BOAS PRINS

Who am I

I’m Boas, a creative developer with a passion for blending design and technology. My focus is on building interactive, visually engaging digital experiences that tell stories.

Specialities

I specialize in modern web development, 3D graphics, and interactive design. From dynamic front-end interfaces to immersive 3D environments, I enjoy pushing the boundaries of what’s possible online.

Experience

I’ve worked on projects ranging from machine learning visualizations to interactive portfolios and web applications. My background combines computer vision, data science, and design to create projects that are both functional and aesthetic.

My Approach

I believe great digital experiences come from combining creativity with problem-solving. My process always starts with the user — ensuring every interaction feels intuitive, fluid, and engaging.

Current Focus

I’m currently exploring opportunities to collaborate on innovative web projects. Whether it’s freelance work, creative partnerships, or new challenges, I’m excited to keep building experiences that inspire.

Skills

Projects

Drone Safe Space Landing Detection

Minor Computer Vision & Data Science

This technical paper focuses on creating a failure-safe landing system for BLOVS UAVs, to achieve fully autonomous drones which comply with the European Union Aviation Safety Agency regulations. This research tackled a specific part of a larger project, which aims to find a suitable segmentation model which can detect and avoid ground obstacles in the scenario of an emergency landing performed by an unmanned aerial vehicle. A deep learning approach is used, involving two segmentation architectures, U-Net and U-Net++, supported by different experiments to improve the performance of these models and in the end determine the best performing architecture. From the results, was concluded that using a segmentation approach is a suitable method to apply in this project, although a few limitations must be first settled to test this method in a real-life scenario.

Visual Inspection Anode Screens

Photonis B.V.

Quality control is an important part for the production of anode screen. When this process is automated by means of computer vision, it is quickly apparent that there is a lot involved in realizing this. The specifications of the camera, the light sources and the angle at which they illuminate must be considered. In addition, this report discusses how a neural network can be used to inspect anode shields.

Tomato Ripeness Detection

Batenburg Beenen B.V.

This study discusses which specifications are important for cameras and lighting for setting up a camera setup. Different types of lighting and ways of lighting are discussed. This report explains the method of stroboscopic lighting, which is a method that overwhelms and blurs the ambient light. Stroboscopic lighting reduces the effect of ambient light and makes it easier to determine the ripeness of tomatoes. In addition, some methods to regulate the reflection of light on tomatoes are also discussed. Machine learning is then introduced, where algorithms learn from human input and can generate similar output as a result. The training and validation process of a machine learning algorithm is dissected and the results evaluated. Finally, this report discusses how the detections of the machine learning algorithm can be linked to a color scale. Afterwards, depending on the results described, a conclusion is described.

Contact Me

Phone: +1 234 567 890

Email: example@email.com

Socials

Profile

Driven by curiosity and a love for technology, I combine my Electrical Engineering background with Robotics and AI to develop innovative, high-tech solutions. I’m constantly exploring new tools and methods to transform ideas into functional systems.

Experience

Engineering Intern Photonis Netherlands

Feb. 2022 – Jul. 2022 (6 Months)

  • Adapted a computer vision setup and implemented a continuous supervised machine learning model to detect defects on night vision anode screens.
  • Analyzed and processed image data to improve defect detection accuracy.
  • Collaborated with engineers to integrate the system into existing production workflows.

R&D Engineering Intern Batenburg Beenen

Feb. 2023 – Jul. 2023 (6 Months)

  • Built a computer vision system and implemented a machine learning model to detect tomato ripeness for an autonomous harvesting robot.
  • Worked in a cross-functional team to design and test a robot that navigates tomato plantations and harvests ripe tomatoes.
  • Developed data pipelines and optimized image processing algorithms to improve detection accuracy.
  • Researched and implemented state-of-the-art machine learning models.

Education

B.Sc. Electrical Engineering

NHL Stenden | University of Applied Sciences

2019 – 2023

Studied electrical systems, electronics, and automation, completing practical and theoretical projects in engineering.

Pre-Master Biomedical Engineering

University of Twente

2023 – 2024 (Discontinued)

Completed coursework in biomedical instrumentation, signal processing, and applied engineering principles.

Pre-Master Robotics

University of Twente

2024 – 2025 (Discontinued)

Participated in courses and projects on robotics, control systems, and mathematics.

Courses

Computer Vision and Data Science

NHL Stenden | University of Applied Sciences

Aug. 2021 – Feb. 2022

Explored computer vision techniques, machine learning algorithms, and data analysis through practical projects and hands-on exercises.