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Related Experiment Video

Updated: Jan 4, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

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Published on: May 8, 2021

A Python-based laboratory course for image and video signal processing on embedded systems.

Karina Jaskolka¹, Jürgen Seiler¹, Frank Beyer¹

¹Multimedia Communications and Signal Processing, Friedrich-Alexander-University Erlangen-Nürnberg, 91058 Erlangen, Germany.

|November 1, 2019

Summary

This summary is machine-generated.

A new lab course uses Raspberry Pi and Python to teach embedded systems for image processing. Students gain practical skills and outperform peers in exams, demonstrating effective learning in embedded image and video signal processing.

Keywords:

Computer science Education Embedded system Image and video signal processing Laboratory course Python

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Area of Science:

Computer Engineering
Electrical Engineering
Signal Processing

Background:

Embedded systems are vital in daily life, handling complex image processing tasks.
Limitations in size and computational power pose challenges for embedded systems.
Effective teaching methods are needed to address these embedded system challenges.

Purpose of the Study:

To introduce a new lab course on "Image and Video Signal Processing on Embedded Systems."
To provide students with hands-on experience in developing embedded image processing systems.
To assess the effectiveness of the lab course in enhancing student understanding and performance.

Main Methods:

Utilized Raspberry Pi 3 Model B and Python for low-cost, accessible hardware and software.
Designed lab experiments covering Python basics, Raspberry Pi integration, and image processing algorithms.
Implemented practical exercises such as corner detection and panorama image creation.
Assessed learning through pre- and post-course questionnaires and compared exam performance with a control group.

Main Results:

Students developed a deep understanding of embedded image and video processing algorithms.
Participants demonstrated superior performance in the lecture exam compared to students attending traditional exercises.
The lab course effectively bridges the gap between theoretical knowledge and practical implementation in embedded systems.

Conclusions:

The developed lab course is an effective pedagogical tool for teaching embedded image and video processing.
Using Raspberry Pi and Python offers a cost-effective and practical approach to embedded systems education.
Hands-on lab experience significantly enhances student comprehension and academic achievement in this field.