389.166 - Signal Processing 1previously 389.068

Deterministische Signalverarbeitung (DSV)Content

Univ.-Prof., Dr.-Ing. Markus RuppWS 18/19

Th 14:00-15:15 EI3A, Fr 8:45-10:00 EI4,

Last change: 29.8.2018

2Univ.-Prof. Dr.-Ing.

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Relation of Lectures in Master Telecommunications 7.Semester

8.Semester

9. Semester

SP1 SP2

DC1 WC1

DC2

LabWC2

3Univ.-Prof. Dr.-Ing.

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Learning Goals SP1 Reanimate basics of linear algebra to

actively use them Exercise calculus techniques Know proof techniques passively To exercise the relation of technical

context and formal problem description To learn basic knowledge and methods of

digital signal processing To be able to follow today’s literature

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Learning Goals SP1 1) Basics: the classics (4U)

Notation: Vector, matrix, random numbers, deterministic numbers

Description of linear systems Convolution Polynomial operators, Bezout Matrix and vector Notation State-space descriptions, time-invariant systems Properties of linear systems

Sampling theorems

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Learning Goals SP1 2) Vector Spaces and Applications of Linear

Algebra in Signal Processing (6U) Metric spaces, sequences, Cauchy-sequences, supremum,

infimum Groups, Vector spaces, linear combination, linear

independence, basis and dimension, orthogonality,blind channel estimation

Norms and normed vector spaces Applications of norms: robustness descriptions,

feedback systems with nonlinear elements: the small gain theorem

Inner vector products and inner product spaces, Hilbert and Banach spaces

Induced norms, Cauchy-Schwarz inequality: matched filter and correlation coefficient, time-frequency uncertainty

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Learning Goals SP1 3) Representation and Approximation in

Vector Spaces (4U) Approximation problem in the Hilbert space Orthogonality principle

Minimization with gradient method Least Squares Filtering,

linear and nonlinear regression, parametric estimation, iterative LS problem, iterative receivers

Signal transformation and generalized Fourier series Examples for orthogonal functions, Wavelets

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Learning Goals SP14) Linear Operators (4U) Linear Transformations, Functionals Null- and other spaces Orthog. Subspaces, Matrix Rank, Projections Factorization

Eigenvalue-decomposition, Hermitian matrices Filter design based on Eigenfilters

Subspace techniques: PHD,MUSIC,ESPRIT Singular value decomposition SVD condition number, MIMO transmissions, blind source

separation

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Learning Goals SP1 5) Matrix Computation (2U)

Kronecker products and sums Vec-operator Tensors for Big Data Hadamard transforms, DFT,FFT Toeplitz and circulant matrices

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Procedure The lecture is a VU with 3.0 SWS, about 2.0 SWS

lecture part and 1.0 SWS exercise. Exercises are held by

Stefan Pratschner(38947, CG0518) as well as Bashar Tahir (389742, CG0410).

Email: sp1exercise@nt.tuwien.ac.at The exercises contain calculus (into box “SP1” at

door 5th floor). All problems will be made available approx two

weeks in advance. Students can start working on them right away.

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Procedure Note that the exercises are part of the course. -

The goal is to improve your skills in the usage of DSP techniques.

The exercises as well as the midterm are optional. You don't need to do them all.

However, we grant you points for them, and...

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Procedure In order to qualify for the final oral exam,

you need to have at least 18 points all together form calculus, Python ,test and blackboard.

Oral exam: 67 Points Calculus exercise: 12 Points Python-exercise: 8 Points Test: 15 Points blackboard: max. 4 Points Minimum passing 40 Points

Procedure Grading system (100 points) Grade „sehr gut“ >=90 points Grade „gut“ 70-89 points Grade „befriedigend“ 55-69 points Grade „genügend“ 40-54 points

That is you can pass with one point from the oral exam!

With just 3 exercises you still can get a „sehr gut“

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13Univ.-Prof. Dr.-Ing.

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Procedure Python-exercises (analytical part, discussion and

results (figures) box “SP 1” at 5th floor, py-files via TUWEL upload)

The Python code should run on its own and show all demanded results automatically.

Python exercises can be prepared in groups (max. 3 people/group). All group members have to be mentioned in the code. Copied Python code without mentioning the group is considered a fraud and will result in zero points!

Note that calculus exercises are to be done individually!!!

Procedure We offer a Python Introduction 5.10.2018 from 15:00 to 16:30 in EI4

Everybody is welcome

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Procedure The exercises contain calculus (into box

“SP 1” at door 5th floor if written by hand, upload per TUWEL if digitally available).

The calculus as well as the Python exercises are to be handed in not later than two days (48h) before the exercise date. They will be presented by the students on the blackboard (with LCD projector).

In case you hand them in later but before the presentation on the blackboard, you get only half the points and you cannot present at the blackboard.15

Univ.-Prof. Dr.-Ing. Markus Rupp

Procedure Note that the exercises Python as well as calculus are

part of the course. The goal is that you become more fluent in the usage of

DSP techniques. The exercises (as well as the midterm) are optional. You

do not need to do them all! However we grant you points for them so that the final

oral exam becomes easier! The more you work during the semester, the more

points you have, the better trained you are and the better the final result is!!!!

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Procedure If a student cannot present her/his exercise at

the blackboard, correctly handed in before, she or he will loose all points achieved so far!

Every problem correctly presented at the blackboard results in an additional point.

We expect students that hand in their exercise to be present at the exercise. If for some reason you cannot attend the exercise in person, we find some solution just for you….

Let us know in advance.

Procedure Furthermore, there will be a written

test (midterm) midth of the semesterTu.18.12.2018 at 15:00-18:00 in EI9.

Here, you have to prove your “skills” It should be very easy for those that

invested time in exercises. The participation of the midterm is

optional!18

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19Univ.-Prof. Dr.-Ing.

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LiteratureMain text book:

T.K.Moon and W.C.Stirling: Mathematical Methods and Algorithms for Signal Processing

Alternative: M. Vetterli, J. Kovacevic, and V. K. Goyal,Signal Processing: Foundationshttp://www.fourierandwavelets.org

Also for basics (part I of V): Papoulis, Signal Analysis, McGraw Hill Proakis, Manolakis, Digital Signal Processing Oppenheim, Schafer, Discrete Time Signal Processing Unbehauen, Systemtheorie, Oldenbourg

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Literature Script:

~200 page additional scriptum (23Euro) and in form of a slide collection https://www.nt.tuwien.ac.at/teaching/wi

nter-term/389-166/ Or alternatively printed for you at the

graphical center (Graphisches Zentrum) Freihaus.

Erasmus There is still open places for the

summer (SS18) Some exciting destinations

EURECOM/Sophia Antipolis, Cannes, Master-LVA in Telecommunications in English

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Other…

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To motivate you a bit to keep youawake, I considered to release

two or three black mambas beforethe start of the lectures…

After this lecture For those who did not do the

Bachelor studies with us, please notethat on October 5 at noon, there will be a welcome/infomation meeting in Lecture hall EI2

Learn all the ropes needed at TU Wien, Telecom Masters

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Markus Rupp