Prerequisite/Recommended prerequisite for participation in the module

Basic knowledge of linear algebra and statistics.
Course on “Development of ICT and media services” or similar qualifications.

Content, progress and pedagogy of the module

Learning objectives

Knowledge

• Must have knowledge about high level smart sensors (e.g. cameras, 3D sensors, EEG sensors)
• Must have knowledge about advanced artificial intelligence and pattern recognition algorithms (e.g. kernel methods, neural networks)
• Must have knowledge about artificial intelligence in the context of data mining
• Must have knowledge about hardware processing platforms (e.g. Arduino, Raspberry Pi) for sensor integration
• Must have a clear understanding of the smart sensor processing technology

Skills

• Must be able to use and integrate various high level smart sensors to acquire data
• Must be able to apply machine learning and pattern recognition techniques on acquired sensor data
• Must be able to design and develop smart sensor systems using hardware (e.g. Arduino, Raspberry Pi) for real-time data processing

Competences

• Must have the competency to compare and choose the most relevant high-level smart sensors for a given application
• Must have the competency to assess the use of various artificial intelligence and pattern recognition techniques for a given application
• Must have the competency to compare and assess the use of various hardware platforms for data processing and sensor integration

Type of instruction

Types of instruction are listed at the start of Chapter 3.

Exam

Exams

Name of exam	Smart Sensor Data Processing
Type of exam	Written or oral exam
ECTS	5
Assessment	7-point grading scale
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures