subjects. 05_processed_dataset_creation_doppler_spectrogram.ipynbĬreates the final datasets for the CNN model from the micro-Doppler spectrogram images. Using the same processing as demonstrated in "03_data_processing_demonstration.ipynb", create the micro-Doppler signature images from the raw data. 04_interim_dataset_creation_doppler_spectrogram.ipynb 03_data_processing_demonstration.ipynbĭemonstration of the stages of processing applied to the radar data. This notebook replaces the i with j for each data file. As it uses the mathematical convention of i to represent the imaginary component this is not compatible with python which uses the engineering convention of j. The raw RADAR data is represented as complex numbers. 02_interim_dataset_creation_convert_i_to_j.ipynb This notebook explores the composition of the dataset to get a better understanding of the number of measurements and identify potential class imbalances. Notebook Guide 01_dataset_composition_analysis.ipynb
#Radar 10 homeopathic graphical representation code#
This investigation has been conducted using Python versions >= 3.6.6 however the code may be compatible with older versions.
#Radar 10 homeopathic graphical representation install#
The requirements have also been stored in the file "requirements.txt" and can be installed using pip with the command pip install -r requirements.txt. To install the requirements using Conda, the command conda env create -f environment.yml. It is recommended to handle this using the Conda environment management software. If you intend not to use the Google Colaboratory environment you will need to install all the required python packages. These variables are often called either "OVERWRITE_RESULTS" or "SAVE_RESULTS". If you want to re-run the experiments, in each notebook there will be one or two boolean variables that must be set to true to allow overwriting of the results. In the notebooks, these results are then loaded back in to allow graphical visualization of the results. This format is read-only, the cells cannot be executed.Īs many of the experiments conducted take a very long time to execute, the results from previous executions have been saved in the 'results' folder as. This allows them to be viewed directly by use of a web browser. The one package it does not come with is Scikit-Optimize, however, this is installed within the notebooks using a cell containing: ! pip install git+ (this will need to be uncommented out).Īlternatively, the folder "notesbooks_as_html" contains all the Jupyter notebooks in HTML format.
#Radar 10 homeopathic graphical representation software#
As Google Colaboratory works in the cloud you will not have to install any additional software to your device and the environment it provides all but one of the necessary Python packages pre-installed.
![radar 10 homeopathic graphical representation radar 10 homeopathic graphical representation](https://i.ytimg.com/vi/KLDXXB2G-Xg/mqdefault.jpg)
Many IDEs have built-in support for these files however I would recommend using either Jupyter Notebook or Google Colaboratory.
#Radar 10 homeopathic graphical representation download#
To download the data, please use the link in the file "gdrive_data_link.env".Īll experiments were created in the Jupyter Notebook format and are stored in the 'notebooks' folder. The project was supervised by Professor Roderick Murray-Smith.ĭue to the large size of the dataset used for this project (total folder size >300GB) the data has not been included.
![radar 10 homeopathic graphical representation radar 10 homeopathic graphical representation](https://reader017.staticloud.net/reader017/html5/2019112409/54537964b1af9f241b8b4d1f/bg4.png)
Level 4 Honours project at the University of Glasgow by Andrew Mackay. Angelov who collected the dataset, developed the original code for processing the data and created the CNN that was applied to the micro-Doppler signatures. This project builds on work conducted by A. This technique is compared with the leadingĪpproach in the literature of creating micro-Doppler spectrogram images from the data and classifying these images using a To classify radar data directly from the range profiles representation. This project adapts the WaveNet deep learning model Adapting the WaveNet Deep Learning Model for RADAR ClassificationĪn investigation into radar classification.