Fundamentals of microprocessor technology
Course: Physics
Structural unit: Faculty of Physics
Title
Fundamentals of microprocessor technology
Code
ВКП 8.
Module type
Вибіркова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2023/2024
Semester/trimester when the component is delivered
6 Semester
Number of ECTS credits allocated
3
Learning outcomes
1.1 know the architecture of modern microprocessors
2.1 Practical schemes using microprocessors for problems of nuclear physics experiment and robotics
3.1 Algorithms of computer vision for nuclear robotics problems
4.1 Apply neural networks in microprocessor devices for the tasks of nuclear physical experiment and nuclear robotics
Form of study
Full-time form
Prerequisites and co-requisites
-Successful mastery of basic courses in physics: "Mechanics", "Molecular Physics", "Electricity", "Optics".
-Be able to solve problems in basic physics courses.
-Know the C ++ and Python programming languages.
-Student must know the basics of analog and digital spectrometric paths.
-Understand the principles of operation of logical elements and functional devices of computer technology.
Course content
providing students with:
- necessary theoretical information on the use of microprocessors in nuclear physics experiment and nuclear robotics;
- practical skills of work with automated devices of nuclear electronics and programming of microcontrollers;
- ability to research and design microcontroller devices for the tasks of nuclear physical experiment and nuclear robotics.
Recommended or required reading and other learning resources/tools
[1] Tanenbaum Endrew, Austin Todd. Computer architecture (6 topics). 2013.
[2] Robert F. Stengel. Robotics and Intelligent Systems. Princeton. University Princeton, NJ. 2017. http://stengel.mycpanel.princeton.edu/RISVirText.html
[3] Robert Bukarev. Fundamentals of Robotics 3rd ed. 2010.
[4] Joe Minichino and Joseph Howse. Learning OpenCV 4 Computer Vision with Python 3. 2020.
[5] Henrik Brink, Joseph Richards, Mark Feverolf. "Machine learning" 2017.
[6] Francis X. Govers. Artificial Intelligence for Robotics: Build Intelligent Robots that Perform Human Tasks Using AI Techniques. 2018.
Planned learning activities and teaching methods
Lecture demonstration; individual work; consultations.
Assessment methods and criteria
semester assessment:
1. Survey during lectures (maximum - 50 points).
2. Execution of independent practical tasks (maximum - 50 points).
3. Final assessment in the form of a test
Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Departments
The following departments are involved in teaching the above discipline