Popis predmeta

Course Description

Time discrete signals. Signal sampling and reconstruction. Nyquist-Shannon sampling theorem. Fourier transforms. Representation of signals and systems in Fourier domain and in the Laplace and Z domains. Spectral analysis of signals. Fast Fourier transform. Invertible systems and deconvolution. Finite and infinite impulse response systems. Linear phase systems. Selective filters. All-pass filters. Minimum and maximum phase filters. Phase correctors. Classical design of digital filters. Computer-aided design of optimal digital filters. Implementation of digital filters. Digital biquad section. Filter structures. Number representation and overflow. Dynamic range scaling. Quantization of filter coefficients. Finite world-length effects. Digital signal processors. Multirate digital signal processing. Decimation and interpolation. Filter banks.

Learning Outcomes

  1. define the basic concepts of digital signal processing
  2. state and explain the Nyquist-Shannon sampling theorem
  3. apply the fast Fourier transform as a signal analysis and processing tool
  4. identify a filter type and determine its frequency response
  5. compare digital filter structures
  6. select which digital filter to use depending on the application and the specification
  7. design optimal digital filter using a computer
  8. analyze and implement time-discrete and digital system using block diagrams and signal-flow graphs
  9. combine basic blocks of multirate systems to realize a multirate filter bank

Forms of Teaching

Lectures

Lectures present theoretical concepts.

Exercises

Recitations include solving practical tasks and discussing solving procedures.

Laboratory

Laboratory exercises introduce students to digital signal processing hardware.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 50 % 20 % 50 % 20 %
Homeworks 0 % 5 % 0 % 0 %
Mid Term Exam: Written 0 % 25 % 0 %
Final Exam: Written 0 % 30 %
Final Exam: Oral 20 %
Exam: Written 50 % 50 %
Exam: Oral 30 %
Comment:

Mandatory prerequisites for the final oral exam are at least 50% achieved on the midterm and on the final exam combined, and at least 50% on the laboratory.
The mandatory prerequisite for the comprehensive written exam is at least 50% achieved on the laboratory.
A minimum of 5 points is required to pass the final oral exam.
A minimum of 7 points is required to pass the comprehensive oral exam.

Week by Week Schedule

  1. Introduction. Review: time discrete signals and systems; Fourier transforms (DFT, DTFT and CTFT).
  2. Review: representation of signals and systems in transform domain (Fourier, Laplace and Z transforms).
  3. Signal decomposition. Signal sampling and reconstruction. The sampling theorem.
  4. Convolution and deconvolution. Invertible systems. Minimum phase and maximum phase systems. Relation between amplitude and phase reposnse for minimum phase systems.
  5. Interpretation of magnitude and phase responses. Phase delay. Group delay. Phase distortions. Linear phase systems. Negative group delay.
  6. Relation between time-continuous and time-discrete systems. Euler transform. Bilinear transform.
  7. Filtration. Digital filter specification. Filter types: selective filters; all-pass filters; minimum phase filters; and phase correctors.
  8. Midterm exam
  9. FIR filter design: window method; least-squares design; Parks-McClellan FIR filter design.
  10. IIR filter design: impulse invariance method; bilinear transform; Yule-Walker IIR filter design.
  11. Implementation of digital filters. Digital biquad section. Filter structures.
  12. Number representation and overflow. Dynamic range scaling. Quantization of filter coefficients. Finite world-length effects. Digital signal processors.
  13. Multirate digital signal processing. Decimation and interpolation. Filter banks.
  14. Fast Fourier transform. Efficient computation of convolution and of correlation between finite duration signals.
  15. Final exam

Study Programmes

University graduate
Audio Technologies and Electroacoustics (profile)
Free Elective Courses (1. semester) (3. semester)
Communication and Space Technologies (profile)
Elective Courses of the Profile (1. semester) Free Elective Courses (3. semester)
Computational Modelling in Engineering (profile)
Free Elective Courses (1. semester) (3. semester)
Computer Engineering (profile)
Elective Course of the Profile (1. semester)
Computer Science (profile)
Free Elective Courses (1. semester) (3. semester)
Control Systems and Robotics (profile)
Free Elective Courses (1. semester) (3. semester)
Data Science (profile)
Free Elective Courses (1. semester) (3. semester)
Electrical Power Engineering (profile)
Free Elective Courses (1. semester) (3. semester)
Electric Machines, Drives and Automation (profile)
Free Elective Courses (1. semester) (3. semester)
Electronic and Computer Engineering (profile)
(1. semester)
Electronics (profile)
Elective Courses of the Profile (1. semester) (3. semester)
Information and Communication Engineering (profile)
(1. semester)
Network Science (profile)
Free Elective Courses (1. semester) (3. semester)
Software Engineering and Information Systems (profile)
Free Elective Courses (1. semester) (3. semester)

Literature

Sanjit Kumar Mitra (2010.), Digital Signal Processing, McGraw-Hill
Alan V. Oppenheim, Ronald W. Schafer (2011.), Discrete-time Signal Processing, Prentice Hall
John G. Proakis, Dimitris G. Manolakis (2006.), Digital Signal Processing, Pearson
Paolo Prandoni, Martin Vetterli (2008.), Signal Processing for Communications, EPFL Press

For students

General

ID 222528
  Winter semester
5 ECTS
L0 English Level
L1 e-Learning
45 Lectures
10 Exercises
20 Laboratory exercises

Grading System

87 Excellent
75 Very Good
64 Good
51 Acceptable

Learning Outcomes

  1. define the basic concepts of digital signal processing
  2. state and explain the Nyquist-Shannon sampling theorem
  3. apply the fast Fourier transform as a signal analysis and processing tool
  4. identify a filter type and determine its frequency response
  5. compare digital filter structures
  6. select which digital filter to use depending on the application and the specification
  7. design optimal digital filter using a computer
  8. analyze and implement time-discrete and digital system using block diagrams and signal-flow graphs
  9. combine basic blocks of multirate systems to realize a multirate filter bank

Forms of Teaching

Lectures

Lectures present theoretical concepts.

Exercises

Recitations include solving practical tasks and discussing solving procedures.

Laboratory

Laboratory exercises introduce students to digital signal processing hardware.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 50 % 20 % 50 % 20 %
Homeworks 0 % 5 % 0 % 0 %
Mid Term Exam: Written 0 % 25 % 0 %
Final Exam: Written 0 % 30 %
Final Exam: Oral 20 %
Exam: Written 50 % 50 %
Exam: Oral 30 %
Comment:

Mandatory prerequisites for the final oral exam are at least 50% achieved on the midterm and on the final exam combined, and at least 50% on the laboratory.
The mandatory prerequisite for the comprehensive written exam is at least 50% achieved on the laboratory.
A minimum of 5 points is required to pass the final oral exam.
A minimum of 7 points is required to pass the comprehensive oral exam.

Week by Week Schedule

  1. Introduction. Review: time discrete signals and systems; Fourier transforms (DFT, DTFT and CTFT).
  2. Review: representation of signals and systems in transform domain (Fourier, Laplace and Z transforms).
  3. Signal decomposition. Signal sampling and reconstruction. The sampling theorem.
  4. Convolution and deconvolution. Invertible systems. Minimum phase and maximum phase systems. Relation between amplitude and phase reposnse for minimum phase systems.
  5. Interpretation of magnitude and phase responses. Phase delay. Group delay. Phase distortions. Linear phase systems. Negative group delay.
  6. Relation between time-continuous and time-discrete systems. Euler transform. Bilinear transform.
  7. Filtration. Digital filter specification. Filter types: selective filters; all-pass filters; minimum phase filters; and phase correctors.
  8. Midterm exam
  9. FIR filter design: window method; least-squares design; Parks-McClellan FIR filter design.
  10. IIR filter design: impulse invariance method; bilinear transform; Yule-Walker IIR filter design.
  11. Implementation of digital filters. Digital biquad section. Filter structures.
  12. Number representation and overflow. Dynamic range scaling. Quantization of filter coefficients. Finite world-length effects. Digital signal processors.
  13. Multirate digital signal processing. Decimation and interpolation. Filter banks.
  14. Fast Fourier transform. Efficient computation of convolution and of correlation between finite duration signals.
  15. Final exam

Study Programmes

University graduate
Audio Technologies and Electroacoustics (profile)
Free Elective Courses (1. semester) (3. semester)
Communication and Space Technologies (profile)
Elective Courses of the Profile (1. semester) Free Elective Courses (3. semester)
Computational Modelling in Engineering (profile)
Free Elective Courses (1. semester) (3. semester)
Computer Engineering (profile)
Elective Course of the Profile (1. semester)
Computer Science (profile)
Free Elective Courses (1. semester) (3. semester)
Control Systems and Robotics (profile)
Free Elective Courses (1. semester) (3. semester)
Data Science (profile)
Free Elective Courses (1. semester) (3. semester)
Electrical Power Engineering (profile)
Free Elective Courses (1. semester) (3. semester)
Electric Machines, Drives and Automation (profile)
Free Elective Courses (1. semester) (3. semester)
Electronic and Computer Engineering (profile)
(1. semester)
Electronics (profile)
Elective Courses of the Profile (1. semester) (3. semester)
Information and Communication Engineering (profile)
(1. semester)
Network Science (profile)
Free Elective Courses (1. semester) (3. semester)
Software Engineering and Information Systems (profile)
Free Elective Courses (1. semester) (3. semester)

Literature

Sanjit Kumar Mitra (2010.), Digital Signal Processing, McGraw-Hill
Alan V. Oppenheim, Ronald W. Schafer (2011.), Discrete-time Signal Processing, Prentice Hall
John G. Proakis, Dimitris G. Manolakis (2006.), Digital Signal Processing, Pearson
Paolo Prandoni, Martin Vetterli (2008.), Signal Processing for Communications, EPFL Press

For students

General

ID 222528
  Winter semester
5 ECTS
L0 English Level
L1 e-Learning
45 Lectures
10 Exercises
20 Laboratory exercises

Grading System

87 Excellent
75 Very Good
64 Good
51 Acceptable