We regularly measure/estimate distance, time, pressure, temperature, cost - and more. Others measure our blood sugar level, weight, satisfaction – and more. Measurements are an integral part of our cognitive processes. While each profession has its own measurement technology, there is also a common background and technical apparatus, the knowledge of which greatly helps in mastering the learning processes of the various fields and effective cooperation. Measurement theory undertakes to present this.

The subject presents the basics of the theoretical background of engineering methods that help to learn about the surrounding material world and to characterize it quantitatively and qualitatively. It reviews signal and system theory, estimation, and decision theory as well as data and signal processing methods with the aim of facilitating the solution of complex measurement, modelling, and information processing tasks. Primarily related to continuous and hybrid systems, it significantly develops conscious modelling and problem-solving skills. It achieves all of this by placing measurement and modelling problems in a unified framework. This framework also includes the basic concepts of signal transmission systems. The methods learned in the course serve as a foundation and background for solving research and development tasks.

Students who successfully fulfil the requirements of the subject are expected to:

1. Know the place, role and relationship of measurement and modelling in cognitive processes.

2. When solving practical problems, they can apply basic signal and system theory, as well as estimation and decision theory procedures.

3. They should be aware of the basic methods of model fitting (identification and adaptation), as well as the different techniques of optimization, regarding recursive procedures that can be implemented in real time.
4. Know the most frequently used recursive signal processing techniques and their implementation aspects.