Flip Lingoes is a language exchange platform connecting participants to practice their target and native languages in a balanced, collaborative environment. By fostering cultural exchange and immersive sessions, it enhances language learning effectively. The service can be explored at Flip Lingoes.

In this project, I am responsible for all technical development, building the platform from the ground up. Here is the technical stack:

• Backend: Django for clean and scalable development.

• Web Server: NGINX for handling requests securely and efficiently.

• Version Control & CI/CD: GitHub, GitHub Actions, AWS CodeBuild.

• Email Services: AWS SES for reliable communication.

• Deployment: Docker, AWS ECR, EC2 for containerization and hosting.

• Database: Amazon RDS (PostgreSQL) for scalable storage.

• Optimization algorithm: A custom-built algorithm optimally assigns participants to event tables based on language proficiencies, ensuring balanced groups and maximizing learning outcomes.

Master Thesis in MSc in Autonomous Systems (DTU).

Implementing a novel offline calibration for on-vehicle 2D camera-LiDAR sensor setups by using a spherical target. For this purpose, these tools were used: C++, ROS, OpenCV and PCL. The calibration method was tested in a simulation environment (Gazebo) as well as in real life. The algorithm archives an accuracy of around 1.5cm in translation and 0.25º in rotation. 

You can know more details about the project by accessing the report here.

The code can be found in this Github Repository .

Special course in MSc in Autonomous Systems (DTU).

In this special course, the performance of several 3D sensors were compared to each other. Some of the tests included getting the performance depending on the depth range, material reflectivity, angle of incidence and lightning conditions. With these tests, it was possible to learn how the specifications of the sensors can affect its performance in different environments. For example, how the baseline of a stereo camera can affect the accuracy depending on the depth.

You can know more details about the project by accessing the report in the following link: Report

You can also implement the tests on another selection of sensors yourself using the code: Git Hub repository