Details - Tematika

Digital Signal Processing (GAINBAN-DIGIJELF-1)

Basic data

Name and type of the study programme

Computer Science Engineering, undergraduate program

Curriculum

2026

Classes / consultation hours

2 + 0 + 2 (L+S+Labs)

Credits

5 credits

Theory – Practice

Theory: 50%, Practice: 50%

Recommended semester

Semester 6

Study mode

full-time

Prerequisites

Signals and Systems

Evaluation type

Mid-term evaluation

Course category

Compulsory elective in the specialization

Language

English

Instructors

Responsible instructor

Dr. Csík Norbert

Responsible department

Department of Information Technologies

Instructor(s)

Dr. Csík Norbert

Checked by

Kovács Márk

Course objectives

The aim of the course is to introduce the fundamental concepts and basic algorithms of digital signal processing.

Course content

Lectures

- Classification of signals; Fourier series. - Sampling, the sampling theorem, and quantization noise. - DFT and IDFT (derivation and properties). - FFT and IFFT (derivation, properties, and examples). - Spectrum analysis, spectral operations, and windowing techniques. - Laplace transform and convolution. - Digital filtering – principles of FIR filtering. - Properties of the z-transform. - IIR filter design. - Nonlinear filters. - Methods for the analysis of non-stationary signals (correlation analysis, STFT, CWT, DWT, MM, and TDAH).

Labs

- Introduction to laboratory procedures, hardware, and software environment. - Introduction to MATLAB: scripts, variables, vectors, and control structures. - Introduction to MATLAB: file handling, signal generation, and noise generation. - Visualization techniques in MATLAB; generation of sinusoidal and square-wave signals; synthesis of square waves using sinusoidal components. - Creation and visualization of transfer functions in MATLAB. - Review and synthesis session. - Generation of various signal types. - Operations on noisy spectra: averaging, thresholding, filtering, and signal-to-noise ratio (SNR) analysis. - FIR filter demonstration and practice. - IIR filter demonstration and practice. - Review and synthesis session. - STFT demonstration and analysis. - Additional applications and effects in audio signal processing.

Acquired competences

Knowledge

- Has knowledge of the scientific principles and methods required for the study and practice of the field of information technology, including mathematics, physics, and other natural sciences. - Possesses fundamental knowledge and an engineering approach related to signal processing, the modeling, simulation, and control of systems and networks. - Is familiar with the terminology and professional language used in information technology and engineering, in both own and English, at least at a basic level.

Skills

- Applies the scientific principles and methods required for the practice of information technology—including mathematics, physics, and other natural sciences—in engineering work aimed at the design and development of information systems. - Is capable of specifying and implementing embedded systems by applying the knowledge acquired during their studies. - Collaborates effectively with IT professionals and electrical engineers in team-based projects, as well as with representatives of other disciplines during requirements analysis and the development of solutions to specific problems.

Attitude

- Strives to understand the overall technical system beyond the boundaries of their own area of responsibility. - Is open to exploring other fields that utilize information technology tools and to developing IT-based solutions in collaboration with specialists from those fields. - Maintains a strong commitment to the security and confidentiality of the data and information of colleagues, clients, and other stakeholders.

Autonomy and responsibilities

–

Requirements, evaluation and grading

Mid-term study requirements

Written assessments based on the course material.

Generative AI usage

Use of GAI tools is permitted in a limited manner (e.g., for literature search support or specific tools). In this case, the course instructor is responsible for defining where and how GAI tools may be used in assignments. The course description must specify in detail how GAI tools may be used during the course.

Study aids, laboratory background

Computer laboratory with a capacity of 15 students, equipped with Scilab / MatLab software.

Readings

Compulsory readings

[1] Anand R.: Digital Signal Processing: An Introduction. Mercury Learning & Information, 2022. ISBN-978-16-839-2802-7