
Advanced Signal Processing, Lectures
Extent
5 crCourse dates
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Advanced Signal Processing, 5 cr
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After completing the course, the student
- Is familiar with the most important advanced signal processingr generic problems: optimal design, convergence, recursiveness in time, spectrum estimation;
- Is able to start from the formulation of a problem formulation and utilize a number of typical algorithmic tools to derive the solution;
- Knows what are the most important algorithms for optimal and adaptive filters: LMS, NLMS,RLS etc.
- Acquires practice on simulating optimal and adaptive algorithms with given input data and extracting useful performance indices helpful in comparing various algorithms.
- Knows how to integrate an optimal or adaptive filter in a number of important applications: echo cancelation, noise cancellation, channel equalization etc.
Core content
- 1. Deterministic and random signals: review of Fourier transform, Z transform, random variables, random signals, correlation, AR,MA, ARMA
- 2. Optimal filter design (Wiener filter, Least squares, essentials of estimation, MLE, CramerRao)
- 3. Adaptive filter design (LMS, NLMS, RLS )
- 4. Application areas of Optimal filter design and Adaptive filter design
- 5. Spectrum estimation:Frequency spectrum (needed in machine function regime diagnosis, finding periodicities in time series), Direction of Arrival spectrum
- 6. Nonlinear filters (median and order statistics filter family)
Common
Lecture:
25.10.2022 08:00 - 10:00
01.11.2022 08:00 - 10:00
08.11.2022 08:00 - 10:00
15.11.2022 08:00 - 10:00
22.11.2022 08:00 - 10:00
29.11.2022 08:00 - 10:00
Groups
Group 1: Group 1
27.10.2022 10:00 - 12:00
03.11.2022 10:00 - 12:00
10.11.2022 10:00 - 12:00
17.11.2022 10:00 - 12:00
24.11.2022 10:00 - 12:00
01.12.2022 10:00 - 12:00
08.12.2022 10:00 - 12:00
Group 2: Group 2
27.10.2022 08:00 - 10:00
03.11.2022 08:00 - 10:00
10.11.2022 08:00 - 10:00
17.11.2022 08:00 - 10:00
24.11.2022 08:00 - 10:00
01.12.2022 08:00 - 10:00
08.12.2022 08:00 - 10:00
Prerequisites
Prerequisite
- Code: SGN-11000
- Name: Basic Course in Signal Processing
- ECTS credits: 5
- Mandatory: Advisable
Prerequisite
- Code: SGN-11007
- Name: Introduction to Signal Processing
- ECTS credits: 5
- Mandatory: Advisable
Recommended Prerequisites
- Introduction to Signal Processing, COMP.SGN.100, 5 cr
Material
- Type: Book
- Name: Adaptive Filter Theory
- Author: Simon O. Haykin
- Exam material: No
- Language: English
Material
- Type: Book
- Name: Spectral analysis of signals
- Author: Petre Stoica and Randolph Moses
- Exam material: No
- Language: English
Material
- Type: Lecture slides
- Author: Ioan Tabus
- Exam material: Yes
- URL: http://www.cs.tut.fi/~tabus/course/AdvSP.html
- Language: English
Material
- Type: Book
- Name: Optimum Signal Processing
- Author: S. J. Orfanidis
- Exam material: No
- URL: http://eceweb1.rutgers.edu/~orfanidi/osp2e/index.html
- Language: English
General scale, 0-5
Contact information
Email: open.studies.tau [at] tuni.fi
Phone: 0294 520 200
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tuni.fi/open-university
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