Name in English: Signal Processing Algorithms
Main field of study (if applicable): Telecommunications Specialization (if applicable): ……….. Level and form of studies: 1st level, full-time Kind of subject: obligatory
Subject code: ETEK102 Group of courses: YES
Lecture Classes Laboratory Project Seminar
Number of hours of organized classes in University (ZZU)
30 30
Number of hours of total student workload (CNPS)
60 90
Form of crediting Crediting with grade
Crediting with grade* For group of courses
mark (X) final course X
Number of ECTS points 5 including number of
ECTS points for practical
(P) classes 3
including number of ECTS points for direct teacher-student contact (BK) classes
1 1
*delete as applicable
PREREQUISITES RELATING TO KNOWLEDGE, SKILLS AND OTHER COMPETENCES
1. K1TEL_W14, K1TEL_U12
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SUBJECT OBJECTIVES
C1 Possesion of basic knowledge in the area of theory and processing of stochastic signals and their applications in modern digital communications systems, with the use of linear orthogonal digital least- squares algorithms for stationary and non-stationary 2-nd order time-series.
C2 Posession of ability to practical applications of computerized programming tools (Matlab
SUBJECT EDUCATIONAL EFFECTS
relating to knowledge:
PEK_W01: posseses knowledge in the area of stochastic signal processing methods.
PEK_W02: knows effective algorithms as well as techniques of estimation of basic stochastic signals parameters.
PEK_W03: knows basic problems of optimal and adaptive filtering, orthogonal parametrization and digital synthesis of stochastic signals.
relating to skills:
PEK_U01 – is able to perform analysis of stochastic signals properties in their applications in telecommunications.
PEK_U02 – is able to apply programming tools (Matlab environment) in analysis and filtering of stochastic signals.
PEK_U03 – is able to perform computer-aided simulations.
PROGRAMME CONTENT
Form of classes - lecture Number of hours Lec 1 Introduction. Classification of signals. Deterministic and stochastic
signals. Representiation of deterministic signals in the time- and
frequency-domains. Frequency analysis of deterministic signals (DFT and FFT algorithms and their properties).
2222
Lec 2 Sampling, spectrum leakage, quantization. Linear transformations of deterministic signals. Classical digital filtering of deterministic signals. The Z-transformation. Design of FIR and IIR digital filters.
2 Lec 3 Stochastic signals: description, properties and basic parameters. 2-nd order
stochastic signals. Stationary and non-stationary signals. Ergodic signals. Linear transformations of 2-nd order stochastic signals.
2 Lec 4 Comparison of the problems of classical linear filtering of deterministic
signals versus linear optimal filtering of 2-nd order stochastic signals. Similarities and differences.
2 Lec 5 Linear prediction of stationary 2-nd order stochastic signals. A set of
normal equations. Covariance matrix of 2-nd order stationary stochastic signals and its properties. An idea of fast solution of the set of normal equations.
2
Lec 6 Forward and backward prediction errors. Normalized Levinson algorithm as an efficient method of solving the prediction problem. Interpretation and example of the algorithm operation and evaluation of its convergence speed.
2
2
Lec 7 J-orthogonal realization of the Levinson filter and its properties. Schur coefficients. Innovations signal and its properties. An idea of parametric estimation of 2-nd order stochastic signals power spectrum.
2 Lec 8 Linear innovations filter. Orthogonal parametrization of 2-nd order signals.
Innovations filtering of 2-nf order signals. 2
Lec 9 Inverse filter problem. Causal stability conditions of the inverse filter.
Modeling filter algorithm and its properties. 2
Lec 11 The LPC method of transmission of stochastic signals. Compression of
information. Applications in digital communication systems. 2
Lec 12 Adaptive orthogonal filtering of non-stationary time-series. 2 Lec 13 Time-frequency transformations of non-stationary time-series and their
applications. 2
Lec 14 Future perspectives and directions of development of digital signal
processing in communication systems. 2
Lec 15 GraGGrading.
2
Total hours 3030
Form of classes - class Number of
hours Cl 1 Cl 2 Cl 3 Cl 4 .. Total hours
Form of classes - laboratory Number of
hours
Lab 1 Introduction. Generation of deterministic and stochastic signals. Estimation of basic parameters of signals.
2
Lab 2 Normalized Levinson algorithm. 4
Lab 3 Innovations filtering of stationary time-series. 4
Lab 4 Three methods of orthogonal parametrization of 2-nd order signals. 4
Lab 5 Stochastic modeling of stationary time-series. 4
Lab 6 Adaptive orthogonal filtering of non-stationary time-series. 4
Lab 7 Parametric estimation of power spectral density of stationary time-series. 4 Lab 8 Parametric estimation of power spectral density of non-stationary time-series.
Time-frequency domains transformations.
4
Total hours 30
Form of classes - project Number of h o u r s Proj 1 Proj 2 Proj 3 Proj 4
…
Total hours
Form of classes - seminar Number of
hours Sem 1 Sem 2 Sem 3 … Total hours
TEACHING TOOLS USED
N1. Lecture
N2. Discussion: questions and answers N3. Laboratory
N4. Consultations
N5. Student’s workload – lecture preparation
N6. Student’s workload – laboratory reports preparation
EVALUATION OF SUBJECT EDUCATIONAL EFFECTS ACHIEVEMENT
Evaluation (F – forming
(during semester), P – concluding (at semester end)
Educational effect number
Way of evaluating educational effect achievement
F1 PEK_W01, PEK_W02,
PEK_W03
Grading of colloquium, grading of student’s answers
F2 PEK_U01, PEK_U02
PEK_U03
Grading of simulation concepts, grading of quality of laboratory reports.
C= 0,5F1 + 0,5F2
PRIMARY AND SECONDARY LITERATURE PRIMARY LITERATURE:
[1] Zarzycki J. Cyfrowa filtracja ortogonalna sygnałów losowych, WNT, Warszawa 1998 [2] Lyons R.G. Wprowadzenie do cyfrowego przetwarzania sygnałów, WKŁ, Warszawa 1997 [3] Zieliński T., Od teorii do cyfrowego przetwarzania sygnałów, WKŁ, Warszawa, 2006
SECONDARY LITERATURE:
[1] Szabatin J., Podstawy teorii sygnałów, Warszawa, WKŁ, 2000
[3] Journal papers recommended by the supervisor.
SUBJECT SUPERVISOR (NAME AND SURNAME, E-MAIL ADDRESS)
MATRIX OF CORRELATION BETWEEN EDUCATIONAL EFFECTS FOR SUBJECT
Signal Processing Algorithms
AND EDUCATIONAL EFFECTS FOR MAIN FIELD OF STUDY Telecommunications
AND SPECIALIZATION ……….. Subject educational effect Correlation between subject
educational effect and educational effects defined for main field of
study and specialization (if applicable)** Subject objectives*** Programme content*** Teaching tool number*** PEK_W01