Course detail

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.

Prerequisites

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.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

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

Czech

Work placements

Not applicable.

Aims

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).

Classification of course in study plans

  • Programme M2I-P Master's

    branch M-VSR , 2. year of study, summer semester, 4 credits, optional (voluntary)

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

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