TEL320 Embedded Systems
Credits (ECTS):10
Responsible faculty:Fakultet for realfag og teknologi
Course responsible:Alex Mason
Campus / Online:Taught campus Ås
Teaching language:English
Course frequency:yearly
Nominal workload:Expected approx. 250 hours, including lectures, practical tutorials and self-study.
Teaching and exam period:Autumn semester
About this course
Embedded systems are all around us, increasingly so with the desire for smart and connected devices, ie, the "Internet of Things". This course will introduce the core principles of embedded systems, including an understanding of common architectures, scales, terms, components and practices. The course will be delivered via a combination of theoretical and practical sessions, which will enable students to begin development of their own embedded systems projects. The course will emphasize the following areas:
- Structure and typical microcontroller architecture (MCU), with examples from major suppliers, and at different scales - from high power systems suitable for AI applications, to ultra-power devices for long-term use in remote environments;
- Common MCU features and limitations (eg, race conditions, timing, power, task scheduling);
- MCU communication protocols (eg, UART, SPI, I2C);
- Use of analog and digital interfaces with common devices (eg, sensors, buttons, displays);
- Wireless embedded systems (eg, Wi-Fi, Zigbee, Bluetooth, LoRaWAN);
- The key differences between hobby- and industrial-level embedded systems;
- Embedded systems application development, particularly using the C programming language and a popular integrated development environment (IDE).
Learning outcome
Upon completion of this module, a student should be able to:
- Demonstrate knowledge of typical MCU architectures and their specific limitations.
- Characterize and select the most appropriate protocols and components for the simple embedded systems, comprising several inputs and/or output devices.
- Design and demonstrate an embedded system with several input sources capable of outputting data to address a complex task.
Learning activities
Teaching support
Syllabus
Prerequisites
Recommended prerequisites
Assessment method
About use of AI
Teaching hours