Sensorics and Artificial Intelligence
FSI-GSUAcad. year: 2018/2019
The course is closely linked with industrial production, machine construction, intelligent systems and mechatronic systems. It focuses especially on sensors working according to various physical principles. Modern technologies of sensor production enable a high integrity of complex measurement chain. Unified I/O and standard communication interfaces lead to an easy and user-friendly implementation within more sophisticated control systems. Sensors are tools for monitoring the internal state of the machine and the interactions between the machine (system) and the environment. The sensors are today applied extensively in industrial automation, transportation and real technical world and a rudimentary knowledge of sensors belongs to the basic technical knowledge.
Learning outcomes of the course unit
Basic theoretical and practical knowledge from the area of sensors. The application will be presented especially in the field of robotics, automation technology and mechatronic systems. Obtained knowledge allow the student to work in team and deal with intelligent systems.
Students are expected to have basic knowledge from the areas of electrical engineering and electronics. Also important is basic knowledge of mathematical algorithms for data processing.
Recommended optional programme components
Recommended or required reading
1. Everett H. R.: Sensors for mobile robots. Theory and application. AK Peters Ltd., 1995
1. Daďo S., Kreidel M.: Senzory a měřící obvody. ČVUT, Praha. 1996
2. Husák M.: Senzorové systémy. ČVUT, Praha. 1993
2. Webster J. G.: The measurement, instrumentation and sensors. IEEE Press, CRC Press, 1999
3. Borenstein J., Everett H. R. and Feng L.: Navigating mobile robots. Systems and Techniques. A. K. Peters, Ltd., Wellesley, MA. 1999
3. Zehnula K.: Měření neelektrických veličin. VUT, Brno. 1988
Martinek R.SENZORY V PRŮMYSLOVÉ PRAXI, Technická literatura BEN, 2004
Planned learning activities and teaching methods
The course is taught through lectures explaining the basic principles and theory of the discipline. According to the possibility of teaching can be organized lectures for students by practitioners and excursions to companies focused on activities related to the course content.
Assesment methods and criteria linked to learning outcomes
The exam has a written and an oral part. In the written part, student processes an assigned problem (example problems and topics will be given early in the second half of the course). The oral part of the exam tests student’s skills to apply the acquired theoretical and practical knowledge to solving of particular technical problem.
Language of instruction
The objective of the course is to familiarise students with basic measuring and signal processing principles. They will have an overview of selected sensor types, working on different physical principles, and their application possibilities in technical practice. Another goal of the course is to acquaint students with modern trends of the multi-sensor systems and data fusion with utilisation of artificial intelligence components and methods.
Specification of controlled education, way of implementation and compensation for absences
Course-unit credit is conditional on attendance at lessons (80%) and independent processing of at least one task (topics of tasks will be set in 1st lesson).
Type of course unit
26 hours, optionally
Teacher / Lecturer
1. Introduction. Sensor’s role in measuring technology.
2. General classification of sensors.
3. Classical sensors (1st generation).
4. Integrated microelectronics sensors (2nd generation).
5. Fibre optoelectronic sensors (3rd generation).
6. Modern technological trends in sensor construction (smart sensors).
7. Selected methods of signal processing (measuring network).
8. Application of sensors in robotics, automation technology and mechatronic systems.
9. Multi-sensorial systems.
10. Summary of the basic deterministic and non-deterministic mathematical methods of signal processing and data fusion.
11. Intelligent systems.
12. AI - artificial intelligence.
13. Overview of sensor types for industrial application.
eLearning: currently opened course