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#40234 / #3

SS 2017 - WiSe 2023/24

English

Compressive Sensing and Inverse Problems in Signal Processing

6

Caire, Giuseppe

benotet

Portfolioprüfung

Zugehörigkeit


Fakultät IV

Institut für Telekommunikationssysteme

34331600 FG Theoretische Grundlagen der Kommunikationstechnik

No information

Kontakt


HFT 6

Jung, Peter

Peter.Jung@tu-berlin.de

Learning Outcomes

In this module the students will learn the theory of compressed sensing (CS) and sparse approximation and will become familar with certain inverse problems and CS algorithms in signal processing, mostly in the area of wireless communication and information processing. The module is formed by two courses in sequence. In the first course "Compressed Sensing" the basic theory is presented with focus on the mathematical concepts and tools. The second course "Sparse Signal Processing, Applications and Algorithms" is a seminar where in addition also students will read and work on recent papers in area beyond standard compressed sensing and present concepts in a talk (project presentation). The topics will range from non-standard algorithms to special inverse problems in signal processing (some sample topics are given below).

Content

- Basic theory of compressed including: sparse solutions to underdetermined linear equations, coherence, Welch-bounds, frames and redundancy, union of bases, nullspace property and best k-term approximation, noisy sparse estimation, the restricted isometry property (RIP), random matrices and the RIP property (stable low-dimensional embeddings), l1-minimization and algorithms (BPDN, LASSO) - Advanced topics in compressed sensing (partially in the form seminar work and paper reading) including: Structured measurements, Graph-based constructions and special recovery/decoding algorithms, (approximate) message passing algorithms, embdedding and recovery low-dimensional/low-rank signal structures

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Compressed SensingVL0432 L 664WiSeNo information2
Sparse Signal Processing, Applications and AlgorithmsSEM34331600 L 009SoSeNo information2

Workload and Credit Points

Compressed Sensing (VL):

Workload descriptionMultiplierHoursTotal
Attendance time15.02.0h30.0h
Examination preparation1.030.0h30.0h
Preparation and postprocessing15.02.0h30.0h
90.0h(~3 LP)

Sparse Signal Processing, Applications and Algorithms (SEM):

Workload descriptionMultiplierHoursTotal
Attendance time15.02.0h30.0h
Examination preparation1.030.0h30.0h
Preparation and postprocessing15.02.0h30.0h
90.0h(~3 LP)
The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

The module consists of (i) conventional frontal teaching in class, developing theoretical and mathematical concepts, (ii) paper reading (homework) and presentation in class and (iii) discussion of applications and exercises in order to develop problem-solving skills and reinforce comprehension of the theory.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Prerequisite for participation to courses are a mathematical background at the level of MS students (mid semester) in Electrical Engineering (multivariate calculus, Fourier transforms, signals and systems, linear algebra and notions of matrix theory, probability theory). The course is open to students enrolled in any MSc in EE CS, Mathematics and Physics

Mandatory requirements for the module test application:

This module has no requirements.

Module completion

Grading

graded

Type of exam

Portfolio examination

Type of portfolio examination

No information

Language

English

Test elements

NamePoints/WeightCategorieDuration/Extent
(Deliverable assessment) Project presentation50oral20min
(Examination) Oral examination/discussion50oral20min

Grading scale

Notenschlüssel »Notenschlüssel 3: Fak IV (3)«

Gesamtpunktzahl1.01.31.72.02.32.73.03.33.74.0
100.0pt85.0pt80.0pt75.0pt70.0pt65.0pt60.0pt55.0pt50.0pt45.0pt40.0pt

Test description (Module completion)

The final grade according to § 47 (2) AllgStuPO will be calculated according to Notenschlüssel 3 of Faculty IV.

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
2 Semester.

This module may be commenced in the following semesters:
Winter- und Sommersemester.

Maximum Number of Participants

This module is not limited to a number of students.

Registration Procedures

Course teaching and organization (not module examination enrollment at Examination office/Prüfungsamt) is supported by an ISIS course. Registration details are provided at the beginning of the module.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  available

 

Literature

Recommended literature
S. Foucart and H. Rauhut, "A Mathematical Introduction to Compressive Sensing"
Y. Eldar and G. Kutyniok, "Compressed Sensing: Theory and Applications"

Assigned Degree Programs


This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Computer Engineering (M. Sc.)182SS 2017WiSe 2023/24
Computer Science (Informatik) (M. Sc.)122WS 2017/18WiSe 2023/24
Elektrotechnik (M. Sc.)169SS 2017WiSe 2023/24
Information Systems Management (Wirtschaftsinformatik) (M. Sc.)113WS 2017/18WiSe 2023/24
Technomathematik (B. Sc.)113WS 2017/18WiSe 2023/24
Technomathematik (M. Sc.)113WS 2017/18WiSe 2023/24
Wirtschaftsingenieurwesen (M. Sc.)147WS 2017/18WiSe 2023/24

Students of other degrees can participate in this module without capacity testing.

Miscellaneous

No information