IKT459 Embedded Sensors, Signal Processing and Machine Learning for Autonomous Systems (Spring 2024)
- ECTS Credits:
- 7.5
- Responsible department:
- Faculty of Engineering and Science
- Course Leader:
- Linga Reddy Cenkeramaddi
- Lecture Semester:
- Spring
- Teaching language:
- English.
- Duration:
- ½ year
Table of contents
The course is connected to the following study programs
- Artificial Intelligence and The Internet of Things, Master's Programme
- Artificial Intelligence, 5-year master programme
Teaching language
English.
Recommended previous knowledge
Basic knowledge in sensors and signal processing, electronic circuit design and programming.
Learning outcomes
On successful completion of the course, the students should be able to:
- Understand the specifications of the sensors.
- Capture raw data from sensors.
- Apply sensor fusion techniques.
- Apply various time-frequency analysis techniques on raw sensor’s data.
- Apply machine learning techniques on the sensor’s data.
- Design and implement end-to-end systems using sensors.
Contents
The main goal of this course is to teach students the fundamental concepts of various sensors used in the autonomous systems, sensor fusion techniques, ROS, time-frequency analysis techniques on the sensors data and machine learning techniques using edge computing devices.
- Introduction to ultrasound sensors, vision-based sensors such as RGB cameras, RGBD cameras, night vision cameras, IR cameras …etc.
- Introduction to thermal imaging
- Introduction to LiDAR imaging
- Introduction to mmWave Radars
- Range, velocity and estimation of angle of arrival of targets using mmWave Radars
- Introduction to sensor fusion and ROS
- Time-frequency analysis techniques such as STFT, SWT, and EWT
- Introduction to Machine learning techniques using edge devices such as FPGAs, Raspberry pi and Nvidia …etc.
Teaching and learning methods
The course is organized in combination of lectures, assignments, paper studies, labs, report writing and self-study. The tasks are done individually or in small groups with group supervision.
The workload for the average student is approximately 200 hours.
Examinations
Graded portfolio assessment, individually or in groups. Groups are given joint grades. Information about the content of the portfolio will be given in Canvas at the start of the semester.
Student evaluation
The person responsible for the course decides, in cooperation with student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.
Offered as a free-standing course
Yes, if there are places available
Admission Requirement if given as a free-standing course
Admission requirements for the course are the same as for the master’s programme in ICT.
Last updated from FS (Felles studentsystem)
Oct. 15, 2026 10:55:47 AM