Module also offered within study programmes:
General information:
Name:
Digital Signal Processing
Course of study:
2017/2018
Code:
IES-1-402-s
Faculty of:
Computer Science, Electronics and Telecommunications
Study level:
First-cycle studies
Specialty:
-
Field of study:
Electronics and Telecommunications
Semester:
4
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Full-time studies
Course homepage:
 
Responsible teacher:
prof. dr hab. inż. Zieliński Tomasz (tzielin@agh.edu.pl)
Academic teachers:
dr inż. Bułat Jarosław (kwant@agh.edu.pl)
Wszołek Jacek (jwszolek@kt.agh.edu.pl)
prof. dr hab. inż. Zieliński Tomasz (tzielin@agh.edu.pl)
Module summary

Introduction to digital spectral analysis and digital filtering with practical applications.

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Social competence
M_K001 Student understand necessity of giving the society actual and clear information and opinions upon digital signal processing methods ES1A_K06 Participation in a discussion
Skills
M_U001 Students can use tools and algorithms of digital signal processing ES1A_U08, ES1A_W13 Examination,
Test,
Execution of laboratory classes
M_U002 Students can analyze signals and systems in time domain and frequency domain ES1A_U08, ES1A_W18, ES1A_W13 Examination,
Test,
Execution of laboratory classes
M_U003 Student can design basic digital signal processing systems ES1A_W20, ES1A_W13 Examination,
Test,
Execution of laboratory classes
M_U004 Student can interpret information from literature about signal processing algorithms ES1A_K01, ES1A_U01, ES1A_U06 Examination,
Test,
Execution of laboratory classes
Knowledge
M_W001 Student knows definitions, concepts and algorithms of digital signal processing ES1A_U08, ES1A_W18, ES1A_W20, ES1A_W13 Examination,
Test,
Execution of laboratory classes
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Others
E-learning
Social competence
M_K001 Student understand necessity of giving the society actual and clear information and opinions upon digital signal processing methods + - + - - - - - - - -
Skills
M_U001 Students can use tools and algorithms of digital signal processing + - + - - - - - - - -
M_U002 Students can analyze signals and systems in time domain and frequency domain + - + - - - - - - - -
M_U003 Student can design basic digital signal processing systems + - + - - - - - - - -
M_U004 Student can interpret information from literature about signal processing algorithms + - - - - - - - - - -
Knowledge
M_W001 Student knows definitions, concepts and algorithms of digital signal processing + - + - - - - - - - -
Module content
Lectures:

LECTURES (28h)

Discrete signals (8h):
1. Signal classification, basic signal features (measures) and their calculation, correlation function. Sampling analog signals. Signal generation in Matlab.
2. Vector spaces of signals, signal decomposition into orthogonal components, introduction to frequency analysis.
3. Fundamentals of frequency analysis using discrete-time Fourier transform (DtFT) and discrete Fourier transform (DFT). Sampling theorem.
4. Fast Fourier transform algorithms (FFT). Optimization of frequency analysis making use of FFT.
5. Frequency analysis techniques: window functions, frequency resolution, amplitude resolution, interpolated DFT, periodogram (power spectral density), spectrogram (short-time Fourier transform).

Discrete systems (filters) (8h):
6. Mathematical description, Z transform, system transfer function, frequency response, impulse response, convolution of two signals, digital filter structures, digital filter design based on placement of zeros and poles of the transfer function.
7-8. Analog filter design (Butterworth, Chebyshev, elliptic). Designing recursive IIR digital filters using transformation of their analog prototypes (bilinear transformation method).
9. Designing non-recursive FIR digital filters: window method, frequency sampling method and least-squares optimization method.
10. Special filters: Hilbert filter and analytic signal, differentiation filter, filters for interpolation (up-sampling) and decimation (down-sampling). Sampling rate conversion.

Selected topics (12h):
11. Adaptive filters and their applications.
12. Discrete linear and circular convolution. Fast convolution algorithms making use of the FFT.
13. FFT application in xDSL modems and OFDM systems. Modulation and demodulation, cyclic prefix, channel identification, time (TEQ) and frequency (FEQ) channel equalizers.
14. Speech compression algorithms. Speech and speaker recognition. Audio compression.
15. Basics of image analysis and processing. Fundamentals of image and video compression.

Laboratory classes:

LABORRATORY EXERCISES (28h) in Matlab programming language

1. Sampling analog signals. Generation of discrete-time signals. Correlation function. Histogram.
2. Signal orthogonal transformations.
3. Basics of frequency analysis making use of DtFT and DFT, illustration of sampling theorem.
4. Fast Fourier transform algorithms.
5. Frequency estimation: role of window functions, interpolated DFT, periodogram, spectrogram.
6. Designing analog and digital filters by transfer function zeros & poles placement.
7. Designing Butterworth, Chebyshev and elliptic analog filters.
8. Designing IIR digital filters using bilinear transformation. Recursive digital signal filtration.
9. Designing FIR digital filters by window method. Non-recursive digital signal filtration – convolution.
10. Hilbert filter, analytic signal and its applications. Interpolation and decimation of signals.
11. Adaptive filters and their applications.
12. Fast convolution and correlation algorithms using FFT.
13. FFT usage in xDSL modems and OFDM transmission systems.
14. Speech coding by mean of LPC-10 algorithm.

Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 126 h
Module ECTS credits 5 ECTS
Participation in lectures 28 h
Realization of independently performed tasks 28 h
Participation in laboratory classes 28 h
Preparation for classes 42 h
Additional information
Method of calculating the final grade:

1. Positive final evaluation from, both, laboratory exercises and examination is required.
2. Mean value is calculated from marks obtained by a student during all final laboratory evaluations and all examination dates.
3. Final mark is calculated using the following formulae:
if mean>4.75 then OK:=5.0 else
if mean>4.25 then OK:=4.5 else
if mean>3.75 then OK:=4.0 else
if mean>3.25 then OK:=3.5 else OK:=3
4. If positive results from laboratory and examination are obtained during the first attempt (date) and the final mark is lower than 5.0, then the final mark is increased by 0.5.

Prerequisites and additional requirements:

Elementary/basic knowledge of: mathematics, numerical methods, theory of signals and systems, programming in Matlab.

Recommended literature and teaching resources:

1. A.V. Oppenheim, R.W. Schafer: Discrete-Time Signal Processing, Prentice-Hall, 1989.
2. R. G. Lyons: Understanding Digital Signal Processing. Addison Wesley Longman, 1997, 2004.
3. E.C. Ifeachor, B.W. Jervis: Digital Signal Processing: A Practical Approach. Addison-Wesley 1993.
4. D.K. Manolakis, V.K. Ingle: Applied Digital Signal Processing. Cambridge University Press 2011.
5. S.D. Stearns, R.A. David: Signal Processing Algorithms in Matlab. Prentice Hall 1996.
6. S. W. Smith: The Scientist and Engineer’s Guide to Digital Signal Processing, California Technical Publishing 1997.
7. J.G. Proakis, D.K. Manolakis: Digital Signal Processing: Principles, Algorithms, Applications. Prentice-Hall 2006.

Scientific publications of module course instructors related to the topic of the module:

1. Zieliński T.P.: „Od teorii do cyfrowego przetwarzania sygnałów”, 576 str. Wydział EAIiE-AGH, Kraków 2002, 2004.
2. Zieliński T.P.: „Cyfrowe przetwarzanie sygnałów. Od teorii do zastosowań”, 832 str., Wydawnictwa Komunikacji i Łączności, Warszawa 2005, 2007, 2009, 2014.
3. Zieliński T.P., Korohoda P., Rumian R. (redakcja całości): „Cyfrowe przetwarzanie sygnałów w telekomunikacji: podstawy, multimedia, transmisja”, autorstwo 131 stron, współautorstwo 87 stron, PWN, Warszawa 2014.
4. Szyper M., Zielinski T.P., Sroka R.: “Spectral Analysis of Nonstationary Signals in the System with Wide Phase Modulation”, IEEE Trans. on Instrumentation and Measurement, vol. 41, no. 6, pp. 919-920, IF=1.79 (2014), 1992.
5. Zielinski T.P.: “Joint Time-Frequency Resolution of Signal Analysis with Gabor Transform“, IEEE Trans. on Instrumentation and Measurement, vol. 50, no. 5, pp.1436-1444, IF=1.79 (2014), 2001.
6. Bułat J., Duda K., Socha M., Turcza P., Zieliński T.P., Duplaga M.: “Computational Tasks in Computer-Assisted Transbronchial Biopsy”, Future Generation Computer Systems (Elsevier), vol. 26, iss. 3, str. 455–461, IF 2.229, 2010.
7. K. Duda, L. B. Magalas, M. Majewski, T. P. Zieliński: “DFT based Estimation of Damped Oscillation’s Parameters in Low–frequency Mechanical Spectroscopy”, IEEE Trans. on Instrumentation and Measurement, str. 3608-3618, IF 0.978 (2011), IF 1.382 (5-cio letni), 2011.
8. Zieliński T.P., Duda K.: “Frequency and Damping Estimation Methods – An Overview“, Metrology and Measurement Systems: Quaterly of Polish Academy of Sciences, vol. 18, no. 4, str. 505–528, IF=0.587 (2010), IF=0.982 (2012), 2011.
9. Duda K., Zielinski T.P.: “Efficacy of the Frequency and Damping Estimation of a Real-Value Sinusoid“, IEEE Instrumentation and Measurement Magazine, vol. 16, iss. 2, pp. 48-58, IF=0.556, (2012), IF=0.828 (5-cio letni), April 2013.
10. Wiśniewski M., Zieliński T.P.: „Joint Application of Audio Spectral Envelope and Tonality Index in an E-Asthma Monitoring System”, IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 4, pp. 1009-1018, IF=2.072 (2013), 2015.
11. Tomasz Zieliński, Krzysztof Duda, Katarzyna Ostrowska: “Fast MinMax energy-based phase correction method for NMR spectra with linear phase distortion”, Journal of Magnetic Resonance, vol. 281, s. 104–117, 2017.
12. Cisek G., Zieliński T.: “Frequency domain multipath fading channel simulator integrated with OFDM transmitter for E-UTRAN baseband traffic generator”, 25th European Signal Processing Conference, Kos Island, Greece, : 28 August – 2 September 2017.
13. Cisek G., Zieliński T.: “Frequency-domain modeling of OFDM transmission with insufficient cyclic prefix using Toeplitz matrices”, 2018 IEEE Vehicular Technology Conference VTC2018-Fall, Chicago, USA, 27-30 August 2018.
14. Cisek G., Zieliński T.: “Frequency-domain multi-user OFDMA fast fading channel simulation in high-mobility scenarios”, 15th International Conference on Wireless Communications Systems ISWCS 2018, Lisbon, Portugal, : 28–31 August 2018.

Additional information:

Participation in EU POWER English language improvement course in 2018.