**Course title, code:** Digital Signal Processing, GAINBAN-DIGIJELF-1

**Name and type of the study programme:**

**Curriculum:**2021

**Number of classes per week (lectures+seminars+labs):**2+0+2

**Credits:**5

**Theory:**50 %

**Practice:**50 %

**Recommended semester:**6

**Study mode:**full-time

**Prerequisites:**Calculus 2 + 100 cr

**Evaluation type:**term mark

**Course category:**required optional

**Language:**english

**Responsible instructor:**Dr. Pintér István

**Responsible department:**Department of Information Technologies

**Instructor(s):**Dr. Csík Norbert , Dömötör Zénó István

**Course objectives:**

Introduction to the basic concepts and fundamental algorithms of digital signal processing.

**Course content - lectures:**

01. Review: Classification of signals, transfer-function, Bode-diagram, sampling 02. Review: Sampling, Fourier- and Laplace-transformation, transfer function is the operator space, convolution 03. Fourier series, complex unit vectors, derivation of the DFT-IDFT. Applications. 04. Derivation of the FFT-IFFT, features of the discrete spectrum, aliasing errors 05. DFT-FFT calculations for spectrums, operations on the spectrum, the effetcs of the windowing 06. Some visualization possibilities of non-stationary signals (STFFT, MM, TDAH, WT) 07. Synthesis class. 08. Digital filtering, the theory of the FIR filters. 09. Features of the z-transform. 10. Applications of the z-transform: construction of an IIR filter. 11. Nonlinear digital filters. 12. Synthesis class. 13. Demo class.

**Course content - labs:**

01. Introduction to the course lab. 02. Introduction to the MatLab software, script, variables, vectors, control structures. 03. Introduction to the MatLab software, file handling, generating signals and noises. 04. Visualization possibilities of signals in MatLab, sinus ans square signals, construction square signal from sinusoids. 05. Transfer function, visualization of transfer function in MatLab. 06. Synthesis class. 07. Generation spectra of various signals 08. Operations for noisy spectrums. Averaging, tresholds, filtering, SNR 09. FIR demo practice 10. IIR demo practice 11. Synthesis class. 12. STFFT demo 13. Other applications, effects on audio sounds.

**Acquired competences:**

__Knowledge:__

- Knowledge of the principles and methods of natural sciences (mathematics, physics, other natural sciences) relevant to the field of IT.
- He/she posesses a basic knowledge and engineering approach to signal processing, modelling, simulation and control of systems and networks.
- He knows the vocabulary and special terms of the engineering profession in the Hungarian and English languages at least on the basic level.

__Skills:__

- He/she uses the principles and methods of natural sciences (mathematics, physics, other natural sciences) relevant to the field of information technology in his/her engineering work for the design of information systems.
- He/she is abile to specify and implement embedded systems using the knowledge gained from his/her studies.
- He/she cooperates with other computer science engineers, electrical engineers during team work, and with other experts during the analysis and solution of a problems.

__Attitude:__

- He/she aims to see through the entire engineering system not only his/her own field.
- He/she is open to get to know other fields which employ information technology tools, and open to work out information technology soultions in cooperation with the experts of other areas.
- He/she keeps in mind and ensures the security of his/her employees' and customers' data and information.

__Autonomy and responsibilities:__

- He/she feels responsible for IT systems analysis, development and operation, both individually and as part of a team.
- He/she has a security-conscious attitude in posession of his/her professional knowledge, and is aware of potential threats and opportunities for attack, as well as is prepared to prevent them.

__Additional professional competences:__

- The technological revolution which started in industry; Industry 4.0 based operation and realed knowledge, cyber physical systems, self-organising mechanisms

**Requirements, evaluation, grading:**

**Mid-term study requirements:**

Graded assignments based on the course material.

**Exam requirements:**

**Study aids, laboratory background:**

**Compulsory readings:**

[1] Tomas Holton: Digital Signal Processing: Principles and Applications, 2021, ISBN-978-11-084-1844-7 [2] Navas, Jayadevan: Lab Primer Through MATLAB, 2014., ISBN-978-81-203-4932-2

**Recommended readings:**

[1] Moon Todd: Advanced Signal Processing, 2022, ISBN-978-12-604-5893-0 [2] Diniz Paulo S. R.: Digital Signal Processing, 2010, ISBN-978-05-218-8775-5