Frequency Selection Tool for Undersampling Multisine Signals

Undersampling Multisine Signals

Undersampling Multisine Signals is a tool for selecting frequencies of a multisine signal that do not overlap in the frequency domain in undersampling conditions. A detailed example is described in the conference proceedings of 1. An application example for multisine signals can be found in 2.

• Getting Started
• Example Usage
• Colophon
• Further Information
• FAQ
• Sources

We want to find a frequency combination that can be used for multisine signals with undersampling. It is assumed that the following signal path exists:
.
The following could therefore be measured in the frequency range:
.
To solve this problem, we first create an adjacency matrix that contains information on whether any two frequencies can be combined:
.
This adjacency matrix is then mapped onto a graph. In this graph, cliques are then searched for that are equivalent to frequencies that can be used together for excitation.
.

Getting Started

1. Clone the project. bash git clone https://git.rwth-aachen.de/isea/undersampling-multisine-signals.git
2. Install Python 3.11.9 (https://www.python.org/downloads/)
3. Open an prompt and navigate to the path of this project bash cd path_to_this_project

4. Follow the instructions of the Python Read the Docs to create an virtual environment (venv) and activate it. E.g.:
   Windows bash python -m venv .venv .\.venv\Scripts\activate Linux / Mac bash python -m venv .venv . .venv/bin/activate

5. Install all necessary packages with:
   Windows bash pip install -r requirements_windows.txt Linux / Mac bash pip install -r requirements.txt

6. (Optional) Clean up your venv: bash python -m pip cache purge

7. Open this project with an Jupyter-Notebook editor of your choice, e.g. VS Code (needs to be installed separatly) with: bash code

Example Usage

Details and step-by-step explanations can be found in the Jupyter Notebooks: - multisine.ipynb

Colophon

Thanks to Hendrik Zappen , who supported me Alexander Blömeke .

Related Publications / Citation

You can cite an archived version of this prepository: https://doi.org/10.18154/RWTH-2024-09502.
Please cite our papers: https://publications.rwth-aachen.de/record/816942, https://doi.org/10.3390/batteries4040064

Archived versions of this git:
Release v0.1.0: https://doi.org/10.18154/RWTH-2024-09502

License

This project is licensed according to the file LICENSE.

Further Information

Developer Info

This project is written in Python 3.11.9 using Visual Studio Code and Jupyter Notebooks in an Python virtual environment on Windows. A requirements.txt can be created by:

bash pip freeze -l > requirements.txt

Git and Jupyter Notebooks

Consider to ignore the Jupyter Outputs in Git:

bash git config filter.strip-notebook-output.clean 'jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to=notebook --stdin --stdout --log-level=ERROR'

FAQ

• pywin32 fails: https://github.com/microsoft/vscode-jupyter/wiki/Failure-to-start-kernel-due-to-failures-related-to-win32api-module

Sources

[1] https://publications.rwth-aachen.de/record/816942
[2] https://doi.org/10.3390/batteries4040064