Module Manual

Master

Computational Science and Engineering

Cohort: Winter Term 2017

Updated: 8th July 2017

Program description

Content


Core qualification

Module M0523: Business & Management

Module Responsible Prof. Matthias Meyer
Admission Requirements None
Recommended Previous Knowledge None
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students are able to find their way around selected special areas of management within the scope of business management.
  • Students are able to explain basic theories, categories, and models in selected special areas of business management.
  • Students are able to interrelate technical and management knowledge.


Skills
  • Students are able to apply basic methods in selected areas of business management.
  • Students are able to explain and give reasons for decision proposals on practical issues in areas of business management.


Personal Competence
Social Competence
Autonomy
  • Students are capable of acquiring necessary knowledge independently by means of research and preparation of material.


Workload in Hours Depends on choice of courses
Credit points 6
Courses
Information regarding lectures and courses can be found in the corresponding module handbook published separately.

Module M0524: Nontechnical Elective Complementary Courses for Master

Module Responsible Dagmar Richter
Admission Requirements None
Recommended Previous Knowledge None
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The Nontechnical Academic Programms (NTA)

imparts skills that, in view of the TUHH’s training profile, professional engineering studies require but are not able to cover fully. Self-reliance, self-management, collaboration and professional and personnel management competences. The department implements these training objectives in its teaching architecture, in its teaching and learning arrangements, in teaching areas and by means of teaching offerings in which students can qualify by opting for specific competences and a competence level at the Bachelor’s or Master’s level. The teaching offerings are pooled in two different catalogues for nontechnical complementary courses.

The Learning Architecture

consists of a cross-disciplinarily study offering. The centrally designed teaching offering ensures that courses in the nontechnical academic programms follow the specific profiling of TUHH degree courses.

The learning architecture demands and trains independent educational planning as regards the individual development of competences. It also provides orientation knowledge in the form of “profiles”.

The subjects that can be studied in parallel throughout the student’s entire study program - if need be, it can be studied in one to two semesters. In view of the adaptation problems that individuals commonly face in their first semesters after making the transition from school to university and in order to encourage individually planned semesters abroad, there is no obligation to study these subjects in one or two specific semesters during the course of studies.

Teaching and Learning Arrangements

provide for students, separated into B.Sc. and M.Sc., to learn with and from each other across semesters. The challenge of dealing with interdisciplinarity and a variety of stages of learning in courses are part of the learning architecture and are deliberately encouraged in specific courses.

Fields of Teaching

are based on research findings from the academic disciplines cultural studies, social studies, arts, historical studies, communication studies, migration studies and sustainability research, and from engineering didactics. In addition, from the winter semester 2014/15 students on all Bachelor’s courses will have the opportunity to learn about business management and start-ups in a goal-oriented way.

The fields of teaching are augmented by soft skills offers and a foreign language offer. Here, the focus is on encouraging goal-oriented communication skills, e.g. the skills required by outgoing engineers in international and intercultural situations.

The Competence Level

of the courses offered in this area is different as regards the basic training objective in the Bachelor’s and Master’s fields. These differences are reflected in the practical examples used, in content topics that refer to different professional application contexts, and in the higher scientific and theoretical level of abstraction in the B.Sc.

This is also reflected in the different quality of soft skills, which relate to the different team positions and different group leadership functions of Bachelor’s and Master’s graduates in their future working life.

Specialized Competence (Knowledge)

Students can

  • explain specialized areas in context of the relevant non-technical disciplines,
  • outline basic theories, categories, terminology, models, concepts or artistic techniques in the disciplines represented in the learning area,
  • different specialist disciplines relate to their own discipline and differentiate it as well as make connections, 
  • sketch the basic outlines of how scientific disciplines, paradigms, models, instruments, methods and forms of representation in the specialized sciences are subject to individual and socio-cultural interpretation and historicity,
  • Can communicate in a foreign language in a manner appropriate to the subject.
Skills

Professional Competence (Skills)

In selected sub-areas students can

  • apply basic and specific methods of the said scientific disciplines,
  • aquestion a specific technical phenomena, models, theories from the viewpoint of another, aforementioned specialist discipline,
  • to handle simple and advanced questions in aforementioned scientific disciplines in a sucsessful manner,
  • justify their decisions on forms of organization and application in practical questions in contexts that go beyond the technical relationship to the subject.



Personal Competence
Social Competence

Personal Competences (Social Skills)

Students will be able

  • to learn to collaborate in different manner,
  • to present and analyze problems in the abovementioned fields in a partner or group situation in a manner appropriate to the addressees,
  • to express themselves competently, in a culturally appropriate and gender-sensitive manner in the language of the country (as far as this study-focus would be chosen), 
  • to explain nontechnical items to auditorium with technical background knowledge.





Autonomy

Personal Competences (Self-reliance)

Students are able in selected areas

  • to reflect on their own profession and professionalism in the context of real-life fields of application
  • to organize themselves and their own learning processes      
  • to reflect and decide questions in front of a broad education background
  • to communicate a nontechnical item in a competent way in writen form or verbaly
  • to organize themselves as an entrepreneurial subject country (as far as this study-focus would be chosen)     



Workload in Hours Depends on choice of courses
Credit points 6
Courses
Information regarding lectures and courses can be found in the corresponding module handbook published separately.

Module M0804: Research Project and Seminar

Courses
Title Typ Hrs/wk CP
Project Work (L1761) Projection Course 10 15
Seminar (L0817) Seminar 2 3
Module Responsible Prof. Karl-Heinz Zimmermann
Admission Requirements None
Recommended Previous Knowledge Basic knowledge and techniques in the chosen field of specialization.
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Students are able to acquire advanced knowledge in a specific field of Computer Science or a closely related subject.
Skills Students are able to work self-dependent in a field of Computer Science or a closely related field.
Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 372, Study Time in Lecture 168
Credit points 18
Examination according to Subject Specific Regulations
Examination duration and scale Presentation on a current research topic (25-30 min and 5 min discussion). The research work is a project work according to the statutes of the ASPO and FSPO.
Assignment for the Following Curricula Computer Science: Core qualification: Compulsory
Computational Science and Engineering: Core qualification: Compulsory
Information and Communication Systems: Core qualification: Compulsory
Course L1761: Project Work
Typ Projection Course
Hrs/wk 10
CP 15
Workload in Hours Independent Study Time 310, Study Time in Lecture 140
Lecturer Dozenten des SD E
Language DE/EN
Cycle WiSe
Content

Current research topics of the chosen specialization.

Literature

Aktuelle Literatur zu Forschungsthemen aus der gewählten Vertiefungsrichtung.
/
Current literature on research topics of the chosen specialization.

Course L0817: Seminar
Typ Seminar
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Dozenten des SD E
Language DE/EN
Cycle WiSe
Content
  • Seminar presentations by enrolled students about the research work carried out by the students
  • Active participation in discussions
Literature Wird vom Veranstalter bekanntgegeben.

Specialization Information and Communication Technology

Module M1244: Technical Complementary Course I for IIWMS (according to Subject Specific Regulations)

Courses
Title Typ Hrs/wk CP
Module Responsible Prof. Volker Turau
Admission Requirements None
Recommended Previous Knowledge None
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students acquire advanced knowledge in a technical subject available at TUHH.

Skills The students acquire professional competence in a technical subject available at TUHH.
Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 180, Study Time in Lecture 0
Credit points 6
Examination according to Subject Specific Regulations
Examination duration and scale
Assignment for the Following Curricula Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory

Module M1336: Soft Computing

Courses
Title Typ Hrs/wk CP
Soft Computing (L1869) Lecture 4 6
Module Responsible Prof. Karl-Heinz Zimmermann
Admission Requirements None
Recommended Previous Knowledge
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
Skills
Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation General Process Engineering: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation Bioprocess Engineering: Elective Compulsory
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
Course L1869: Soft Computing
Typ Lecture
Hrs/wk 4
CP 6
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Lecturer Prof. Karl-Heinz Zimmermann
Language DE/EN
Cycle WiSe
Content
Literature

Module M0667: Algorithmic Algebra

Courses
Title Typ Hrs/wk CP
Algorithmic Algebra (L0422) Lecture 3 5
Algorithmic Algebra (L0423) Recitation Section (small) 1 1
Module Responsible Dr. Prashant Batra
Admission Requirements None
Recommended Previous Knowledge

Mathe I-III (Real analysis,computing in Vector spaces , principle of complete induction)  Diskrete Mathematik I (gropus, rings, ideals, fields; euclidean algorithm)

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students can discuss logical connections between the following concepts and explain them by means of examples: Smith normal form, Chinese remainder theorem, grid point sets, integer solution of inequality systems.

Skills

Students are able to access independently further logical connections between the concepts with which they have become familiar and are able to verify them.

Students are able to develop a suitable solution approach to given problems, to pursue it and to evaluate the results critically, such as in solving multivariate equation systems and in grid point theory.

Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Course L0422: Algorithmic Algebra
Typ Lecture
Hrs/wk 3
CP 5
Workload in Hours Independent Study Time 108, Study Time in Lecture 42
Lecturer Dr. Prashant Batra
Language DE
Cycle WiSe
Content

Extended euclidean algorithm, solution of the Bezout-equation

Division with remainder (over rings)

 fast arithmetic algorithms (conversion, fast multiplications)

discrete Fourier-transformation over rings

Computation with  modular remainders, solving of remainder systems (chinese remainder theorem), solvability of integer linear systems over the integers

linearization of polynomial equations-- matrix approach

Sylvester-matrix, elimination

elimination in rings, elimination of many variables

Buchberger algorithm, Gröbner basis

Minkowskis Lattice Point theorem and integer-valued  optimization

LLL-algorithm for construction of  'short' lattice vectors in polynomial time

Literature von zur Gathen, Joachim; Gerhard, Jürgen

Modern computer algebra. 3rd ed. (English) Zbl 1277.68002
Cambridge: Cambridge University Press (ISBN 978-1-107-03903-2/hbk; 978-1-139-85606-5/ebook).


Yap, Chee Keng
Fundamental problems of algorithmic algebra. (English) Zbl 0999.68261
Oxford: Oxford University Press. xvi, 511 p. $ 87.00 (2000).


Free download for students from author's website: http://cs.nyu.edu/yap/book/berlin/

Cox, David; Little, John; O’Shea, Donal
Ideals, varieties, and algorithms. An introduction to computational algebraic geometry and commutative algebra. 3rd ed. (English) Zbl 1118.13001
Undergraduate Texts in Mathematics. New York, NY: Springer (ISBN 978-0-387-35650-1/hbk; 978-0-387-35651-8/ebook). xv, 551 p.

eBook: http://dx.doi.org/10.1007/978-0-387-35651-8


Concrete abstract algebra : from numbers to Gröbner bases / Niels Lauritzen
Verfasser: 
Lauritzen, Niels
Ausgabe: 
Reprinted with corr.
Erschienen: 
Cambridge [u.a.] : Cambridge Univ. Press, 2006
Umfang: 
XIV, 240 S. : graph. Darst.
Anmerkung: 
Includes bibliographical references and index
ISBN: 
0-521-82679-9, 978-0-521-82679-2 (hbk.) : GBP 55.00
0-521-53410-0, 978-0-521-53410-9 (pbk.) : USD 39.99

Koepf, Wolfram
Computer algebra. An algorithmic oriented introduction. (Computeralgebra. Eine algorithmisch orientierte Einführung.) (German) Zbl 1161.68881
Berlin: Springer (ISBN 3-540-29894-0/pbk). xiii, 515 p.

springer eBook: http://dx.doi.org/10.1007/3-540-29895-9

Kaplan, Michael
Computer algebra. (Computeralgebra.) (German) Zbl 1093.68148
Berlin: Springer (ISBN 3-540-21379-1/pbk). xii, 391 p.

springer eBook:

http://dx.doi.org/10.1007/b137968



Course L0423: Algorithmic Algebra
Typ Recitation Section (small)
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Dr. Prashant Batra
Language DE
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0676: Digital Communications

Courses
Title Typ Hrs/wk CP
Digital Communications (L0444) Lecture 2 3
Digital Communications (L0445) Recitation Section (large) 1 2
Laboratory Digital Communications (L0646) Laboratory Course 1 1
Module Responsible Prof. Gerhard Bauch
Admission Requirements None
Recommended Previous Knowledge
  • Mathematics 1-3
  • Signals and Systems
  • Fundamentals of Communications and Random Processes
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge The students are able to understand, compare and design modern digital information transmission schemes. They are familiar with the properties of linear and non-linear digital modulation methods. They can describe distortions caused by transmission channels and design and evaluate detectors including channel estimation and equalization. They know the principles of single carrier transmission and multi-carrier transmission as well as the fundamentals of basic multiple access schemes.
Skills The students are able to design and analyse a digital information transmission scheme including multiple access. They are able to choose a digital modulation scheme taking into account transmission rate, required bandwidth, error probability, and further signal properties. They can design an appropriate detector including channel estimation and equalization taking into account performance and complexity properties of suboptimum solutions. They are able to set parameters of a single carrier or multi carrier transmission scheme and trade the properties of both approaches against each other.
Personal Competence
Social Competence

The students can jointly solve specific problems.

Autonomy

The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Electrical Engineering: Core qualification: Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems: Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory
Course L0444: Digital Communications
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Gerhard Bauch
Language DE/EN
Cycle WiSe
Content
  • Digital modulation methods

  • Coherent and non-coherent detection

  • Channel estimation and equalization

  • Single-Carrier- and multi carrier transmission schemes, multiple access schemes (TDMA, FDMA, CDMA, OFDM)

Literature

K. Kammeyer: Nachrichtenübertragung, Teubner

P.A. Höher: Grundlagen der digitalen Informationsübertragung, Teubner.

J.G. Proakis, M. Salehi: Digital Communications. McGraw-Hill.

S. Haykin: Communication Systems. Wiley

R.G. Gallager: Principles of Digital Communication. Cambridge

A. Goldsmith: Wireless Communication. Cambridge.

D. Tse, P. Viswanath: Fundamentals of Wireless Communication. Cambridge.

Course L0445: Digital Communications
Typ Recitation Section (large)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Gerhard Bauch
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course
Course L0646: Laboratory Digital Communications
Typ Laboratory Course
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Gerhard Bauch
Language DE/EN
Cycle WiSe
Content

- DSL transmission

- Random processes

- Digital data transmission

Literature

K. Kammeyer: Nachrichtenübertragung, Teubner

P.A. Höher: Grundlagen der digitalen Informationsübertragung, Teubner.

J.G. Proakis, M. Salehi: Digital Communications. McGraw-Hill.

S. Haykin: Communication Systems. Wiley

R.G. Gallager: Principles of Digital Communication. Cambridge

A. Goldsmith: Wireless Communication. Cambridge.

D. Tse, P. Viswanath: Fundamentals of Wireless Communication. Cambridge.

Module M0836: Communication Networks I - Analysis and Structure

Courses
Title Typ Hrs/wk CP
Analysis and Structure of Communication Networks (L0897) Lecture 2 2
Selected Topics of Communication Networks (L0899) Problem-based Learning 2 2
Communication Networks Excercise (L0898) Problem-based Learning 1 2
Module Responsible Prof. Andreas Timm-Giel
Admission Requirements None
Recommended Previous Knowledge
  • Fundamental stochastics
  • Basic understanding of computer networks and/or communication technologies is beneficial
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to describe the principles and structures of communication networks in detail. They can explain the formal description methods of communication networks and their protocols. They are able to explain how current and complex communication networks work and describe the current research in these examples.

Skills

Students are able to evaluate the performance of communication networks using the learned methods. They are able to work out problems themselves and apply the learned methods. They can apply what they have learned autonomously on further and new communication networks.

Personal Competence
Social Competence

Students are able to define tasks themselves in small teams and solve these problems together using the learned methods. They can present the obtained results. They are able to discuss and critically analyse the solutions.

Autonomy

Students are able to obtain the necessary expert knowledge for understanding the functionality and performance capabilities of new communication networks independently.

Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Examination Colloquium
Examination duration and scale 1.5 hours colloquium with three students, therefore about 30 min per student. Topics of the colloquium are the posters from the previous poster session and the topics of the module.
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Electrical Engineering: Specialisation Control and Power Systems: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory
Course L0897: Analysis and Structure of Communication Networks
Typ Lecture
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Andreas Timm-Giel
Language EN
Cycle WiSe
Content
Literature
  • Skript des Instituts für Kommunikationsnetze
  • Tannenbaum, Computernetzwerke, Pearson-Studium


Further literature is announced at the beginning of the lecture.

Course L0899: Selected Topics of Communication Networks
Typ Problem-based Learning
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Dr. Maciej Mühleisen
Language EN
Cycle WiSe
Content Example networks selected by the students will be researched on in a PBL course by the students in groups and will be presented in a poster session at the end of the term.
Literature
  • see lecture
Course L0898: Communication Networks Excercise
Typ Problem-based Learning
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Dr. Maciej Mühleisen
Language EN
Cycle WiSe
Content Part of the content of the lecture Communication Networks are reflected in computing tasks in groups, others are motivated and addressed in the form of a PBL exercise.
Literature
  • announced during lecture

Module M0586: Efficient Algorithms

Courses
Title Typ Hrs/wk CP
Efficient Algorithms (L0120) Lecture 2 3
Efficient Algorithms (L1207) Recitation Section (small) 2 3
Module Responsible Prof. Siegfried Rump
Admission Requirements None
Recommended Previous Knowledge

Programming in Matlab and/or C

Basic knowledge in discrete mathematics
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students are able to explain the basic theory and methods of network algorithms and in particular their data structures. They are able to analyze the computational behavior and computing time of linear programming algorithms as well network algorithms. Moreover the students can distinguish between efficiently solvable and NP-hard problems.

Skills

The students are able to analyze complex tasks and can determine possibilities to transform them into networking algorithms. In particular they can efficiently implement basic algorithms and data structures of LP- and network algorithms and identify possible weaknesses. They are able to distinguish between different efficient data structures and are able to use them appropriately.

Personal Competence
Social Competence

The students have the skills to solve problems together in small groups and to present the achieved results in an appropriate manner.

Autonomy

The students are able to retrieve necessary informations from the given literature and to combine them with the topics of the lecture. Throughout the lecture they can check their abilities and knowledge on the basis of given exercises and test questions providing an aid to optimize their learning process.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Electrical Engineering: Specialisation Modeling and Simulation: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Course L0120: Efficient Algorithms
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Siegfried Rump
Language DE
Cycle WiSe
Content

- Linear Programming

- Data structures

- Leftist heaps

- Minimum spanning tree

- Shortest path

- Maximum flow

- NP-hard problems via max-cut

Literature

R. E. Tarjan: Data Structures and Network Algorithms. CBMS 44, Society for Industrial and Applied Mathematics, Philadelphia, PA, 1983.

Wesley, 2011 http://algs4.cs.princeton.edu/home/

V. Chvátal, ``Linear Programming'', Freeman, New York, 1983.

Course L1207: Efficient Algorithms
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Siegfried Rump
Language DE
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0926: Distributed Algorithms

Courses
Title Typ Hrs/wk CP
Distributed Algorithms (L1071) Lecture 2 3
Distributed Algorithms (L1072) Recitation Section (large) 2 3
Module Responsible Prof. Volker Turau
Admission Requirements None
Recommended Previous Knowledge
  • Algorithms and data structures
  • Distributed systems
  • Discrete mathematics
  • Graph theory
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Students know the main abstractions of distributed algorithms (synchronous/asynchronous model, message passing and shared memory model). They are able to describe complexity measures for distributed algorithms (round , message and memory complexity). They explain well known distributed algorithms for important problems such as leader election, mutual exclusion, graph coloring, spanning trees. They know the fundamental techniques used for randomized algorithms.
Skills Students design their own distributed algorithms and analyze their complexity. They make use of known standard algorithms. They compute the complexity of randomized algorithms.
Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 45 min
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Course L1071: Distributed Algorithms
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Volker Turau
Language DE/EN
Cycle WiSe
Content
  • Leader Election
  • Colorings & Independent Sets
  • Tree Algorithms
  • Minimal Spanning Trees
  • Randomized Distributed Algorithms
  • Mutual Exclusion
Literature
  1. David Peleg: Distributed Computing - A Locality-Sensitive Approach. SIAM Monograph, 2000

  2. Gerard Tel: Introduction to Distributed Algorithms, Cambridge University Press, 2nd edition, 2000
  3. Nancy Lynch: Distributed Algorithms. Morgan Kaufmann, 1996
  4. Volker Turau: Algorithmische Graphentheorie. Oldenbourg Wissenschaftsverlag, 3. Auflage, 2004.


Course L1072: Distributed Algorithms
Typ Recitation Section (large)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Volker Turau
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0942: Software Security

Courses
Title Typ Hrs/wk CP
Software Security (L1103) Lecture 2 3
Software Security (L1104) Recitation Section (small) 2 3
Module Responsible Prof. Dieter Gollmann
Admission Requirements None
Recommended Previous Knowledge Familiarity with C/C++, web programming
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students can 

  • name the main causes for security vulnerabilities in software 
  • explain current methods for identifying and avoiding security vulnerabilities 
  • explain the fundamental concepts of code-based access control 
Skills

Students are capable of 

  • performing a software vulnerability analysis 
  • developing secure code 
Personal Competence
Social Competence None
Autonomy Students are capable of acquiring knowledge independently from professional publications, technical  standards, and other sources, and are capable of applying newly acquired knowledge to new problems. 
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 120 minutes
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory
Course L1103: Software Security
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Dieter Gollmann
Language EN
Cycle WiSe
Content
  • Reliabilty and Software Security
  • Attacks exploiting character and integer representations
  • Buffer overruns
  • Vulnerabilities in memory managemet: double free attacks
  • Race conditions
  • SQL injection
  • Cross-site scripting and cross-site request forgery
  • Testing for security; taint analysis
  • Type safe languages
  • Development proceses for secure software
  • Code-based access control


Literature

M. Howard, D. LeBlanc: Writing Secure Code, 2nd edition, Microsoft Press (2002)

G. Hoglund, G. McGraw: Exploiting Software, Addison-Wesley (2004)

L. Gong, G. Ellison, M. Dageforde: Inside Java 2 Platform Security, 2nd edition, Addison-Wesley (2003)

B. LaMacchia, S. Lange, M. Lyons, R. Martin, K. T. Price: .NET Framework Security, Addison-Wesley Professional (2002)

D. Gollmann: Computer Security, 3rd edition (2011)


Course L1104: Software Security
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Dieter Gollmann
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0753: Software Verification

Courses
Title Typ Hrs/wk CP
Software Verification (L0629) Lecture 2 3
Software Verification (L0630) Recitation Section (small) 2 3
Module Responsible Prof. Sibylle Schupp
Admission Requirements None
Recommended Previous Knowledge
  • Automata theory and formal languages
  • Computational logic
  • Object-oriented programming, algorithms, and data structures
  • Functional programming or procedural programming
  • Concurrency
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students apply the major verification techniques in model checking and deductive verification. They explain in formal terms syntax and semantics of the underlying logics, and assess the expressivity of different logics as well as their limitations. They classify formal properties of software systems. They find flaws in formal arguments, arising from modeling artifacts or underspecification. 

Skills

Students formulate provable properties of a software system in a formal language. They develop logic-based models that properly abstract from the software under verification and, where necessary, adapt model or property. They construct proofs and property checks by hand or using tools for model checking or deductive verification, and reflect on the scope of the results. Presented with a verification problem in natural language, they select the appropriate verification technique and justify their choice.   

Personal Competence
Social Competence

Students discuss relevant topics in class. They defend their solutions orally. They communicate in English. 

Autonomy

Using accompanying on-line material for self study, students can assess their level of knowledge continuously and adjust it appropriately.  Working on exercise problems, they receive additional feedback. Within limits, they can set their own learning goals. Upon successful completion, students can identify and precisely formulate new problems in academic or applied research in the field of software verification. Within this field, they can conduct independent studies to acquire the necessary competencies and compile their findings in academic reports. They can devise plans to arrive at new solutions or assess existing ones. 

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
Course L0629: Software Verification
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sibylle Schupp
Language EN
Cycle WiSe
Content
  • Syntax and semantics of logic-based systems
  • Deductive verification
    • Specification
    • Proof obligations
    • Program properties
    • Automated vs. interactive theorem proving
  • Model checking
    • Foundations
    • Property languages
    • Tool support
  • Timed automata
  • Recent developments of verification techniques and applications
Literature
  • C. Baier and J-P. Katoen, Principles of Model Checking, MIT Press 2007.
  • M. Huth and M. Bryan, Logic in Computer Science. Modelling and Reasoning about Systems, 2nd Edition, 2004.
  • Selected Research Papers
Course L0630: Software Verification
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sibylle Schupp
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1245: Technical Complementary Course II for IIWMS (according to Subject Specific Regulations)

Courses
Title Typ Hrs/wk CP
Module Responsible Prof. Volker Turau
Admission Requirements None
Recommended Previous Knowledge None
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students acquire advanced knowledge in a technical subject available at TUHH.

Skills

The students acquire professional competence in a technical subject available at TUHH.

Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 180, Study Time in Lecture 0
Credit points 6
Examination according to Subject Specific Regulations
Examination duration and scale
Assignment for the Following Curricula Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory

Module M1337: Codes and Cryptosystems

Courses
Title Typ Hrs/wk CP
Codes and Cryptosystems (L1870) Lecture 4 6
Module Responsible Prof. Karl-Heinz Zimmermann
Admission Requirements None
Recommended Previous Knowledge
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
Skills
Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Course L1870: Codes and Cryptosystems
Typ Lecture
Hrs/wk 4
CP 6
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Lecturer Prof. Karl-Heinz Zimmermann
Language DE/EN
Cycle SoSe
Content
Literature

Module M1318: Wireless Sensor Networks

Courses
Title Typ Hrs/wk CP
Selected Topics of Wireless Sensor Networks (L1819) Problem-based Learning 1 2
Wireless Sensor Networks (L1815) Lecture 2 2
Wireless Sensor Networks (L1816) Recitation Section (small) 1 2
Module Responsible Prof. Bernd-Christian Renner
Admission Requirements None
Recommended Previous Knowledge
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
Skills
Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Course L1819: Selected Topics of Wireless Sensor Networks
Typ Problem-based Learning
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Bernd-Christian Renner
Language EN
Cycle SoSe
Content

Selected topics on sensor network research will be researched in a PBL course by the students in groups and will be presented in a poster session at the end of the term. Topics are:

  • Energy-efficient / low-power Medium Access
  • Energy-efficient / low-power Routing (Data Collection and Data Dissemination)
  • Energy Harvesting
  • Intermittently Powered Sensor Nodes
  • Energy-Aware Load Adaptation and Scheduling
  • Additional Topics will be provided on demand / depending on the number of participants
Literature

Will be provided individually

Course L1815: Wireless Sensor Networks
Typ Lecture
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Bernd-Christian Renner
Language EN
Cycle SoSe
Content
Literature
Course L1816: Wireless Sensor Networks
Typ Recitation Section (small)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Bernd-Christian Renner
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0637: Advanced Concepts of Wireless Communications

Courses
Title Typ Hrs/wk CP
Advanced Concepts of Wireless Communications (L0297) Lecture 3 4
Advanced Concepts of Wireless Communications (L0298) Recitation Section (large) 1 2
Module Responsible Dr. Rainer Grünheid
Admission Requirements None
Recommended Previous Knowledge
  • Lecture "Signals and Systems"
  • Lecture "Fundamentals of Telecommunications and Stochastic Processes"
  • Lecture "Digital Communications"
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Students are able to explain the general as well as advanced principles and techniques that are applied to wireless communications. They understand the properties of wireless channels and the corresponding mathematical description. Furthermore, students are able to explain the physical layer of wireless transmission systems. In this context, they are proficient in the concepts of multicarrier transmission (OFDM), modulation, error control coding, channel estimation and multi-antenna techniques (MIMO). Students can also explain methods of multiple access. On the example of contemporary communication systems (UMTS, LTE) they can put the learnt content into a larger context.
Skills

Using the acquired knowledge, students are able to understand the design of current and future wireless systems. Moreover, given certain constraints, they can choose appropriate parameter settings of communication systems. Students are also able to assess the suitability of technical concepts for a given application.

Personal Competence
Social Competence Students can jointly elaborate tasks in small groups and present their results in an adequate fashion.
Autonomy Students are able to extract necessary information from given literature sources and put it into the perspective of the lecture. They can continuously check their level of expertise with the help of accompanying measures (such as online tests, clicker questions, exercise tasks) and, based on that, to steer their learning process accordingly. They can relate their acquired knowledge to topics of other lectures, e.g., "Fundamentals of Communications and Stochastic Processes" and "Digital Communications".
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 90 minutes; scope: content of lecture and exercise
Assignment for the Following Curricula Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory
Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory
Course L0297: Advanced Concepts of Wireless Communications
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Dr. Rainer Grünheid
Language EN
Cycle SoSe
Content

The lecture deals with technical principles and related concepts of mobile communications. In this context, the main focus is put on the physical and data link layer of the ISO-OSI stack.

In the lecture, the transmission medium, i.e., the mobile radio channel, serves as the starting point of all considerations. The characteristics and the mathematical descriptions of the radio channel are discussed in detail. Subsequently, various physical layer aspects of wireless transmission are covered, such as channel coding, modulation/demodulation, channel estimation, synchronization, and equalization. Moreover, the different uses of multiple antennas at the transmitter and receiver, known as MIMO techniques, are described. Besides these physical layer topics, concepts of multiple access schemes in a cellular network are outlined.

In order to illustrate the above-mentioned technical solutions, the lecture will also provide a system view, highlighting the basics of some contemporary wireless systems, including UMTS/HSPA, LTE, LTE Advanced, and WiMAX.


Literature

John G. Proakis, Masoud Salehi: Digital Communications. 5th Edition, Irwin/McGraw Hill, 2007

David Tse, Pramod Viswanath: Fundamentals of Wireless Communication. Cambridge, 2005

Bernard Sklar: Digital Communications: Fundamentals and Applications. 2nd Edition, Pearson, 2013

Stefani Sesia, Issam Toufik, Matthew Baker: LTE - The UMTS Long Term Evolution. Second Edition, Wiley, 2011

Course L0298: Advanced Concepts of Wireless Communications
Typ Recitation Section (large)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Dr. Rainer Grünheid
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1301: Software Testing

Courses
Title Typ Hrs/wk CP
Software Testing (L1791) Lecture 2 3
Software Testing (L1792) Problem-based Learning 2 3
Module Responsible Prof. Sibylle Schupp
Admission Requirements None
Recommended Previous Knowledge
  • Software Engineering
  • Higher Programming Languages
  • Object-Oriented Programming
  • Algorithms and Data Structures
  • Experience with (Small) Software Projects
  • Statistics
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
Students explain the different phases of testing, describe fundamental
techniques of different types of testing, and paraphrase the basic
principles of the corresponding test process. They give examples of
software development scenarios and the corresponding test type and
technique. They explain algorithms used for particular testing
techniques and describe possible advantages and limitations.
Skills
Students identify the appropriate testing type and technique for a given
problem. They adapt and execute respective algorithms to execute a
concrete test technique properly. They interpret testing results and
execute corresponding steps for proper re-test scenarios. They write and
analyze test specifications. They apply bug finding techniques for
non-trivial problems.
Personal Competence
Social Competence

Students discuss relevant topics in class. They defend their solutions orally.
They communicate in English.

Autonomy

Students can assess their level of knowledge continuously and adjust it appropriately, based on feedback and on self-guided studies. Within limits, they can set their own learning goals. Upon successful completion, students can identify and precisely formulate new problems in academic or applied research in the field of software testing. Within this field, they can conduct independent studies to acquire the necessary competencies and compile their findings in academic reports. They can devise plans to arrive at new solutions or assess existing ones

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Project
Examination duration and scale Software
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory
Course L1791: Software Testing
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sibylle Schupp
Language EN
Cycle SoSe
Content
  • Fundamentals of software testing
  • Regression-testing techniques
  • Search-based testing
  • Combinatorial testing
  • Product-line testing
  • Debugging
  • Model-based testing
Literature
  • M. Pezze and M. Young, Software Testing and Analysis, John Wiley 2008.
  • P. Ammann and J. Offutt, "Introduction to Software Testing", 2nd edition 2016.
  • A. Zeller: "Why Programs Fail: A Guide to Systematic Debugging", 2nd edition 2012.
Course L1792: Software Testing
Typ Problem-based Learning
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sibylle Schupp
Language EN
Cycle SoSe
Content
  • Fundamentals of software testing
  • Regression-testing techniques
  • Search-based testing
  • Combinatorial testing
  • Product-line testing
  • Debugging
  • Model-based testing
Literature
  • M. Pezze and M. Young, Software Testing and Analysis, John Wiley 2008.
  • P. Ammann and J. Offutt, "Introduction to Software Testing", 2nd edition 2015.
  • A. Zeller: "Why Programs Fail: A Guide to Systematic Debugging", 2nd edition 2012.

Module M0711: Numerical Mathematics II

Courses
Title Typ Hrs/wk CP
Numerical Mathematics II (L0568) Lecture 2 3
Numerical Mathematics II (L0569) Recitation Section (small) 2 3
Module Responsible Prof. Sabine Le Borne
Admission Requirements None
Recommended Previous Knowledge
  • Numerical Mathematics I
  • MATLAB knowledge
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to

  • name advanced numerical methods for interpolation, integration, linear least squares problems, eigenvalue problems, nonlinear root finding problems and explain their core ideas,
  • repeat convergence statements for the numerical methods,
  • sketch convergence proofs,
  • explain practical aspects of numerical methods concerning runtime and storage needs


    explain aspects regarding the practical implementation of numerical methods with respect to computational and storage complexity.


Skills

Students are able to

  • implement, apply and compare advanced numerical methods in MATLAB,
  • justify the convergence behaviour of numerical methods with respect to the problem and solution algorithm and to transfer it to related problems,
  • for a given problem, develop a suitable solution approach, if necessary through composition of several algorithms, to execute this approach and to critically evaluate the results


Personal Competence
Social Competence

Students are able to

  • work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge), explain theoretical foundations and support each other with practical aspects regarding the implementation of algorithms.
Autonomy

Students are capable

  • to assess whether the supporting theoretical and practical excercises are better solved individually or in a team,
  • to assess their individual progess and, if necessary, to ask questions and seek help.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Course L0568: Numerical Mathematics II
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sabine Le Borne, Dr. Patricio Farrell
Language DE/EN
Cycle SoSe
Content
  1. Error and stability: Notions and estimates
  2. Interpolation: Rational and trigonometric interpolation
  3. Quadrature: Gaussian quadrature, orthogonal polynomials
  4. Linear systems: Perturbation theory of decompositions, structured matrices
  5. Eigenvalue problems: LR-, QD-, QR-Algorithmus
  6. Krylov space methods: Arnoldi-, Lanczos methods
Literature
  • Stoer/Bulirsch: Numerische Mathematik 1, Springer
  • Dahmen, Reusken: Numerik für Ingenieure und Naturwissenschaftler, Springer
Course L0569: Numerical Mathematics II
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sabine Le Borne, Dr. Patricio Farrell
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1248: Compilers for Embedded Systems

Courses
Title Typ Hrs/wk CP
Compilers for Embedded Systems (L1692) Lecture 3 4
Compilers for Embedded Systems (L1693) Laboratory 1 2
Module Responsible Prof. Heiko Falk
Admission Requirements None
Recommended Previous Knowledge

Module "Embedded Systems"

C/C++ Programming skills

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The relevance of embedded systems increases from year to year. Within such systems, the amount of software to be executed on embedded processors grows continuously due to its lower costs and higher flexibility. Because of the particular application areas of embedded systems, highly optimized and application-specific processors are deployed. Such highly specialized processors impose high demands on compilers which have to generate code of highest quality. After the successful attendance of this course, the students are able

  • to illustrate the structure and organization of such compilers,
  • to distinguish and explain intermediate representations of various abstraction levels, and
  • to assess optimizations and their underlying problems in all compiler phases.

The high demands on compilers for embedded systems make effective code optimizations mandatory. The students learn in particular,

  • which kinds of optimizations are applicable at the source code level,
  • how the translation from source code to assembly code is performed,
  • which kinds of optimizations are applicable at the assembly code level,
  • how register allocation is performed, and
  • how memory hierarchies can be exploited effectively.

Since compilers for embedded systems often have to optimize for multiple objectives (e.g., average- or worst-case execution time, energy dissipation, code size), the students learn to evaluate the influence of optimizations on these different criteria.

Skills

After successful completion of the course, students shall be able to translate high-level program code into machine code. They will be enabled to assess which kind of code optimization should be applied most effectively at which abstraction level (e.g., source or assembly code) within a compiler.

While attending the labs, the students will learn to implement a fully functional compiler including optimizations.

Personal Competence
Social Competence

Students are able to solve similar problems alone or in a group and to present the results accordingly.

Autonomy

Students are able to acquire new knowledge from specific literature and to associate this knowledge with other classes.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Course L1692: Compilers for Embedded Systems
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Heiko Falk
Language DE/EN
Cycle SoSe
Content
  • Introduction and Motivation
  • Compilers for Embedded Systems - Requirements and Dependencies
  • Internal Structure of Compilers
  • Pre-Pass Optimizations
  • HIR Optimizations and Transformations
  • Code Generation
  • LIR Optimizations and Transformations
  • Register Allocation
  • WCET-Aware Compilation
  • Outlook
Literature
  • Peter Marwedel. Embedded System Design - Embedded Systems Foundations of Cyber-Physical Systems. 2nd Edition, Springer, 2012.
  • Steven S. Muchnick. Advanced Compiler Design and Implementation. Morgan Kaufmann, 1997.
  • Andrew W. Appel. Modern compiler implementation in C. Oxford University Press, 1998.
Course L1693: Compilers for Embedded Systems
Typ Laboratory
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Heiko Falk
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1304: Security in Embedded Hardware

Courses
Title Typ Hrs/wk CP
Security in Embedded Hardware (L1804) Lecture 2 3
Security in Embedded Hardware (L1805) Recitation Section (small) 2 3
Module Responsible Prof. Daniel Ziener
Admission Requirements None
Recommended Previous Knowledge

Computer Engineering

Basic knowledge in embedded systems

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Course coverage:

  • Attack scenarios
    • Examples of attack scenarios
    • Attacks on cryptographic algorithms and their implementations
  • Code injection attacks
    • Different type of code injection attacks
    • Countermeasures
  • Invasive physical attacks
    • Microprobing
    • Prevention and detection of single event effects
    • Reverse engineering
    • IP Protection
    • Watermarking
  • Non-invasive logical attacks
    • Phishing
    • Forged authenticity
    • Countermeasures
  • Non-invasive physical attacks
    • Eavesdroping
    • Side-channel attacks
  • Case study: Security in automotive applications


Skills


  • The students show the influence of attacks and the corresponding countermeasures on the dependability of embedded systems
  • The students describe the different countermeasures of attacks
  • The students summarize different security facilities and measures for embedded systems
  • The students show the overhead (area, time) of security facilities
  • The students classify different types of attack on embedded systems


Personal Competence
Social Competence
  • The students develop concepts in groups with subsequent implementations
Autonomy
  • The students acquire new knowledge from specific literature and to associate this knowledge with other classes.


Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Course L1804: Security in Embedded Hardware
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer NN
Language DE/EN
Cycle SoSe
Content

Course coverage:

  • Attack scenarios
    • Examples of attack scenarios
    • Attacks on cryptographic algorithms and their implementations
  • Code injection attacks
    • Different type of code injection attacks
    • Countermeasures
  • Invasive physical attacks
    • Microprobing
    • Prevention and detection of single event effects
    • Reverse engineering
    • IP Protection
    • Watermarking
  • Non-invasive logical attacks
    • Phishing
    • Forged authenticity
    • Countermeasures
  • Non-invasive physical attacks
    • Eavesdroping
    • Side-channel attacks
  • Case study: Security in automotive applications
Literature
  • Catherine H. Gebotys Security in Embedded Devices. Springer 2010.
  • Benoit Badrignans et al. Security Trends for FPGAs. Springer 2011.
  • Daniel Ziener Techniques for Increasing Security and Reliability of IP Cores Embedded in FPGA and ASIC Designs. Dr. Hut 2010.
Course L1805: Security in Embedded Hardware
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer NN
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0837: Communication Networks II - Simulation and Modeling

Courses
Title Typ Hrs/wk CP
Simulation and Modelling of Communication Networks (L0887) Problem-based Learning 5 6
Module Responsible Prof. Andreas Timm-Giel
Admission Requirements None
Recommended Previous Knowledge
  • Knowledge of computer and communication networks
  • Basic programming skills
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to explain the necessary stochastics, the discrete event simulation technology and modelling of networks for performance evaluation.

Skills

Students are able to apply the method of simulation for performance evaluation to different, also not practiced, problems of communication networks. The students can analyse the obtained results and explain the effects observed in the network. They are able to question their own results.

Personal Competence
Social Competence

Students are able to acquire expert knowledge in groups, present the results, and discuss solution approaches and results. They are able to work out solutions for new problems in small teams.

Autonomy

Students are able to transfer independently and in discussion with others the acquired method and expert knowledge to new problems. They can identify missing knowledge and acquire this knowledge independently.

Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Examination Colloquium
Examination duration and scale 45-60 minutes colloquium with two students, therefore about 30 minutes per student.
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory
Course L0887: Simulation and Modelling of Communication Networks
Typ Problem-based Learning
Hrs/wk 5
CP 6
Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Lecturer Prof. Andreas Timm-Giel
Language EN
Cycle SoSe
Content

In the course necessary basic stochastics and the discrete event simulation are introduced. Also simulation models for communication networks, for example, traffic models, mobility models and radio channel models are presented in the lecture. Students work with a simulation tool, where they can directly try out the acquired skills, algorithms and models. At the end of the course increasingly complex networks and protocols are considered and their performance is determined by simulation.

Literature
  • Skript des Instituts für Kommunikationsnetze

Further literature is announced at the beginning of the lecture.

Module M0673: Information Theory and Coding

Courses
Title Typ Hrs/wk CP
Information Theory and Coding (L0436) Lecture 3 4
Information Theory and Coding (L0438) Recitation Section (large) 1 2
Module Responsible Prof. Gerhard Bauch
Admission Requirements None
Recommended Previous Knowledge
  • Mathematics 1-3
  • Probability theory and random processes
  • Basic knowledge of communications engineering (e.g. from lecture "Fundamentals of Communications and Random Processes")
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge The students know the basic definitions for quantification of information in the sense of information theory. They know Shannon's source coding theorem and channel coding theorem and are able to determine theoretical limits of data compression and error-free data transmission over noisy channels. They understand the principles of source coding as well as error-detecting and error-correcting channel coding. They are familiar with the principles of decoding, in particular with modern methods of iterative decoding. They know fundamental coding schemes, their properties and decoding algorithms. 
Skills The students are able to determine the limits of data compression as well as of data transmission through noisy channels and based on those limits to design basic parameters of a transmission scheme. They can estimate the parameters of an error-detecting or error-correcting channel coding scheme for achieving certain performance targets. They are able to compare the properties of basic channel coding and decoding schemes regarding error correction capabilities, decoding delay, decoding complexity and to decide for a suitable method. They are capable of implementing basic coding and decoding schemes in software.
Personal Competence
Social Competence

The students can jointly solve specific problems.

Autonomy

The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Information and Communication Systems: Core qualification: Compulsory
International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Course L0436: Information Theory and Coding
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Gerhard Bauch
Language DE/EN
Cycle SoSe
Content
  • Fundamentals of information theory

    • Self information, entropy, mutual information

    • Source coding theorem, channel coding theorem

    • Channel capacity of various channels

  • Fundamental source coding algorithms:

    • Huffman Code, Lempel Ziv Algorithm

  • Fundamentals of channel coding

    • Basic parameters of channel coding and respective bounds

    • Decoding principles: Maximum-A-Posteriori Decoding, Maximum-Likelihood Decoding, Hard-Decision-Decoding and Soft-Decision-Decoding

    • Error probability

  • Block codes

  • Low Density Parity Check (LDPC) Codes and iterative Ddecoding

  • Convolutional codes and Viterbi-Decoding

  • Turbo Codes and iterative decoding

  • Coded Modulation

Literature

Bossert, M.: Kanalcodierung. Oldenbourg.

Friedrichs, B.: Kanalcodierung. Springer.

Lin, S., Costello, D.: Error Control Coding. Prentice Hall.

Roth, R.: Introduction to Coding Theory.

Johnson, S.: Iterative Error Correction. Cambridge.

Richardson, T., Urbanke, R.: Modern Coding Theory. Cambridge University Press.

Gallager, R. G.: Information theory and reliable communication. Whiley-VCH

Cover, T., Thomas, J.: Elements of information theory. Wiley.

Course L0438: Information Theory and Coding
Typ Recitation Section (large)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Gerhard Bauch
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1307: Cryptography

Courses
Title Typ Hrs/wk CP
Cryptography (L1806) Lecture 2 3
Cryptography (L1807) Recitation Section (small) 2 3
Module Responsible Prof. Chris Brzuska
Admission Requirements None
Recommended Previous Knowledge

Prerequisites:
Mathematical reasoning will be used throughout the course and is essential. It is helpful if you have been to introduction to IT Security and know that the concept of an algorithm can be formalized (e.g., via the concept of a Turing Maschine) and used to measure running time. It is also useful if you know the complexity classes P and NP. We will need some basic probability analysis, too.

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Knowledge of cryptographic primitives such as one-way-functions, digitalen signatures, encryption, key exchange, zero-knowledge proofs as well as implications between the primitives, knowledge of formal security definitions of cryptographic prmitives, connections between cryptography and complexity theory, in particular to the P vs. NP problem.

Skills Ability to discuss and devellop security models for cryptographic pimitives. Constructing reductions between cryptographic primitives and ability to say whether small tweaks might harm the security of a cryptographic primitive.
Personal Competence
Social Competence Ability to critically question schemes and methods that seem intuitively secure.
Autonomy
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory
Technomathematics: Specialisation II. Informatics: Elective Compulsory
Course L1806: Cryptography
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Chris Brzuska
Language DE/EN
Cycle SoSe
Content

Content:
This course is about the foundations of cryptography. We introduce cryptographic security models and concepts and understand the relations between them. We then apply the learnt concepts and techniques to real-world problems. In particular, we cover:
- One-way functions
- Pseudorandomness
- Pseudorandom generators
- Pseudorandom functions
- symmetric encryption
- asymmetric encryption
- message authentication codes
- signature schemes
- secure channels
- recent attacks on real-life protocols such as TLS, IPsec,...

Literature

Literatur:

- Foundations of Cryptography: Volume 1, Basic Tools, Oded Goldreich, Cambridge University Press 2007, ISBN-10: 0521035368, ISBN-13: 978-0521035361

- Foundations of Cryptography: Volume 2, Basic Applications, Oded Goldreich, Cambridge University Press 2009, ISBN-10: 052111991X, ISBN-13: 978-0521119917

Course L1807: Cryptography
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Chris Brzuska
Language DE/EN
Cycle SoSe
Content
Literature

Literatur:

- Foundations of Cryptography: Volume 1, Basic Tools, Oded Goldreich, Cambridge University Press 2007, ISBN-10: 0521035368, ISBN-13: 978-0521035361

- Foundations of Cryptography: Volume 2, Basic Applications, Oded Goldreich, Cambridge University Press 2009, ISBN-10: 052111991X, ISBN-13: 978-0521119917

Module M0943: Network Security

Courses
Title Typ Hrs/wk CP
Network Security (L1105) Lecture 3 3
Network Security (L1106) Recitation Section (small) 2 3
Module Responsible Prof. Dieter Gollmann
Admission Requirements None
Recommended Previous Knowledge Discrete Mathematics, Computer Networks (TCP/IP)
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students can

  • explain the fundamental security services that can be implemented with the methods of modern cryptography, 
  • describe current standardized network security protocols and mechanisms, 
  • follow current methods for the formal analysis of security protocols.
Skills

Students are capable of 

  • performing an analysis of network security solutions. 
  • identifying suitable security solutions for given requirements.  
  • recognizing the limitations of existing standard solutions,
  • performing a formal analysis of security protocos.
Personal Competence
Social Competence None
Autonomy Students are capable of acquiring knowledge independently from professional publications, technical  standards, and other sources, and are capable of applying newly acquired knowledge to new problems.
Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Examination Written exam
Examination duration and scale 120 minutes
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory
Course L1105: Network Security
Typ Lecture
Hrs/wk 3
CP 3
Workload in Hours Independent Study Time 48, Study Time in Lecture 42
Lecturer Prof. Dieter Gollmann
Language EN
Cycle SoSe
Content
  • Security objectives
  • Security services and cryptographic mechanisms
  • Key establishment: Diffie-Hellman, Kerberos
  • IPsec protocols, mobile IPv6
  • SSL/TLS
  • GSM/UMTS/LTE security protocols
  • WLAN security
  • Firewalls and Intrusion Detection Systems
  • Formal analysis of security protocols
Literature

W. Stallings: Cryptography and Network Security: Principles and Practice, 6th edition (2013)

A. Menezes, P. van Oorschot, S. Vanstone: Handbook of Applied Cryptography, CRC Press (1997)

D. Gollmann: Computer Security, 3rd edition, Wiley (2011)

V. Niemi, K. Nyberg: UMTS Security, Wiley (2003)

Course L1106: Network Security
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Dieter Gollmann
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0924: Software for Embedded Systems

Courses
Title Typ Hrs/wk CP
Software for Embdedded Systems (L1069) Lecture 2 3
Software for Embdedded Systems (L1070) Recitation Section (small) 3 3
Module Responsible Prof. Volker Turau
Admission Requirements None
Recommended Previous Knowledge
  • Good knowledge and experience in programming language C
  • Basis knowledge in software engineering
  • Basic understanding of assembly language
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Students know the basic principles and procedures of software engineering for embedded systems. They are able to describe the usage and pros of event based programming using interrupts. They know the components and functions of a concrete microcontroller. The participants explain requirements of real time systems. They know at least three scheduling algorithms for real time operating systems including their pros and cons.
Skills Students build interrupt-based programs for a concrete microcontroller. They build and use a preemptive scheduler. They use peripheral components (timer, ADC, EEPROM) to realize complex tasks for embedded systems. To interface with external components they utilize serial protocols.
Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Course L1069: Software for Embdedded Systems
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Volker Turau
Language DE/EN
Cycle SoSe
Content
  • General-Purpose Processors
  • Programming the Atmel AVR
  • Interrupts
  • C for Embedded Systems
  • Standard Single Purpose Processors: Peripherals
  • Finite-State Machines
  • Memory
  • Operating Systems for Embedded Systems
  • Real-Time Embedded Systems
  • Boot loader and Power Management
Literature
  1. Embedded System Design,  F. Vahid and T. Givargis,  John Wiley
  2. Programming Embedded Systems: With C and Gnu Development Tools, M. Barr and A. Massa, O'Reilly

  3. C und C++ für Embedded Systems,  F. Bollow, M. Homann, K. Köhn,  MITP
  4. The Art of Designing  Embedded Systems, J. Ganssle, Newnses

  5. Mikrocomputertechnik mit Controllern der Atmel AVR-RISC-Familie,  G. Schmitt, Oldenbourg
  6. Making Embedded Systems: Design Patterns for Great Software, E. White, O'Reilly

Course L1070: Software for Embdedded Systems
Typ Recitation Section (small)
Hrs/wk 3
CP 3
Workload in Hours Independent Study Time 48, Study Time in Lecture 42
Lecturer Prof. Volker Turau
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0758: Application Security

Courses
Title Typ Hrs/wk CP
Application Security (L0726) Lecture 3 3
Application Security (L0729) Recitation Section (small) 2 3
Module Responsible Prof. Dieter Gollmann
Admission Requirements None
Recommended Previous Knowledge Familiarity with Information security, fundamentals of cryptography, Web protocols and the architecture of the Web
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Students can name current approaches for securing selected applications, in particular of web applications
Skills

Students are capable of

  • performing a security analysis
  • developing security solutions for distributed applications
  • recognizing the limitations of existing standard solutions  




Personal Competence
Social Competence Students are capable of appreciating the impact of security problems on  those affected and of the potential responsibilities for their resolution. 
Autonomy Students are capable of acquiring knowledge independently from professional publications, technical  standards, and other sources, and are capable of applying newly acquired knowledge to new problems. 
Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Examination Written exam
Examination duration and scale 120 minutes
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
Technomathematics: Specialisation II. Informatics: Elective Compulsory
Technomathematics: Core qualification: Elective Compulsory
Course L0726: Application Security
Typ Lecture
Hrs/wk 3
CP 3
Workload in Hours Independent Study Time 48, Study Time in Lecture 42
Lecturer Prof. Dieter Gollmann
Language EN
Cycle SoSe
Content
  • Email security 
  • Web Services security
  • Security in Web applications
  • Access control
  • Trust Management
  • Trusted Computing
  • Digital Rights Management
  • Security Solutions for selected applications
Literature

Webseiten der OMG, W3C, OASIS, WS-Security, OECD, TCG

D. Gollmann: Computer Security, 3rd edition, Wiley (2011)

R. Anderson: Security Engineering, 2nd edition, Wiley (2008)

U. Lang: CORBA Security, Artech House, 2002

Course L0729: Application Security
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Dieter Gollmann
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0913: CMOS Nanoelectronics with Practice

Courses
Title Typ Hrs/wk CP
CMOS Nanoelectronics (L0764) Lecture 2 3
CMOS Nanoelectronics (L1063) Laboratory Course 2 2
CMOS Nanoelectronics (L1059) Recitation Section (small) 1 1
Module Responsible NN
Admission Requirements None
Recommended Previous Knowledge Fundamentals of MOS devices and electronic circuits
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can explain the functionality of very small  MOS transistors and explain the problems occurring due to scaling-down the minimum feature size.
  • Students are able to explain the basic steps of processing of very small MOS devices.
  • Students can exemplify the functionality of volatile and non-volatile memories und give their specifications.
  • Students can describe the limitations of advanced MOS technologies.
  • Students can explain measurement methods for MOS quality control.


Skills
  • Students can quantify the current-voltage-behavior of very small MOS transistors and list possible applications.
  • Students can describe larger electronic systems by their functional blocks.
  • Students can name the existing options for the specific applications and select the most appropriate ones.


Personal Competence
Social Competence
  • Students can team up with one or several partners who may have different professional backgrounds
  • Students are able to work by their own or in small groups for solving problems and answer scientific questions.


Autonomy
  • Students are able to assess their knowledge in a realistic manner.
  • The students are able to draw scenarios for estimation of the impact of advanced mobile electronics on the future lifestyle of the society.


Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory
Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Microelectronics and Microsystems: Core qualification: Elective Compulsory
Course L0764: CMOS Nanoelectronics
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer NN
Language EN
Cycle WiSe
Content
  • Ideal and non-ideal MOS devices
  • Threshold voltage, Parasitic charges, Work function difference
  • I-V behavior
  • Scaling-down rules
  • Details of very small MOS transistors
  • Basic CMOS process flow
  • Memory Technology, SRAM, DRAM, embedded DRAM
  • Gain memory cells
  • Non-volatile memories, Flash memory circuits
  • Methods for Quality Control, C(V)-technique, Charge pumping, Uniform injection
  • Systems with extremely small CMOS transistors
Literature
  • S. Deleonibus, Electronic Device Architectures for the Nano-CMOS Era, Pan Stanford Publishing, 2009.
  • Y. Taur and T.H. Ning, Fundamentals of Modern VLSI Devices, Cambridge University Press, 2nd edition.
  • R.F. Pierret, Advanced Semiconductor Fundamentals, Prentice Hall, 2003.
  • F. Schwierz, H. Wong, J. J. Liou, Nanometer CMOS, Pan Stanford Publishing, 2010.
  • H.-G. Wagemann und T. Schönauer, Silizium-Planartechnologie, Grundprozesse, Physik und Bauelemente
    Teubner-Verlag, 2003, ISBN 3519004674


Course L1063: CMOS Nanoelectronics
Typ Laboratory Course
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer NN
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course
Course L1059: CMOS Nanoelectronics
Typ Recitation Section (small)
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer NN
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0839: Traffic Engineering

Courses
Title Typ Hrs/wk CP
Seminar Traffic Engineering (L0902) Seminar 2 2
Traffic Engineering (L0900) Lecture 2 2
Traffic Engineering Exercises (L0901) Recitation Section (small) 1 2
Module Responsible Prof. Andreas Timm-Giel
Admission Requirements None
Recommended Previous Knowledge
  • Fundamentals of communication or computer networks
  • Stochastics
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to describe methods for planning, optimisation and performance evaluation of communication networks.

Skills

Students are able to solve typical planning and optimisation tasks for communication networks. Furthermore they are able to evaluate the network performance using queuing theory.

Students are able to apply independently what they have learned to other and new problems. They can present their results in front of experts and discuss them.

Personal Competence
Social Competence
Autonomy

Students are able to acquire the necessary expert knowledge to understand the functionality and performance of new communication networks independently.

Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory
Course L0902: Seminar Traffic Engineering
Typ Seminar
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Andreas Timm-Giel
Language EN
Cycle WiSe
Content Selected applications of methods for planning, optimization, and performance evaluation of communication networks, which have been introduced in the traffic engineering lecture are prepared by the students and presented in a seminar.
Literature
  • U. Killat, Entwurf und Analyse von Kommunikationsnetzen, Vieweg + Teubner
  • further literature announced in the lecture
Course L0900: Traffic Engineering
Typ Lecture
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Andreas Timm-Giel
Language EN
Cycle WiSe
Content

Network Planning and Optimization
• Linear Programming (LP)
• Network planning with LP solvers
• Planning of communication networks
Queueing Theory for Communication Networks
• Stochastic processes
• Queueing systems
• Switches (circuit- and packet switching)
• Network of queues

Literature

Literatur:
U. Killat, Entwurf und Analyse von Kommunikationsnetzen, Springer
Weitere Literatur wird in der Lehrveranstaltung bekanntgegeben
/
 Literature:
U. Killat, Entwurf und Analyse von Kommunikationsnetzen, Springer
further literature announced in the lecture

Course L0901: Traffic Engineering Exercises
Typ Recitation Section (small)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Andreas Timm-Giel
Language EN
Cycle WiSe
Content

Accompanying exercise for the traffic engineering course

Literature

Literatur:
U. Killat, Entwurf und Analyse von Kommunikationsnetzen, Springer
Weitere Literatur wird in der Lehrveranstaltung bekanntgegeben / Literature:
U. Killat, Entwurf und Analyse von Kommunikationsnetzen, Springer
further literature announced in the lecture

Module M0919: Laboratory: Analog and Digital Circuit Design

Courses
Title Typ Hrs/wk CP
Laboratory: Analog Circuit Design (L0692) Laboratory Course 2 3
Laboratory: Digital Circuit Design (L0694) Laboratory Course 2 3
Module Responsible NN
Admission Requirements None
Recommended Previous Knowledge Basic knowledge of semiconductor devices and circuit design
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can explain the structure and philosophy of the software framework for circuit design.
  • Students can determine all necessary input parameters for circuit simulation.
  • Students know the basics physics of the analog behavior.
  • Students are able to explain the functions of the logic gates of their digital design.
  • Students can explain the algorithms of checking routines.
  • Students are able to select the appropriate transistor models for fast and accurate simulations.


Skills
  • Students can activate and execute all necessary checking routines for verification of proper circuit functionality.
  • Students are able to run the input desks for definition of their electronic circuits.
  • Students can define the specifications of the electronic circuits to be designed.
  • Students can optimize the electronic circuits for low-noise and low-power.
  • Students can develop analog circuits for mobile medical applications.
  • Students can define the building blocks of digital systems.


Personal Competence
Social Competence
  • Students are trained to work through complex circuits in teams.
  • Students are able to share their knowledge for efficient design work.
  • Students can help each other to understand all the details and options of the design software.
  • Students are aware of their limitations regarding circuit design, so they do not go ahead, but they involve experts when required.
  • Students can present their design approaches for easy checking by more experienced experts.


Autonomy
  • Students are able to realistically judge the status of their knowledge and to define actions for improvements when necessary.
  • Students can break down their design work in sub-tasks and can schedule the design work in a realistic way.
  • Students can handle the complex data structures of their design task and document it in consice but understandable way.
  • Students are able to judge the amount of work for a major design project.


Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 60 min
Assignment for the Following Curricula Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Microelectronics and Microsystems: Core qualification: Elective Compulsory
Course L0692: Laboratory: Analog Circuit Design
Typ Laboratory Course
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer NN
Language DE
Cycle WiSe
Content
  • Input desk for circuits
  • Algorithms for simulation
  • MOS transistor model
  • Simulation of analog circuits
  • Placement and routing     
  • Generation of layouts
  • Design checking routines
  • Postlayout simulations



Literature Handouts to be distributed
Course L0694: Laboratory: Digital Circuit Design
Typ Laboratory Course
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer NN
Language DE
Cycle SoSe
Content
  • Definition of specifications
  • Architecture studies
  • Digital simulation flow
  • Philosophy of standard cells
  • Placement and routing of standard cells
  • Layout generation
  • Design checking routines


Literature Handouts will be distributed

Module M0910: Advanced System-on-Chip Design (Lab)

Courses
Title Typ Hrs/wk CP
Advanced System-on-Chip Design (L1061) Problem-based Learning 3 6
Module Responsible Prof. Heiko Falk
Admission Requirements None
Recommended Previous Knowledge

Successful completion of the practical FPGA lab of module "Computer Architecture" is a mandatory prerequisite.

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

This module provides in-depth, hands-on experience on advanced concepts of computer architecture. Using the Hardware Description Language VHDL and using reconfigurable FPGA hardware boards, students learn how to design complex computer systems (so-called systems-on-chip, SoCs), that are commonly found in the domain of embedded systems, in actual hardware.

Starting with a simple processor architecture, the students learn to how realize instruction-processing of a computer processor according to the principle of pipelining. They implement different styles of cache-based memory hierarchies, examine strategies for dynamic scheduling of machine instructions and for branch prediction, and finally construct a complex MPSoC system (multi-processor system-on-chip) that consists of multiple processor cores that are connected via a shared bus.

Skills Students will be able to analyze, how highly specific and individual computer systems can be constructed using a library of given standard components. They evaluate the interferences between the physical structure of a computer system and the software executed thereon. This way, they will be enabled to estimate the effects of design decision at the hardware level on the performance of the entire system, to evaluate the whole and complex system and to propose design options to improve a system.
Personal Competence
Social Competence

Students are able to solve similar problems alone or in a group and to present the results accordingly.

Autonomy

Students are able to acquire new knowledge from specific literature, to transform this knowledge into actual implementations of complex hardware structures, and to associate this knowledge with contents of other classes.

Workload in Hours Independent Study Time 138, Study Time in Lecture 42
Credit points 6
Examination Project
Examination duration and scale VHDL Codes and FPGA-based implementations
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Course L1061: Advanced System-on-Chip Design
Typ Problem-based Learning
Hrs/wk 3
CP 6
Workload in Hours Independent Study Time 138, Study Time in Lecture 42
Lecturer Prof. Heiko Falk
Language DE/EN
Cycle WiSe
Content
  • Introduction into fundamental technologies (FPGAs, MIPS single-cycle machine)
  • Pipelined instruction execution
  • Cache-based memory hierarchies
  • Busses and their arbitration
  • Multi-Processor Systems-on-Chip
  • Optional: Advanced pipelining concepts (dynamic scheduling, branch prediction)
Literature
  • D. Patterson, J. Hennessy. Rechnerorganisation und -entwurf. Elsevier, 2005.
  • A. Tanenbaum, J. Goodman. Computerarchitektur. Pearson, 2001.
  • A. Clements. The Principles of Computer Hardware. 3. Auflage, Oxford University Press, 2000.

Module M0733: Software Analysis

Courses
Title Typ Hrs/wk CP
Software Analysis (L0631) Lecture 2 3
Software Analysis (L0632) Recitation Section (small) 2 3
Module Responsible Prof. Sibylle Schupp
Admission Requirements None
Recommended Previous Knowledge
  • Basic knowledge of software-engineering activities
  • Discrete algebraic structures
  • Object-oriented programming, algorithms, and data structures
  • Functional programming or Procedural programming
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students apply the major approaches to data-flow analysis, control-flow analysis, and type-based analysis, along with their classification schemes, and employ abstract interpretation. They explain the standard forms of internal representations and models, including their mathematical structure and properties, and evaluate their suitability for a particular analysis. They explain and categorize the major analysis algorithms. They distinguish precise solutions from approximative approaches, and show termination and soundness properties. 

Skills

Presented with an analytical task for a software artifact, students select appropriate approaches from software analysis, and justify their choice. They design suitable representations by modifying standard representations. They develop customized analyses and devise them as safe overapproximations. They formulate analyses in a formal way and construct arguments for their correctness, behavior, and precision.

Personal Competence
Social Competence

Students discuss relevant topics in class. They defend their solutions orally. They communicate in English. 

Autonomy

Using accompanying on-line material for self study, students can assess their level of knowledge continuously and adjust it appropriately.  Working on exercise problems, they receive additional feedback. Within limits, they can set their own learning goals. Upon successful completion, students can identify and precisely formulate new problems in academic or applied research in the field of software analysis. Within this field, they can conduct independent studies to acquire the necessary competencies and compile their findings in academic reports. They can devise plans to arrive at new solutions or assess existing ones. 

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
Course L0631: Software Analysis
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sibylle Schupp
Language EN
Cycle WiSe
Content


  • Modeling: Control-Flow Modeling, Data Dependences, Intermediate Languages)
  • Classical Bit-Vector Analyses (Reaching Definition, Very Busy Expressions, Liveness, Available Expressions, May/Must, Forward/Backward)
  • Monotone Frameworks (Lattices, Transfer Functions, Ascending Chain Condition, Distributivity, Constant Propagation)
  • Theory of Data-Flow Analysis (Tarski's Fixed Point Theorem,  Data-Flow Equations, MFP Solution, MOP Solution, Worklist Algorithm)
  • Non-Classical Data-Flow Analyses
  • Abstract Interpretation (Galois Connections, Approximating Fixed Points, Construction Techniques)
  • Type Systems (Type Derivation, Inference Trees, Algorithm W, Unification)
  • Recent Developments of Analysis Techniques and Applications


Literature
  • Flemming Nielsen, Hanne Nielsen, and Chris Hankin. Principles of Program Analysis. Springer, 2nd. ed. 2005.
  • Uday Khedker, Amitabha Sanyal, and Bageshri Karkara. Data Flow Analysis: Theory and Practice. CRC Press, 2009.
  • Selected research papers
Course L0632: Software Analysis
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sibylle Schupp
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Specialization Systems Engineering and Robotics

Module M1244: Technical Complementary Course I for IIWMS (according to Subject Specific Regulations)

Courses
Title Typ Hrs/wk CP
Module Responsible Prof. Volker Turau
Admission Requirements None
Recommended Previous Knowledge None
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students acquire advanced knowledge in a technical subject available at TUHH.

Skills The students acquire professional competence in a technical subject available at TUHH.
Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 180, Study Time in Lecture 0
Credit points 6
Examination according to Subject Specific Regulations
Examination duration and scale
Assignment for the Following Curricula Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory

Module M0563: Robotics

Courses
Title Typ Hrs/wk CP
Robotics: Modelling and Control (L0168) Lecture 3 3
Robotics: Modelling and Control (L1305) Recitation Section (small) 2 3
Module Responsible Prof. Uwe Weltin
Admission Requirements None
Recommended Previous Knowledge

Fundamentals of electrical engineering

Broad knowledge of mechanics

Fundamentals of control theory

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Students are able to describe fundamental properties of robots and solution approaches for multiple problems in robotics.
Skills

Students are able to derive and solve equations of motion for various manipulators.

Students can generate trajectories in various coordinate systems.

Students can design linear and partially nonlinear controllers for robotic manipulators.

Personal Competence
Social Competence Students are able to work goal-oriented in small mixed groups.
Autonomy

Students are able to recognize and improve knowledge deficits independently.

With instructor assistance, students are able to evaluate their own knowledge level and define a further course of study.

Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Examination Written exam
Examination duration and scale 120 min
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Aircraft Systems Engineering: Specialisation Aircraft Systems: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
International Production Management: Specialisation Production Technology: Elective Compulsory
International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory
International Management and Engineering: Specialisation II. Product Development and Production: Elective Compulsory
Mechanical Engineering and Management: Core qualification: Compulsory
Mechatronics: Core qualification: Compulsory
Product Development, Materials and Production: Specialisation Product Development: Elective Compulsory
Product Development, Materials and Production: Specialisation Production: Elective Compulsory
Product Development, Materials and Production: Specialisation Materials: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Product Development and Production: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Course L0168: Robotics: Modelling and Control
Typ Lecture
Hrs/wk 3
CP 3
Workload in Hours Independent Study Time 48, Study Time in Lecture 42
Lecturer Prof. Uwe Weltin
Language EN
Cycle WiSe
Content

Fundamental kinematics of rigid body systems

Newton-Euler equations for manipulators

Trajectory generation

Linear and nonlinear control of robots

Literature

Craig, John J.: Introduction to Robotics Mechanics and Control, Third Edition, Prentice Hall. ISBN 0201-54361-3

Spong, Mark W.; Hutchinson, Seth;  Vidyasagar, M. : Robot Modeling and Control. WILEY. ISBN 0-471-64990-2


Course L1305: Robotics: Modelling and Control
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Uwe Weltin
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0846: Control Systems Theory and Design

Courses
Title Typ Hrs/wk CP
Control Systems Theory and Design (L0656) Lecture 2 4
Control Systems Theory and Design (L0657) Recitation Section (small) 2 2
Module Responsible Prof. Herbert Werner
Admission Requirements None
Recommended Previous Knowledge Introduction to Control Systems
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can explain how linear dynamic systems are represented as state space models; they can interpret the system response to initial states or external excitation as trajectories in state space
  • They can explain the system properties controllability and observability, and their relationship to state feedback and state estimation, respectively
  • They can explain the significance of a minimal realisation
  • They can explain observer-based state feedback and how it can be used to achieve tracking and disturbance rejection
  • They can extend all of the above to multi-input multi-output systems
  • They can explain the z-transform and its relationship with the Laplace Transform
  • They can explain state space models and transfer function models of discrete-time systems
  • They can explain the experimental identification of ARX models of dynamic systems, and how the identification problem can be solved by solving a normal equation
  • They can explain how a state space model can be constructed from a discrete-time impulse response

Skills
  • Students can transform transfer function models into state space models and vice versa
  • They can assess controllability and observability and construct minimal realisations
  • They can design LQG controllers for multivariable plants
  •  They can carry out a controller design both in continuous-time and discrete-time domain, and decide which is  appropriate for a given sampling rate
  • They can identify transfer function models and state space models of dynamic systems from experimental data
  • They can carry out all these tasks using standard software tools (Matlab Control Toolbox, System Identification Toolbox, Simulink)

Personal Competence
Social Competence

Students can work in small groups on specific problems to arrive at joint solutions. 

Autonomy

Students can obtain information from provided sources (lecture notes, software documentation, experiment guides) and use it when solving given problems.

They can assess their knowledge in weekly on-line tests and thereby control their learning progress.


Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 120 min
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Electrical Engineering: Core qualification: Compulsory
Energy Systems: Core qualification: Elective Compulsory
Aircraft Systems Engineering: Specialisation Aircraft Systems: Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory
International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory
Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory
Mechatronics: Core qualification: Compulsory
Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory
Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory
Biomedical Engineering: Specialisation Medical Technology and Control Theory: Compulsory
Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory
Product Development, Materials and Production: Core qualification: Elective Compulsory
Theoretical Mechanical Engineering: Core qualification: Compulsory
Course L0656: Control Systems Theory and Design
Typ Lecture
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Lecturer Prof. Herbert Werner
Language EN
Cycle WiSe
Content

State space methods (single-input single-output)

• State space models and transfer functions, state feedback 
• Coordinate basis, similarity transformations 
• Solutions of state equations, matrix exponentials, Caley-Hamilton Theorem
• Controllability and pole placement 
• State estimation, observability, Kalman decomposition 
• Observer-based state feedback control, reference tracking 
• Transmission zeros
• Optimal pole placement, symmetric root locus 
Multi-input multi-output systems
• Transfer function matrices, state space models of multivariable systems, Gilbert realization 
• Poles and zeros of multivariable systems, minimal realization 
• Closed-loop stability
• Pole placement for multivariable systems, LQR design, Kalman filter 

Digital Control
• Discrete-time systems: difference equations and z-transform 
• Discrete-time state space models, sampled data systems, poles and zeros 
• Frequency response of sampled data systems, choice of sampling rate 

System identification and model order reduction 
• Least squares estimation, ARX models, persistent excitation 
• Identification of state space models, subspace identification 
• Balanced realization and model order reduction 

Case study
• Modelling and multivariable control of a process evaporator using Matlab and Simulink 
Software tools
• Matlab/Simulink

Literature
  • Werner, H., Lecture Notes „Control Systems Theory and Design“
  • T. Kailath "Linear Systems", Prentice Hall, 1980
  • K.J. Astrom, B. Wittenmark "Computer Controlled Systems" Prentice Hall, 1997
  • L. Ljung "System Identification - Theory for the User", Prentice Hall, 1999
Course L0657: Control Systems Theory and Design
Typ Recitation Section (small)
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Herbert Werner
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1336: Soft Computing

Courses
Title Typ Hrs/wk CP
Soft Computing (L1869) Lecture 4 6
Module Responsible Prof. Karl-Heinz Zimmermann
Admission Requirements None
Recommended Previous Knowledge
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
Skills
Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation General Process Engineering: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation Bioprocess Engineering: Elective Compulsory
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
Course L1869: Soft Computing
Typ Lecture
Hrs/wk 4
CP 6
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Lecturer Prof. Karl-Heinz Zimmermann
Language DE/EN
Cycle WiSe
Content
Literature

Module M0667: Algorithmic Algebra

Courses
Title Typ Hrs/wk CP
Algorithmic Algebra (L0422) Lecture 3 5
Algorithmic Algebra (L0423) Recitation Section (small) 1 1
Module Responsible Dr. Prashant Batra
Admission Requirements None
Recommended Previous Knowledge

Mathe I-III (Real analysis,computing in Vector spaces , principle of complete induction)  Diskrete Mathematik I (gropus, rings, ideals, fields; euclidean algorithm)

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students can discuss logical connections between the following concepts and explain them by means of examples: Smith normal form, Chinese remainder theorem, grid point sets, integer solution of inequality systems.

Skills

Students are able to access independently further logical connections between the concepts with which they have become familiar and are able to verify them.

Students are able to develop a suitable solution approach to given problems, to pursue it and to evaluate the results critically, such as in solving multivariate equation systems and in grid point theory.

Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Course L0422: Algorithmic Algebra
Typ Lecture
Hrs/wk 3
CP 5
Workload in Hours Independent Study Time 108, Study Time in Lecture 42
Lecturer Dr. Prashant Batra
Language DE
Cycle WiSe
Content

Extended euclidean algorithm, solution of the Bezout-equation

Division with remainder (over rings)

 fast arithmetic algorithms (conversion, fast multiplications)

discrete Fourier-transformation over rings

Computation with  modular remainders, solving of remainder systems (chinese remainder theorem), solvability of integer linear systems over the integers

linearization of polynomial equations-- matrix approach

Sylvester-matrix, elimination

elimination in rings, elimination of many variables

Buchberger algorithm, Gröbner basis

Minkowskis Lattice Point theorem and integer-valued  optimization

LLL-algorithm for construction of  'short' lattice vectors in polynomial time

Literature von zur Gathen, Joachim; Gerhard, Jürgen

Modern computer algebra. 3rd ed. (English) Zbl 1277.68002
Cambridge: Cambridge University Press (ISBN 978-1-107-03903-2/hbk; 978-1-139-85606-5/ebook).


Yap, Chee Keng
Fundamental problems of algorithmic algebra. (English) Zbl 0999.68261
Oxford: Oxford University Press. xvi, 511 p. $ 87.00 (2000).


Free download for students from author's website: http://cs.nyu.edu/yap/book/berlin/

Cox, David; Little, John; O’Shea, Donal
Ideals, varieties, and algorithms. An introduction to computational algebraic geometry and commutative algebra. 3rd ed. (English) Zbl 1118.13001
Undergraduate Texts in Mathematics. New York, NY: Springer (ISBN 978-0-387-35650-1/hbk; 978-0-387-35651-8/ebook). xv, 551 p.

eBook: http://dx.doi.org/10.1007/978-0-387-35651-8


Concrete abstract algebra : from numbers to Gröbner bases / Niels Lauritzen
Verfasser: 
Lauritzen, Niels
Ausgabe: 
Reprinted with corr.
Erschienen: 
Cambridge [u.a.] : Cambridge Univ. Press, 2006
Umfang: 
XIV, 240 S. : graph. Darst.
Anmerkung: 
Includes bibliographical references and index
ISBN: 
0-521-82679-9, 978-0-521-82679-2 (hbk.) : GBP 55.00
0-521-53410-0, 978-0-521-53410-9 (pbk.) : USD 39.99

Koepf, Wolfram
Computer algebra. An algorithmic oriented introduction. (Computeralgebra. Eine algorithmisch orientierte Einführung.) (German) Zbl 1161.68881
Berlin: Springer (ISBN 3-540-29894-0/pbk). xiii, 515 p.

springer eBook: http://dx.doi.org/10.1007/3-540-29895-9

Kaplan, Michael
Computer algebra. (Computeralgebra.) (German) Zbl 1093.68148
Berlin: Springer (ISBN 3-540-21379-1/pbk). xii, 391 p.

springer eBook:

http://dx.doi.org/10.1007/b137968



Course L0423: Algorithmic Algebra
Typ Recitation Section (small)
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Dr. Prashant Batra
Language DE
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0550: Digital Image Analysis

Courses
Title Typ Hrs/wk CP
Digital Image Analysis (L0126) Lecture 4 6
Module Responsible Prof. Rolf-Rainer Grigat
Admission Requirements None
Recommended Previous Knowledge

System theory of one-dimensional signals (convolution and correlation, sampling theory, interpolation and decimation, Fourier transform, linear time-invariant systems), linear algebra (Eigenvalue decomposition, SVD), basic stochastics and statistics (expectation values, influence of sample size, correlation and covariance, normal distribution and its parameters), basics of Matlab, basics in optics

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students can

  • Describe imaging processes
  • Depict the physics of sensorics
  • Explain linear and non-linear filtering of signals
  • Establish interdisciplinary connections in the subject area and arrange them in their context
  • Interpret effects of the most important classes of imaging sensors and displays using mathematical methods and physical models.


Skills

Students are able to

  • Use highly sophisticated methods and procedures of the subject area
  • Identify problems and develop and implement creative solutions.

Students can solve simple arithmetical problems relating to the specification and design of image processing and image analysis systems.

Students are able to assess different solution approaches in multidimensional decision-making areas.

Students can undertake a prototypical analysis of processes in Matlab.


Personal Competence
Social Competence k.A.


Autonomy

Students can solve image analysis tasks independently using the relevant literature.


Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 60 Minutes, Content of Lecture and materials in StudIP
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Electrical Engineering: Specialisation Medical Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Course L0126: Digital Image Analysis
Typ Lecture
Hrs/wk 4
CP 6
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Lecturer Prof. Rolf-Rainer Grigat
Language EN
Cycle WiSe
Content
  • Image representation, definition of images and volume data sets, illumination, radiometry, multispectral imaging, reflectivities, shape from shading
  • Perception of luminance and color, color spaces and transforms, color matching functions, human visual system, color appearance models
  • imaging sensors (CMOS, CCD, HDR, X-ray, IR), sensor characterization(EMVA1288), lenses and optics
  • spatio-temporal sampling (interpolation, decimation, aliasing, leakage, moiré, flicker, apertures)
  • features (filters, edge detection, morphology, invariance, statistical features, texture)
  • optical flow ( variational methods, quadratic optimization, Euler-Lagrange equations)
  • segmentation (distance, region growing, cluster analysis, active contours, level sets, energy minimization and graph cuts)
  • registration (distance and similarity, variational calculus, iterative closest points)
Literature

Bredies/Lorenz, Mathematische Bildverarbeitung, Vieweg, 2011
Wedel/Cremers, Stereo Scene Flow for 3D Motion Analysis, Springer 2011
Handels, Medizinische Bildverarbeitung, Vieweg, 2000
Pratt, Digital Image Processing, Wiley, 2001
Jain, Fundamentals of Digital Image Processing, Prentice Hall, 1989

Module M0881: Mathematical Image Processing

Courses
Title Typ Hrs/wk CP
Mathematical Image Processing (L0991) Lecture 3 4
Mathematical Image Processing (L0992) Recitation Section (small) 1 2
Module Responsible Prof. Marko Lindner
Admission Requirements None
Recommended Previous Knowledge
  • Analysis: partial derivatives, gradient, directional derivative
  • Linear Algebra: eigenvalues, least squares solution of a linear system
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to 

  • characterize and compare diffusion equations
  • explain elementary methods of image processing
  • explain methods of image segmentation and registration
  • sketch and interrelate basic concepts of functional analysis 
Skills

Students are able to 

  • implement and apply elementary methods of image processing  
  • explain and apply modern methods of image processing
Personal Competence
Social Competence

Students are able to work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge) and to explain theoretical foundations.

Autonomy
  • Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them.
  • Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 20 min
Assignment for the Following Curricula Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Modeling and Simulation: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Process Engineering: Specialisation Process Engineering: Elective Compulsory
Course L0991: Mathematical Image Processing
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Marko Lindner
Language DE/EN
Cycle WiSe
Content
  • basic methods of image processing
  • smoothing filters
  • the diffusion / heat equation
  • variational formulations in image processing
  • edge detection
  • image segmentation
  • image registration
Literature Bredies/Lorenz: Mathematische Bildverarbeitung
Course L0992: Mathematical Image Processing
Typ Recitation Section (small)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Marko Lindner
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0677: Digital Signal Processing and Digital Filters

Courses
Title Typ Hrs/wk CP
Digital Signal Processing and Digital Filters (L0446) Lecture 3 4
Digital Signal Processing and Digital Filters (L0447) Recitation Section (large) 1 2
Module Responsible Prof. Gerhard Bauch
Admission Requirements None
Recommended Previous Knowledge
  • Mathematics 1-3
  • Signals and Systems
  • Fundamentals of signal and system theory as well as random processes.
  • Fundamentals of spectral transforms (Fourier series, Fourier transform, Laplace transform)
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge The students know and understand basic algorithms of digital signal processing. They are familiar with the spectral transforms of discrete-time signals and are able to describe and analyse signals and systems in time and image domain. They know basic structures of digital filters and can identify and assess important properties including stability. They are aware of the effects caused by quantization of filter coefficients and signals. They are familiar with the basics of adaptive filters. They can perform traditional and parametric methods of spectrum estimation, also taking a limited observation window into account.
Skills The students are able to apply methods of digital signal processing to new problems. They can choose and parameterize suitable filter striuctures. In particular, the can design adaptive filters according to the minimum mean squared error (MMSE) criterion and develop an efficient implementation, e.g. based on the LMS or RLS algorithm.  Furthermore, the students are able to apply methods of spectrum estimation and to take the effects of a limited observation window into account.
Personal Competence
Social Competence

The students can jointly solve specific problems.

Autonomy

The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Electrical Engineering: Specialisation Control and Power Systems: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory
Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Microelectronics and Microsystems: Specialisation Microelectronics Complements: Elective Compulsory
Course L0446: Digital Signal Processing and Digital Filters
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Gerhard Bauch
Language EN
Cycle WiSe
Content
  • Transforms of discrete-time signals:

    • Discrete-time Fourier Transform (DTFT)

    • Discrete Fourier-Transform (DFT), Fast Fourier Transform (FFT)

    • Z-Transform

  • Correspondence of continuous-time and discrete-time signals, sampling, sampling theorem

  • Fast convolution, Overlap-Add-Method, Overlap-Save-Method

  • Fundamental structures and basic types of digital filters

  • Characterization of digital filters using pole-zero plots, important properties of digital filters

  • Quantization effects

  • Design of linear-phase filters

  • Fundamentals of stochastic signal processing and adaptive filters

    • MMSE criterion

    • Wiener Filter

    • LMS- and RLS-algorithm

  • Traditional and parametric methods of spectrum estimation

Literature

K.-D. Kammeyer, K. Kroschel: Digitale Signalverarbeitung. Vieweg Teubner.

V. Oppenheim, R. W. Schafer, J. R. Buck: Zeitdiskrete Signalverarbeitung. Pearson StudiumA. V.

W. Hess: Digitale Filter. Teubner.

Oppenheim, R. W. Schafer: Digital signal processing. Prentice Hall.

S. Haykin:  Adaptive flter theory.

L. B. Jackson: Digital filters and signal processing. Kluwer.

T.W. Parks, C.S. Burrus: Digital filter design. Wiley.

Course L0447: Digital Signal Processing and Digital Filters
Typ Recitation Section (large)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Gerhard Bauch
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0633: Industrial Process Automation

Courses
Title Typ Hrs/wk CP
Industrial Process Automation (L0344) Lecture 2 3
Industrial Process Automation (L0345) Recitation Section (small) 2 3
Module Responsible Prof. Alexander Schlaefer
Admission Requirements None
Recommended Previous Knowledge

mathematics and optimization methods
principles of automata 
principles of algorithms and data structures
programming skills

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students can evaluate and assess disctrete event systems. They can evaluate properties of processes and explain methods for process analysis. The students can compare methods for process modelling and select an appropriate method for actual problems. They can discuss scheduling methods in the context of actual problems and give a detailed explanation of advantages and disadvantages of different programming methods.


Skills

The students are able to develop and model processes and evaluate them accordingly. This involves taking into account optimal scheduling, understanding algorithmic complexity and implementation using PLCs.

Personal Competence
Social Competence

The students work in teams to solve problems.


Autonomy

The students can reflect their knowledge and document the results of their work. 


Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 90 minutes
Assignment for the Following Curricula Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation Chemical Process Engineering: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation General Process Engineering: Elective Compulsory
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Control and Power Systems: Elective Compulsory
Aircraft Systems Engineering: Specialisation Cabin Systems: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
International Production Management: Specialisation Production Technology: Elective Compulsory
International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory
Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Process Engineering: Specialisation Chemical Process Engineering: Elective Compulsory
Process Engineering: Specialisation Process Engineering: Elective Compulsory
Course L0344: Industrial Process Automation
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Alexander Schlaefer
Language EN
Cycle WiSe
Content

- foundations of problem solving and system modeling, discrete event systems
- properties of processes, modeling using automata and Petri-nets
- design considerations for processes (mutex, deadlock avoidance, liveness)
- optimal scheduling for processes
- optimal decisions when planning manufacturing systems, decisions under uncertainty
- software design and software architectures for automation, PLCs

Literature

J. Lunze: „Automatisierungstechnik“, Oldenbourg Verlag, 2012
Reisig: Petrinetze: Modellierungstechnik, Analysemethoden, Fallstudien; Vieweg+Teubner 2010
Hrúz, Zhou: Modeling and Control of Discrete-event Dynamic Systems; Springer 2007
Li, Zhou: Deadlock Resolution in Automated Manufacturing Systems, Springer 2009
Pinedo: Planning and Scheduling in Manufacturing and Services, Springer 2009

Course L0345: Industrial Process Automation
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Alexander Schlaefer
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0586: Efficient Algorithms

Courses
Title Typ Hrs/wk CP
Efficient Algorithms (L0120) Lecture 2 3
Efficient Algorithms (L1207) Recitation Section (small) 2 3
Module Responsible Prof. Siegfried Rump
Admission Requirements None
Recommended Previous Knowledge

Programming in Matlab and/or C

Basic knowledge in discrete mathematics
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students are able to explain the basic theory and methods of network algorithms and in particular their data structures. They are able to analyze the computational behavior and computing time of linear programming algorithms as well network algorithms. Moreover the students can distinguish between efficiently solvable and NP-hard problems.

Skills

The students are able to analyze complex tasks and can determine possibilities to transform them into networking algorithms. In particular they can efficiently implement basic algorithms and data structures of LP- and network algorithms and identify possible weaknesses. They are able to distinguish between different efficient data structures and are able to use them appropriately.

Personal Competence
Social Competence

The students have the skills to solve problems together in small groups and to present the achieved results in an appropriate manner.

Autonomy

The students are able to retrieve necessary informations from the given literature and to combine them with the topics of the lecture. Throughout the lecture they can check their abilities and knowledge on the basis of given exercises and test questions providing an aid to optimize their learning process.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Electrical Engineering: Specialisation Modeling and Simulation: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Course L0120: Efficient Algorithms
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Siegfried Rump
Language DE
Cycle WiSe
Content

- Linear Programming

- Data structures

- Leftist heaps

- Minimum spanning tree

- Shortest path

- Maximum flow

- NP-hard problems via max-cut

Literature

R. E. Tarjan: Data Structures and Network Algorithms. CBMS 44, Society for Industrial and Applied Mathematics, Philadelphia, PA, 1983.

Wesley, 2011 http://algs4.cs.princeton.edu/home/

V. Chvátal, ``Linear Programming'', Freeman, New York, 1983.

Course L1207: Efficient Algorithms
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Siegfried Rump
Language DE
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0676: Digital Communications

Courses
Title Typ Hrs/wk CP
Digital Communications (L0444) Lecture 2 3
Digital Communications (L0445) Recitation Section (large) 1 2
Laboratory Digital Communications (L0646) Laboratory Course 1 1
Module Responsible Prof. Gerhard Bauch
Admission Requirements None
Recommended Previous Knowledge
  • Mathematics 1-3
  • Signals and Systems
  • Fundamentals of Communications and Random Processes
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge The students are able to understand, compare and design modern digital information transmission schemes. They are familiar with the properties of linear and non-linear digital modulation methods. They can describe distortions caused by transmission channels and design and evaluate detectors including channel estimation and equalization. They know the principles of single carrier transmission and multi-carrier transmission as well as the fundamentals of basic multiple access schemes.
Skills The students are able to design and analyse a digital information transmission scheme including multiple access. They are able to choose a digital modulation scheme taking into account transmission rate, required bandwidth, error probability, and further signal properties. They can design an appropriate detector including channel estimation and equalization taking into account performance and complexity properties of suboptimum solutions. They are able to set parameters of a single carrier or multi carrier transmission scheme and trade the properties of both approaches against each other.
Personal Competence
Social Competence

The students can jointly solve specific problems.

Autonomy

The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Electrical Engineering: Core qualification: Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems: Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory
Course L0444: Digital Communications
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Gerhard Bauch
Language DE/EN
Cycle WiSe
Content
  • Digital modulation methods

  • Coherent and non-coherent detection

  • Channel estimation and equalization

  • Single-Carrier- and multi carrier transmission schemes, multiple access schemes (TDMA, FDMA, CDMA, OFDM)

Literature

K. Kammeyer: Nachrichtenübertragung, Teubner

P.A. Höher: Grundlagen der digitalen Informationsübertragung, Teubner.

J.G. Proakis, M. Salehi: Digital Communications. McGraw-Hill.

S. Haykin: Communication Systems. Wiley

R.G. Gallager: Principles of Digital Communication. Cambridge

A. Goldsmith: Wireless Communication. Cambridge.

D. Tse, P. Viswanath: Fundamentals of Wireless Communication. Cambridge.

Course L0445: Digital Communications
Typ Recitation Section (large)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Gerhard Bauch
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course
Course L0646: Laboratory Digital Communications
Typ Laboratory Course
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Gerhard Bauch
Language DE/EN
Cycle WiSe
Content

- DSL transmission

- Random processes

- Digital data transmission

Literature

K. Kammeyer: Nachrichtenübertragung, Teubner

P.A. Höher: Grundlagen der digitalen Informationsübertragung, Teubner.

J.G. Proakis, M. Salehi: Digital Communications. McGraw-Hill.

S. Haykin: Communication Systems. Wiley

R.G. Gallager: Principles of Digital Communication. Cambridge

A. Goldsmith: Wireless Communication. Cambridge.

D. Tse, P. Viswanath: Fundamentals of Wireless Communication. Cambridge.

Module M0926: Distributed Algorithms

Courses
Title Typ Hrs/wk CP
Distributed Algorithms (L1071) Lecture 2 3
Distributed Algorithms (L1072) Recitation Section (large) 2 3
Module Responsible Prof. Volker Turau
Admission Requirements None
Recommended Previous Knowledge
  • Algorithms and data structures
  • Distributed systems
  • Discrete mathematics
  • Graph theory
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Students know the main abstractions of distributed algorithms (synchronous/asynchronous model, message passing and shared memory model). They are able to describe complexity measures for distributed algorithms (round , message and memory complexity). They explain well known distributed algorithms for important problems such as leader election, mutual exclusion, graph coloring, spanning trees. They know the fundamental techniques used for randomized algorithms.
Skills Students design their own distributed algorithms and analyze their complexity. They make use of known standard algorithms. They compute the complexity of randomized algorithms.
Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 45 min
Assignment for the Following Curricula Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Course L1071: Distributed Algorithms
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Volker Turau
Language DE/EN
Cycle WiSe
Content
  • Leader Election
  • Colorings & Independent Sets
  • Tree Algorithms
  • Minimal Spanning Trees
  • Randomized Distributed Algorithms
  • Mutual Exclusion
Literature
  1. David Peleg: Distributed Computing - A Locality-Sensitive Approach. SIAM Monograph, 2000

  2. Gerard Tel: Introduction to Distributed Algorithms, Cambridge University Press, 2nd edition, 2000
  3. Nancy Lynch: Distributed Algorithms. Morgan Kaufmann, 1996
  4. Volker Turau: Algorithmische Graphentheorie. Oldenbourg Wissenschaftsverlag, 3. Auflage, 2004.


Course L1072: Distributed Algorithms
Typ Recitation Section (large)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Volker Turau
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0629: Intelligent Autonomous Agents and Cognitive Robotics

Courses
Title Typ Hrs/wk CP
Intelligent Autonomous Agents and Cognitive Robotics (L0341) Lecture 2 4
Intelligent Autonomous Agents and Cognitive Robotics (L0512) Recitation Section (small) 2 2
Module Responsible Rainer Marrone
Admission Requirements None
Recommended Previous Knowledge Vectors, matrices, Calculus
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students can explain the agent abstraction, define intelligence in terms of rational behavior, and give details about agent design (goals, utilities, environments). They can describe the main features of environments. The notion of adversarial agent cooperation can be discussed in terms of decision problems and algorithms for solving these problems. For dealing with uncertainty in real-world scenarios, students can summarize how Bayesian networks can be employed as a knowledge representation and reasoning formalism in static and dynamic settings. In addition, students can define decision making procedures in simple and sequential settings, with and with complete access to the state of the environment. In this context, students can describe techniques for solving (partially observable) Markov decision problems, and they can recall techniques for measuring the value of information. Students can identify techniques for simultaneous localization and mapping, and can explain planning techniques for achieving desired states. Students can explain coordination problems and decision making in a multi-agent setting in term of different types of equilibria, social choice functions, voting protocol, and mechanism design techniques.

Skills

Students can select an appropriate agent architecture for concrete agent application scenarios. For simplified agent application students can derive decision trees and apply basic optimization techniques. For those applications they can also create Bayesian networks/dynamic Bayesian networks and apply bayesian reasoning for simple queries. Students can also name and apply different sampling techniques for simplified agent scenarios. For simple and complex decision making students can compute the best action or policies for concrete settings. In multi-agent situations students will apply techniques for finding different equilibria states,e.g., Nash equilibria. For multi-agent decision making students will apply different voting protocols and compare and explain the results.


Personal Competence
Social Competence

Students are able to discuss their solutions to problems with others. They communicate in English

Autonomy

Students are able of checking their understanding of complex concepts by solving varaints of concrete problems

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 90 minutes
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
International Production Management: Specialisation Production Technology: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory
Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory
Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory
Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory
Course L0341: Intelligent Autonomous Agents and Cognitive Robotics
Typ Lecture
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Lecturer Rainer Marrone
Language EN
Cycle WiSe
Content
  • Definition of agents, rational behavior, goals, utilities, environment types
  • Adversarial agent cooperation: 
    Agents with complete access to the state(s) of the environment, games, Minimax algorithm, alpha-beta pruning, elements of chance
  • Uncertainty: 
    Motivation: agents with no direct access to the state(s) of the environment, probabilities, conditional probabilities, product rule, Bayes rule, full joint probability distribution, marginalization, summing out, answering queries, complexity, independence assumptions, naive Bayes, conditional independence assumptions
  • Bayesian networks: 
    Syntax and semantics of Bayesian networks, answering queries revised (inference by enumeration), typical-case complexity, pragmatics: reasoning from effect (that can be perceived by an agent) to cause (that cannot be directly perceived).
  • Probabilistic reasoning over time:
    Environmental state may change even without the agent performing actions, dynamic Bayesian networks, Markov assumption, transition model, sensor model, inference problems: filtering, prediction, smoothing, most-likely explanation, special cases: hidden Markov models, Kalman filters, Exact inferences and approximations
  • Decision making under uncertainty:
    Simple decisions: utility theory, multivariate utility functions, dominance, decision networks, value of informatio
    Complex decisions: sequential decision problems, value iteration, policy iteration, MDPs
    Decision-theoretic agents: POMDPs, reduction to multidimensional continuous MDPs, dynamic decision networks
  • Simultaneous Localization and Mapping
  • Planning
  • Game theory (Golden Balls: Split or Share) 
    Decisions with multiple agents, Nash equilibrium, Bayes-Nash equilibrium
  • Social Choice 
    Voting protocols, preferences, paradoxes, Arrow's Theorem,
  • Mechanism Design 
    Fundamentals, dominant strategy implementation, Revelation Principle, Gibbard-Satterthwaite Impossibility Theorem, Direct mechanisms, incentive compatibility, strategy-proofness, Vickrey-Groves-Clarke mechanisms, expected externality mechanisms, participation constraints, individual rationality, budget balancedness, bilateral trade, Myerson-Satterthwaite Theorem
Literature
  1. Artificial Intelligence: A Modern Approach (Third Edition), Stuart Russell, Peter Norvig, Prentice Hall, 2010, Chapters 2-5, 10-11, 13-17
  2. Probabilistic Robotics, Thrun, S., Burgard, W., Fox, D. MIT Press 2005

  3. Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, Yoav Shoham, Kevin Leyton-Brown, Cambridge University Press, 2009

Course L0512: Intelligent Autonomous Agents and Cognitive Robotics
Typ Recitation Section (small)
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Rainer Marrone
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1245: Technical Complementary Course II for IIWMS (according to Subject Specific Regulations)

Courses
Title Typ Hrs/wk CP
Module Responsible Prof. Volker Turau
Admission Requirements None
Recommended Previous Knowledge None
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students acquire advanced knowledge in a technical subject available at TUHH.

Skills

The students acquire professional competence in a technical subject available at TUHH.

Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 180, Study Time in Lecture 0
Credit points 6
Examination according to Subject Specific Regulations
Examination duration and scale
Assignment for the Following Curricula Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory

Module M1302: Applied Humanoid Robotics

Courses
Title Typ Hrs/wk CP
Humanoid Robotics (L1794) Problem-based Learning 6 6
Module Responsible Prof. Herbert Werner
Admission Requirements None
Recommended Previous Knowledge
  • Object oriented programming; algorithms and data structures
  • Introduction to control systems
  • Control systems theory and design
  • Mechanics
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can explain humanoid robots.
  • Students can explain the basic concepts, relationships and methods of forward- and inverse kinematics
  • Students learn to apply basic control concepts for different tasks in humanoid robotics.
Skills
  • Students can implement models for humanoid robotic systems in Matlab and C++, and use these models for robot motion or other tasks.
  • They are capable of using models in Matlab for simulation and testing these models if necessary with C++ code on the real robot system.
  • They are capable of selecting methods for solving abstract problems, for which no standard methods are available, and apply it successfully.
Personal Competence
Social Competence
  • Students can develop joint solutions in mixed teams and present these.
  • They can provide appropriate feedback to others, and  constructively handle feedback on their own results
Autonomy
  • Students are able to obtain required information from provided literature sources, and to put in into the context of the lecture.
  • They can independently define tasks and apply the appropriate means to solve them.
Workload in Hours Independent Study Time 96, Study Time in Lecture 84
Credit points 6
Examination Colloquium
Examination duration and scale
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Bio- and Medical Technology: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Course L1794: Humanoid Robotics
Typ Problem-based Learning
Hrs/wk 6
CP 6
Workload in Hours Independent Study Time 96, Study Time in Lecture 84
Lecturer Prof. Herbert Werner
Language DE/EN
Cycle SoSe
Content
  • Fundamentals of kinematics
  • Static and dynamic stability of humanoid robotic systems
  • Combination of different software environments (Matlab, C++, etc.)
  • Introduction to the necessary  software frameworks
  • Team project
  • Presentation and Demonstration of intermediate and final results
Literature
  • B. Siciliano, O. Khatib. "Handbook of Robotics. Part A: Robotics Foundations", Springer (2008)

Module M0747: Microsystem Design

Courses
Title Typ Hrs/wk CP
Microsystem Design (L0683) Lecture 2 3
Microsystem Design (L0684) Laboratory Course 3 3
Module Responsible Prof. Manfred Kasper
Admission Requirements None
Recommended Previous Knowledge

Mathematical Calculus, Linear Algebra, Microsystem Engineering

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students know about the most important and most common simulation and design methods used in microsystem design. The scientific background of finite element methods and the basic theory of these methods are known.

Skills

Students are able to apply simulation methods and commercial simulators in a goal oriented approach to complex design tasks. Students know to apply the theory in order achieve estimates of expected accuracy and can judge and verify the correctness of results. Students are able to develop a design approach even if only incomplete information about material data or constraints are available. Student can make use of approximate and reduced order models in a preliminary design stage or a system simulation.

Personal Competence
Social Competence

Students are able to solve specific problems alone or in a group and to present the results accordingly. Students can develop and explain their solution approach and subdivide the design task to subproblems which are solved separately by group members.

Autonomy

Students are able to acquire particular knowledge using specialized literature and to integrate and associate this knowledge with other fields.

Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Electrical Engineering: Specialisation Nanoelectronics and Microsystems Technology: Elective Compulsory
Electrical Engineering: Specialisation Modeling and Simulation: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Microelectronics and Microsystems: Core qualification: Elective Compulsory
Course L0683: Microsystem Design
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Manfred Kasper
Language EN
Cycle SoSe
Content

Finite difference methods

Approximation error

Finite element method

Order of convergence

Error estimation, mesh refinement

Makromodeling

Reduced order modeling

Black-box models

System identification

Multi-physics systems

System simulation

Levels of simulation, network simulation

Transient problems

Non-linear problems

Introduction to Comsol

Application to thermal, electric, electromagnetic, mechanical and fluidic problems

Literature

M. Kasper: Mikrosystementwurf, Springer (2000)

S. Senturia: Microsystem Design, Kluwer (2001)

Course L0684: Microsystem Design
Typ Laboratory Course
Hrs/wk 3
CP 3
Workload in Hours Independent Study Time 48, Study Time in Lecture 42
Lecturer Prof. Manfred Kasper
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0840: Optimal and Robust Control

Courses
Title Typ Hrs/wk CP
Optimal and Robust Control (L0658) Lecture 2 3
Optimal and Robust Control (L0659) Recitation Section (small) 2 3
Module Responsible Prof. Herbert Werner
Admission Requirements None
Recommended Previous Knowledge
  • Classical control (frequency response, root locus)
  • State space methods
  • Linear algebra, singular value decomposition
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can explain the significance of the matrix Riccati equation for the solution of LQ problems.
  • They can explain the duality between optimal state feedback and optimal state estimation.
  • They can explain how the H2 and H-infinity norms are used to represent stability and performance constraints.
  • They can explain how an LQG design problem can be formulated as special case of an H2 design problem.
  • They  can explain how model uncertainty can be represented in a way that lends itself to robust controller design
  • They can explain how - based on the small gain theorem - a robust controller can guarantee stability and performance for an uncertain plant.
  • They understand how analysis and synthesis conditions on feedback loops can be represented as linear matrix inequalities.
Skills
  • Students are capable of designing and tuning LQG controllers for multivariable plant models.
  • They are capable of representing a H2 or H-infinity design problem in the form of a generalized plant, and of using standard software tools for solving it.
  • They are capable of translating time and frequency domain specifications for control loops into constraints on closed-loop sensitivity functions, and of carrying out a mixed-sensitivity design.
  • They are capable of constructing an LFT uncertainty model for an uncertain system, and of designing a mixed-objective robust controller.
  • They are capable of formulating analysis and synthesis conditions as linear matrix inequalities (LMI), and of using standard LMI-solvers for solving them.
  • They can carry out all of the above using standard software tools (Matlab robust control toolbox).
Personal Competence
Social Competence Students can work in small groups on specific problems to arrive at joint solutions. 
Autonomy

Students are able to find required information in sources provided (lecture notes, literature, software documentation) and use it to solve given problems. 


Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Control and Power Systems: Elective Compulsory
Energy Systems: Core qualification: Elective Compulsory
Aircraft Systems Engineering: Specialisation Aircraft Systems: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory
Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory
Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory
Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory
Product Development, Materials and Production: Specialisation Product Development: Elective Compulsory
Product Development, Materials and Production: Specialisation Production: Elective Compulsory
Product Development, Materials and Production: Specialisation Materials: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Core qualification: Elective Compulsory
Course L0658: Optimal and Robust Control
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Herbert Werner
Language EN
Cycle SoSe
Content
  • Optimal regulator problem with finite time horizon, Riccati differential equation
  • Time-varying and steady state solutions, algebraic Riccati equation, Hamiltonian system
  • Kalman’s identity, phase margin of LQR controllers, spectral factorization
  • Optimal state estimation, Kalman filter, LQG control
  • Generalized plant, review of LQG control
  • Signal and system norms, computing H2 and H∞ norms
  • Singular value plots, input and output directions
  • Mixed sensitivity design, H∞ loop shaping, choice of weighting filters
  • Case study: design example flight control
  • Linear matrix inequalities, design specifications as LMI constraints (H2, H∞ and pole region)
  • Controller synthesis by solving LMI problems, multi-objective design
  • Robust control of uncertain systems, small gain theorem, representation of parameter uncertainty
Literature
  • Werner, H., Lecture Notes: "Optimale und Robuste Regelung"
  • Boyd, S., L. El Ghaoui, E. Feron and V. Balakrishnan "Linear Matrix Inequalities in Systems and Control", SIAM, Philadelphia, PA, 1994
  • Skogestad, S. and I. Postlewhaite "Multivariable Feedback Control", John Wiley, Chichester, England, 1996
  • Strang, G. "Linear Algebra and its Applications", Harcourt Brace Jovanovic, Orlando, FA, 1988
  • Zhou, K. and J. Doyle "Essentials of Robust Control", Prentice Hall International, Upper Saddle River, NJ, 1998
Course L0659: Optimal and Robust Control
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Herbert Werner
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0551: Pattern Recognition and Data Compression

Courses
Title Typ Hrs/wk CP
Pattern Recognition and Data Compression (L0128) Lecture 4 6
Module Responsible Prof. Rolf-Rainer Grigat
Admission Requirements None
Recommended Previous Knowledge

Linear algebra (including PCA, unitary transforms), stochastics and statistics, binary arithmetics

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students can name the basic concepts of pattern recognition and data compression.

Students are able to discuss logical connections between the concepts covered in the course and to explain them by means of examples.


Skills

Students can apply statistical methods to classification problems in pattern recognition and to prediction in data compression. On a sound theoretical and methodical basis they can analyze characteristic value assignments and classifications and describe data compression and video signal coding. They are able to use highly sophisticated methods and processes of the subject area. Students are capable of assessing different solution approaches in multidimensional decision-making areas.



Personal Competence
Social Competence

k.A.

Autonomy

Students are capable of identifying problems independently and of solving them scientifically, using the methods they have learnt.


Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 60 Minutes, Content of Lecture and materials in StudIP
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Course L0128: Pattern Recognition and Data Compression
Typ Lecture
Hrs/wk 4
CP 6
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Lecturer Prof. Rolf-Rainer Grigat
Language EN
Cycle SoSe
Content

Structure of a pattern recognition system, statistical decision theory, classification based on statistical models, polynomial regression, dimension reduction, multilayer perceptron regression, radial basis functions, support vector machines, unsupervised learning and clustering, algorithm-independent machine learning, mixture models and EM, adaptive basis function models and boosting, Markov random fields

Information, entropy, redundancy, mutual information, Markov processes, basic coding schemes (code length, run length coding, prefix-free codes), entropy coding (Huffman, arithmetic coding), dictionary coding (LZ77/Deflate/LZMA2, LZ78/LZW), prediction, DPCM, CALIC, quantization (scalar and vector quantization), transform coding, prediction, decorrelation (DPCM, DCT, hybrid DCT, JPEG, JPEG-LS), motion estimation, subband coding, wavelets, HEVC (H.265,MPEG-H)

Literature

Schürmann: Pattern Classification, Wiley 1996
Murphy, Machine Learning, MIT Press, 2012
Barber, Bayesian Reasoning and Machine Learning, Cambridge, 2012
Duda, Hart, Stork: Pattern Classification, Wiley, 2001
Bishop: Pattern Recognition and Machine Learning, Springer 2006

Salomon, Data Compression, the Complete Reference, Springer, 2000
Sayood, Introduction to Data Compression, Morgan Kaufmann, 2006
Ohm, Multimedia Communication Technology, Springer, 2004
Solari, Digital video and audio compression, McGraw-Hill, 1997
Tekalp, Digital Video Processing, Prentice Hall, 1995

Module M0630: Robotics and Navigation in Medicine

Courses
Title Typ Hrs/wk CP
Robotics and Navigation in Medicine (L0335) Lecture 2 3
Robotics and Navigation in Medicine (L0338) Project Seminar 2 2
Robotics and Navigation in Medicine (L0336) Recitation Section (small) 1 1
Module Responsible Prof. Alexander Schlaefer
Admission Requirements None
Recommended Previous Knowledge
  • principles of math (algebra, analysis/calculus)
  • principles of programming, e.g., in Java or C++
  • solid R or Matlab skills
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students can explain kinematics and tracking systems in clinical contexts and illustrate systems and their components in details. Systems can be evaluated with respect to collision detection and  safety and regulations. Students can assess typical systems regarding design and  limitations.

Skills

The students are able to design and evaluate navigation systems and robotic systems for medical applications.


Personal Competence
Social Competence

The students discuss the results of other groups, provide helpful feedback and can incoorporate feedback into their work.

Autonomy

The students can reflect their knowledge and document the results of their work. They can present the results in an appropriate manner.

Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Examination Written exam
Examination duration and scale 90 minutes
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Medical Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory
Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory
Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory
Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory
Product Development, Materials and Production: Specialisation Product Development: Elective Compulsory
Product Development, Materials and Production: Specialisation Production: Elective Compulsory
Product Development, Materials and Production: Specialisation Materials: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Bio- and Medical Technology: Elective Compulsory
Course L0335: Robotics and Navigation in Medicine
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Alexander Schlaefer
Language EN
Cycle SoSe
Content

- kinematics
- calibration
- tracking systems
- navigation and image guidance
- motion compensation
The seminar extends and complements the contents of the lecture with respect to recent research results.


Literature

Spong et al.: Robot Modeling and Control, 2005
Troccaz: Medical Robotics, 2012
Further literature will be given in the lecture.

Course L0338: Robotics and Navigation in Medicine
Typ Project Seminar
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Alexander Schlaefer
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course
Course L0336: Robotics and Navigation in Medicine
Typ Recitation Section (small)
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Alexander Schlaefer
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0673: Information Theory and Coding

Courses
Title Typ Hrs/wk CP
Information Theory and Coding (L0436) Lecture 3 4
Information Theory and Coding (L0438) Recitation Section (large) 1 2
Module Responsible Prof. Gerhard Bauch
Admission Requirements None
Recommended Previous Knowledge
  • Mathematics 1-3
  • Probability theory and random processes
  • Basic knowledge of communications engineering (e.g. from lecture "Fundamentals of Communications and Random Processes")
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge The students know the basic definitions for quantification of information in the sense of information theory. They know Shannon's source coding theorem and channel coding theorem and are able to determine theoretical limits of data compression and error-free data transmission over noisy channels. They understand the principles of source coding as well as error-detecting and error-correcting channel coding. They are familiar with the principles of decoding, in particular with modern methods of iterative decoding. They know fundamental coding schemes, their properties and decoding algorithms. 
Skills The students are able to determine the limits of data compression as well as of data transmission through noisy channels and based on those limits to design basic parameters of a transmission scheme. They can estimate the parameters of an error-detecting or error-correcting channel coding scheme for achieving certain performance targets. They are able to compare the properties of basic channel coding and decoding schemes regarding error correction capabilities, decoding delay, decoding complexity and to decide for a suitable method. They are capable of implementing basic coding and decoding schemes in software.
Personal Competence
Social Competence

The students can jointly solve specific problems.

Autonomy

The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Information and Communication Systems: Core qualification: Compulsory
International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Course L0436: Information Theory and Coding
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Gerhard Bauch
Language DE/EN
Cycle SoSe
Content
  • Fundamentals of information theory

    • Self information, entropy, mutual information

    • Source coding theorem, channel coding theorem

    • Channel capacity of various channels

  • Fundamental source coding algorithms:

    • Huffman Code, Lempel Ziv Algorithm

  • Fundamentals of channel coding

    • Basic parameters of channel coding and respective bounds

    • Decoding principles: Maximum-A-Posteriori Decoding, Maximum-Likelihood Decoding, Hard-Decision-Decoding and Soft-Decision-Decoding

    • Error probability

  • Block codes

  • Low Density Parity Check (LDPC) Codes and iterative Ddecoding

  • Convolutional codes and Viterbi-Decoding

  • Turbo Codes and iterative decoding

  • Coded Modulation

Literature

Bossert, M.: Kanalcodierung. Oldenbourg.

Friedrichs, B.: Kanalcodierung. Springer.

Lin, S., Costello, D.: Error Control Coding. Prentice Hall.

Roth, R.: Introduction to Coding Theory.

Johnson, S.: Iterative Error Correction. Cambridge.

Richardson, T., Urbanke, R.: Modern Coding Theory. Cambridge University Press.

Gallager, R. G.: Information theory and reliable communication. Whiley-VCH

Cover, T., Thomas, J.: Elements of information theory. Wiley.

Course L0438: Information Theory and Coding
Typ Recitation Section (large)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Gerhard Bauch
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0711: Numerical Mathematics II

Courses
Title Typ Hrs/wk CP
Numerical Mathematics II (L0568) Lecture 2 3
Numerical Mathematics II (L0569) Recitation Section (small) 2 3
Module Responsible Prof. Sabine Le Borne
Admission Requirements None
Recommended Previous Knowledge
  • Numerical Mathematics I
  • MATLAB knowledge
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to

  • name advanced numerical methods for interpolation, integration, linear least squares problems, eigenvalue problems, nonlinear root finding problems and explain their core ideas,
  • repeat convergence statements for the numerical methods,
  • sketch convergence proofs,
  • explain practical aspects of numerical methods concerning runtime and storage needs


    explain aspects regarding the practical implementation of numerical methods with respect to computational and storage complexity.


Skills

Students are able to

  • implement, apply and compare advanced numerical methods in MATLAB,
  • justify the convergence behaviour of numerical methods with respect to the problem and solution algorithm and to transfer it to related problems,
  • for a given problem, develop a suitable solution approach, if necessary through composition of several algorithms, to execute this approach and to critically evaluate the results


Personal Competence
Social Competence

Students are able to

  • work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge), explain theoretical foundations and support each other with practical aspects regarding the implementation of algorithms.
Autonomy

Students are capable

  • to assess whether the supporting theoretical and practical excercises are better solved individually or in a team,
  • to assess their individual progess and, if necessary, to ask questions and seek help.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Course L0568: Numerical Mathematics II
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sabine Le Borne, Dr. Patricio Farrell
Language DE/EN
Cycle SoSe
Content
  1. Error and stability: Notions and estimates
  2. Interpolation: Rational and trigonometric interpolation
  3. Quadrature: Gaussian quadrature, orthogonal polynomials
  4. Linear systems: Perturbation theory of decompositions, structured matrices
  5. Eigenvalue problems: LR-, QD-, QR-Algorithmus
  6. Krylov space methods: Arnoldi-, Lanczos methods
Literature
  • Stoer/Bulirsch: Numerische Mathematik 1, Springer
  • Dahmen, Reusken: Numerik für Ingenieure und Naturwissenschaftler, Springer
Course L0569: Numerical Mathematics II
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sabine Le Borne, Dr. Patricio Farrell
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1310: Methods and Applications of Differential Geometry

Courses
Title Typ Hrs/wk CP
Methods and Applications of Differential Geometry (L1808) Lecture 4 6
Module Responsible Prof. Karl-Heinz Zimmermann
Admission Requirements None
Recommended Previous Knowledge Linear Algebra, Multivariate Calculus 
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The lectures start by reviewing basics from linear algebra and analysis under the aspect of abstraction from coordinates and proceed to methods of differential geometry with applications to computer graphics, robotics, and physical field equations. As part of a computer science curriculum, they discuss  relations between the mathematical and the computer data types, and possible computer implementations of mathematical constructions. Keywords:

Data types, algorithms, numbers and number codes, discretisation of coninuous structures, systems of coordinates; vector spaces, tensors, quaternions, exterior algebra, Clifford algebras, Lie algebras; coordinate-free vector analysis, vector fields, Lie deivative, diffferential equations, variational calculus, differential forms and operators;  surfaces in space, curvature, covariant derivative, geodesics; manifolds, fibre bundles, transformation groups, Riemannian metrics, symplectic structures; groups of symmetries, invariants, special functions


Skills
Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Course L1808: Methods and Applications of Differential Geometry
Typ Lecture
Hrs/wk 4
CP 6
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Lecturer Prof. Georg Friedrich Mayer-Lindenberg
Language DE/EN
Cycle SoSe
Content

The lectures start by reviewing basics from linear algebra and analysis under the aspect of abstraction from coordinates and proceed to methods of differential geometry with applications to computer graphics, robotics, and physical field equations. As part of a computer science curriculum, they discuss  relations between the mathematical and the computer data types, and possible computer implementations of mathematical constructions. Keywords:

Data types, algorithms, numbers and number codes, discretisation of coninuous structures, systems of coordinates; vector spaces, tensors, quaternions, exterior algebra, Clifford algebras, Lie algebras; coordinate-free vector analysis, vector fields, Lie deivative, diffferential equations, variational calculus, differential forms and operators;  surfaces in space, curvature, covariant derivative, geodesics; manifolds, fibre bundles, transformation groups, Riemannian metrics, symplectic structures; groups of symmetries, invariants, special functions

Literature

Agricola, Friedrich,    Vektoranalysis,                                                Vieweg/Teubner 2010

A.C. Da Silva,           Lectures on Symplectic Geometry,                    Springer L.N. Math. 1764

J. Snygg,                  Differential Geometry using Clifford's Algebra,   Birkhäuser 2010

T. Frankel                 The Geometry of Physics                                 Cambridge U. P. 2012

M.Desbrun et al.       Discrete exterior calculus,                                arXiv:math/0508341v2

J.Marsden  et al.       Discrete Mechanics and Variational Integrators, Acta numerica. 2001

Module M0627: Machine Learning and Data Mining

Courses
Title Typ Hrs/wk CP
Machine Learning and Data Mining (L0340) Lecture 2 4
Machine Learning and Data Mining (L0510) Recitation Section (small) 2 2
Module Responsible NN
Admission Requirements None
Recommended Previous Knowledge
  • Calculus
  • Stochastics
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students can explain the difference between instance-based and model-based learning approaches, and they can enumerate basic machine learning technique for each of the two basic approaches, either on the basis of static data, or on the basis of incrementally incoming data . For dealing with uncertainty, students can describe suitable representation formalisms, and they explain how axioms, features, parameters, or structures used in these formalisms can be learned automatically with different algorithms. Students are also able to sketch different clustering techniques. They depict how the performance of learned classifiers can be improved by ensemble learning, and they can summarize how this influences computational learning theory. Algorithms for reinforcement learning can also be explained by students.

Skills

Student derive decision trees and, in turn, propositional rule sets from simple and static data tables and are able to name and explain basic optimization techniques. They present and apply the basic idea of first-order inductive leaning. Students apply the BME, MAP, ML, and EM algorithms for learning parameters of Bayesian networks and compare the different algorithms. They also know how to carry out Gaussian mixture learning. They can contrast kNN classifiers, neural networks, and support vector machines, and name their basic application areas and algorithmic properties. Students can describe basic clustering techniques and explain the basic components of those techniques. Students compare related machine learning techniques, e.g., k-means clustering and nearest neighbor classification. They can distinguish various ensemble learning techniques and compare the different goals of those techniques.




Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 90 minutes
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Course L0340: Machine Learning and Data Mining
Typ Lecture
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Lecturer Rainer Marrone
Language EN
Cycle SoSe
Content
  • Decision trees
  • First-order inductive learning
  • Incremental learning: Version spaces
  • Uncertainty
  • Bayesian networks
  • Learning parameters of Bayesian networks
    BME, MAP, ML, EM algorithm
  • Learning structures of Bayesian networks
  • Gaussian Mixture Models
  • kNN classifier, neural network classifier, support vector machine (SVM) classifier
  • Clustering
    Distance measures, k-means clustering, nearest neighbor clustering
  • Kernel Density Estimation
  • Ensemble Learning
  • Reinforcement Learning
  • Computational Learning Theory
Literature
  1. Artificial Intelligence: A Modern Approach (Third Edition), Stuart Russel, Peter Norvig, Prentice Hall, 2010, Chapters 13, 14, 18-21
  2. Machine Learning: A Probabilistic Perspective, Kevin Murphy, MIT Press 2012
Course L0510: Machine Learning and Data Mining
Typ Recitation Section (small)
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Rainer Marrone
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0549: Scientific Computing and Accuracy

Courses
Title Typ Hrs/wk CP
Verification Methods (L0122) Lecture 2 3
Verification Methods (L1208) Recitation Section (small) 2 3
Module Responsible Prof. Siegfried Rump
Admission Requirements None
Recommended Previous Knowledge

Basic knowledge in numerics

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students have deeper knowledge of numerical and semi-numerical methods with the goal to compute principally exact and accurate error bounds. For several fundamental problems they know algorithms with the verification of the correctness of the computed result.

Skills

The students can devise algorithms for several basic problems which compute rigorous error bounds for the solution and analyze the sensitivity with respect to variation of the input data as well.

Personal Competence
Social Competence

The students have the skills to solve problems together in small groups and to present the achieved results in an appropriate manner.

Autonomy

The students are able to retrieve necessary informations from the given literature and to combine them with the topics of the lecture. Throughout the lecture they can check their abilities and knowledge on the basis of given exercises and test questions providing an aid to optimize their learning process.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Technomathematics: Specialisation II. Informatics: Elective Compulsory
Process Engineering: Specialisation Process Engineering: Elective Compulsory
Process Engineering: Specialisation Chemical Process Engineering: Elective Compulsory
Course L0122: Verification Methods
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Siegfried Rump
Language DE
Cycle WiSe
Content
  • Fast and accurate interval arithmetic

  • Error-free transformations

  • Verification methods for linear and nonlinear systems

  • Verification methods for finite integrals

  • Treatment of multiple zeros

  • Automatic differentiation

  • Implementation in Matlab/INTLAB

  • Practical applications

Literature

Neumaier: Interval Methods for Systems of Equations. In: Encyclopedia of Mathematics and its  Applications. Cambridge University Press, 1990

S.M. Rump. Verification methods: Rigorous results using floating-point arithmetic. Acta Numerica, 19:287-449, 2010.
Course L1208: Verification Methods
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Siegfried Rump
Language DE
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0832: Advanced Topics in Control

Courses
Title Typ Hrs/wk CP
Advanced Topics in Control (L0661) Lecture 2 3
Advanced Topics in Control (L0662) Recitation Section (small) 2 3
Module Responsible Prof. Herbert Werner
Admission Requirements None
Recommended Previous Knowledge H-infinity optimal control, mixed-sensitivity design, linear matrix inequalities 
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can explain the advantages and shortcomings of the classical gain scheduling approach
  • They can explain the representation of nonlinear systems in the form of quasi-LPV systems
  • They can explain how stability and performance conditions for LPV systems can be formulated as LMI conditions
  • They can explain how gridding techniques can be used to solve analysis and synthesis problems for LPV systems
  • They are familiar with polytopic and LFT representations of LPV systems and some of the basic synthesis techniques associated with each of these model structures


  • Students can explain how graph theoretic concepts are used to represent the communication topology of multiagent systems
  • They can explain the convergence properties of  first order consensus protocols
  • They can explain analysis and synthesis conditions for formation control loops involving either LTI or LPV agent models


  • Students can explain the state space representation of spatially invariant distributed systems that are discretized according to an actuator/sensor array
  • They can explain (in outline) the extension of the bounded real lemma to such distributed systems and the associated synthesis conditions for distributed controllers

Skills
  • Students are capable of constructing LPV models of nonlinear plants and carry out a mixed-sensitivity design of gain-scheduled controllers; they can do this using polytopic, LFT or general LPV models 
  • They are able to use standard software tools (Matlab robust control toolbox) for these tasks


  • Students are able to design distributed formation controllers for groups of agents with either LTI or LPV dynamics, using Matlab tools provided


  • Students are able to design distributed controllers for spatially interconnected systems, using the Matlab MD-toolbox
Personal Competence
Social Competence Students can work in small groups and arrive at joint results.
Autonomy

Students are able to find required information in sources provided (lecture notes, literature, software documentation) and use it to solve given problems. 


 
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Control and Power Systems: Elective Compulsory
Electrical Engineering: Specialisation Control and Power Systems: Elective Compulsory
Aircraft Systems Engineering: Specialisation Aircraft Systems: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory
Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory
Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory
Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory
Theoretical Mechanical Engineering: Core qualification: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Course L0661: Advanced Topics in Control
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Herbert Werner
Language EN
Cycle WiSe
Content
  • Linear Parameter-Varying (LPV) Gain Scheduling

    - Linearizing gain scheduling, hidden coupling
    - Jacobian linearization vs. quasi-LPV models
    - Stability and induced L2 norm of LPV systems
    - Synthesis of LPV controllers based on the two-sided projection lemma
    - Simplifications: controller synthesis for polytopic and LFT models
    - Experimental identification of LPV models
    - Controller synthesis based on input/output models
    - Applications: LPV torque vectoring for electric vehicles, LPV control of a robotic manipulator
  • Control of Multi-Agent Systems

    - Communication graphs
    - Spectral properties of the graph Laplacian
    - First and second order consensus protocols
    - Formation control, stability and performance
    - LPV models for agents subject to nonholonomic constraints
    - Application: formation control for a team of quadrotor helicopters
  • Control of Spatially Interconnected Systems

    - Multidimensional signals, l2 and L2 signal norm
    - Multidimensional systems in Roesser state space form
    - Extension of real-bounded lemma to spatially interconnected systems
    - LMI-based synthesis of distributed controllers
    - Spatial LPV control of spatially varying systems
    - Applications: control of temperature profiles, vibration damping for an actuated beam
Literature
  • Werner, H., Lecture Notes "Advanced Topics in Control"
  • Selection of relevant research papers made available as pdf documents via StudIP
Course L0662: Advanced Topics in Control
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Herbert Werner
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0768: Microsystems Technology in Theory and Practice

Courses
Title Typ Hrs/wk CP
Microsystems Technology (L0724) Lecture 2 4
Microsystems Technology (L0725) Problem-based Learning 2 2
Module Responsible Prof. Hoc Khiem Trieu
Admission Requirements None
Recommended Previous Knowledge

Basics in physics, chemistry, mechanics and semiconductor technology

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able

     to present and to explain current fabrication techniques for microstructures and especially methods for the fabrication of microsensors and microactuators, as well as the integration thereof in more complex systems

     to explain in details operation principles of microsensors and microactuators and

     to discuss the potential and limitation of microsystems in application.


Skills

Students are capable

     to analyze the feasibility of microsystems,

     to develop process flows for the fabrication of microstructures and

     to apply them.




Personal Competence
Social Competence


Students are able to prepare and perform their lab experiments in team work as well as to present and discuss the results in front of audience.


Autonomy

None

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Electrical Engineering: Specialisation Nanoelectronics and Microsystems Technology: Elective Compulsory
Electrical Engineering: Specialisation Medical Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory
Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory
Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory
Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory
Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory
Microelectronics and Microsystems: Core qualification: Elective Compulsory
Course L0724: Microsystems Technology
Typ Lecture
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Lecturer Prof. Hoc Khiem Trieu
Language EN
Cycle WiSe
Content
  • Introduction (historical view, scientific and economic relevance, scaling laws)
  • Semiconductor Technology Basics, Lithography (wafer fabrication, photolithography, improving resolution, next-generation lithography, nano-imprinting, molecular imprinting)
  • Deposition Techniques (thermal oxidation, epitaxy, electroplating, PVD techniques: evaporation and sputtering; CVD techniques: APCVD, LPCVD, PECVD and LECVD; screen printing)
  • Etching and Bulk Micromachining (definitions, wet chemical etching, isotropic etch with HNA, electrochemical etching, anisotropic etching with KOH/TMAH: theory, corner undercutting, measures for compensation and etch-stop techniques; plasma processes, dry etching: back sputtering, plasma etching, RIE, Bosch process, cryo process, XeF2 etching)
  • Surface Micromachining and alternative Techniques (sacrificial etching, film stress, stiction: theory and counter measures; Origami microstructures, Epi-Poly, porous silicon, SOI, SCREAM process, LIGA, SU8, rapid prototyping)
  • Thermal and Radiation Sensors (temperature measurement, self-generating sensors: Seebeck effect and thermopile; modulating sensors: thermo resistor, Pt-100, spreading resistance sensor, pn junction, NTC and PTC; thermal anemometer, mass flow sensor, photometry, radiometry, IR sensor: thermopile and bolometer)
  • Mechanical Sensors (strain based and stress based principle, capacitive readout, piezoresistivity,  pressure sensor: piezoresistive, capacitive and fabrication process; accelerometer: piezoresistive, piezoelectric and capacitive; angular rate sensor: operating principle and fabrication process)
  • Magnetic Sensors (galvanomagnetic sensors: spinning current Hall sensor and magneto-transistor; magnetoresistive sensors: magneto resistance, AMR and GMR, fluxgate magnetometer)
  • Chemical and Bio Sensors (thermal gas sensors: pellistor and thermal conductivity sensor; metal oxide semiconductor gas sensor, organic semiconductor gas sensor, Lambda probe, MOSFET gas sensor, pH-FET, SAW sensor, principle of biosensor, Clark electrode, enzyme electrode, DNA chip)
  • Micro Actuators, Microfluidics and TAS (drives: thermal, electrostatic, piezo electric and electromagnetic; light modulators, DMD, adaptive optics, microscanner, microvalves: passive and active, micropumps, valveless micropump, electrokinetic micropumps, micromixer, filter, inkjet printhead, microdispenser, microfluidic switching elements, microreactor, lab-on-a-chip, microanalytics)
  • MEMS in medical Engineering (wireless energy and data transmission, smart pill, implantable drug delivery system, stimulators: microelectrodes, cochlear and retinal implant; implantable pressure sensors, intelligent osteosynthesis, implant for spinal cord regeneration)
  • Design, Simulation, Test (development and design flows, bottom-up approach, top-down approach, testability, modelling: multiphysics, FEM and equivalent circuit simulation; reliability test, physics-of-failure, Arrhenius equation, bath-tub relationship)
  • System Integration (monolithic and hybrid integration, assembly and packaging, dicing, electrical contact: wire bonding, TAB and flip chip bonding; packages, chip-on-board, wafer-level-package, 3D integration, wafer bonding: anodic bonding and silicon fusion bonding; micro electroplating, 3D-MID)


Literature

M. Madou: Fundamentals of Microfabrication, CRC Press, 2002

N. Schwesinger: Lehrbuch Mikrosystemtechnik, Oldenbourg Verlag, 2009

T. M. Adams, R. A. Layton:Introductory MEMS, Springer, 2010

G. Gerlach; W. Dötzel: Introduction to microsystem technology, Wiley, 2008

Course L0725: Microsystems Technology
Typ Problem-based Learning
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Hoc Khiem Trieu
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0746: Microsystem Engineering

Courses
Title Typ Hrs/wk CP
Microsystem Engineering (L0680) Lecture 2 4
Microsystem Engineering (L0682) Problem-based Learning 1 1
Microsystem Engineering (L0681) Recitation Section (small) 1 1
Module Responsible Prof. Manfred Kasper
Admission Requirements None
Recommended Previous Knowledge Basic courses in physics, mathematics and electric engineering
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students know about the most important technologies and materials of MEMS as well as their applications in sensors and actuators.

Skills

Students are able to analyze and describe the functional behaviour of MEMS components and to evaluate the potential of microsystems.

Personal Competence
Social Competence

Students are able to solve specific problems alone or in a group and to present the results accordingly.

Autonomy

Students are able to acquire particular knowledge using specialized literature and to integrate and associate this knowledge with other fields.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 2h
Assignment for the Following Curricula Electrical Engineering: Core qualification: Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory
International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory
Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory
Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory
Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory
Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory
Microelectronics and Microsystems: Core qualification: Elective Compulsory
Course L0680: Microsystem Engineering
Typ Lecture
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Lecturer Prof. Manfred Kasper
Language EN
Cycle WiSe
Content

Object and goal of MEMS

Scaling Rules

Lithography

Film deposition

Structuring and etching

Energy conversion and force generation

Electromagnetic Actuators

Reluctance motors

Piezoelectric actuators, bi-metal-actuator

Transducer principles

Signal detection and signal processing

Mechanical and physical sensors

Acceleration sensor, pressure sensor

Sensor arrays

System integration

Yield, test and reliability

Literature

M. Kasper: Mikrosystementwurf, Springer (2000)

M. Madou: Fundamentals of Microfabrication, CRC Press (1997)

Course L0682: Microsystem Engineering
Typ Problem-based Learning
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Manfred Kasper
Language EN
Cycle WiSe
Content

Examples of MEMS components

Layout consideration

Electric, thermal and mechanical behaviour

Design aspects

Literature

Wird in der Veranstaltung bekannt gegeben

Course L0681: Microsystem Engineering
Typ Recitation Section (small)
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Manfred Kasper
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0552: 3D Computer Vision

Courses
Title Typ Hrs/wk CP
3D Computer Vision (L0129) Lecture 2 3
3D Computer Vision (L0130) Recitation Section (small) 2 3
Module Responsible Prof. Rolf-Rainer Grigat
Admission Requirements None
Recommended Previous Knowledge
  • Knowlege of the modules Digital Image Analysis and Pattern Recognition and Data Compression are used in the practical task
  • Linear Algebra (including PCA, SVD), nonlinear optimization (Levenberg-Marquardt), basics of stochastics and basics of Matlab are required and cannot be explained in detail during the lecture.
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students can explain and describe the field of projective geometry.

Skills

Students are capable of

  • Implementing an exemplary 3D or volumetric analysis task
  • Using highly sophisticated methods and procedures of the subject area
  • Identifying problems and
  • Developing and implementing creative solution suggestions.

With assistance from the teacher students are able to link the contents of the three subject areas (modules)

  • Digital Image Analysis 
  • Pattern Recognition and Data Compression
    and 
  • 3D Computer Vision 

in practical assignments.

Personal Competence
Social Competence

Students can collaborate in a small team on the practical realization and testing of a system to reconstruct a three-dimensional scene or to evaluate volume data sets.

Autonomy

Students are able to solve simple tasks independently with reference to the contents of the lectures and the exercise sets.

Students are able to solve detailed problems independently with the aid of the tutorial’s programming task.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 60 Minutes, Content of Lecture and materials in StudIP
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory
Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory
Course L0129: 3D Computer Vision
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Rolf-Rainer Grigat
Language EN
Cycle WiSe
Content
  • Projective Geometry and Transformations in 2D und 3D in homogeneous coordinates
  • Projection matrix, calibration
  • Epipolar Geometry, fundamental and essential matrices, weak calibration, 5 point algorithm
  • Homographies 2D and 3D
  • Trifocal Tensor
  • Correspondence search
Literature
  • Skriptum Grigat/Wenzel
  • Hartley, Zisserman: Multiple View Geometry in Computer Vision. Cambridge 2003.
Course L0130: 3D Computer Vision
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Rolf-Rainer Grigat
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1249: Numerical Methods for Medical Imaging

Courses
Title Typ Hrs/wk CP
Numerical Methods for Medical Imaging (L1694) Lecture 2 3
Numerical Methods for Medical Imaging (L1695) Recitation Section (small) 2 3
Module Responsible Prof. Tobias Knopp
Admission Requirements None
Recommended Previous Knowledge
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
Skills
Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Modeling and Simulation: Elective Compulsory
Electrical Engineering: Specialisation Medical Technology: Elective Compulsory
Electrical Engineering: Specialisation Medical Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Course L1694: Numerical Methods for Medical Imaging
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Tobias Knopp
Language DE
Cycle WiSe
Content
Literature

Bildgebende Verfahren in der Medizin; O. Dössel; Springer, Berlin, 2000

Bildgebende Systeme für die medizinische Diagnostik; H. Morneburg (Hrsg.); Publicis MCD, München, 1995

Introduction to the Mathematics of Medical Imaging; C. L.Epstein; Siam, Philadelphia, 2008

Medical Image Processing, Reconstruction and Restoration; J. Jan; Taylor and Francis, Boca Raton, 2006

Principles of Magnetic Resonance Imaging; Z.-P. Liang and P. C. Lauterbur; IEEE Press, New York, 1999

Course L1695: Numerical Methods for Medical Imaging
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Tobias Knopp
Language DE
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0738: Digital Audio Signal Processing

Courses
Title Typ Hrs/wk CP
Digital Audio Signal Processing (L0650) Lecture 3 4
Digital Audio Signal Processing (L0651) Recitation Section (large) 1 2
Module Responsible Prof. Udo Zölzer
Admission Requirements None
Recommended Previous Knowledge

Signals and Systems

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Die Studierenden können die grundlegenden Verfahren und Methoden der digitalen Audiosignalverarbeitung erklären. Sie können die wesentlichen physikalischen Effekte bei der Sprach- und Audiosignalverarbeitung erläutern und in Kategorien einordnen. Sie können einen Überblick der numerischen Methoden und messtechnischen Charakterisierung von Algorithmen zur Audiosignalverarbeitung geben. Sie können die erarbeiteten Algorithmen auf weitere Anwendungen im Bereich der Informationstechnik und Informatik abstrahieren.

Skills

The students will be able to apply methods and techniques from audio signal processing in the fields of mobile and internet communication. They can rely on elementary algorithms of audio signal processing in form of Matlab code and interactive JAVA applets. They can study parameter modifications and evaluate the influence on human perception and technical applications in a variety of applications beyond audio signal processing. Students can perform measurements in time and frequency domain in order to give objective and subjective quality measures with respect to the methods and applications.

Personal Competence
Social Competence

The students can work in small groups to study special tasks and problems and will be enforced to present their results with adequate methods during the exercise.

Autonomy

The students will be able to retrieve information out of the relevant literature in the field and putt hem into the context of the lecture. They can relate their gathered knowledge and relate them to other lectures (signals and systems, digital communication systems, image and video processing, and pattern recognition). They will be prepared to understand and communicate problems and effects in the field audio signal processing.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 45 min
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory
Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory
Course L0650: Digital Audio Signal Processing
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Udo Zölzer
Language EN
Cycle WiSe
Content
  • Introduction (Studio Technology,  Digital Transmission Systems, Storage Media, Audio Components at Home)

  • Quantization (Signal Quantization, Dither, Noise Shaping, Number Representation)

  • AD/DA Conversion (Methods, AD Converters, DA Converters, Audio Processing Systems, Digital Signal Processors, Digital Audio Interfaces, Single-Processor Systems, Multiprocessor Systems)

  • Equalizers (Recursive Audio Filters, Nonrecursive Audio Filters, Multi-Complementary Filter Bank)

  • Room Simulation (Early Reflections, Subsequent Reverberation, Approximation of Room Impulse Responses)

  • Dynamic Range Control (Static Curve, Dynamic Behavior, Implementation, Realization Aspects)

  • Sampling Rate Conversion (Synchronous Conversion, Asynchronous Conversion, Interpolation Methods)

  • Data Compression (Lossless Data Compression, Lossy Data Compression, Psychoacoustics, ISO-MPEG1 Audio Coding)

Literature

- U. Zölzer, Digitale Audiosignalverarbeitung, 3. Aufl., B.G. Teubner, 2005.

- U. Zölzer, Digitale Audio Signal Processing, 2nd Edition, J. Wiley & Sons, 2005.


- U. Zölzer (Ed), Digital Audio Effects, 2nd Edition, J. Wiley & Sons, 2011.


 






Course L0651: Digital Audio Signal Processing
Typ Recitation Section (large)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Udo Zölzer
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0623: Intelligent Systems in Medicine

Courses
Title Typ Hrs/wk CP
Intelligent Systems in Medicine (L0331) Lecture 2 3
Intelligent Systems in Medicine (L0334) Project Seminar 2 2
Intelligent Systems in Medicine (L0333) Recitation Section (small) 1 1
Module Responsible Prof. Alexander Schlaefer
Admission Requirements None
Recommended Previous Knowledge
  • principles of math (algebra, analysis/calculus)
  • principles of stochastics
  • principles of programming, Java/C++ and R/Matlab
  • advanced programming skills
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students are able to analyze and solve clinical treatment planning and decision support problems using methods for search, optimization, and planning. They are able to explain methods for classification and their respective advantages and disadvantages in clinical contexts. The students can compare  different methods for representing medical knowledge. They can evaluate methods in the context of clinical data  and explain challenges due to the clinical nature of the data and its acquisition and due to privacy and safety requirements.

Skills

The students can give reasons for selecting and adapting methods for classification, regression, and prediction. They can assess the methods based on actual patient data and evaluate the implemented methods.

Personal Competence
Social Competence

The students discuss the results of other groups, provide helpful feedback and can incoorporate feedback into their work.

Autonomy

The students can reflect their knowledge and document the results of their work. They can present the results in an appropriate manner.

Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Examination Written exam
Examination duration and scale 90 minutes
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Medical Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory
Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory
Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory
Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Bio- and Medical Technology: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Course L0331: Intelligent Systems in Medicine
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Alexander Schlaefer
Language EN
Cycle WiSe
Content

- methods for search, optimization,  planning,  classification, regression and prediction in a clinical context
- representation of medical knowledge
- understanding challenges due to clinical and patient related data and data acquisition
The students will work in groups to apply the methods introduced during the lecture using problem based learning.


Literature

Russel & Norvig: Artificial Intelligence: a Modern Approach, 2012
Berner: Clinical Decision Support Systems: Theory and Practice, 2007
Greenes: Clinical Decision Support: The Road Ahead, 2007
Further literature will be given in the lecture


Course L0334: Intelligent Systems in Medicine
Typ Project Seminar
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Alexander Schlaefer
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course
Course L0333: Intelligent Systems in Medicine
Typ Recitation Section (small)
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Alexander Schlaefer
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Specialization Scientific Computing

Module M1244: Technical Complementary Course I for IIWMS (according to Subject Specific Regulations)

Courses
Title Typ Hrs/wk CP
Module Responsible Prof. Volker Turau
Admission Requirements None
Recommended Previous Knowledge None
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students acquire advanced knowledge in a technical subject available at TUHH.

Skills The students acquire professional competence in a technical subject available at TUHH.
Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 180, Study Time in Lecture 0
Credit points 6
Examination according to Subject Specific Regulations
Examination duration and scale
Assignment for the Following Curricula Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory

Module M0716: Hierarchical Algorithms

Courses
Title Typ Hrs/wk CP
Hierarchical Algorithms (L0585) Lecture 2 3
Hierarchical Algorithms (L0586) Recitation Section (small) 2 3
Module Responsible Prof. Sabine Le Borne
Admission Requirements None
Recommended Previous Knowledge
  • Mathematics I, II, III for Engineering students (german or english) or Analysis & Linear Algebra I + II as well as Analysis III for Technomathematicians
  • Programming experience in C
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to

  • name representatives of hierarchical algorithms and list their characteristics,
  • explain construction techniques for hierarchical algorithms,
  • discuss aspects regarding the efficient implementation of hierarchical algorithms.
Skills

Students are able to

  • implement the hierarchical algorithms discussed in the lecture,
  • analyse the storage and computational complexities of the algorithms,
  • adapt algorithms to problem settings of various applications and thus develop problem adapted variants.
Personal Competence
Social Competence

Students are able to

  • work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge), explain theoretical foundations and support each other with practical aspects regarding the implementation of algorithms.
Autonomy

Students are capable

  • to assess whether the supporting theoretical and practical excercises are better solved individually or in a team,
  • to work on complex problems over an extended period of time,
  • to assess their individual progess and, if necessary, to ask questions and seek help.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 20 min
Assignment for the Following Curricula Electrical Engineering: Specialisation Modeling and Simulation: Elective Compulsory
Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Course L0585: Hierarchical Algorithms
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sabine Le Borne
Language DE/EN
Cycle WiSe
Content
  • Low rank matrices
  • Separable expansions
  • Hierarchical matrix expansions
  • Hierarchical matrices
  • Formatted matrix operations
  • Applications
  • Additional topics
Literature W. Hackbusch: Hierarchische Matrizen: Algorithmen und Analysis
Course L0586: Hierarchical Algorithms
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sabine Le Borne
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0586: Efficient Algorithms

Courses
Title Typ Hrs/wk CP
Efficient Algorithms (L0120) Lecture 2 3
Efficient Algorithms (L1207) Recitation Section (small) 2 3
Module Responsible Prof. Siegfried Rump
Admission Requirements None
Recommended Previous Knowledge

Programming in Matlab and/or C

Basic knowledge in discrete mathematics
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students are able to explain the basic theory and methods of network algorithms and in particular their data structures. They are able to analyze the computational behavior and computing time of linear programming algorithms as well network algorithms. Moreover the students can distinguish between efficiently solvable and NP-hard problems.

Skills

The students are able to analyze complex tasks and can determine possibilities to transform them into networking algorithms. In particular they can efficiently implement basic algorithms and data structures of LP- and network algorithms and identify possible weaknesses. They are able to distinguish between different efficient data structures and are able to use them appropriately.

Personal Competence
Social Competence

The students have the skills to solve problems together in small groups and to present the achieved results in an appropriate manner.

Autonomy

The students are able to retrieve necessary informations from the given literature and to combine them with the topics of the lecture. Throughout the lecture they can check their abilities and knowledge on the basis of given exercises and test questions providing an aid to optimize their learning process.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Electrical Engineering: Specialisation Modeling and Simulation: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Course L0120: Efficient Algorithms
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Siegfried Rump
Language DE
Cycle WiSe
Content

- Linear Programming

- Data structures

- Leftist heaps

- Minimum spanning tree

- Shortest path

- Maximum flow

- NP-hard problems via max-cut

Literature

R. E. Tarjan: Data Structures and Network Algorithms. CBMS 44, Society for Industrial and Applied Mathematics, Philadelphia, PA, 1983.

Wesley, 2011 http://algs4.cs.princeton.edu/home/

V. Chvátal, ``Linear Programming'', Freeman, New York, 1983.

Course L1207: Efficient Algorithms
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Siegfried Rump
Language DE
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0955: Matrix Theory

Courses
Title Typ Hrs/wk CP
Numerical Analysis and Matrix Theory (L0123) Lecture 2 3
Numerical Analysis and Matrix Theory (L1209) Recitation Section (small) 2 3
Module Responsible Prof. Siegfried Rump
Admission Requirements None
Recommended Previous Knowledge

Basic knowledge in discrete mathematics

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students know basic theories, connections and methods in matrix theory. Moreover they know about possible connections between matrix theory and other subareas in mathematics, computer science and engineering sciences.

Skills

The students are able to analyze complex problems in matrix theory and solve them with unorthodox methods.

Personal Competence
Social Competence

The students have the skills to solve problems together in small groups and to present the achieved results in an appropriate manner.

Autonomy

The students are able to retrieve necessary informations from the given literature and to combine them with the topics of the lecture. Throughout the lecture they can check their abilities and knowledge on the basis of given exercises and test questions providing an aid to optimize their learning process.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Course L0123: Numerical Analysis and Matrix Theory
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Siegfried Rump
Language DE
Cycle WiSe
Content Selected chapters of matrix theory
Literature

R.A. Horn and Ch. Johnson, Matrix Analysis. Cambridge University Press, 1985

M. Fiedler: Special matrices and their applications in numerical mathematics. Martinus Nijhoff Publishers, Dordrecht, 1986
 

G.H. Golub, Ch. Van Loan: Matrix Computations. third edition. Johns Hopkins University Press, Baltimore, 1996

Course L1209: Numerical Analysis and Matrix Theory
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Siegfried Rump
Language DE
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0720: Matrix Algorithms

Courses
Title Typ Hrs/wk CP
Matrix Algorithms (L0984) Lecture 2 3
Matrix Algorithms (L0985) Recitation Section (small) 2 3
Module Responsible Dr. Jens-Peter Zemke
Admission Requirements None
Recommended Previous Knowledge
  • Mathematics I - III
  • Numerical Mathematics/ Numerics
  • Basic knowledge of the programming languages Matlab and C
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to

  1. name, state and classify state-of-the-art Krylov subspace  methods for the solution of the core problems of the engineering sciences, namely, eigenvalue problems, solution of linear systems, and model reduction;
  2. state approaches for the solution of matrix equations (Sylvester, Lyapunov, Riccati).
Skills

Students are capable to

  1. implement and assess basic Krylov subspace methods for the solution of eigenvalue problems, linear systems, and model reduction;
  2. assess methods used in modern software with respect to computing time, stability, and domain of applicability;
  3. adapt the approaches learned to new, unknown types of problem.
Personal Competence
Social Competence

Students can

  • develop and document joint solutions in small teams;
  • form groups to further develop the ideas and transfer them to other areas of applicability;
  • form a team to develop, build, and advance a software library.
Autonomy

Students are able to

  • correctly assess the time and effort of self-defined work;
  • assess whether the supporting theoretical and practical excercises are better solved individually or in a team;
  • define test problems for testing and expanding the methods;
  • assess their individual progess and, if necessary, to ask questions and seek help.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Electrical Engineering: Specialisation Modeling and Simulation: Elective Compulsory
Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Course L0984: Matrix Algorithms
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Dr. Jens-Peter Zemke
Language DE
Cycle WiSe
Content
  • Part A: Krylov Subspace Methods:
    • Basics (derivation, basis, Ritz, OR, MR)
    • Arnoldi-based methods (Arnoldi, GMRes)
    • Lanczos-based methods (Lanczos, CG, BiCG, QMR, SymmLQ, PvL)
    • Sonneveld-based methods (IDR, BiCGStab, TFQMR, IDR(s))
  • Part B: Matrix Equations:
    • Sylvester Equation
    • Lyapunov Equation
    • Algebraic Riccati Equation
Literature Skript
Course L0985: Matrix Algorithms
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Dr. Jens-Peter Zemke
Language DE
Cycle WiSe
Content
Literature Siehe korrespondierende Vorlesung

Module M0808: Finite Elements Methods

Courses
Title Typ Hrs/wk CP
Finite Element Methods (L0291) Lecture 2 3
Finite Element Methods (L0804) Recitation Section (large) 2 3
Module Responsible Prof. Otto von Estorff
Admission Requirements None
Recommended Previous Knowledge

Mechanics I (Statics, Mechanics of Materials) and Mechanics II (Hydrostatics, Kinematics, Dynamics)
Mathematics I, II, III (in particular differential equations)

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students possess an in-depth knowledge regarding the derivation of the finite element method and are able to give an overview of the theoretical and methodical basis of the method.



Skills

The students are capable to handle engineering problems by formulating suitable finite elements, assembling the corresponding system matrices, and solving the resulting system of equations.



Personal Competence
Social Competence -
Autonomy

The students are able to independently solve challenging computational problems and develop own finite element routines. Problems can be identified and the results are critically scrutinized.



Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 120 min
Assignment for the Following Curricula Civil Engineering: Core qualification: Compulsory
Energy Systems: Core qualification: Elective Compulsory
Aircraft Systems Engineering: Specialisation Aircraft Systems: Elective Compulsory
Aircraft Systems Engineering: Specialisation Air Transportation Systems: Elective Compulsory
Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory
International Management and Engineering: Specialisation II. Product Development and Production: Elective Compulsory
Mechatronics: Core qualification: Compulsory
Biomedical Engineering: Specialisation Implants and Endoprostheses: Compulsory
Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory
Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory
Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory
Product Development, Materials and Production: Core qualification: Compulsory
Technomathematics: Specialisation III. Engineering Science: Elective Compulsory
Technomathematics: Core qualification: Elective Compulsory
Theoretical Mechanical Engineering: Core qualification: Compulsory
Course L0291: Finite Element Methods
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Otto von Estorff
Language EN
Cycle WiSe
Content

- General overview on modern engineering
- Displacement method
- Hybrid formulation
- Isoparametric elements
- Numerical integration
- Solving systems of equations (statics, dynamics)
- Eigenvalue problems
- Non-linear systems
- Applications

- Programming of elements (Matlab, hands-on sessions)
- Applications

Literature

Bathe, K.-J. (2000): Finite-Elemente-Methoden. Springer Verlag, Berlin

Course L0804: Finite Element Methods
Typ Recitation Section (large)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Otto von Estorff
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1150: Continuum Mechanics

Courses
Title Typ Hrs/wk CP
Continuum Mechanics (L1533) Lecture 2 3
Continuum Mechanics Exercise (L1534) Recitation Section (small) 2 3
Module Responsible Prof. Swantje Bargmann
Admission Requirements None
Recommended Previous Knowledge

Mechanics I

Mechanics II

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge


The students can explain the fundamental concepts to calculate the mechanical behavior of materials.


Skills

The students can set up balance laws and apply basics of deformation theory to specific aspects, both in applied contexts as in research contexts.

Personal Competence
Social Competence

The students are able to present solutions to specialists and to develop ideas further.

Autonomy

The students are able to assess their own strengths and weaknesses and to define tasks themselves. They can solve exercises in the area of continuum mechanics on their own.


Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Materials Science: Specialisation Modeling: Elective Compulsory
Mechanical Engineering and Management: Specialisation Materials: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory
Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory
Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory
Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory
Product Development, Materials and Production: Core qualification: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Course L1533: Continuum Mechanics
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Swantje Bargmann
Language DE/EN
Cycle WiSe
Content
  • kinematics of undeformed and deformed bodies
  • balance equations (balance of mass, balance of energy, …)
  • stress states
  • material modelling


Literature

R. Greve: Kontinuumsmechanik: Ein Grundkurs für Ingenieure und Physiker

I-S. Liu: Continuum Mechanics, Springer


Course L1534: Continuum Mechanics Exercise
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Swantje Bargmann
Language DE/EN
Cycle WiSe
Content
  • kinematics of undeformed and deformed bodies
  • balance equations (balance of mass, balance of energy, …)
  • stress states
  • material modelling


Literature

R. Greve: Kontinuumsmechanik: Ein Grundkurs für Ingenieure und Physiker

I-S. Liu: Continuum Mechanics, Springer


Module M0751: Vibration Theory

Courses
Title Typ Hrs/wk CP
Vibration Theory (L0701) Lecture 4 6
Module Responsible Prof. Norbert Hoffmann
Admission Requirements None
Recommended Previous Knowledge
  • Calculus
  • Linear Algebra
  • Engineering Mechanics
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Students are able to denote terms and concepts of Vibration Theory and develop them further.
Skills Students are able to denote methods of Vibration Theory and develop them further.
Personal Competence
Social Competence Students can reach working results also in groups.
Autonomy Students are able to approach individually research tasks in Vibration Theory.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 2 Hours
Assignment for the Following Curricula Energy Systems: Core qualification: Elective Compulsory
Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory
Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory
Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory
Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory
Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory
Product Development, Materials and Production: Core qualification: Compulsory
Naval Architecture and Ocean Engineering: Core qualification: Elective Compulsory
Theoretical Mechanical Engineering: Core qualification: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Course L0701: Vibration Theory
Typ Lecture
Hrs/wk 4
CP 6
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Lecturer Prof. Norbert Hoffmann
Language DE/EN
Cycle WiSe
Content Linear and Nonlinear Single and Multiple Degree of Freedom Oscillations and Waves.
Literature K. Magnus, K. Popp, W. Sextro: Schwingungen. Physikalische Grundlagen und mathematische Behandlung von Schwingungen. Springer Verlag, 2013.

Module M1245: Technical Complementary Course II for IIWMS (according to Subject Specific Regulations)

Courses
Title Typ Hrs/wk CP
Module Responsible Prof. Volker Turau
Admission Requirements None
Recommended Previous Knowledge None
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students acquire advanced knowledge in a technical subject available at TUHH.

Skills

The students acquire professional competence in a technical subject available at TUHH.

Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 180, Study Time in Lecture 0
Credit points 6
Examination according to Subject Specific Regulations
Examination duration and scale
Assignment for the Following Curricula Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory

Module M1152: Modeling Across The Scales

Courses
Title Typ Hrs/wk CP
Modeling Across The Scales (L1537) Lecture 2 3
Modeling Across The Scales - Excercise (L1538) Recitation Section (small) 2 3
Module Responsible Prof. Swantje Bargmann
Admission Requirements None
Recommended Previous Knowledge

mechanics I

mechanics II

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge The students can describe different deformation mechanisms on different scales and can name the appropriate kind of modeling concept suited for its description.
Skills The students are able to predict first estimates of the effective material behavior based on the material's microstructure. They are able to correlate and describe the damage behavior of materials based on their micromechanical behavior. In particular, they are able to apply their knowledge to different problems of material science and evaluate and implement material models into a finite element code.
Personal Competence
Social Competence

The students are able to present solutions to specialists and to develop ideas further.

Autonomy

The students are able to assess their own strengths and weaknesses and to define tasks themselves.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale
Assignment for the Following Curricula Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Materials Science: Specialisation Modeling: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Materials Science: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Course L1537: Modeling Across The Scales
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Swantje Bargmann
Language DE/EN
Cycle SoSe
Content
  • modeling of deformation mechanisms in materials at different scales (e.g., molecular dynamics, crystal plasticity, phenomenological models, ...)
  • relationship between microstructure and macroscopic mechanical material behavior
  • Eshelby problem
  • effective material properties, concept of RVE 
  • homogenisation methods, coupling of scales (micro-meso-macro)
  • micromechanical concepts for the description of damage and failure behavior 


Literature

D. Gross, T. Seelig, Bruchmechanik: Mit einer Einführung in die Mikromechanik, Springer

T. Zohdi, P. Wriggers: An Introduction to Computational Micromechanics

D. Raabe: Computational Materials Science, The Simulation of Materials, Microstructures and Properties, Wiley-Vch

G. Gottstein., Physical Foundations of Materials Science, Springer


Course L1538: Modeling Across The Scales - Excercise
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Swantje Bargmann
Language DE/EN
Cycle SoSe
Content


  • modeling of deformation mechanisms in materials at different scales (e.g., molecular dynamics, crystal plasticity, phenomenological models, ...)
  • relationship between microstructure and macroscopic mechanical material behavior
  • Eshelby problem
  • effective material properties, concept of RVE 
  • homogenisation methods, coupling of scales (micro-meso-macro)
  • micromechanical concepts for the description of damage and failure behavior 
Literature


D. Gross, T. Seelig, Bruchmechanik: Mit einer Einführung in die Mikromechanik, Springer

T. Zohdi, P. Wriggers: An Introduction to Computational Micromechanics

D. Raabe: Computational Materials Science, The Simulation of Materials, Microstructures and Properties, Wiley-Vch

G. Gottstein., Physical Foundations of Materials Science, Springer

Module M0692: Approximation and Stability

Courses
Title Typ Hrs/wk CP
Approximation and Stability (L0487) Lecture 3 4
Approximation and Stability (L0488) Recitation Section (small) 1 2
Module Responsible Prof. Marko Lindner
Admission Requirements None
Recommended Previous Knowledge
  • Linear Algebra: systems of linear equations, least squares problems, eigenvalues, singular values
  • Analysis: sequences, series, differentiation, integration
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to

  • sketch and interrelate basic concepts of functional analysis (Hilbert space, operators),
  • name and understand concrete approximation methods,
  • name and explain basic stability theorems,
  • discuss spectral quantities, conditions numbers and methods of regularisation

Skills

Students are able to

  • apply basic results from functional analysis,
  • apply approximation methods,
  • apply stability theorems,
  • compute spectral quantities,
  • apply regularisation methods.
Personal Competence
Social Competence

Students are able to solve specific problems in groups and to present their results appropriately (e.g. as a seminar presentation).

Autonomy
  • Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them.
  • Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 20 min
Assignment for the Following Curricula Electrical Engineering: Specialisation Control and Power Systems: Elective Compulsory
Electrical Engineering: Specialisation Modeling and Simulation: Elective Compulsory
Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Course L0487: Approximation and Stability
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Marko Lindner
Language DE/EN
Cycle SoSe
Content

This course is about solving the following basic problems of Linear Algebra,

  • systems of linear equations,
  • least squares problems,
  • eigenvalue problems

but now in function spaces (i.e. vector spaces of infinite dimension) by a stable approximation of the problem in a space of finite dimension.

Contents:

  • crash course on Hilbert spaces: metric, norm, scalar product, completeness
  • crash course on operators: boundedness, norm, compactness, projections
  • uniform vs. strong convergence, approximation methods
  • applicability and stability of approximation methods, Polski's theorem
  • Galerkin methods, collocation, spline interpolation, truncation
  • convolution and Toeplitz operators
  • crash course on C*-algebras
  • convergence of condition numbers
  • convergence of spectral quantities: spectrum, eigen values, singular values, pseudospectra
  • regularisation methods (truncated SVD, Tichonov)
Literature
  • R. Hagen, S. Roch, B. Silbermann: C*-Algebras in Numerical Analysis
  • H. W. Alt: Lineare Funktionalanalysis
  • M. Lindner: Infinite matrices and their finite sections
Course L0488: Approximation and Stability
Typ Recitation Section (small)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Marko Lindner
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0714: Numerical Treatment of Ordinary Differential Equations

Courses
Title Typ Hrs/wk CP
Numerical Treatment of Ordinary Differential Equations (L0576) Lecture 2 3
Numerical Treatment of Ordinary Differential Equations (L0582) Recitation Section (small) 2 3
Module Responsible Prof. Sabine Le Borne
Admission Requirements None
Recommended Previous Knowledge
  • Mathematik I, II, III für Ingenieurstudierende (deutsch oder englisch) oder Analysis & Lineare Algebra I + II sowie Analysis III für Technomathematiker
  • Basic MATLAB knowledge
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to

  • list numerical methods for the solution of ordinary differential equations and explain their core ideas,
  • repeat convergence statements for the treated numerical methods (including the prerequisites tied to the underlying problem),
  • explain aspects regarding the practical execution of a method.
  • select the appropriate numerical method for concrete problems, implement the numerical algorithms efficiently and interpret the numerical results
Skills

Students are able to

  • implement (MATLAB), apply and compare numerical methods for the solution of ordinary differential equations,
  • to justify the convergence behaviour of numerical methods with respect to the posed problem and selected algorithm,
  • for a given problem, develop a suitable solution approach, if necessary by the composition of several algorithms, to execute this approach and to critically evaluate the results.


Personal Competence
Social Competence

Students are able to

  • work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge), explain theoretical foundations and support each other with practical aspects regarding the implementation of algorithms.
Autonomy

Students are capable

  • to assess whether the supporting theoretical and practical excercises are better solved individually or in a team,
  • to assess their individual progress and, if necessary, to ask questions and seek help.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation Chemical Process Engineering: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation General Process Engineering: Elective Compulsory
Electrical Engineering: Specialisation Control and Power Systems: Elective Compulsory
Electrical Engineering: Specialisation Modeling and Simulation: Elective Compulsory
Energy Systems: Core qualification: Elective Compulsory
Aircraft Systems Engineering: Specialisation Aircraft Systems: Elective Compulsory
Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Core qualification: Compulsory
Process Engineering: Specialisation Chemical Process Engineering: Elective Compulsory
Process Engineering: Specialisation Process Engineering: Elective Compulsory
Course L0576: Numerical Treatment of Ordinary Differential Equations
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sabine Le Borne, Dr. Patricio Farrell
Language DE/EN
Cycle SoSe
Content

Numerical methods for Initial Value Problems

  • single step methods
  • multistep methods
  • stiff problems
  • differential algebraic equations (DAE) of index 1

Numerical methods for Boundary Value Problems

  • initial value methods
  • multiple shooting method
  • difference methods
  • variational methods


Literature
  • E. Hairer, S. Noersett, G. Wanner: Solving Ordinary Differential Equations I: Nonstiff Problems
  • E. Hairer, G. Wanner: Solving Ordinary Differential Equations II: Stiff and Differential-Algebraic Problems
Course L0582: Numerical Treatment of Ordinary Differential Equations
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sabine Le Borne, Dr. Patricio Farrell
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1281: Advanced Topics in Vibration

Courses
Title Typ Hrs/wk CP
Advanced Topics in Vibration (L1743) Problem-based Learning 4 6
Module Responsible Prof. Norbert Hoffmann
Admission Requirements None
Recommended Previous Knowledge Vibration Theory
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Students are able to reflect existing terms and concepts of Advanced Vibrations and to develop and research new terms and concepts.
Skills Students are able to apply existing methods and procesures of Advanced Vibrations and to develop novel methods and procedures.
Personal Competence
Social Competence Students can reach working results also in groups.
Autonomy Students are able to approach given research tasks individually and to identify and follow up novel research tasks by themselves.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 2 Hours
Assignment for the Following Curricula Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Product Development and Production: Elective Compulsory
Course L1743: Advanced Topics in Vibration
Typ Problem-based Learning
Hrs/wk 4
CP 6
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Lecturer Prof. Norbert Hoffmann
Language DE/EN
Cycle SoSe
Content Research Topics in Vibrations.
Literature Aktuelle Veröffentlichungen

Module M0752: Nonlinear Dynamics

Courses
Title Typ Hrs/wk CP
Nonlinear Dynamics (L0702) Lecture 4 6
Module Responsible Prof. Norbert Hoffmann
Admission Requirements None
Recommended Previous Knowledge
  • Calculus
  • Linear Algebra
  • Engineering Mechanics
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Students are able to reflect existing terms and concepts in Nonlinear Dynamics and to develop and research new terms and concepts.
Skills Students are able to apply existing methods and procesures of Nonlinear Dynamics and to develop novel methods and procedures.
Personal Competence
Social Competence Students can reach working results also in groups.
Autonomy Students are able to approach given research tasks individually and to identify and follow up novel research tasks by themselves.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 2 Hours
Assignment for the Following Curricula Aircraft Systems Engineering: Specialisation Aircraft Systems: Elective Compulsory
Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory
Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory
Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory
Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory
Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory
Product Development, Materials and Production: Core qualification: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Core qualification: Elective Compulsory
Course L0702: Nonlinear Dynamics
Typ Lecture
Hrs/wk 4
CP 6
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Lecturer Prof. Norbert Hoffmann
Language DE/EN
Cycle SoSe
Content Fundamentals of Nonlinear Dynamics.
Literature S. Strogatz: Nonlinear Dynamics and Chaos. Perseus, 2013.

Module M0711: Numerical Mathematics II

Courses
Title Typ Hrs/wk CP
Numerical Mathematics II (L0568) Lecture 2 3
Numerical Mathematics II (L0569) Recitation Section (small) 2 3
Module Responsible Prof. Sabine Le Borne
Admission Requirements None
Recommended Previous Knowledge
  • Numerical Mathematics I
  • MATLAB knowledge
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to

  • name advanced numerical methods for interpolation, integration, linear least squares problems, eigenvalue problems, nonlinear root finding problems and explain their core ideas,
  • repeat convergence statements for the numerical methods,
  • sketch convergence proofs,
  • explain practical aspects of numerical methods concerning runtime and storage needs


    explain aspects regarding the practical implementation of numerical methods with respect to computational and storage complexity.


Skills

Students are able to

  • implement, apply and compare advanced numerical methods in MATLAB,
  • justify the convergence behaviour of numerical methods with respect to the problem and solution algorithm and to transfer it to related problems,
  • for a given problem, develop a suitable solution approach, if necessary through composition of several algorithms, to execute this approach and to critically evaluate the results


Personal Competence
Social Competence

Students are able to

  • work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge), explain theoretical foundations and support each other with practical aspects regarding the implementation of algorithms.
Autonomy

Students are capable

  • to assess whether the supporting theoretical and practical excercises are better solved individually or in a team,
  • to assess their individual progess and, if necessary, to ask questions and seek help.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Course L0568: Numerical Mathematics II
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sabine Le Borne, Dr. Patricio Farrell
Language DE/EN
Cycle SoSe
Content
  1. Error and stability: Notions and estimates
  2. Interpolation: Rational and trigonometric interpolation
  3. Quadrature: Gaussian quadrature, orthogonal polynomials
  4. Linear systems: Perturbation theory of decompositions, structured matrices
  5. Eigenvalue problems: LR-, QD-, QR-Algorithmus
  6. Krylov space methods: Arnoldi-, Lanczos methods
Literature
  • Stoer/Bulirsch: Numerische Mathematik 1, Springer
  • Dahmen, Reusken: Numerik für Ingenieure und Naturwissenschaftler, Springer
Course L0569: Numerical Mathematics II
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sabine Le Borne, Dr. Patricio Farrell
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0807: Boundary Element Methods

Courses
Title Typ Hrs/wk CP
Boundary Element Methods (L0523) Lecture 2 3
Boundary Element Methods (L0524) Recitation Section (large) 2 3
Module Responsible Prof. Otto von Estorff
Admission Requirements None
Recommended Previous Knowledge

Mechanics I (Statics, Mechanics of Materials) and Mechanics II (Hydrostatics, Kinematics, Dynamics)
Mathematics I, II, III (in particular differential equations)

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students possess an in-depth knowledge regarding the derivation of the boundary element method and are able to give an overview of the theoretical and methodical basis of the method.



Skills

The students are capable to handle engineering problems by formulating suitable boundary elements, assembling the corresponding system matrices, and solving the resulting system of equations.



Personal Competence
Social Competence -
Autonomy

The students are able to independently solve challenging computational problems and develop own boundary element routines. Problems can be identified and the results are critically scrutinized.



Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Civil Engineering: Specialisation Structural Engineering: Elective Compulsory
Civil Engineering: Specialisation Geotechnical Engineering: Elective Compulsory
Civil Engineering: Specialisation Coastal Engineering: Elective Compulsory
Energy Systems: Core qualification: Elective Compulsory
Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Mechanical Engineering and Management: Specialisation Product Development and Production: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Product Development, Materials and Production: Core qualification: Elective Compulsory
Technomathematics: Specialisation III. Engineering Science: Elective Compulsory
Technomathematics: Core qualification: Elective Compulsory
Theoretical Mechanical Engineering: Core qualification: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Course L0523: Boundary Element Methods
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Otto von Estorff
Language EN
Cycle SoSe
Content

- Boundary value problems
- Integral equations
- Fundamental Solutions
- Element formulations
- Numerical integration
- Solving systems of equations (statics, dynamics)
- Special BEM formulations
- Coupling of FEM and BEM

- Hands-on Sessions (programming of BE routines)
- Applications

Literature

Gaul, L.; Fiedler, Ch. (1997): Methode der Randelemente in Statik und Dynamik. Vieweg, Braunschweig, Wiesbaden
Bathe, K.-J. (2000): Finite-Elemente-Methoden. Springer Verlag, Berlin

Course L0524: Boundary Element Methods
Typ Recitation Section (large)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Otto von Estorff
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0653: High-Performance Computing

Courses
Title Typ Hrs/wk CP
Fundamentals of High-Performance Computing (L0242) Lecture 2 3
Fundamentals of High-Performance Computing (L1416) Problem-based Learning 2 3
Module Responsible Prof. Thomas Rung
Admission Requirements None
Recommended Previous Knowledge
  • Basic knowledge in usage of modern IT environment
  • Programming skills
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to outline the fundamentals of numerical algorithms for high-performance computers by reference to modern hardware examples. Students can explain the relation between hard- and software aspects for the design of algorithms.

Skills Student can perform a critical assesment of the computational efficiency of simulation approaches. 
Personal Competence
Social Competence Students are able to develop and code algorithms in a team.
Autonomy


Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 1.5h
Assignment for the Following Curricula Electrical Engineering: Specialisation Modeling and Simulation: Elective Compulsory
Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Naval Architecture and Ocean Engineering: Core qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Course L0242: Fundamentals of High-Performance Computing
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Thomas Rung
Language DE/EN
Cycle SoSe
Content

Fundamentals of modern hardware architectur, critical hard- & software aspects for efficient processing of exemplary algorithms, concepts for shared- and distributed-memory systems, implementations for accelerator hardware (GPGPUs)

Literature
Course L1416: Fundamentals of High-Performance Computing
Typ Problem-based Learning
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Thomas Rung
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1020: Numerics of Partial Differential Equations

Courses
Title Typ Hrs/wk CP
Numerics of Partial Differential Equations (L1247) Lecture 2 3
Numerics of Partial Differential Equations (L1248) Recitation Section (small) 2 3
Module Responsible Prof. Sabine Le Borne
Admission Requirements None
Recommended Previous Knowledge
  • Mathematik I - IV (for Engineering Students) or Analysis & Linear Algebra I + II for Technomathematicians
  • Numerical mathematics 1
  • Numerical treatment of ordinary differential equations
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can classify partial differential equations according to the three basic types.
  • For each type, students know suitable numerical approaches.
  • Students know the theoretical convergence results for these approaches.
Skills Students are capable to formulate solution strategies for given problems involving partial differential equations, to comment on theoretical properties concerning convergence and to implement and test these methods in practice.
Personal Competence
Social Competence

Students are able to work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge) and to explain theoretical foundations.

Autonomy
  • Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them.
  • Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Technomathematics: Core qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Course L1247: Numerics of Partial Differential Equations
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sabine Le Borne, Dr. Patricio Farrell
Language DE/EN
Cycle WiSe
Content

Elementary Theory and Numerics of PDEs

  • types of PDEs
  • well posed problems
  • finite differences
  • finite elements
  • finite volumes
  • applications
Literature

Dietrich Braess: Finite Elemente: Theorie, schnelle Löser und Anwendungen in der Elastizitätstheorie, Berlin u.a., Springer 2007

Susanne Brenner, Ridgway Scott: The Mathematical Theory of Finite Element Methods, Springer, 2008

Peter Deuflhard, Martin Weiser: Numerische Mathematik 3
Course L1248: Numerics of Partial Differential Equations
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sabine Le Borne, Dr. Patricio Farrell
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0549: Scientific Computing and Accuracy

Courses
Title Typ Hrs/wk CP
Verification Methods (L0122) Lecture 2 3
Verification Methods (L1208) Recitation Section (small) 2 3
Module Responsible Prof. Siegfried Rump
Admission Requirements None
Recommended Previous Knowledge

Basic knowledge in numerics

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students have deeper knowledge of numerical and semi-numerical methods with the goal to compute principally exact and accurate error bounds. For several fundamental problems they know algorithms with the verification of the correctness of the computed result.

Skills

The students can devise algorithms for several basic problems which compute rigorous error bounds for the solution and analyze the sensitivity with respect to variation of the input data as well.

Personal Competence
Social Competence

The students have the skills to solve problems together in small groups and to present the achieved results in an appropriate manner.

Autonomy

The students are able to retrieve necessary informations from the given literature and to combine them with the topics of the lecture. Throughout the lecture they can check their abilities and knowledge on the basis of given exercises and test questions providing an aid to optimize their learning process.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory
Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Technomathematics: Specialisation II. Informatics: Elective Compulsory
Process Engineering: Specialisation Process Engineering: Elective Compulsory
Process Engineering: Specialisation Chemical Process Engineering: Elective Compulsory
Course L0122: Verification Methods
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Siegfried Rump
Language DE
Cycle WiSe
Content
  • Fast and accurate interval arithmetic

  • Error-free transformations

  • Verification methods for linear and nonlinear systems

  • Verification methods for finite integrals

  • Treatment of multiple zeros

  • Automatic differentiation

  • Implementation in Matlab/INTLAB

  • Practical applications

Literature

Neumaier: Interval Methods for Systems of Equations. In: Encyclopedia of Mathematics and its  Applications. Cambridge University Press, 1990

S.M. Rump. Verification methods: Rigorous results using floating-point arithmetic. Acta Numerica, 19:287-449, 2010.
Course L1208: Verification Methods
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Siegfried Rump
Language DE
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1268: Linear and Nonlinear Waves

Courses
Title Typ Hrs/wk CP
Linear and Nonlinear Waves (L1737) Problem-based Learning 4 6
Module Responsible Prof. Norbert Hoffmann
Admission Requirements None
Recommended Previous Knowledge Good Knowledge in Mathematics, Mechanics and Dynamics.
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Students are able to reflect existing terms and concepts in Wave Mechanics and to develop and research new terms and concepts.
Skills Students are able to apply existing methods and procesures of Wave Mechanics and to develop novel methods and procedures.
Personal Competence
Social Competence Students can reach working results also in groups.
Autonomy Students are able to approach given research tasks individually and to identify and follow up novel research tasks by themselves.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Written exam
Examination duration and scale 2 Hours
Assignment for the Following Curricula Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Naval Architecture and Ocean Engineering: Core qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Maritime Technology: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Course L1737: Linear and Nonlinear Waves
Typ Problem-based Learning
Hrs/wk 4
CP 6
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Lecturer Prof. Norbert Hoffmann
Language DE/EN
Cycle WiSe
Content Introduction into the Dynamics of Linear and Nonlinear Waves.
Literature

G.B. Witham, Linear and Nonlinear Waves. Wiley 1999.

C.C. Mei, Theory and Applications of Ocean Surface Waves. World Scientific 2004.

Module M1151: Material Modeling

Courses
Title Typ Hrs/wk CP
Material Modeling (L1535) Lecture 2 3
Material Modeling (L1536) Recitation Section (small) 2 3
Module Responsible Prof. Swantje Bargmann
Admission Requirements None
Recommended Previous Knowledge

mechanics I

mechanics II

 continuum mechanics

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge The students can explain the fundamentals of multidimensional consitutive material laws
Skills The students can implement their own material laws in finite element codes. In particular, the students can apply their knowledge to various problems of material science and evaluate the corresponding material models.
Personal Competence
Social Competence

The students are able to develop solutions, to present them to specialists and to develop ideas further.

Autonomy

The students are able to assess their own strengths and weaknesses and to define tasks themselves. They can solve exercises in the area of continuum mechanics on their own.


Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory
Materials Science: Specialisation Modeling: Elective Compulsory
Mechanical Engineering and Management: Specialisation Materials: Elective Compulsory
Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory
Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory
Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory
Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory
Product Development, Materials and Production: Core qualification: Elective Compulsory
Course L1535: Material Modeling
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Swantje Bargmann
Language DE/EN
Cycle WiSe
Content
  • fundamentals of finite element methods
  • fundamentals of material modeling
  • introduction to numerical implementation of material laws 
  • overview of modelling of different classes of materials
  • combination of macroscopic quantities to material microstructure


Literature

D. Raabe: Computational Materials Science, The Simulation of Materials, Microstructures and Properties, Wiley-Vch

J. Bonet, R.D. Wood, Nonlinear Continuum Mechanics for Finite Element Analysis, Cambridge

G. Gottstein., Physical Foundations of Materials Science, Springer


Course L1536: Material Modeling
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Swantje Bargmann
Language DE/EN
Cycle WiSe
Content


  • fundamentals of finite element methods
  • fundamentals of material modeling
  • introduction to numerical implementation of material laws 
  • overview of modelling of different classes of materials
  • combination of macroscopic quantities to material microstructure
Literature

D. Raabe: Computational Materials Science, The Simulation of Materials, Microstructures and Properties, Wiley-Vch

J. Bonet, R.D. Wood, Nonlinear Continuum Mechanics for Finite Element Analysis, Cambridge

G. Gottstein., Physical Foundations of Materials Science, Springer


Thesis

Module M-002: Master Thesis

Courses
Title Typ Hrs/wk CP
Module Responsible Professoren der TUHH
Admission Requirements
  • According to General Regulations §24 (1):

    At least 78 credit points have to be achieved in study programme. The examinations board decides on exceptions.

Recommended Previous Knowledge
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • The students can use specialized knowledge (facts, theories, and methods) of their subject competently on specialized issues.
  • The students can explain in depth the relevant approaches and terminologies in one or more areas of their subject, describing current developments and taking up a critical position on them.
  • The students can place a research task in their subject area in its context and describe and critically assess the state of research.


Skills

The students are able:

  • To select, apply and, if necessary, develop further methods that are suitable for solving the specialized problem in question.
  • To apply knowledge they have acquired and methods they have learnt in the course of their studies to complex and/or incompletely defined problems in a solution-oriented way.
  • To develop new scientific findings in their subject area and subject them to a critical assessment.
Personal Competence
Social Competence

Students can

  • Both in writing and orally outline a scientific issue for an expert audience accurately, understandably and in a structured way.
  • Deal with issues competently in an expert discussion and answer them in a manner that is appropriate to the addressees while upholding their own assessments and viewpoints convincingly.


Autonomy

Students are able:

  • To structure a project of their own in work packages and to work them off accordingly.
  • To work their way in depth into a largely unknown subject and to access the information required for them to do so.
  • To apply the techniques of scientific work comprehensively in research of their own.
Workload in Hours Independent Study Time 900, Study Time in Lecture 0
Credit points 30
Examination according to Subject Specific Regulations
Examination duration and scale see FSPO
Assignment for the Following Curricula Civil Engineering: Thesis: Compulsory
Bioprocess Engineering: Thesis: Compulsory
Chemical and Bioprocess Engineering: Thesis: Compulsory
Computer Science: Thesis: Compulsory
Electrical Engineering: Thesis: Compulsory
Energy and Environmental Engineering: Thesis: Compulsory
Energy Systems: Thesis: Compulsory
Environmental Engineering: Thesis: Compulsory
Aircraft Systems Engineering: Thesis: Compulsory
Global Innovation Management: Thesis: Compulsory
Computational Science and Engineering: Thesis: Compulsory
Information and Communication Systems: Thesis: Compulsory
International Production Management: Thesis: Compulsory
International Management and Engineering: Thesis: Compulsory
Joint European Master in Environmental Studies - Cities and Sustainability: Thesis: Compulsory
Logistics, Infrastructure and Mobility: Thesis: Compulsory
Materials Science: Thesis: Compulsory
Mechanical Engineering and Management: Thesis: Compulsory
Mechatronics: Thesis: Compulsory
Biomedical Engineering: Thesis: Compulsory
Microelectronics and Microsystems: Thesis: Compulsory
Product Development, Materials and Production: Thesis: Compulsory
Renewable Energies: Thesis: Compulsory
Naval Architecture and Ocean Engineering: Thesis: Compulsory
Ship and Offshore Technology: Thesis: Compulsory
Theoretical Mechanical Engineering: Thesis: Compulsory
Process Engineering: Thesis: Compulsory
Water and Environmental Engineering: Thesis: Compulsory