AutoSFC (Auto Space Filling Curve) is a web-based demo showcasing research activities around the use of Space-Filling Curves (SFCs) for encoding and reducing the dimensionality of automotive data. While the primary focus is on automotive applications, the approach can be extended to other types of data.
Encoding demo
- Displays original signal plots alongside encoded signal blocks.
- Demonstrates how multidimensional signals are transformed into a one-dimensional signal.
- Built with reusable UI components (TypeScript, React, Material UI).
Characteristic Stripe Pattern Comparison (CSP) Demo
- Upload two different files for comparison.
- View the Characteristic Stripe Pattern.
- Apply/reset transformations.
- Adjust ranges displayed.
- Experiment with different dimensionality reduction algorithms and parameters.
Previous work
- Includes cards linking to relevant publications from the team and external sources.
TypeScript Provides strong typing, class-based structure, and robust tooling for building complex UI components.
React Library for building fast, interactive user interfaces.
Material UI Modern UI framework with reusable components. Features like drag & drop are directly integrated to speed up development.
Deployment & CI/CD
- GitHub Actions automatically check, build, and deploy the app to GitHub Pages whenever changes are pushed to the
mainbranch. - Dependencies are managed through
package.json.
Clone the repository and install dependencies:
git clone https://github.com/beatrizcabdan/AutoSFC.git
cd AutoSFC
npm installRun the development server locally:
npm startBuild the project for production:
npm run buildThe production-ready build will be output to the build/ directory.
- Access the deployed version on GitHub Pages after pushing changes to
main. - GitHub Actions automatically deploy changes to GitHub Pages whenever commits are merged into
main.
This project supports ongoing research into dimensionality reduction using Space-Filling Curves. For more information, see the included research papers section on the website for references and related publications.
- You can also check: PCICF: A Pedestrian Crossing Identification and Classification Framework
Contributions are welcome! Please fork the repository and create a pull request with your proposed changes.
This project is licensed under the MIT License – see the LICENSE file for details.
Anton J Olsson
Beatriz Cabrero-Daniel