PA190 Digital Signal Processing

Faculty of Informatics
Spring 2018
Extent and Intensity
2/0/0. 2 credit(s) (plus 2 credits for an exam). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).
Teacher(s)
prof. Ing. Karel Hájek, CSc. (lecturer), prof. Ing. Václav Přenosil, CSc. (deputy)
prof. Ing. Václav Přenosil, CSc. (alternate examiner)
Guaranteed by
doc. RNDr. Aleš Horák, Ph.D.
Department of Machine Learning and Data Processing - Faculty of Informatics
Contact Person: prof. Ing. Václav Přenosil, CSc.
Supplier department: Department of Machine Learning and Data Processing - Faculty of Informatics
Timetable
Thu 8:00–9:50 B411
Prerequisites
This is the introductory courese of the study branch.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 20 student(s).
Current registration and enrolment status: enrolled: 1/20, only registered: 0/20, only registered with preference (fields directly associated with the programme): 0/20
fields of study / plans the course is directly associated with
Course objectives
To introduce the fundamentals of digital signal processing and related applications. This course will cover linear system analysis, z-transform, discrete Fourier transform (DFT) and its applications, FFT algorithms, digital filter (FIR and IIR) design and multi-rate signal processing.
Learning outcomes
At the end of the course, students will be able to:
- use linear system analysis;
- apply z-tranformation;
- use discrete Fourier transformation;
- apply Fast Furier Transoemation for signal processing;
- analyze and design FIR and IIR digital filters;
- use multi-rate signal processing.
Syllabus
  • 1) An Introduction to digital signal processing (DSP), signals and their types
  • 2) Analog to Digital Converter (ADC) and Digital to Analog Converter (DAC)
  • 3) Amplitude quantization errors for rounding and truncation, statistical parameters of the quantization errors
  • 4) Principal characteristics of a signal, mean values, power, energy, autocorrelation, cross-correlation
  • 5) Spectrum of a signal, time sampling and amplitude quantization of a signal
  • 6) Digital filters, digital filters with finite impulse response (FIR) and infinite impulse response (IIR)
  • 7) Discrete Fourier Transform - DFT, Fast Fourier Transform - FFT, Parametric and Nonparametric Spectral Estimation in Use
  • 8) Direct Frequency Synthesis - DDS
  • 9) RF telemetric modules for data transmission
  • 10) Software Defined Radio principles
  • 11) Radio Frequency Identification RFID principles
  • 12) Radar principles, radar signal processing
  • 13) Using DSP in audio and telemetric application
  • 14) Introduction of the MATLAB Signal Processing Toolbox
Literature
    recommended literature
  • Sanjit K. Mitra, "Digital Signal Processing: A Computer-Based Approach", second edition, McGraw-Hill.
  • John G. Proakis, Dimitris K Manolakis, "Digital Signal Processing: Principles, Algorithms, and Applications", third edition, Prentice Hall.
Teaching methods
During the semester, students are required to do tasks involving the use of Matlab software package. The course concludes with a final test.
Assessment methods
Final examination consists of 3 parts:
1) defense of the project - implementation of the design from laboratory lessons and discussion about protocol,
2) written test - logical algebra, design of the digital circuits and analysis of the digital circuits,
3) oral exam - theoretical tools for design of the digital circuits.
Language of instruction
English
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021.
  • Enrolment Statistics (Spring 2018, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2018/PA190