Moduł oferowany także w ramach programów studiów:
Informacje ogólne:
Nazwa:
Digital Signal Processing
Tok studiów:
2017/2018
Kod:
IES-1-402-s
Wydział:
Informatyki, Elektroniki i Telekomunikacji
Poziom studiów:
Studia I stopnia
Specjalność:
-
Kierunek:
Electronics and Telecommunications
Semestr:
4
Profil kształcenia:
Ogólnoakademicki (A)
Język wykładowy:
Angielski
Forma i tryb studiów:
Stacjonarne
Strona www:
 
Osoba odpowiedzialna:
prof. dr hab. inż. Zieliński Tomasz (tzielin@agh.edu.pl)
Osoby prowadzące:
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)
Krótka charakterystyka modułu

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

Opis efektów kształcenia dla modułu zajęć
Kod EKM Student, który zaliczył moduł zajęć wie/umie/potrafi Powiązania z EKK Sposób weryfikacji efektów kształcenia (forma zaliczeń)
Wiedza
M_W001 Student knows definitions, concepts and algorithms of digital signal processing ES1A_U08, ES1A_W18, ES1A_W20, ES1A_W13 Egzamin,
Kolokwium,
Wykonanie ćwiczeń laboratoryjnych
Umiejętności
M_U001 Students can use tools and algorithms of digital signal processing ES1A_U08, ES1A_W13 Egzamin,
Kolokwium,
Wykonanie ćwiczeń laboratoryjnych
M_U002 Students can analyze signals and systems in time domain and frequency domain ES1A_U08, ES1A_W18, ES1A_W13 Egzamin,
Kolokwium,
Wykonanie ćwiczeń laboratoryjnych
M_U003 Student can design basic digital signal processing systems ES1A_W20, ES1A_W13 Egzamin,
Kolokwium,
Wykonanie ćwiczeń laboratoryjnych
M_U004 Student can interpret information from literature about signal processing algorithms ES1A_K01, ES1A_U01, ES1A_U06 Egzamin,
Kolokwium,
Wykonanie ćwiczeń laboratoryjnych
Kompetencje społeczne
M_K001 Student understand necessity of giving the society actual and clear information and opinions upon digital signal processing methods ES1A_K06 Udział w dyskusji
Matryca efektów kształcenia w odniesieniu do form zajęć
Kod EKM Student, który zaliczył moduł zajęć wie/umie/potrafi Forma zajęć
Wykład
Ćwicz. aud
Ćwicz. lab
Ćw. proj.
Konw.
Zaj. sem.
Zaj. prakt
Zaj. terenowe
Zaj. warsztatowe
Inne
E-learning
Wiedza
M_W001 Student knows definitions, concepts and algorithms of digital signal processing + - + - - - - - - - -
Umiejętności
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 + - - - - - - - - - -
Kompetencje społeczne
M_K001 Student understand necessity of giving the society actual and clear information and opinions upon digital signal processing methods + - + - - - - - - - -
Treść modułu zajęć (program wykładów i pozostałych zajęć)
Wykład:

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.

Ćwiczenia laboratoryjne:

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.

Nakład pracy studenta (bilans punktów ECTS)
Forma aktywności studenta Obciążenie studenta
Sumaryczne obciążenie pracą studenta 126 godz
Punkty ECTS za moduł 5 ECTS
Udział w wykładach 28 godz
Samodzielne studiowanie tematyki zajęć 28 godz
Udział w ćwiczeniach laboratoryjnych 28 godz
Przygotowanie do zajęć 42 godz
Pozostałe informacje
Sposób obliczania oceny końcowej:

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.
5. Positive laboratory evaluation can be obtained also during one additional date in examination session. But this possibility is only for students having no less than 40% of laboratory points.

Wymagania wstępne i dodatkowe:

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

Zalecana literatura i pomoce naukowe:

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.

Publikacje naukowe osób prowadzących zajęcia związane z tematyką modułu:

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.

Informacje dodatkowe:

Participation in EU POWER English language improvement course in 2018.