Program description

Content

The consecutive international master program "Mechatronics" extends the education in engineering, mathematics and natural science of the bachelor studies. It provides systematic, scientific and autonomous problem solving capabilities needed in industry and research.

The program covers the methods of computation, design and implementation of mechatronic systems.Students specialize in one out of two concentrations and develop the ability to work in the interfaces of the interconnected sub-disciplines. Based on personal interest, students are able to adapt their study programs within a broad catalogue of elective courses.


Career prospects

The consecutive international Master course "Mechatronics" prepares graduates for a wide range of job profiles in mechatronics engineering.

Graduates can work in multiple disciplines directly, e.g. System Design or Intelligent Systems and Robotics.

Additionally graduates have a multifaceted knowledge of methods for interdisciplinary topics.

Graduates may decide for direct entry into companies or to take up academic careers, e.g. Ph.D. studies, in universities or other research institutions. In companies they can take up jobs as specialists or subsequently qualify for demanding management tasks in the technical area (e.g. project, group, or team leader; R&D director).

The program is designed to be universal and allows graduates to work in a variety of different industrial sectors and with different projects.


Learning target

Graduates of the program are able to transfer the individually acquired specialized knowledge to new, unknown topics, to comprehend, to analyze and to scientifically solve complex problems of their discipline. They can find missing information and plan as well as execute theoretical and experimental studies. They are able to judge, evaluate and question scientific engineering results critically as well as making decisions based on this foundation and draw further conclusions. They are able to act methodically, to organize smaller projects, to select new technologies and scientific methods and to advance these further, if necessary.

Graduates can develop and document new ideas and solutions, independently or in teams. They are capable of presenting and discussing results to and with professionals. They can estimate their own strengths and weaknesses as well as possible consequences of their actions. They are capable of familiarizing themselves with complex tasks, defining new tasks and developing the necessary knowledge to solve them using systematically applied, appropriate means.

Graduates learn to work systematically and methodically on difficult design (or) mechatronic tasks. They have a broad knowledge of new engineering methods, in automation and simulation. Graduates can select appropriate solution strategies and use them autonomously to develop new intelligent systems. They are able to use methods of integrated system development such as simulation or modern test and inspection procedures.


Program structure

The course is designed modularly and is based on the university-wide standardized course structure with uniform module sizes (multiples of six credit points (CP)).

The program combines the disciplines of mechanical and electrical engineering and supports concentration in interdisciplinary fields of system design and system implementation.

All modules in the first semester are mandatory. This helps especially students from abroad to familiarize themselves with the university and culture.

Afterwards the students can broadly personalize their studies due to the high number and variety of elective courses.

In the common core skills, students take the following modules:

  • Finite element analysis and Vibration theory (12 CP)
  • Theory and design of control systems and Design and implementation of software systems (12 CP)
  • Robotics (6 CP)
  • Complementary courses business and management (catalogue) (6 CP)
  • Nontechnical elective complementary courses (catalogue) (6 CP).

Students specialize by selecting modules amountig to 36 CP, which can be chosen form a module catalog containing modules with a size of six credits. Instead, open modules can be attend to the maximum extent of twelve credit points, in which smaller specialized courses can be combined, individually.

Students write a master thesis and one additional research project.

  • Research project (12 CP)
  • Master thesis (30 CP)

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

  • Students are able to communicate in small interdisciplinary groups and to jointly develop solutions for complex problems
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: Non-technical 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 M0563: Robotics
Courses
Title Typ Hrs/wk CP
Robotics: Modelling and Control (L0168) Integrated Lecture 4 4
Robotics: Modelling and Control (L1305) Project-/problem-based Learning 2 2
Module Responsible Dr. Martin Gomse
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 96, Study Time in Lecture 84
Credit points 6
Course achievement
Compulsory Bonus Form Description
Yes None Subject theoretical and practical work Teilnahme an PBL-Einheiten sowie Erreichen des Gesamtziels und der jeweiligen Session-Ziele
Examination Written exam
Examination duration and scale 120 min
Assignment for the Following Curricula Aircraft Systems Engineering: Core Qualification: Elective Compulsory
International Management and Engineering: Specialisation II. Product Development and Production: Elective Compulsory
International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory
Aeronautics: Core Qualification: 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: Specialisation Robotics and Computer Science: Elective Compulsory
Course L0168: Robotics: Modelling and Control
Typ Integrated Lecture
Hrs/wk 4
CP 4
Workload in Hours Independent Study Time 64, Study Time in Lecture 56
Lecturer Dr. Martin Gomse
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 Project-/problem-based Learning
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Dr. Martin Gomse
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

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. Benedikt Kriegesmann
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

Students can work in small groups on specific problems to arrive at joint solutions.
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
Course achievement
Compulsory Bonus Form Description
No 20 % Midterm
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: Core Qualification: Elective Compulsory
International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory
International Management and Engineering: Specialisation II. Product Development and Production: Elective Compulsory
Aeronautics: Core Qualification: 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
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. Benedikt Kriegesmann
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. Benedikt Kriegesmann
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 NN
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
Course achievement None
Examination Written exam
Examination duration and scale 120 min
Assignment for the Following Curricula Electrical Engineering: Core Qualification: Compulsory
Energy Systems: Core Qualification: Elective Compulsory
Aircraft Systems Engineering: Core Qualification: Elective Compulsory
Aeronautics: Core Qualification: 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 NN
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 NN
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1222: Design and Implementation of Software Systems
Courses
Title Typ Hrs/wk CP
Design and Implementation of Software Systems (L1657) Lecture 2 3
Design and Implementation of Software Systems (L1658) Project-/problem-based Learning 2 3
Module Responsible Prof. Bernd-Christian Renner
Admission Requirements None
Recommended Previous Knowledge

- Imperativ programming languages (C, Pascal, Fortran or similar)

- Simple data types (integer, double, char, boolean), arrays, if-then-else, for, while, procedure and function calls
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to describe mechatronic systems and define requirements.
Skills

Students are able to design and implement mechatronic systems. They are able to argue the combination of Hard- and Software and the interfaces.
Personal Competence
Social Competence

Students are able to work goal-oriented in small mixed groups, learning and broadening teamwork abilities and define task within the team.
Autonomy

Students are able to solve individually exercises related to this lecture with instructional direction. Students are able to plan, execute and summarize a mechatronic experiment.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement
Compulsory Bonus Form Description
No 10 % Attestation
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Mechatronics: Core Qualification: Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Course L1657: Design and Implementation of Software Systems
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Bernd-Christian Renner
Language EN
Cycle WiSe
Content This course covers software design and implementation of mechatronic systems, tools for automation in Java.

Content:

  • Introduction to software techniques
  • Procedural Programming
  • Object oriented software design
  • Java
  • Event based programming
  • Formal methods
Literature
  • “The Pragmatic Programmer: From Journeyman to Master”Andrew Hunt, David Thomas, Ward Cunningham
  • “Core LEGO MINDSTORMS Programming: Unleash the Power of the Java Platform” Brian Bagnall Prentice Hall PTR, 1st edition (March, 2002) ISBN 0130093645
  • “Objects First with Java: A Practical Introduction using BlueJ” David J. Barnes & Michael Kölling Prentice Hall/ Pearson Education; 2003, ISBN 0-13-044929-6
Course L1658: Design and Implementation of Software Systems
Typ Project-/problem-based Learning
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Bernd-Christian Renner
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0751: Vibration Theory
Courses
Title Typ Hrs/wk CP
Vibration Theory (L0701) Integrated 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.
  • Students know methods of modeling and simulation for free, driven, self-excited and parameter driven vibrations.
  • Students know about concepts of linear and nonlinear vibration problems.
  • Students know basic tasks of vibration problems of discrete and continuous systems.
Skills
  • Students are able to denote methods of Vibration Theory and develop them further.
  • Students are able to apply and expand methods of modeling and simulation for free, forced, self-excited and parameter driven vibrations.
  • Students are able to solve linear and nonlinear vibration problems.
Personal Competence
Social Competence
  • Students can analyze vibration problems, work on them, and reach working results also in teams or groups.
  • Students are able to document the results of vibration studies also in groups.
Autonomy
  • Students are able to individually analyze and solve vibration problems.
  • 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
Course achievement None
Examination Written exam
Examination duration and scale 2 Hours
Assignment for the Following Curricula Energy Systems: Core Qualification: 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: 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
Course L0701: Vibration Theory
Typ Integrated 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 Vibrations

  • Free vibration
  • Self-excited vibration
  • Parameter driven vibration
  • Forced vibration
  • Multi degree of freedom vibration
  • Continuum vibration
  • Irregular vibration
Literature

German - K. Magnus, K. Popp, W. Sextro: Schwingungen. Physikalische Grundlagen und mathematische Behandlung von Schwingungen.

English - K. Magnus: Vibrations. 

Module M1223: Selected Topics of Mechatronics (Alternative A: 12 LP)
Courses
Title Typ Hrs/wk CP
Applied Automation (L1592) Project-/problem-based Learning 3 3
Advanced Training Course SE-ZERT (L2739) Project-/problem-based Learning 2 3
Development Management for Mechatronics (L1512) Lecture 2 3
Fatigue & Damage Tolerance (L0310) Lecture 2 3
Industry 4.0 for Engineers (L2012) Lecture 2 3
Microcontroller Circuits: Implementation in Hardware and Software (L0087) Seminar 2 2
Microsystems Technology (L0724) Lecture 2 4
Model-Based Systems Engineering (MBSE) with SysML/UML (L1551) Project-/problem-based Learning 3 3
Sustainable Industrial Production (L2863) Lecture 2 4
Process Measurement Engineering (L1077) Lecture 2 3
Process Measurement Engineering (L1083) Recitation Section (large) 1 1
Feedback Control in Medical Technology (L0664) Lecture 2 3
Module Responsible Dr. Martin Gomse
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 express their extended knowledge and discuss the connection of different special fields or application areas of mechatronics
  • Students are qualified to connect different special fields with each other
Skills
  • Students can apply specialized solution strategies and new scientific methods in selected areas
  • Students are able to transfer learned skills to new and unknown problems and can develop own solution approaches
Personal Competence
Social Competence None
Autonomy
  • Students are able to develop their knowledge and skills by autonomous election of courses.
Workload in Hours Depends on choice of courses
Credit points 12
Assignment for the Following Curricula Mechatronics: Core Qualification: Elective Compulsory
Course L1592: Applied Automation
Typ Project-/problem-based Learning
Hrs/wk 3
CP 3
Workload in Hours Independent Study Time 48, Study Time in Lecture 42
Examination Form Mündliche Prüfung
Examination duration and scale 30 Minuten
Lecturer Prof. Thorsten Schüppstuhl
Language DE
Cycle WiSe
Content 
-Project Based Learning
-Robot Operating System
-Robot structure and description
-Motion description
-Calibration
-Accuracy
Literature 
John J. Craig
Introduction to Robotics - Mechanics and Control 
ISBN: 0131236296
 Pearson Education, Inc., 2005

Stefan Hesse
Grundlagen der Handhabungstechnik
ISBN: 3446418725
 München Hanser, 2010

K. Thulasiraman and M. N. S. Swamy
Graphs: Theory and Algorithms
ISBN: 9781118033104  %CITAVIPICKER£9781118033104£Titel anhand dieser ISBN in Citavi-Projekt übernehmen£%
John Wüey & Sons, Inc., 1992
Course L2739: Advanced Training Course SE-ZERT
Typ Project-/problem-based Learning
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Examination Form Klausur
Examination duration and scale 120 min
Lecturer Prof. Ralf God
Language DE
Cycle SoSe
Content
Literature

INCOSE Systems Engineering Handbuch - Ein Leitfaden für Systemlebenszyklus-Prozesse und -Aktivitäten, GfSE (Hrsg. der deutschen Übersetzung), ISBN 978-3-9818805-0-2.

ISO/IEC 15288 System- und Software-Engineering - System-Lebenszyklus-Prozesse (Systems and Software Engineering - System Life Cycle Processes).
Course L1512: Development Management for Mechatronics
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Examination Form Mündliche Prüfung
Examination duration and scale 30 Minuten
Lecturer NN, Dr. Johannes Nicolas Gebhardt
Language DE
Cycle SoSe
Content
  • Processes and methods of product development - from idea to market launch
    • identification of market and technology potentials
    • development of a common product architecture
    • Synchronized product development across all engineering disciplines
    • product validation incl. customer view
  • Steering and optimization of product development
    • Design of processes for product development
    • IT systems for product development
    • Establishment of management standards
    • Typical types of organization
Literature
  • Bender: Embedded Systems - qualitätsorientierte Entwicklung 
  • Ehrlenspiel: Integrierte Produktentwicklung: Denkabläufe, Methodeneinsatz, Zusammenarbeit 
  • Gausemeier/Ebbesmeyer/Kallmeyer: Produktinnovation - Strategische Planung und Entwicklung der Produkte von morgen
  • Haberfellner/de Weck/Fricke/Vössner: Systems Engineering: Grundlagen und Anwendung
  • Lindemann: Methodische Entwicklung technischer Produkte: Methoden flexibel und situationsgerecht anwenden
  • Pahl/Beitz: Konstruktionslehre: Grundlagen erfolgreicher Produktentwicklung. Methoden und Anwendung 
  • VDI-Richtlinie 2206: Entwicklungsmethodik für mechatronische Systeme
Course L0310: Fatigue & Damage Tolerance
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Examination Form Mündliche Prüfung
Examination duration and scale 45 min
Lecturer Dr. Martin Flamm
Language EN
Cycle WiSe
Content Design principles, fatigue strength, crack initiation and crack growth, damage calculation, counting methods, methods to improve fatigue strength, environmental influences
Literature Jaap Schijve, Fatigue of Structures and Materials. Kluver Academic Puplisher, Dordrecht, 2001 E. Haibach. Betriebsfestigkeit Verfahren und Daten zur Bauteilberechnung. VDI-Verlag, Düsseldorf, 1989
Course L2012: Industry 4.0 for Engineers
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Examination Form Klausur
Examination duration and scale 120 min
Lecturer Prof. Thorsten Schüppstuhl
Language DE
Cycle SoSe
Content
Literature
Course L0087: Microcontroller Circuits: Implementation in Hardware and Software
Typ Seminar
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Examination Form Schriftliche Ausarbeitung
Examination duration and scale 10 min. Vortrag + anschließende Diskussion
Lecturer Prof. Siegfried Rump
Language DE
Cycle WiSe/SoSe
Content
Literature

ATmega16A 8-bit  Microcontroller with 16K Bytes In-System Programmable Flash - DATASHEET, Atmel Corporation 2014

Atmel AVR 8-bit Instruction Set Instruction Set Manual, Atmel Corporation 2016
Course L0724: Microsystems Technology
Typ Lecture
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Examination Form Mündliche Prüfung
Examination duration and scale 30 min
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 L1551: Model-Based Systems Engineering (MBSE) with SysML/UML
Typ Project-/problem-based Learning
Hrs/wk 3
CP 3
Workload in Hours Independent Study Time 48, Study Time in Lecture 42
Examination Form Schriftliche Ausarbeitung
Examination duration and scale ca. 10 Seiten
Lecturer Prof. Ralf God
Language DE
Cycle SoSe
Content

Objectives of the problem-oriented course are the acquisition of knowledge on system design using the formal languages SysML/UML, learning about tools for modeling and finally the implementation of a project with methods and tools of Model-Based Systems Engineering (MBSE) on a realistic hardware platform (e.g. Arduino®, Raspberry Pi®):
• What is a model? 
• What is Systems Engineering? 
• Survey of MBSE methodologies
• The modelling languages SysML /UML 
• Tools for MBSE 
• Best practices for MBSE 
• Requirements specification, functional architecture, specification of a solution
• From model to software code 
• Validation and verification: XiL methods
• Accompanying MBSE project
Literature

- Skript zur Vorlesung
- Weilkiens, T.: Systems Engineering mit SysML/UML: Modellierung, Analyse, Design. 2. Auflage, dpunkt.Verlag, 2008
- Holt, J., Perry, S.A., Brownsword, M.: Model-Based Requirements Engineering. Institution Engineering & Tech, 2011
Course L2863: Sustainable Industrial Production
Typ Lecture
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Examination Form Klausur
Examination duration and scale 60 min
Lecturer Dr. Simon Markus Kothe
Language DE
Cycle SoSe
Content

Industrial production deals with the manufacture of physical products to satisfy human needs using various manufacturing processes that change the form and physical properties of raw materials. Manufacturing is a central driver of economic development and has a major impact on the well-being of humanity. However, the scale of current manufacturing activities results in enormous global energy and material demands that are harmful to both the environment and people. Historically, industrial activities were mostly oriented towards economic constraints, while social and environmental consequences were only hardly considered. As a result, today's global consumption rates of many resources and associated emissions often exceed the natural regeneration rate of our planet. In this respect, current industrial production can mostly be described as unsustainable. This is emphasized each year by the Earth Overshoot Day, which marks the day when humanity's ecological footprint exceeds the Earth's annual regenerative capacity. 

This lecture aims to provide the motivation, analytical methods as well as approaches for sustainable industrial production and to clarify the influence of the production phase in relation to the raw material, use and recycling phases in the entire life cycle of products. For this, the following topics will be highlighted:

- Motivation for sustainable production, the 17 Sustainable Development Goals (SDGs) of the UN and their relevance for tomorrow's manufacturing;

- raw material vs. production phase vs. use phase vs. recycling/end-of-life phase: importance of the production phase for the environmental impact of manufactured products;

- Typical energy- and resource-intensive processes in industrial production and innovative approaches to increase energy and resource efficiency;

- Methodology for optimizing the energy and resource efficiency of industrial manufacturing chains with the three steps of modeling (1), evaluating (2) and improving (3);

- Resource efficiency of industrial manufacturing value chains and its assessment using life cycle analysis (LCA);

- Exercise: LCA analysis of a manufacturing process (thermoplastic joining of an aircraft fuselage segment) as part of a product life cycle assessment.
Literature

Literatur:

- Stefan Alexander (2020): Resource efficiency in manufacturing value chains. Cham: Springer International Publishing.

- Hauschild, Michael Z.; Rosenbaum, Ralph K.; Olsen, Stig Irving (Hg.) (2018): Life Cycle Assessment. Theory and Practice. Cham: Springer International Publishing.

- Kishita, Yusuke; Matsumoto, Mitsutaka; Inoue, Masato; Fukushige, Shinichi (2021): EcoDesign and sustainability. Singapore: Springer.

- Schebek, Liselotte; Herrmann, Christoph; Cerdas, Felipe (2019): Progress in Life Cycle Assessment. Cham: Springer International Publishing.

- Thiede, Sebastian; Hermann, Christoph (2019): Eco-factories of the future. Cham: Springer Nature Switzerland AG.

- Vorlesungsskript.
Course L1077: Process Measurement Engineering
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Examination Form Mündliche Prüfung
Examination duration and scale 45 Minuten
Lecturer Prof. Roland Harig
Language DE/EN
Cycle SoSe
Content
  • Process measurement engineering in the context of process control engineering
    • Challenges of process measurement engineering
    • Instrumentation of processes
    • Classification of pickups
  • Systems theory in process measurement engineering
    • Generic linear description of pickups
    • Mathematical description of two-port systems
    • Fourier and Laplace transformation
  • Correlational measurement
    • Wide band signals
    • Auto- and cross-correlation function and their applications
    • Fault-free operation of correlational methods
  • Transmission of analog and digital measurement signals
    • Modulation process (amplitude and frequency modulation)
    • Multiplexing
    • Analog to digital converter
Literature

- Färber: „Prozeßrechentechnik“, Springer-Verlag 1994

- Kiencke, Kronmüller: „Meßtechnik“, Springer Verlag Berlin Heidelberg, 1995

- A. Ambardar: „Analog and Digital Signal Processing“ (1), PWS Publishing Company, 1995, NTC 339

- A. Papoulis: „Signal Analysis“ (1), McGraw-Hill, 1987, NTC 312 (LB)

- M. Schwartz: „Information Transmission, Modulation and Noise“ (3,4), McGraw-Hill, 1980, 2402095

- S. Haykin: „Communication Systems“ (1,3), Wiley&Sons, 1983, 2419072

- H. Sheingold: „Analog-Digital Conversion Handbook“ (5), Prentice-Hall, 1986, 2440072

- J. Fraden: „AIP Handbook of Modern Sensors“ (5,6), American Institute of Physics, 1993, MTB 346
Course L1083: Process Measurement Engineering
Typ Recitation Section (large)
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Examination Form Mündliche Prüfung
Examination duration and scale
Lecturer Prof. Roland Harig
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course
Course L0664: Feedback Control in Medical Technology
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Examination Form Mündliche Prüfung
Examination duration and scale 20 min
Lecturer Johannes Kreuzer, Christian Neuhaus
Language DE
Cycle SoSe
Content

Always viewed from the engineer's point of view, the lecture is structured as follows:

  •     Introduction to the topic
  •     Fundamentals of physiological modelling
  •     Introduction to Breathing and Ventilation
  •     Physiology and Pathology in Cardiology
  •     Introduction to the Regulation of Blood Glucose
  •     kidney function and renal replacement therapy
  •     Representation of the control technology on the concrete ventilator
  •     Excursion to a medical technology company

Techniques of modeling, simulation and controller development are discussed. In the models, simple equivalent block diagrams for physiological processes are derived and explained how sensors, controllers and actuators are operated. MATLAB and SIMULINK are used as development tools.
Literature
  • Leonhardt, S., & Walter, M. (2016). Medizintechnische Systeme. Berlin, Heidelberg: Springer Vieweg.
  • Werner, J. (2005). Kooperative und autonome Systeme der Medizintechnik. München: Oldenbourg.
  • Oczenski, W. (2017). Atmen : Atemhilfen ; Atemphysiologie und Beatmungstechnik: Georg Thieme Verlag KG.

Module M1224: Selected Topics of Mechatronics (Alternative B: 6 LP)
Courses
Title Typ Hrs/wk CP
Applied Automation (L1592) Project-/problem-based Learning 3 3
Advanced Training Course SE-ZERT (L2739) Project-/problem-based Learning 2 3
Development Management for Mechatronics (L1512) Lecture 2 3
Fatigue & Damage Tolerance (L0310) Lecture 2 3
Industry 4.0 for Engineers (L2012) Lecture 2 3
Microcontroller Circuits: Implementation in Hardware and Software (L0087) Seminar 2 2
Microsystems Technology (L0724) Lecture 2 4
Model-Based Systems Engineering (MBSE) with SysML/UML (L1551) Project-/problem-based Learning 3 3
Sustainable Industrial Production (L2863) Lecture 2 4
Process Measurement Engineering (L1077) Lecture 2 3
Process Measurement Engineering (L1083) Recitation Section (large) 1 1
Feedback Control in Medical Technology (L0664) Lecture 2 3
Module Responsible Dr. Martin Gomse
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 express their extended knowledge and discuss the connection of different special fields or application areas of mechatronics
  • Students are qualified to connect different special fields with each other
Skills
  • Students can apply specialized solution strategies and new scientific methods in selected areas
  • Students are able to transfer learned skills to new and unknown problems and can develop own solution approaches
Personal Competence
Social Competence None
Autonomy
  • Students are able to develop their knowledge and skills by autonomous election of courses.
Workload in Hours Depends on choice of courses
Credit points 6
Assignment for the Following Curricula Mechatronics: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Course L1592: Applied Automation
Typ Project-/problem-based Learning
Hrs/wk 3
CP 3
Workload in Hours Independent Study Time 48, Study Time in Lecture 42
Examination Form Mündliche Prüfung
Examination duration and scale 30 Minuten
Lecturer Prof. Thorsten Schüppstuhl
Language DE
Cycle WiSe
Content 
-Project Based Learning
-Robot Operating System
-Robot structure and description
-Motion description
-Calibration
-Accuracy
Literature 
John J. Craig
Introduction to Robotics - Mechanics and Control 
ISBN: 0131236296
 Pearson Education, Inc., 2005

Stefan Hesse
Grundlagen der Handhabungstechnik
ISBN: 3446418725
 München Hanser, 2010

K. Thulasiraman and M. N. S. Swamy
Graphs: Theory and Algorithms
ISBN: 9781118033104  %CITAVIPICKER£9781118033104£Titel anhand dieser ISBN in Citavi-Projekt übernehmen£%
John Wüey & Sons, Inc., 1992
Course L2739: Advanced Training Course SE-ZERT
Typ Project-/problem-based Learning
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Examination Form Klausur
Examination duration and scale 120 min
Lecturer Prof. Ralf God
Language DE
Cycle SoSe
Content
Literature

INCOSE Systems Engineering Handbuch - Ein Leitfaden für Systemlebenszyklus-Prozesse und -Aktivitäten, GfSE (Hrsg. der deutschen Übersetzung), ISBN 978-3-9818805-0-2.

ISO/IEC 15288 System- und Software-Engineering - System-Lebenszyklus-Prozesse (Systems and Software Engineering - System Life Cycle Processes).
Course L1512: Development Management for Mechatronics
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Examination Form Mündliche Prüfung
Examination duration and scale 30 Minuten
Lecturer NN, Dr. Johannes Nicolas Gebhardt
Language DE
Cycle SoSe
Content
  • Processes and methods of product development - from idea to market launch
    • identification of market and technology potentials
    • development of a common product architecture
    • Synchronized product development across all engineering disciplines
    • product validation incl. customer view
  • Steering and optimization of product development
    • Design of processes for product development
    • IT systems for product development
    • Establishment of management standards
    • Typical types of organization
Literature
  • Bender: Embedded Systems - qualitätsorientierte Entwicklung 
  • Ehrlenspiel: Integrierte Produktentwicklung: Denkabläufe, Methodeneinsatz, Zusammenarbeit 
  • Gausemeier/Ebbesmeyer/Kallmeyer: Produktinnovation - Strategische Planung und Entwicklung der Produkte von morgen
  • Haberfellner/de Weck/Fricke/Vössner: Systems Engineering: Grundlagen und Anwendung
  • Lindemann: Methodische Entwicklung technischer Produkte: Methoden flexibel und situationsgerecht anwenden
  • Pahl/Beitz: Konstruktionslehre: Grundlagen erfolgreicher Produktentwicklung. Methoden und Anwendung 
  • VDI-Richtlinie 2206: Entwicklungsmethodik für mechatronische Systeme
Course L0310: Fatigue & Damage Tolerance
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Examination Form Mündliche Prüfung
Examination duration and scale 45 min
Lecturer Dr. Martin Flamm
Language EN
Cycle WiSe
Content Design principles, fatigue strength, crack initiation and crack growth, damage calculation, counting methods, methods to improve fatigue strength, environmental influences
Literature Jaap Schijve, Fatigue of Structures and Materials. Kluver Academic Puplisher, Dordrecht, 2001 E. Haibach. Betriebsfestigkeit Verfahren und Daten zur Bauteilberechnung. VDI-Verlag, Düsseldorf, 1989
Course L2012: Industry 4.0 for Engineers
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Examination Form Klausur
Examination duration and scale 120 min
Lecturer Prof. Thorsten Schüppstuhl
Language DE
Cycle SoSe
Content
Literature
Course L0087: Microcontroller Circuits: Implementation in Hardware and Software
Typ Seminar
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Examination Form Schriftliche Ausarbeitung
Examination duration and scale 10 min. Vortrag + anschließende Diskussion
Lecturer Prof. Siegfried Rump
Language DE
Cycle WiSe/SoSe
Content
Literature

ATmega16A 8-bit  Microcontroller with 16K Bytes In-System Programmable Flash - DATASHEET, Atmel Corporation 2014

Atmel AVR 8-bit Instruction Set Instruction Set Manual, Atmel Corporation 2016
Course L0724: Microsystems Technology
Typ Lecture
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Examination Form Mündliche Prüfung
Examination duration and scale 30 min
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 L1551: Model-Based Systems Engineering (MBSE) with SysML/UML
Typ Project-/problem-based Learning
Hrs/wk 3
CP 3
Workload in Hours Independent Study Time 48, Study Time in Lecture 42
Examination Form Schriftliche Ausarbeitung
Examination duration and scale ca. 10 Seiten
Lecturer Prof. Ralf God
Language DE
Cycle SoSe
Content

Objectives of the problem-oriented course are the acquisition of knowledge on system design using the formal languages SysML/UML, learning about tools for modeling and finally the implementation of a project with methods and tools of Model-Based Systems Engineering (MBSE) on a realistic hardware platform (e.g. Arduino®, Raspberry Pi®):
• What is a model? 
• What is Systems Engineering? 
• Survey of MBSE methodologies
• The modelling languages SysML /UML 
• Tools for MBSE 
• Best practices for MBSE 
• Requirements specification, functional architecture, specification of a solution
• From model to software code 
• Validation and verification: XiL methods
• Accompanying MBSE project
Literature

- Skript zur Vorlesung
- Weilkiens, T.: Systems Engineering mit SysML/UML: Modellierung, Analyse, Design. 2. Auflage, dpunkt.Verlag, 2008
- Holt, J., Perry, S.A., Brownsword, M.: Model-Based Requirements Engineering. Institution Engineering & Tech, 2011
Course L2863: Sustainable Industrial Production
Typ Lecture
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Examination Form Klausur
Examination duration and scale 60 min
Lecturer Dr. Simon Markus Kothe
Language DE
Cycle SoSe
Content

Industrial production deals with the manufacture of physical products to satisfy human needs using various manufacturing processes that change the form and physical properties of raw materials. Manufacturing is a central driver of economic development and has a major impact on the well-being of humanity. However, the scale of current manufacturing activities results in enormous global energy and material demands that are harmful to both the environment and people. Historically, industrial activities were mostly oriented towards economic constraints, while social and environmental consequences were only hardly considered. As a result, today's global consumption rates of many resources and associated emissions often exceed the natural regeneration rate of our planet. In this respect, current industrial production can mostly be described as unsustainable. This is emphasized each year by the Earth Overshoot Day, which marks the day when humanity's ecological footprint exceeds the Earth's annual regenerative capacity. 

This lecture aims to provide the motivation, analytical methods as well as approaches for sustainable industrial production and to clarify the influence of the production phase in relation to the raw material, use and recycling phases in the entire life cycle of products. For this, the following topics will be highlighted:

- Motivation for sustainable production, the 17 Sustainable Development Goals (SDGs) of the UN and their relevance for tomorrow's manufacturing;

- raw material vs. production phase vs. use phase vs. recycling/end-of-life phase: importance of the production phase for the environmental impact of manufactured products;

- Typical energy- and resource-intensive processes in industrial production and innovative approaches to increase energy and resource efficiency;

- Methodology for optimizing the energy and resource efficiency of industrial manufacturing chains with the three steps of modeling (1), evaluating (2) and improving (3);

- Resource efficiency of industrial manufacturing value chains and its assessment using life cycle analysis (LCA);

- Exercise: LCA analysis of a manufacturing process (thermoplastic joining of an aircraft fuselage segment) as part of a product life cycle assessment.
Literature

Literatur:

- Stefan Alexander (2020): Resource efficiency in manufacturing value chains. Cham: Springer International Publishing.

- Hauschild, Michael Z.; Rosenbaum, Ralph K.; Olsen, Stig Irving (Hg.) (2018): Life Cycle Assessment. Theory and Practice. Cham: Springer International Publishing.

- Kishita, Yusuke; Matsumoto, Mitsutaka; Inoue, Masato; Fukushige, Shinichi (2021): EcoDesign and sustainability. Singapore: Springer.

- Schebek, Liselotte; Herrmann, Christoph; Cerdas, Felipe (2019): Progress in Life Cycle Assessment. Cham: Springer International Publishing.

- Thiede, Sebastian; Hermann, Christoph (2019): Eco-factories of the future. Cham: Springer Nature Switzerland AG.

- Vorlesungsskript.
Course L1077: Process Measurement Engineering
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Examination Form Mündliche Prüfung
Examination duration and scale 45 Minuten
Lecturer Prof. Roland Harig
Language DE/EN
Cycle SoSe
Content
  • Process measurement engineering in the context of process control engineering
    • Challenges of process measurement engineering
    • Instrumentation of processes
    • Classification of pickups
  • Systems theory in process measurement engineering
    • Generic linear description of pickups
    • Mathematical description of two-port systems
    • Fourier and Laplace transformation
  • Correlational measurement
    • Wide band signals
    • Auto- and cross-correlation function and their applications
    • Fault-free operation of correlational methods
  • Transmission of analog and digital measurement signals
    • Modulation process (amplitude and frequency modulation)
    • Multiplexing
    • Analog to digital converter
Literature

- Färber: „Prozeßrechentechnik“, Springer-Verlag 1994

- Kiencke, Kronmüller: „Meßtechnik“, Springer Verlag Berlin Heidelberg, 1995

- A. Ambardar: „Analog and Digital Signal Processing“ (1), PWS Publishing Company, 1995, NTC 339

- A. Papoulis: „Signal Analysis“ (1), McGraw-Hill, 1987, NTC 312 (LB)

- M. Schwartz: „Information Transmission, Modulation and Noise“ (3,4), McGraw-Hill, 1980, 2402095

- S. Haykin: „Communication Systems“ (1,3), Wiley&Sons, 1983, 2419072

- H. Sheingold: „Analog-Digital Conversion Handbook“ (5), Prentice-Hall, 1986, 2440072

- J. Fraden: „AIP Handbook of Modern Sensors“ (5,6), American Institute of Physics, 1993, MTB 346
Course L1083: Process Measurement Engineering
Typ Recitation Section (large)
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Examination Form Mündliche Prüfung
Examination duration and scale
Lecturer Prof. Roland Harig
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course
Course L0664: Feedback Control in Medical Technology
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Examination Form Mündliche Prüfung
Examination duration and scale 20 min
Lecturer Johannes Kreuzer, Christian Neuhaus
Language DE
Cycle SoSe
Content

Always viewed from the engineer's point of view, the lecture is structured as follows:

  •     Introduction to the topic
  •     Fundamentals of physiological modelling
  •     Introduction to Breathing and Ventilation
  •     Physiology and Pathology in Cardiology
  •     Introduction to the Regulation of Blood Glucose
  •     kidney function and renal replacement therapy
  •     Representation of the control technology on the concrete ventilator
  •     Excursion to a medical technology company

Techniques of modeling, simulation and controller development are discussed. In the models, simple equivalent block diagrams for physiological processes are derived and explained how sensors, controllers and actuators are operated. MATLAB and SIMULINK are used as development tools.
Literature
  • Leonhardt, S., & Walter, M. (2016). Medizintechnische Systeme. Berlin, Heidelberg: Springer Vieweg.
  • Werner, J. (2005). Kooperative und autonome Systeme der Medizintechnik. München: Oldenbourg.
  • Oczenski, W. (2017). Atmen : Atemhilfen ; Atemphysiologie und Beatmungstechnik: Georg Thieme Verlag KG.

Module M1281: Advanced Topics in Vibration
Courses
Title Typ Hrs/wk CP
Advanced Topics in Vibration (L1743) Project-/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.
  • Students are able to identify the need to develop and research new terms and concepts in vibrations.
Skills
  • Students are able to apply existing methods and procesures of Advanced Vibrations. 
  • Students are able to develop novel methods and procedures for advanced vibration problems.
Personal Competence
Social Competence
  • Students can reach working results also in groups.
  • Students can present working results also in groups.
Autonomy
  • Students are able to approach given research tasks individually
  • Students are able 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
Course achievement None
Examination Written exam
Examination duration and scale 2 Hours
Assignment for the Following Curricula Mechatronics: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Product Development and Production: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory
Course L1743: Advanced Topics in Vibration
Typ Project-/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

Advanced and Research Topics in Vibrations

  • Rotor Dynamics
  • Modal Analysis
  • Model Order Reduction
  • Stability of Periodic Solutions
  • Random Vibrations
Literature

Aktuelle Veröffentlichungen / Recent research publications

Bücher/Books:

Gasch, Nordmann, Pfützner: Rotordynamik

Gasch, Knothe, Liebich: Strukturdynamik

Module M0835: Humanoid Robotics
Courses
Title Typ Hrs/wk CP
Humanoid Robotics (L0663) Seminar 2 2
Module Responsible Patrick Göttsch
Admission Requirements None
Recommended Previous Knowledge


  • Introduction to control systems
  • Control theory and design
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can explain humanoid robots.
  • Students learn to apply basic control concepts for different tasks in humanoid robotics.
Skills
  • Students acquire knowledge about selected aspects of humanoid robotics, based on specified literature
  • Students generalize developed results and present them to the participants
  • Students practice to prepare and give a presentation
Personal Competence
Social Competence
  • Students are capable of developing solutions in interdisciplinary teams and present them
  • They are able to provide appropriate feedback and handle constructive criticism of their own results
Autonomy
  • Students evaluate advantages and drawbacks of different forms of presentation for specific tasks and select the best solution
  • Students familiarize themselves with a scientific field, are able of introduce it and follow presentations of other students, such that a scientific discussion develops
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Credit points 2
Course achievement None
Examination Presentation
Examination duration and scale 30 min
Assignment for the Following Curricula Mechatronics: Core Qualification: 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 Robotics and Computer Science: Elective Compulsory
Course L0663: Humanoid Robotics
Typ Seminar
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Patrick Göttsch
Language DE
Cycle SoSe
Content
  • Grundlagen der Regelungstechnik
  • Control systems theory and design
Literature

- B. Siciliano, O. Khatib. "Handbook of Robotics. Part A: Robotics Foundations",

Springer (2008).

Module M1269: Lab Cyber-Physical Systems
Courses
Title Typ Hrs/wk CP
Lab Cyber-Physical Systems (L1740) Project-/problem-based Learning 4 6
Module Responsible Prof. Heiko Falk
Admission Requirements None
Recommended Previous Knowledge Module "Embedded Systems"
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Cyber-Physical Systems (CPS) are tightly integrated with their surrounding environment, via sensors, A/D and D/A converters, and actors. Due to their particular application areas, highly specialized sensors, processors and actors are common. Accordingly, there is a large variety of different specification approaches for CPS - in contrast to classical software engineering approaches.

Based on practical experiments using robot kits and computers, the basics of specification and modelling of CPS are taught. The lab introduces into the area (basic notions, characteristical properties) and their specification techniques (models of computation, hierarchical automata, data flow models, petri nets, imperative approaches). Since CPS frequently perform control tasks, the lab's experiments will base on simple control applications. The experiments will use state-of-the-art industrial specification tools (MATLAB/Simulink, LabVIEW, NXC) in order to model cyber-physical models that interact with the environment via sensors and actors.
Skills After successful attendance of the lab, students are able to develop simple CPS. They understand the interdependencies between a CPS and its surrounding processes which stem from the fact that a CPS interacts with the environment via sensors, A/D converters, digital processors, D/A converters and actors. The lab enables students to compare modelling approaches, to evaluate their advantages and limitations, and to decide which technique to use for a concrete task. They will be able to apply these techniques to practical problems. They obtain first experiences in hardware-related software development, in industry-relevant specification tools and in the area of simple control applications.
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
Course achievement None
Examination Written elaboration
Examination duration and scale Execution and documentation of all lab experiments
Assignment for the Following Curricula General Engineering Science (German program, 7 semester): Specialisation Computer Science: Elective Compulsory
Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory
Computer Science in Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Course L1740: Lab Cyber-Physical Systems
Typ Project-/problem-based Learning
Hrs/wk 4
CP 6
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Lecturer Prof. Heiko Falk
Language DE/EN
Cycle SoSe
Content
  • Experiment 1: Programming in NXC
  • Experiment 2: Programming the Robot in Matlab/Simulink
  • Experiment 3: Programming the Robot in LabVIEW
Literature
  • Peter Marwedel. Embedded System Design - Embedded System Foundations of Cyber-Physical Systems. 2nd Edition, Springer, 2012.
  • Begleitende Foliensätze

Module M0838: Linear and Nonlinear System Identifikation
Courses
Title Typ Hrs/wk CP
Linear and Nonlinear System Identification (L0660) Lecture 2 3
Module Responsible Prof. Herbert Werner
Admission Requirements None
Recommended Previous Knowledge
  • Classical control (frequency response, root locus)
  • State space methods
  • Discrete-time systems
  • Linear algebra, singular value decomposition
  • Basic knowledge about stochastic processes
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can explain the general framework of the prediction error method and its application to a variety of linear and nonlinear model structures
  • They can explain how multilayer perceptron networks are used to model nonlinear dynamics
  • They can explain how an approximate predictive control scheme can be based on neural network models
  • They can explain the idea of subspace identification and its relation to Kalman realisation theory
Skills
  • Students are capable of applying the predicition error method to the experimental identification of linear and nonlinear models for dynamic systems
  • They are capable of implementing a nonlinear predictive control scheme based on a neural network model
  • They are capable of applying subspace algorithms to the experimental identification of linear models for dynamic systems
  • They can do the above using standard software tools (including the Matlab System Identification Toolbox)
Personal Competence
Social Competence

Students can work in mixed 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 62, Study Time in Lecture 28
Credit points 3
Course achievement None
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory
Mechatronics: Core Qualification: 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: Compulsory
Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory
Theoretical Mechanical Engineering: Core Qualification: Elective Compulsory
Course L0660: Linear and Nonlinear System Identification
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
  • Prediction error method
  • Linear and nonlinear model structures
  • Nonlinear model structure based on multilayer perceptron network
  • Approximate predictive control based on multilayer perceptron network model
  • Subspace identification
Literature
  • Lennart Ljung, System Identification - Theory for the User, Prentice Hall 1999
  • M. Norgaard, O. Ravn, N.K. Poulsen and L.K. Hansen, Neural Networks for Modeling and Control of Dynamic Systems, Springer Verlag, London 2003
  • T. Kailath, A.H. Sayed and B. Hassibi, Linear Estimation, Prentice Hall 2000

Module M0939: Control Lab A
Courses
Title Typ Hrs/wk CP
Control Lab I (L1093) Practical Course 1 1
Control Lab II (L1291) Practical Course 1 1
Control Lab III (L1665) Practical Course 1 1
Control Lab IV (L1666) Practical Course 1 1
Module Responsible NN
Admission Requirements None
Recommended Previous Knowledge
  • State space methods
  • LQG control
  • H2 and H-infinity optimal control
  • uncertain plant models and robust control
  •  LPV control
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can explain the difference between validation of a control lop in simulation and experimental validation
Skills
  • Students are capable of applying basic system identification tools (Matlab System Identification Toolbox) to identify a dynamic model that can be used for controller synthesis
  • They are capable of using standard software tools (Matlab Control Toolbox) for the design and implementation of LQG controllers
  • They are capable of using standard software tools (Matlab Robust Control Toolbox) for the mixed-sensitivity design and the implementation of H-infinity optimal controllers
  • They are capable of representing model uncertainty, and of designing and implementing a robust controller
  • They are capable of using standard software tools (Matlab Robust Control Toolbox) for the design and the implementation of LPV gain-scheduled controllers
Personal Competence
Social Competence
  • Students can work in teams to conduct experiments and document the results
Autonomy
  • Students can independently carry out simulation studies to design and validate control loops
Workload in Hours Independent Study Time 64, Study Time in Lecture 56
Credit points 4
Course achievement None
Examination Written elaboration
Examination duration and scale 1
Assignment for the Following Curricula Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Course L1093: Control Lab I
Typ Practical Course
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer NN, Adwait Datar, Patrick Göttsch
Language EN
Cycle WiSe/SoSe
Content One of the offered experiments in control theory.
Literature

Experiment Guides
Course L1291: Control Lab II
Typ Practical Course
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer NN, Adwait Datar, Patrick Göttsch
Language EN
Cycle WiSe/SoSe
Content One of the offered experiments in control theory.
Literature

Experiment Guides
Course L1665: Control Lab III
Typ Practical Course
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer NN, Adwait Datar, Patrick Göttsch
Language EN
Cycle WiSe/SoSe
Content One of the offered experiments in control theory.
Literature

Experiment Guides
Course L1666: Control Lab IV
Typ Practical Course
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer NN, Adwait Datar, Patrick Göttsch
Language EN
Cycle WiSe/SoSe
Content One of the offered experiments in control theory.
Literature

Experiment Guides

Module M1229: Control Lab B
Courses
Title Typ Hrs/wk CP
Control Lab V (L1667) Practical Course 1 1
Control Lab VI (L1668) Practical Course 1 1
Module Responsible NN
Admission Requirements None
Recommended Previous Knowledge
  • State space methods
  • LQG control
  • H2 and H-infinity optimal control
  • uncertain plant models and robust control
  •  LPV control
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can explain the difference between validation of a control lop in simulation and experimental validation
Skills
  • Students are capable of applying basic system identification tools (Matlab System Identification Toolbox) to identify a dynamic model that can be used for controller synthesis
  • They are capable of using standard software tools (Matlab Control Toolbox) for the design and implementation of LQG controllers
  • They are capable of using standard software tools (Matlab Robust Control Toolbox) for the mixed-sensitivity design and the implementation of H-infinity optimal controllers
  • They are capable of representing model uncertainty, and of designing and implementing a robust controller
  • They are capable of using standard software tools (Matlab Robust Control Toolbox) for the design and the implementation of LPV gain-scheduled controllers
Personal Competence
Social Competence
  • Students can work in teams to conduct experiments and document the results
Autonomy
  • Students can independently carry out simulation studies to design and validate control loops
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Credit points 2
Course achievement None
Examination Written elaboration
Examination duration and scale 1
Assignment for the Following Curricula Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Course L1667: Control Lab V
Typ Practical Course
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Herbert Werner, Adwait Datar, Patrick Göttsch
Language EN
Cycle WiSe/SoSe
Content One of the offered experiments in control theory.
Literature

Experiment Guides
Course L1668: Control Lab VI
Typ Practical Course
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Herbert Werner, Adwait Datar, Patrick Göttsch
Language EN
Cycle WiSe/SoSe
Content One of the offered experiments in control theory.
Literature

Experiment Guides

Module M1306: Control Lab C
Courses
Title Typ Hrs/wk CP
Control Lab IX (L1836) Practical Course 1 1
Control Lab VII (L1834) Practical Course 1 1
Control Lab VIII (L1835) Practical Course 1 1
Module Responsible Prof. Herbert Werner
Admission Requirements None
Recommended Previous Knowledge
  • State space methods
  • LQG control
  • H2 and H-infinity optimal control
  • uncertain plant models and robust control
  •  LPV control
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can explain the difference between validation of a control lop in simulation and experimental validation
Skills
  • Students are capable of applying basic system identification tools (Matlab System Identification Toolbox) to identify a dynamic model that can be used for controller synthesis
  • They are capable of using standard software tools (Matlab Control Toolbox) for the design and implementation of LQG controllers
  • They are capable of using standard software tools (Matlab Robust Control Toolbox) for the mixed-sensitivity design and the implementation of H-infinity optimal controllers
  • They are capable of representing model uncertainty, and of designing and implementing a robust controller
  • They are capable of using standard software tools (Matlab Robust Control Toolbox) for the design and the implementation of LPV gain-scheduled controllers
Personal Competence
Social Competence
  • Students can work in teams to conduct experiments and document the results
Autonomy
  • Students can independently carry out simulation studies to design and validate control loops
Workload in Hours Independent Study Time 48, Study Time in Lecture 42
Credit points 3
Course achievement None
Examination Written elaboration
Examination duration and scale 1
Assignment for the Following Curricula Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Core Qualification: Elective Compulsory
Course L1836: Control Lab IX
Typ Practical Course
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Herbert Werner, Adwait Datar, Patrick Göttsch
Language EN
Cycle WiSe/SoSe
Content

One of the offered experiments in control theory.
Literature

Experiment Guides
Course L1834: Control Lab VII
Typ Practical Course
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Herbert Werner, Patrick Göttsch
Language EN
Cycle WiSe/SoSe
Content

One of the offered experiments in control theory.
Literature

Experiment Guides
Course L1835: Control Lab VIII
Typ Practical Course
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Herbert Werner, Adwait Datar, Patrick Göttsch
Language EN
Cycle WiSe/SoSe
Content

One of the offered experiments in control theory.
Literature

Experiment Guides

Module M1203: Applied Dynamics: Numerical and experimental methods
Courses
Title Typ Hrs/wk CP
Lab Applied Dynamics (L1631) Practical Course 1 1
Applied Dynamics (L1630) Lecture 4 4
Applied Dynamics (L3103) Recitation Section (small) 1 1
Module Responsible Prof. Robert Seifried
Admission Requirements None
Recommended Previous Knowledge

Mathematics I, II, III, Mechanics I, II, III, IV

Numerical Treatment of Ordinary Differential Equations
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students can represent the most important methods of dynamics after successful completion of the module Technical dynamics and have a good understanding of the main concepts in the technical dynamics.
Skills

Students are able

+ to think holistically

+ to independently, securly and critically analyze and optimize basic problems of the dynamics of rigid and flexible multibody systems

+ to describe dynamics problems mathematically

+ to investigate dynamics problems both experimentally and numerically
Personal Competence
Social Competence

Students are able to

+ solve problems in heterogeneous groups and to document the corresponding results.
Autonomy

Students are able to

+ assess their knowledge by means of exercises and experiments.

+ acquaint themselves with the necessary knowledge to solve research oriented tasks.
Workload in Hours Independent Study Time 96, Study Time in Lecture 84
Credit points 6
Course achievement
Compulsory Bonus Form Description
Yes None Subject theoretical and practical work Versuche Fachlabor
No 20 % Excercises Aufgaben in Matlab
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Mechatronics: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Core Qualification: Compulsory
Course L1631: Lab Applied Dynamics
Typ Practical Course
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Dr. Marc-André Pick
Language DE
Cycle SoSe
Content

Practical exercises are performed in groups. The examples are taken from different areas of applied dynamics, such as numerical simulation, experimental validation and experimental vibration analysis.


Literature

Schiehlen, W.; Eberhard, P.: Technische Dynamik, 4. Auflage, Vieweg+Teubner: Wiesbaden, 2014.
Course L1630: Applied Dynamics
Typ Lecture
Hrs/wk 4
CP 4
Workload in Hours Independent Study Time 64, Study Time in Lecture 56
Lecturer Prof. Robert Seifried, Dr. Marc-André Pick
Language DE
Cycle SoSe
Content
  1. Modelling of Multibody Systems
  2. Basics from kinematics and kinetics
  3. Constraints
  4. Multibody systems in minimal coordinates
  5. State space, linearization and modal analysis
  6. Multibody systems with kinematic constraints
  7. Multibody systems as DAE
  8. Non-holonomic multibody systems
  9. Experimental Methods in Dynamics
Literature

Schiehlen, W.; Eberhard, P.: Technische Dynamik, 4. Auflage, Vieweg+Teubner: Wiesbaden, 2014.

Woernle, C.: Mehrkörpersysteme, Springer: Heidelberg, 2011.

Seifried, R.: Dynamics of Underactuated Multibody Systems, Springer, 2014.
Course L3103: Applied Dynamics
Typ Recitation Section (small)
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Robert Seifried
Language DE
Cycle SoSe
Content See interlocking course
Literature See interlocking course

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 detail. 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 are able to grasp practical tasks in groups, develop solution strategies independently, define work processes and work on them collaboratively.
The students are able to collaboratively organize their work processes and software solutions using virtual communication and software management tools.
The students can critically reflect on the results of other groups, make constructive suggestions for improvement, and also incorporate them into their own work.
Autonomy

The students can assess their level of knowledge and independently control their learning processes on this basis as well as document their work results. They can critically evaluate the results achieved and present them in an appropriate argumentative manner to the other groups.
Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Course achievement
Compulsory Bonus Form Description
Yes 10 % Written elaboration
Yes 10 % Presentation
Examination Written exam
Examination duration and scale 90 minutes
Assignment for the Following Curricula Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory
Data Science: Specialisation III. Applications: Elective Compulsory
Data Science: Specialisation IV. Special Focus Area: Elective Compulsory
Electrical Engineering: Specialisation Medical Technology: Elective Compulsory
Computer Science in Engineering: Specialisation II. Engineering Science: Elective Compulsory
International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory
International Management and Engineering: Specialisation II. Process Engineering and Biotechnology: Elective Compulsory
Mechatronics: Core Qualification: 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: 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 M1212: Technical Complementary Course for IMPMEC (according to Subject Specific Regulations)
Courses
Title Typ Hrs/wk CP
Module Responsible NN
Admission Requirements None
Recommended Previous Knowledge

See selected module according to FSPO
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

see selected module according to FSPO
Skills

see selected module according to FSPO
Personal Competence
Social Competence

see selected module according to FSPO
Autonomy

see selected module according to FSPO
Workload in Hours Depends on choice of courses
Credit points 6
Assignment for the Following Curricula Mechatronics: Core Qualification: Elective Compulsory

Module M1616: Flight Control Laws: Design and Application
Courses
Title Typ Hrs/wk CP
Flight Control Law Design and Application (L2448) Lecture 2 4
Flight Control Law Design and Application (L2449) Project-/problem-based Learning 2 2
Module Responsible Prof. Frank Thielecke
Admission Requirements None
Recommended Previous Knowledge

Basic knowledge in:

  •  Mathematics (linear algebra and ordinary differential equations)
  •  Control Systems (transfer functions and state space representation)
  •  Mechanics (rigid-body kinetics)
  •  Flight mechanics
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to:

  • Describe and understand flight dynamics models for control tasks
  • Assess handling qualities and understand the need for augmentation through control systems
  • Identify fundamental performance limitations of control laws
Skills

Students are able to:

  • Design model-based control laws for stability augmentation
  • Design model-based flight control laws
  • Assess robustness and performance of control laws
Personal Competence
Social Competence

Students are able to:

  • Design control laws in groups as well as discuss the requirements and results
Autonomy

Students are able to:

  • Reflect on the contents of lectures and extend their knowledge through literature research
  • Solve control design tasks with software tools
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement
Compulsory Bonus Form Description
Yes None Attestation Die in der Vorlesung vermittelten Kenntnisse werden in einem semesterbegleitenden Projekt direkt auf das Modell eines Passagierflugzeugs angewendet.
Examination Written exam
Examination duration and scale 60 min
Assignment for the Following Curricula Aircraft Systems Engineering: Core Qualification: Elective Compulsory
Aeronautics: Core Qualification: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Aircraft Systems Engineering: Elective Compulsory
Course L2448: Flight Control Law Design and Application
Typ Lecture
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Lecturer Prof. Frank Thielecke
Language EN
Cycle SoSe
Content
  • Flight dynamics (equations of motion, trim and linearization, linear models of longitudinal and lateral-directional motion, eigenforms)
  • Stability augmentation (modal dynamics, damper design with root-loci, pole placement and eigenstructure assignment)
  • Primary flight control laws and autopilots
  • Design of flight control laws (loopshaping design, robustness criteria and analysis, cascaded control loops, gain-scheduling)
  • Verification of flight control laws in simulation
Literature
  • J. Theis: Lecture Notes Flight Control Law Design
  • D. Schmidt: Modern Flight Dynamics
  • B. Stevens, F. Lewis: Aircraft Control and Simulation
  • D. McGruer, D. Graham, I. Ashkenas: Aircraft Dynamics and Automatic Control
  • SAE Aerospace Standard 94900 - Flight Control Systems
  • The MathWorks: Control Systems Design Toolbox User Guide
Course L2449: Flight Control Law Design and Application
Typ Project-/problem-based Learning
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Frank Thielecke
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1724: Smart Monitoring
Courses
Title Typ Hrs/wk CP
Smart Monitoring (L2762) Integrated Lecture 2 2
Smart Monitoring (L2763) Recitation Section (small) 2 4
Module Responsible Prof. Kay Smarsly
Admission Requirements None
Recommended Previous Knowledge

Basic knowledge or interest in object-oriented modeling, programming, and sensor technologies are helpful. Interest in modern research and teaching areas, such as Internet of Things, Industry 4.0 and cyber-physical systems, as well as the will to deepen skills of scientific working, are required. Basic knowledge in scientific writing and good English skills.
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students will become familiar with the principles and practices of smart monitoring. The students will be able to design decentralized smart systems to be applied for continuous (remote) monitoring of systems in the built and in the natural environment. In addition, the students will learn to design and to implement intelligent sensor systems using state-of-the-art data analysis techniques, modern software design concepts, and embedded computing methodologies. Besides lectures, project work is also part of this module, which will be conducted throughout the semester and will contribute to the grade. In small groups, the students will design smart monitoring systems that integrate a number of “intelligent” sensors to be implemented by the students. Specific focus will be put on the application of machine learning techniques. The smart monitoring systems will be mounted on real-world (built or natural) systems, such as bridges or slopes, or on scaled lab structures for validation purposes. The outcome of every group will be documented in a paper. All students of this module will “automatically” participate with their smart monitoring system in the annual "Smart Monitoring" competition. The written papers and oral examinations form the final grades. The module will be taught in English. Limited enrollment.
Skills
Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Written elaboration
Examination duration and scale 10 pages of work with 15-minute oral presentation
Assignment for the Following Curricula Civil Engineering: Specialisation Water and Traffic: Elective Compulsory
Civil Engineering: Specialisation Geotechnical Engineering: Elective Compulsory
Civil Engineering: Specialisation Coastal Engineering: Elective Compulsory
Civil Engineering: Specialisation Structural Engineering: Elective Compulsory
Environmental Engineering: Specialisation Water Quality and Water Engineering: Elective Compulsory
Environmental Engineering: Specialisation Energy and Resources: Elective Compulsory
Environmental Engineering: Specialisation Environment and Climate: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Water and Environmental Engineering: Specialisation Cities: Elective Compulsory
Water and Environmental Engineering: Specialisation Environment: Elective Compulsory
Water and Environmental Engineering: Specialisation Water: Elective Compulsory
Course L2762: Smart Monitoring
Typ Integrated Lecture
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Kay Smarsly
Language EN
Cycle SoSe
Content

In this course, principles of smart monitoring will be taught, focusing on modern concepts of data acquisition, data storage, and data analysis. Also, fundamentals of intelligent sensors and embedded computing will be illuminated. Autonomous software and decentralized data processing are further crucial parts of the course, including concepts of the Internet of Things, Industry 4.0 and cyber-physical systems. Furthermore, measuring principles, data acquisition systems, data management and data analysis algorithms will be discussed. Besides the theoretical background, numerous practical examples will be shown to demonstrate how smart monitoring may advantageously be used for assessing the condition of systems in the built or natural environment.
Literature
Course L2763: Smart Monitoring
Typ Recitation Section (small)
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Lecturer Prof. Kay Smarsly
Language EN
Cycle SoSe
Content The contents of the exercises are based on the lecture contents. In addition to the exercises, project work will be conducted throughout the semester, which will consume the majority of the workload. As part of the project work, students will design smart monitoring systems that will be tested in the laboratory or in the field. As mentioned in the module description, the students will participate in the “Smart Monitoring” competition, hosted annually by the Institute of Digital and Autonomous Construction. Students are encouraged to contribute their own ideas. The tools required to implement the smart monitoring systems will be taught in the group exercises as well as through external sources, such as video tutorials and literature.
Literature

Module M0805: Technical Acoustics I (Acoustic Waves, Noise Protection, Psycho Acoustics )
Courses
Title Typ Hrs/wk CP
Technical Acoustics I (Acoustic Waves, Noise Protection, Psycho Acoustics ) (L0516) Lecture 2 3
Technical Acoustics I (Acoustic Waves, Noise Protection, Psycho Acoustics ) (L0518) Recitation Section (large) 2 3
Module Responsible Prof. Benedikt Kriegesmann
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 in acoustics regarding acoustic waves, noise protection, and psycho acoustics and are able to give an overview of the corresponding theoretical and methodical basis.
Skills

The students are capable to handle engineering problems in acoustics by theory-based application of the demanding methodologies and measurement procedures treated within the module.
Personal Competence
Social Competence

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

The students are able to independently solve challenging acoustical problems in the areas treated within the module. Possible conflicting issues and limitations can be identified and the results are critically scrutinized.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Aircraft Systems Engineering: Core Qualification: Elective Compulsory
International Management and Engineering: Specialisation II. Aviation Systems: Elective Compulsory
Aeronautics: Core Qualification: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Product Development, Materials and Production: Core Qualification: Elective Compulsory
Technomathematics: Specialisation III. Engineering Science: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Product Development and Production: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory
Course L0516: Technical Acoustics I (Acoustic Waves, Noise Protection, Psycho Acoustics )
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Dr.-Ing. Sören Keuchel
Language EN
Cycle SoSe
Content

- Introduction and Motivation
- Acoustic quantities
- Acoustic waves
- Sound sources, sound radiation
- Sound engergy and intensity
- Sound propagation
- Signal processing
- Psycho acoustics
- Noise
- Measurements in acoustics
Literature

Cremer, L.; Heckl, M. (1996): Körperschall. Springer Verlag, Berlin
Veit, I. (1988): Technische Akustik. Vogel-Buchverlag, Würzburg
Veit, I. (1988): Flüssigkeitsschall. Vogel-Buchverlag, Würzburg
Course L0518: Technical Acoustics I (Acoustic Waves, Noise Protection, Psycho Acoustics )
Typ Recitation Section (large)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Dr.-Ing. Sören Keuchel
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1400: Design of Dependable Systems
Courses
Title Typ Hrs/wk CP
Designing Dependable Systems (L2000) Lecture 2 3
Designing Dependable Systems (L2001) Recitation Section (small) 2 3
Module Responsible Prof. Görschwin Fey
Admission Requirements None
Recommended Previous Knowledge Basic knowledge about data structures and algorithms
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

In the following "dependable" summarizes the concepts Reliability, Availability, Maintainability, Safety and Security.

Knowledge about approaches for designing dependable systems, e.g.,

  • Structural solutions like modular redundancy
  • Algorithmic solutions like handling byzantine faults or checkpointing

Knowledge about methods for the analysis of dependable systems
Skills

Ability to implement dependable systems using the above approaches.

Ability to analyzs the dependability of systems using the above methods for analysis.
Personal Competence
Social Competence

Students

  • discuss relevant topics in class and
  • present their solutions orally.
Autonomy Using accompanying material students independently learn in-depth relations between concepts explained in the lecture and additional solution strategies.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement
Compulsory Bonus Form Description
Yes None Subject theoretical and practical work Die Lösung einer Aufgabe ist Zuslassungsvoraussetzung für die Prüfung. Die Aufgabe wird in Vorlesung und Übung definiert.
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Course L2000: Designing Dependable Systems
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Görschwin Fey
Language DE/EN
Cycle SoSe
Content

Description

The term dependability comprises various aspects of a system. These are typically:
  • Reliability
  • Availability
  • Maintainability
  • Safety
  • Security
This makes dependability a core aspect that has to be considered early in system design, no matter whether software, embedded systems or full scale cyber-physical systems are considered.

Contents

The module introduces the basic concepts for the design and the analysis of dependable systems. Design examples for getting practical hands-on-experience in dependable design techniques. The module focuses towards embedded systems. The following topics are covered:
  • Modelling
  • Fault Tolerance
  • Design Concepts
  • Analysis Techniques
Literature
Course L2001: Designing Dependable Systems
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Görschwin Fey
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1894: Automation Technology and Systems
Courses
Title Typ Hrs/wk CP
Automation Technology and Systems (L2329) Lecture 4 4
Automation Technology and Systems (L2331) Project-/problem-based Learning 1 1
Automation Technology and Systems (L2330) Recitation Section (small) 1 1
Module Responsible Prof. Thorsten Schüppstuhl
Admission Requirements None
Recommended Previous Knowledge

without major course assessment
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students

  • know the characteristic components of an automation systems and have good understanding of their interaction
  • know methods for a systematical analysis of automation tasks and are able to use them
  • have special competences in industrial robot based automation systems
Skills

Students are able to...

  • analyze complex Automation tasks
  • develop application based concepts and solutions
  • design subsystems and integrate into one system
  • investigate and evaluate safety of machinery
  • create simple programs for robots and programmable logic controllers
  • design of circuit for pneumatic applications
Personal Competence
Social Competence

Students are able to ...

- find solutions for automation and handling tasks in groups

 - develop solutions in a production environment with qualified personnel at technical level and represent decisions.
Autonomy

Students are able to ...

  • analyze automation tasks independently
  • generate programs for robots and programmable logic devices autonomously
  • develop solutions for practice oriented tasks of automation independently
  • design safety concepts for automation applications
  • assess consequences of their professional actions and responsibilities
Workload in Hours Independent Study Time 96, Study Time in Lecture 84
Credit points 6
Course achievement
Compulsory Bonus Form Description
No 20 % Subject theoretical and practical work Die Studienleistung umfasst die Ergebnisse der PBL basierten Anteile des Moduls sowie der Präsentation in der Gruppe.
Examination Written exam
Examination duration and scale 120 min
Assignment for the Following Curricula International Management and Engineering: Specialisation II. Product Development and Production: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Product Development, Materials and Production: Specialisation Product Development: Elective Compulsory
Product Development, Materials and Production: Specialisation Production: Compulsory
Product Development, Materials and Production: Specialisation Materials: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Product Development and Production: Elective Compulsory
Course L2329: Automation Technology and Systems
Typ Lecture
Hrs/wk 4
CP 4
Workload in Hours Independent Study Time 64, Study Time in Lecture 56
Lecturer Prof. Thorsten Schüppstuhl
Language DE
Cycle SoSe
Content
Literature
Course L2331: Automation Technology and Systems
Typ Project-/problem-based Learning
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Thorsten Schüppstuhl
Language DE
Cycle SoSe
Content See interlocking course
Literature See interlocking course
Course L2330: Automation Technology and Systems
Typ Recitation Section (small)
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Thorsten Schüppstuhl
Language DE
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1702: Process Imaging
Courses
Title Typ Hrs/wk CP
Process Imaging (L2723) Lecture 3 3
Process Imaging (L2724) Project-/problem-based Learning 3 3
Module Responsible Prof. Alexander Penn
Admission Requirements None
Recommended Previous Knowledge No special prerequisites needed
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Content: The module focuses primarily on discussing established imaging techniques including (a) optical and infrared imaging, (b) magnetic resonance imaging, (c) X-ray imaging and tomography, and (d) ultrasound imaging but also covers a range of more recent imaging modalities. The students will learn:

  1. what these imaging techniques can measure (such as sample density or concentration, material transport, chemical composition, temperature),
  2. how the measurements work (physical measurement principles, hardware requirements, image reconstruction), and
  3. how to determine the most suited imaging methods for a given problem.

Learning goals: After the successful completion of the course, the students shall:

  1. understand the physical principles and practical aspects of the most common imaging methods,
  2. be able to assess the pros and cons of these methods with regard to cost, complexity, expected contrasts, spatial and temporal resolution, and based on this assessment
  3. be able to identify the most suited imaging modality for any specific engineering challenge in the field of chemical and bioprocess engineering.
Skills
Personal Competence
Social Competence In the problem-based interactive course, students work in small teams and set up two process imaging systems and use these systems to measure relevant process parameters in different chemical and bioprocess engineering applications. The teamwork will foster interpersonal communication skills.
Autonomy Students are guided to work in self-motivation due to the challenge-based character of this module. A final presentation improves presentation skills.
Workload in Hours Independent Study Time 96, Study Time in Lecture 84
Credit points 6
Course achievement None
Examination Written exam
Examination duration and scale 120 min
Assignment for the Following Curricula Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory
Bioprocess Engineering: Specialisation B - Industrial Bioprocess Engineering: Elective Compulsory
Bioprocess Engineering: Specialisation C - Bioeconomic Process Engineering, Focus Energy and Bioprocess Technology: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation General Process Engineering: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation Bioprocess Engineering: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation Chemical Process Engineering: Elective Compulsory
Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory
International Management and Engineering: Specialisation II. Process Engineering and Biotechnology: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Process Engineering: Specialisation Process Engineering: Elective Compulsory
Process Engineering: Specialisation Chemical Process Engineering: Elective Compulsory
Process Engineering: Specialisation Environmental Process Engineering: Elective Compulsory
Water and Environmental Engineering: Specialisation Environment: Elective Compulsory
Water and Environmental Engineering: Specialisation Water: Elective Compulsory
Course L2723: Process Imaging
Typ Lecture
Hrs/wk 3
CP 3
Workload in Hours Independent Study Time 48, Study Time in Lecture 42
Lecturer Prof. Alexander Penn
Language EN
Cycle SoSe
Content
Literature

Wang, M. (2015). Industrial Tomography. Cambridge, UK: Woodhead Publishing. 

Available as e-book in the library of TUHH: https://katalog.tub.tuhh.de/Record/823579395
Course L2724: Process Imaging
Typ Project-/problem-based Learning
Hrs/wk 3
CP 3
Workload in Hours Independent Study Time 48, Study Time in Lecture 42
Lecturer Prof. Alexander Penn, Dr. Stefan Benders
Language EN
Cycle SoSe
Content

Content: The module focuses primarily on discussing established imaging techniques including (a) optical and infrared imaging, (b) magnetic resonance imaging, (c) X-ray imaging and tomography, and (d) ultrasound imaging and also covers a range of more recent imaging modalities. The students will learn:

  1. what these imaging techniques can measure (such as sample density or concentration, material transport, chemical composition, temperature),
  2. how the measurements work (physical measurement principles, hardware requirements, image reconstruction), and
  3. how to determine the most suited imaging methods for a given problem.

Learning goals: After the successful completion of the course, the students shall:

  1. understand the physical principles and practical aspects of the most common imaging methods,
  2. be able to assess the pros and cons of these methods with regard to cost, complexity, expected contrasts, spatial and temporal resolution, and based on this assessment
  3. be able to identify the most suited imaging modality for any specific engineering challenge in the field of chemical and bioprocess engineering.
Literature

Wang, M. (2015). Industrial Tomography. Cambridge, UK: Woodhead Publishing. 

Available as e-book in the library of TUHH: https://katalog.tub.tuhh.de/Record/823579395

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
Course achievement
Compulsory Bonus Form Description
Yes None Presentation
Examination Oral exam
Examination duration and scale 20 min
Assignment for the Following Curricula Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: 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 Methods for 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. Daniel Ruprecht
Admission Requirements None
Recommended Previous Knowledge
  • Mathematik I, II, III for Engineers (German or English) or Analysis & Linear Algebra I + II plus Analysis III for Technomathematiker.
  • Basic knowledge of MATLAB, Python or a similar programming language.
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to

  • name numerical methods for the solution of ordinary differential equations and explain their core ideas,
  • formulate convergence statements for the taught numerical methods (including the necessary assumptions about the solved problem),
  • explain aspects regarding the practical realisation of a method,
  • select the appropriate numerical method for specific problems, implement the numerical algorithms efficiently and interpret the numerical results.
Skills

Students are able to

  • implement, apply and compare numerical methods for the solution of ordinary differential equations,
  • explain the convergence behaviour of numerical methods, taking into consideration the solved problem and selected algorithm,
  • develop a suitable solution approach for a given problem, if necessary by combining multiple algorithms, realise this approach and critically evaluate results.
Personal Competence
Social Competence

Students are able to

  • work together in heterogeneous teams (i.e., teams from different study programs and with different 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 provided theoretical and practical excercises are better solved individually or in a team and
  • 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
Course achievement None
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
Computer Science: Specialisation III. Mathematics: Elective Compulsory
Data Science: Specialisation I. Mathematics: Elective Compulsory
Data Science: Specialisation IV. Special Focus Area: Elective Compulsory
Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory
Energy Systems: Core Qualification: Elective Compulsory
Aircraft Systems Engineering: Core Qualification: Elective Compulsory
Interdisciplinary Mathematics: Specialisation II. Numerical - Modelling Training: Compulsory
Aeronautics: Core Qualification: Elective Compulsory
Mechatronics: Core Qualification: 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. Daniel Ruprecht
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

  • multiple shooting method
  • difference 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.
  • D. Griffiths, D. Higham: Numerical Methods for Ordinary Differential Equations.
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. Daniel Ruprecht
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1772: Smart Sensors
Courses
Title Typ Hrs/wk CP
Smart Sensors (L2904) Lecture 2 2
Smart Sensors Lab (L2905) Project-/problem-based Learning 3 4
Module Responsible Prof. Ulf Kulau
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 110, Study Time in Lecture 70
Credit points 6
Course achievement None
Examination Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Electrical Engineering: Specialisation Wireless and Sensor Technologies: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory
Course L2904: Smart Sensors
Typ Lecture
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Ulf Kulau
Language DE/EN
Cycle SoSe
Content
Literature
Course L2905: Smart Sensors Lab
Typ Project-/problem-based Learning
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Ulf Kulau
Language DE/EN
Cycle SoSe
Content
Literature

Module M1810: Autonomous Cyber-Physical Systems
Courses
Title Typ Hrs/wk CP
Autonomous Cyber-Physical Systems (L3000) Lecture 2 3
Autonomous Cyber-Physical Systems (L3001) Recitation Section (small) 2 3
Module Responsible Prof. Bernd-Christian Renner
Admission Requirements None
Recommended Previous Knowledge
  • Very good knowledge and practical experience in programming in the C/C++ language (e.g., module: Procedural Programming for Computer Scientists)
  • Basic knowledge in software engineering
  • Basic knowledge in wired and wireless communication protocols
  • Principal understanding of simple electronic circuits
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Cyber-Physical Systems form the basis for many modern control tasks in automation and for methods for monitoring the environment, infrastructure, etc. Essential aspects in the implementation of such systems are their networking, especially based on wireless technologies, and their autonomous operation, especially on the basis of regenerative energy sources. After successfully attending this event, the students are able to

  • to present the special features of cyber-physical systems and the associated challenges and concepts,
  • describe and evaluate wired and wireless communication on different layers of the ISO/OSI model,
  • explain and compare methods of regenerative energy production,
  • discuss and evaluate procedures for the autonomous and self-sufficient operation of such systems.
Skills

Students will be able to

  • to implement programs for cyber-physical systems in high-level languages ​​and using existing libraries,
  • to assess which communication and networking protocols can be used most sensibly in which application and to use them in real scenarios,
  • select and implement suitable methods for adapting the tasks based on the energy consumption and the future expected energy yield,
  • plan and evaluate scientific experiments.
Personal Competence
Social Competence

After completing the module, the students are able to work on similar tasks alone or in a group and to present the results in a suitable way.
Autonomy

After completing the module, the students are able to independently work on sub-areas of the subject using specialist literature, to summarize and present the knowledge they have acquired and to link it to the content of other courses.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement
Compulsory Bonus Form Description
No 10 % Attestation
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Data Science: Specialisation II. Computer Science: Elective Compulsory
Data Science: Specialisation IV. Special Focus Area: Elective Compulsory
Electrical Engineering: Specialisation Wireless and Sensor Technologies: Elective Compulsory
Computer Science in Engineering: Specialisation II. Engineering Science: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Course L3000: Autonomous Cyber-Physical Systems
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Bernd-Christian Renner
Language EN
Cycle SoSe
Content
Literature
Course L3001: Autonomous Cyber-Physical Systems
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Bernd-Christian Renner
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 NN
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
Course achievement None
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory
Energy Systems: Core Qualification: Elective Compulsory
Aircraft Systems Engineering: Core Qualification: Elective Compulsory
Aeronautics: Core Qualification: Elective Compulsory
Mechatronics: Core Qualification: 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: 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 NN
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 NN
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0803: Embedded Systems
Courses
Title Typ Hrs/wk CP
Embedded Systems (L0805) Lecture 3 3
Embedded Systems (L2938) Project-/problem-based Learning 1 1
Embedded Systems (L0806) Recitation Section (small) 1 2
Module Responsible Prof. Heiko Falk
Admission Requirements None
Recommended Previous Knowledge Computer Engineering
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Embedded systems can be defined as information processing systems embedded into enclosing products. This course teaches the foundations of such systems. In particular, it deals with an introduction into these systems (notions, common characteristics) and their specification languages (models of computation, hierarchical automata, specification of distributed systems, task graphs, specification of real-time applications, translations between different models).

Another part covers the hardware of embedded systems: Sonsors, A/D and D/A converters, real-time capable communication hardware, embedded processors, memories, energy dissipation, reconfigurable logic and actuators. The course also features an introduction into real-time operating systems, middleware and real-time scheduling. Finally, the implementation of embedded systems using hardware/software co-design (hardware/software partitioning, high-level transformations of specifications, energy-efficient realizations, compilers for embedded processors) is covered.
Skills After having attended the course, students shall be able to realize simple embedded systems. The students shall realize which relevant parts of technological competences to use in order to obtain a functional embedded systems. In particular, they shall be able to compare different models of computations and feasible techniques for system-level design. They shall be able to judge in which areas of embedded system design specific risks exist.
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 110, Study Time in Lecture 70
Credit points 6
Course achievement
Compulsory Bonus Form Description
Yes 10 % Subject theoretical and practical work
Examination Written exam
Examination duration and scale 90 minutes, contents of course and labs
Assignment for the Following Curricula General Engineering Science (German program, 7 semester): Specialisation Computer Science: Compulsory
Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Electrical Engineering: Core Qualification: Elective Compulsory
Engineering Science: Specialisation Mechatronics: Elective Compulsory
Engineering Science: Specialisation Electrical Engineering: Elective Compulsory
Aircraft Systems Engineering: Core Qualification: Elective Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechatronics: Elective Compulsory
Computer Science in Engineering: Core Qualification: Compulsory
Aeronautics: Core Qualification: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Mechatronics: Specialisation Naval Engineering: Compulsory
Mechatronics: Specialisation Electrical Systems: Compulsory
Mechatronics: Specialisation Dynamic Systems and AI: Compulsory
Mechatronics: Specialisation Robot- and Machine-Systems: Compulsory
Mechatronics: Specialisation Medical Engineering: Compulsory
Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory
Course L0805: Embedded Systems
Typ Lecture
Hrs/wk 3
CP 3
Workload in Hours Independent Study Time 48, Study Time in Lecture 42
Lecturer Prof. Heiko Falk
Language EN
Cycle SoSe
Content
  • Introduction
  • Specifications and Modeling
  • Embedded/Cyber-Physical Systems Hardware
  • System Software
  • Evaluation and Validation
  • Mapping of Applications to Execution Platforms
  • Optimization
Literature
  • Peter Marwedel. Embedded System Design - Embedded Systems Foundations of Cyber-Physical Systems. 2nd Edition, Springer, 2012., Springer, 2012.
Course L2938: Embedded Systems
Typ Project-/problem-based Learning
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Heiko Falk
Language EN
Cycle SoSe
Content
  • Introduction
  • Specifications and Modeling
  • Embedded/Cyber-Physical Systems Hardware
  • System Software
  • Evaluation and Validation
  • Mapping of Applications to Execution Platforms
  • Optimization
Literature
  • Peter Marwedel. Embedded System Design - Embedded Systems Foundations of Cyber-Physical Systems. 2nd Edition, Springer, 2012., Springer, 2012.
Course L0806: Embedded Systems
Typ Recitation Section (small)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Heiko Falk
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1143: Applied Design Methodology in Mechatronics
Courses
Title Typ Hrs/wk CP
Applied Design Methodology in Mechatronics (L1523) Lecture 2 2
Applied Design Methodology in Mechatronics (L1524) Project-/problem-based Learning 3 4
Module Responsible Prof. Thorsten Kern
Admission Requirements None
Recommended Previous Knowledge Basics of mechanical design, electrical design or computer-sciences
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Science-based working on interdisciplinary product design considering targeted application of specific product design techniques
Skills

Creative handling of processes used for scientific preparation and formulation of complex product design problems / Application of various product design techniques following theoretical aspects.
Personal Competence
Social Competence Students will solve and execute technical-scientific tasks from an industrial context in small design-teams with application of common, creative methodologies.
Autonomy

Students are enabled to optimize the design and development process according to the target and topic of the design

Students are educated to operate in a development team

Students learn about the right application of creative methods in engineering.
Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Course achievement None
Examination Subject theoretical and practical work
Examination duration and scale 30 min Presentation for a group design-work
Assignment for the Following Curricula International Management and Engineering: Specialisation II. Product Development and Production: Elective Compulsory
International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory
Mechanical Engineering and Management: Specialisation Product Development and Production: Elective Compulsory
Mechatronics: Core Qualification: 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 Product Development and Production: Elective Compulsory
Course L1523: Applied Design Methodology in Mechatronics
Typ Lecture
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Thorsten Kern
Language EN
Cycle SoSe
Content
  • Systematic analysis and planning of the design process for products combining a multitude of disciplines
  • Structure of the engineering process with focus on engineering steps (task-definition, functional decomposition, physical principles, elements for solution, combination to systems and products, execution of design, component-tests, system-tests, product-testing and qualification/validation)
  • Creative methods (Basics, methods like lead-user-method, 6-3-5, BrainStorming, Intergalactic Thinking, … - Applications in examples all around mechatronics topics)
  • Several design-supporting methods and tools (functional strcutures, GALFMOS, AEIOU-method, GAMPFT, simulation and its application, TRIZ, design for SixSigma, continous integration and testing, …)
  • Evaluation and final selection of solution (technical and business-considerations, preference-matrix, pair-comparision), dealing with uncertainties, decision-making
  • Value-analysis
  • Derivation of architectures and architectural management
  • Project-tracking and -guidance (project-lead, guiding of employees, organization of multidisciplinary R&D departments, idea-identification, responsibilities and communication)
  • Project-execution methods (Scrum, Kanbaan, …)
  • Presentation-skills
  • Questions of aesthetic product design and design for subjective requirements (industrial design, color, haptic/optic/acoustic interfaces)
  • Evaluation of selected methods at practical examples in small teams
Literature
  • Definition folgt...
  • Pahl, G.; Beitz, W.; Feldhusen, J.; Grote, K.-H.: Konstruktionslehre: Grundlage erfolgreicher Produktentwicklung, Methoden und Anwendung, 7. Auflage, Springer Verlag, Berlin 2007
  • VDI-Richtlinien: 2206; 2221ff
Course L1524: Applied Design Methodology in Mechatronics
Typ Project-/problem-based Learning
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Thorsten Kern
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. Bernd-Christian Renner
Admission Requirements None
Recommended Previous Knowledge
  • Very Good knowledge and practical experience in programming in the C language and its compilation process
  • Basic knowledge in software engineering
  • Basic understanding of assembly language
  • Basic knowledge of electrical engineering
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 advantages 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 design and write hardware-oriented software modules for an embedded system based on a specific microcontroller.
  • They learn to interact with peripherals (timer, ADC, EEPROM), including interrupt-based processing and program flow.
  • They build and use a (preemptive) scheduler for an embedded system.
  • They learn to write independent, reusable software components.
Personal Competence
Social Competence
  • Students are able to work goal-oriented in small mixed groups.
  • They learn and broaden their teamwork abilities.
  • They learn to define and split tasks within the team.
Autonomy

Students are able

  • to solve assignments related to this lecture independently with instructional direction.
  • to design, implement, and test software components for an embedded system independently based on a textual description.
  • to read and understand data sheets and manuals of electronic components (such as micro-controllers and sensors)
Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Course achievement
Compulsory Bonus Form Description
No 10 % Attestation
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Data Science: Specialisation II. Computer Science: Elective Compulsory
Data Science: Specialisation IV. Special Focus Area: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: 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. Bernd-Christian Renner
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. Bernd-Christian Renner
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1302: Applied Humanoid Robotics
Courses
Title Typ Hrs/wk CP
Applied Humanoid Robotics (L1794) Project-/problem-based Learning 6 6
Module Responsible Patrick Göttsch
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
Course achievement None
Examination Written elaboration
Examination duration and scale 5-10 pages
Assignment for the Following Curricula Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory
Data Science: Specialisation III. Applications: Elective Compulsory
Data Science: Specialisation IV. Special Focus Area: Elective Compulsory
Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Bio- and Medical Technology: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Course L1794: Applied Humanoid Robotics
Typ Project-/problem-based Learning
Hrs/wk 6
CP 6
Workload in Hours Independent Study Time 96, Study Time in Lecture 84
Lecturer Patrick Göttsch
Language DE/EN
Cycle WiSe/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 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
Course achievement None
Examination Written exam
Examination duration and scale 90 minutes
Assignment for the Following Curricula Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: 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 M1156: Systems Engineering
Courses
Title Typ Hrs/wk CP
Systems Engineering (L1547) Lecture 3 4
Systems Engineering (L1548) Recitation Section (large) 1 2
Module Responsible Prof. Ralf God
Admission Requirements None
Recommended Previous Knowledge

Basic knowledge in:
• Mathematics
• Mechanics
• Thermodynamics
• Electrical Engineering
• Control Systems

Previous knowledge in:
• Aircraft Cabin Systems
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to:
• understand systems engineering process models, methods and tools for the development of complex Systems
• describe innovation processes and the need for technology Management
• explain the aircraft development process and the process of type certification for aircraft
• explain the system development process, including requirements for systems reliability
• identify environmental conditions and test procedures for airborne Equipment
• value the methodology of requirements-based engineering (RBE) and model-based requirements engineering (MBRE)
Skills

Students are able to:
• plan the process for the development of complex Systems
• organize the development phases and development Tasks
• assign required business activities and technical Tasks
• apply systems engineering methods and tools

Personal Competence
Social Competence

Students are able to:
• understand and accept their tasks within a development team
• be comfortable with their role their tasks within the overall process
• understand and serve their suppliers and customers in large projects
• assume responsibility for people and technology in the development of safety-critical systems
Autonomy

Students are able to:
• interact and communicate in a development team with division of tasks.
• independently research and identify certification specifications
• formulate requirements on their own
• create test plans on their own and accompany certification processes
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Written exam
Examination duration and scale 120 Minutes
Assignment for the Following Curricula Aircraft Systems Engineering: Core Qualification: Compulsory
International Management and Engineering: Specialisation II. Aviation Systems: Elective Compulsory
International Management and Engineering: Specialisation II. Product Development and Production: Elective Compulsory
Aeronautics: Core Qualification: Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Product Development, Materials and Production: Specialisation Product Development: Compulsory
Product Development, Materials and Production: Specialisation Production: Elective Compulsory
Product Development, Materials and Production: Specialisation Materials: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Aircraft Systems Engineering: Elective Compulsory
Course L1547: Systems Engineering
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Ralf God
Language DE
Cycle SoSe
Content

The objective of the lecture with the corresponding exercise is to accomplish the prerequisites for the development and integration of complex systems using the example of commercial aircraft and cabin systems. Competences in the systems engineering process, tools and methods is to be achieved. Regulations, guidelines and certification issues will be known.

Key aspects of the course are processes for innovation and technology management, system design, system integration and certification as well as tools and methods for systems engineering:
• Innovation processes
• IP-protection
• Technology management
• Systems engineering
• Aircraft program
• Certification issues
• Systems development
• Safety objectives and fault tolerance
• Environmental and operating conditions
• Tools for systems engineering
• Requirements-based engineering (RBE)
• Model-based requirements engineering (MBRE)
Literature

- Skript zur Vorlesung
- diverse Normen und Richtlinien (EASA, FAA, RTCA, SAE)
- Hauschildt, J., Salomo, S.: Innovationsmanagement. Vahlen, 5. Auflage, 2010
- NASA Systems Engineering Handbook, National Aeronautics and Space Administration, 2007
- Hinsch, M.: Industrielles Luftfahrtmanagement: Technik und Organisation luftfahrttechnischer Betriebe. Springer, 2010
- De Florio, P.: Airworthiness: An Introduction to Aircraft Certification. Elsevier Ltd., 2010
- Pohl, K.: Requirements Engineering. Grundlagen, Prinzipien, Techniken. 2. korrigierte Auflage, dpunkt.Verlag, 2008
Course L1548: Systems Engineering
Typ Recitation Section (large)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Ralf God
Language DE
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1340: Introduction to Waveguides, Antennas, and Electromagnetic Compatibility
Courses
Title Typ Hrs/wk CP
Introduction to Waveguides, Antennas, and Electromagnetic Compatibility (L1669) Lecture 3 4
Introduction to Waveguides, Antennas, and Electromagnetic Compatibility (L1877) Recitation Section (small) 2 2
Module Responsible Prof. Christian Schuster
Admission Requirements None
Recommended Previous Knowledge Basic principles of physics and electrical engineering
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students can explain the basic principles, relationships, and methods for the design of waveguides and antennas as well as of Electromagnetic Compatibility. Specific topics are:

- Fundamental properties and phenomena of electrical circuits
- Steady-state sinusoidal analysis of electrical circuits
- Fundamental properties and phenomena of electromagnetic fields and waves
- Steady-state sinusoidal description of electromagnetic fields and waves
- Useful microwave network parameters
- Transmission lines and basic results from transmission line theory
- Plane wave propagation, superposition, reflection and refraction
- General theory of waveguides
- Most important types of waveguides and their properties
- Radiation and basic antenna parameters
- Most important types of antennas and their properties
- Numerical techniques and CAD tools for waveguide and antenna design
- Fundamentals of Electromagnetic Compatibility
- Coupling mechanisms and countermeasures
- Shielding, grounding, filtering
- Standards and regulations
- EMC measurement techniques
Skills

Students know how to apply various methods and models for characterization and choice of waveguides and antennas. They are able to assess and qualify their basic electromagnetic properties. They can apply results and strategies from the field of Electromagnetic Compatibilty to the development of electrical components and systems.
Personal Competence
Social Competence

Students are able to work together on subject related tasks in small groups. They are able to present their results effectively in English (e.g. during small group exercises).
Autonomy Students are capable to gather information from subject related, professional publications and relate that information to the context of the lecture. They are able to make a connection between their knowledge obtained in this lecture with the content of other lectures (e.g. theory of electromagnetic fields, fundamentals of electrical engineering / physics). They can discuss technical problems and physical effects in English.
Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Course achievement None
Examination Oral exam
Examination duration and scale 45 min
Assignment for the Following Curricula General Engineering Science (German program, 7 semester): Specialisation Electrical Engineering: Elective Compulsory
Electrical Engineering: Core Qualification: Elective Compulsory
Engineering Science: Specialisation Electrical Engineering: Elective Compulsory
Aircraft Systems Engineering: Core Qualification: Elective Compulsory
Aeronautics: Core Qualification: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Course L1669: Introduction to Waveguides, Antennas, and Electromagnetic Compatibility
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Christian Schuster
Language DE/EN
Cycle SoSe
Content

This course is intended as an introduction to the topics of wave propagation, guiding, sending, and receiving as well as Electromagnetic Compatibility (EMC). It will be useful for engineers that face the technical challenge of transmitting high frequency / high bandwidth data in e.g. medical, automotive, or avionic applications. Both circuit and field concepts of wave propagation and Electromagnetic Compatibility will be introduced and discussed.

Topics:

- Fundamental properties and phenomena of electrical circuits
- Steady-state sinusoidal analysis of electrical circuits
- Fundamental properties and phenomena of electromagnetic fields and waves
- Steady-state sinusoidal description of electromagnetic fields and waves
- Useful microwave network parameters
- Transmission lines and basic results from transmission line theory
- Plane wave propagation, superposition, reflection and refraction
- General theory of waveguides
- Most important types of waveguides and their properties
- Radiation and basic antenna parameters
- Most important types of antennas and their properties
- Numerical techniques and CAD tools for waveguide and antenna design
- Fundamentals of Electromagnetic Compatibility
- Coupling mechanisms and countermeasures
- Shielding, grounding, filtering
- Standards and regulations
- EMC measurement techniques
Literature

- Zinke, Brunswig, "Hochfrequenztechnik 1", Springer (1999)

- J. Detlefsen, U. Siart, "Grundlagen der Hochfrequenztechnik", Oldenbourg (2012)

- D. M. Pozar, "Microwave Engineering", Wiley (2011)

- Y. Huang, K. Boyle, "Antenna: From Theory to Practice", Wiley (2008)

- H. Ott, "Electromagnetic Compatibility Engineering", Wiley (2009)

- A. Schwab, W. Kürner, "Elektromagnetische Verträglichkeit", Springer (2007)
Course L1877: Introduction to Waveguides, Antennas, and Electromagnetic Compatibility
Typ Recitation Section (small)
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Christian Schuster
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0752: Nonlinear Dynamics
Courses
Title Typ Hrs/wk CP
Nonlinear Dynamics (L0702) Integrated 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.
  • Students are able to denote and expand methods of modeling and analysis for nonlinear dynamical systems.
Skills
  • Students are able to apply existing methods and procesures of Nonlinear Dynamics.
  • Students are able to develop novel methods and procedures for nonlinear dynamical systems.
Personal Competence
Social Competence
  • Students can analyze problems of nonlinear dynamics also in groups.
  • Students can achieve solution procedures for problems of nonlinear dynamical systems also in groups.
Autonomy
  • Students are able to approach given research tasks on the basis of given methods individually.
  • Students are able 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
Course achievement None
Examination Written exam
Examination duration and scale 2 Hours
Assignment for the Following Curricula Aircraft Systems Engineering: Core Qualification: Elective Compulsory
International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory
Aeronautics: Core Qualification: Elective Compulsory
Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory
Mechatronics: Core Qualification: 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: Core Qualification: Elective Compulsory
Course L0702: Nonlinear Dynamics
Typ Integrated 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

  • One dimensional problems
    • Linear Stability
    • Local Bifurcations
    • Synchronisation
  • Two dimensional problems
    • Limit Cycles
    • Global Bifurcations
  • Chaos
    • Lorenz Equations
    • Fractals and Strange Attractors
    • Predictability and Horizons
Literature Steven Strogatz: Nonlinear Dynamics and Chaos.

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) Project-/problem-based Learning 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
Course achievement None
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Aircraft Systems Engineering: Core Qualification: Elective Compulsory
Aeronautics: Core Qualification: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: 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 Project-/problem-based Learning
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 M1211: Research Project Mechatronics
Courses
Title Typ Hrs/wk CP
Module Responsible Dozenten des Studiengangs
Admission Requirements None
Recommended Previous Knowledge Subjects of the program of studies.
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students are able to demonstrate their detailed knowledge in the field of mechatronics engineering. They can exemplify the state of technology and application and discuss critically in the context of actual problems and general conditions of science and society.

The students can develop solving strategies and approaches for fundamental and practical problems in mechatronics engineering. They may apply theory based procedures and integrate safety-related, ecological, ethical, and economic view points of science and society.

Scientific work techniques that are used can be described and critically reviewed.
Skills

The students are able to independently select methods for the project work and to justify this choice. They can explain how these methods relate to the field of work and how the context of application has to be adjusted. General findings and further developments may essentially be outlined.
Personal Competence
Social Competence

The students are able to condense the relevance and the structure of the project work, the work steps and the sub-problems for the presentation and discussion in front of a bigger group. They can lead the discussion and give a feedback on the project to their colleagues.
Autonomy

The students are capable of independently planning and documenting the work steps and procedures while considering the given deadlines. This includes the ability to accurately procure the newest scientific information. Furthermore, they can obtain feedback from experts with regard to the progress of the work, and to accomplish results on the state of the art in science and technology.
Workload in Hours Independent Study Time 360, Study Time in Lecture 0
Credit points 12
Course achievement None
Examination Study work
Examination duration and scale lt. FSPO
Assignment for the Following Curricula Mechatronics: Core Qualification: Compulsory

Module M0836: Communication Networks
Courses
Title Typ Hrs/wk CP
Selected Topics of Communication Networks (L0899) Project-/problem-based Learning 2 2
Communication Networks (L0897) Lecture 2 2
Communication Networks Excercise (L0898) Project-/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
Course achievement None
Examination Presentation
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 Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory
Aircraft Systems Engineering: Core Qualification: Elective Compulsory
Computer Science in Engineering: Specialisation I. Computer Science: 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
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
Aeronautics: Core Qualification: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Course L0899: Selected Topics of Communication Networks
Typ Project-/problem-based Learning
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Dr.-Ing. Koojana Kuladinithi
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 L0897: Communication Networks
Typ Lecture
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Dr.-Ing. Koojana Kuladinithi
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 L0898: Communication Networks Excercise
Typ Project-/problem-based Learning
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Dr.-Ing. Koojana Kuladinithi
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 M1598: Image Processing
Courses
Title Typ Hrs/wk CP
Image Processing (L2443) Lecture 2 4
Image Processing (L2444) Recitation Section (small) 2 2
Module Responsible Prof. Tobias Knopp
Admission Requirements None
Recommended Previous Knowledge Signal and Systems
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students know about

  • visual perception
  • multidimensional signal processing
  • sampling and sampling theorem
  • filtering
  • image enhancement
  • edge detection
  • multi-resolution procedures: Gauss and Laplace pyramid, wavelets
  • image compression
  • image segmentation
  • morphological image processing
Skills

The students can

  • analyze, process, and improve multidimensional image data
  • implement simple compression algorithms
  • design custom filters for specific applications
Personal Competence
Social Competence

Students can work on complex problems both independently and in teams. They can exchange ideas with each other and use their individual strengths to solve the problem.
Autonomy

Students are able to independently investigate a complex problem and assess which competencies are required to solve it. 
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Data Science: Core Qualification: Elective Compulsory
Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory
Data Science: Specialisation II. Computer Science: Elective Compulsory
Data Science: Specialisation IV. Special Focus Area: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Electrical Engineering: Specialisation Medical 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 Signal Processing: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Course L2443: Image Processing
Typ Lecture
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Lecturer Prof. Tobias Knopp
Language DE/EN
Cycle WiSe
Content
  • Visual perception
  • Multidimensional signal processing
  • Sampling and sampling theorem
  • Filtering
  • Image enhancement
  • Edge detection
  • Multi-resolution procedures: Gauss and Laplace pyramid, wavelets
  • Image Compression
  • Segmentation
  • Morphological image processing
Literature

Bredies/Lorenz, Mathematische Bildverarbeitung, Vieweg, 2011
Pratt, Digital Image Processing, Wiley, 2001
Bernd Jähne: Digitale Bildverarbeitung - Springer, Berlin 2005
Course L2444: Image Processing
Typ Recitation Section (small)
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Tobias Knopp
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1048: Integrated Circuit Design
Courses
Title Typ Hrs/wk CP
Integrated Circuit Design (L0691) Lecture 3 4
Integrated Circuit Design (L0998) Recitation Section (small) 1 2
Module Responsible NN
Admission Requirements None
Recommended Previous Knowledge

Basic knowledge of (solid-state) physics and mathematics.

Knowledge in fundamentals of electrical engineering and electrical networks.
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can explain basic concepts of electron transport in semiconductor devices (energy bands, generation/recombination, carrier concentrations, drift and diffusion current densities, semiconductor device equations).  
  • Students are able to explain functional principles of pn-diodes, MOS capacitors, and MOSFETs using energy band diagrams.
  • Students can present and discuss current-voltage relationships and small-signal equivalent circuits of these devices.
  • Students can explain the physics and current-voltage behavior transistors based on charged carrier flow.
  • Students are able to explain the basic concepts for static and dynamic logic gates for integrated circuits
  • Students can exemplify approaches for low power consumption on the device and circuit level
  • Students can describe the potential and limitations of analytical expression for device and circuit analysis.
  • Students can explain characterization techniques for MOS devices.
Skills
  • Students can qualitatively construct energy band diagrams of the devices for varying applied voltages.
  • Students are able to qualitatively determine electric field, carrier concentrations, and charge flow from energy band diagrams.
  • Students can understand scientific publications from the field of semiconductor devices.
  • Students can calculate the dimensions of MOS devices in dependence of the circuits properties
  • Students can design complex electronic circuits and anticipate possible problems.
  • Students know procedure for optimization regarding high performance and low power consumption
Personal Competence
Social Competence
  • Students can team up with other experts in the field to work out innovative solutions.
  • Students are able to work by their own or in small groups for solving problems and answer scientific questions.
  • Students have the ability to critically question the value of their contributions to working groups.
Autonomy
  • Students are able to assess their knowledge in a realistic manner.
  • Students are able to define their personal approaches to solve challenging problems
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Electrical Engineering: Specialisation Nanoelectronics and Microsystems 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
Mechatronics: Core Qualification: Elective Compulsory
Microelectronics and Microsystems: Core Qualification: Elective Compulsory
Course L0691: Integrated Circuit Design
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Matthias Kuhl
Language EN
Cycle WiSe
Content
  • Electron transport in semiconductors
  • Electronic operating principles of diodes, MOS capacitors, and MOS field-effect transistors
  • MOS transistor as four terminal device
  • Performace degradation due to short channel effects
  • Scaling-down of MOS technology
  • Digital logic circuits
  • Basic analog circuits
  • Operational amplifiers
  • Bipolar and BiCMOS circuits
Literature


  • Yuan Taur, Tak H. Ning:  Fundamentals of Modern VLSI Devices, Cambridge University Press 1998
  • R. Jacob Baker: CMOS, Circuit Design, Layout and Simulation,  IEEE Press, Wiley Interscience, 3rd Edition, 2010
  • Neil H.E. Weste and David Money Harris, Integrated Circuit Design, Pearson, 4th International Edition, 2013
  • John E. Ayers, Digital Integrated Circuits: Analysis and Design, CRC Press, 2009
  • Richard C. Jaeger and Travis N. Blalock: Microelectronic Circuit Design, Mc Graw-Hill, 4rd. Edition, 2010
Course L0998: Integrated Circuit Design
Typ Recitation Section (small)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Matthias Kuhl
Language 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) 2 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.

The students are familiar with the contents of lecture and tutorials. They can explain and apply them to new problems.
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 110, Study Time in Lecture 70
Credit points 6
Course achievement None
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory
Computer Science in Engineering: Specialisation II. Engineering Science: 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
Mechatronics: Core Qualification: Elective Compulsory
Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: 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 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Gerhard Bauch
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1552: Advanced Machine Learning
Courses
Title Typ Hrs/wk CP
Advanced Machine Learning (L2322) Lecture 2 3
Advanced Machine Learning (L2323) Recitation Section (small) 2 3
Module Responsible Dr. Jens-Peter Zemke
Admission Requirements None
Recommended Previous Knowledge
  1. Mathematics I-III
  2. Numerical Mathematics 1/ Numerics
  3. Programming skills, preferably in Python
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Students are able to name, state and classify state-of-the-art neural networks and their corresponding mathematical basics. They can assess the difficulties of different neural networks.
Skills Students are able to implement, understand, and, tailored to the field of application, apply neural networks.
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
Course achievement None
Examination Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Computer Science: Specialisation III. Mathematics: Elective Compulsory
Data Science: Core Qualification: Compulsory
Computer Science in Engineering: Specialisation III. Mathematics: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Course L2322: Advanced Machine Learning
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/EN
Cycle WiSe
Content
  1. Basics: analogy; layout of neural nets, universal approximation, NP-completeness
  2. Feedforward nets: backpropagation, variants of Stochastistic Gradients
  3. Deep Learning: problems and solution strategies
  4. Deep Belief Networks: energy based models, Contrastive Divergence
  5. CNN: idea, layout, FFT and Winograds algorithms, implementation details
  6. RNN: idea, dynamical systems, training, LSTM
  7. ResNN: idea, relation to neural ODEs
  8. Standard libraries: Tensorflow, Keras, PyTorch
  9. Recent trends
Literature
  1. Skript
  2. Online-Werke:
    • http://neuralnetworksanddeeplearning.com/
    • https://www.deeplearningbook.org/
Course L2323: Advanced Machine Learning
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/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1749: Energy Efficiency in Embedded Systems
Courses
Title Typ Hrs/wk CP
Energy Efficiency in Embedded Systems (L2870) Lecture 2 3
Energy Efficiency in Embedded Systems (L2872) Project-/problem-based Learning 2 2
Energy Efficiency in Embedded Systems (L2871) Recitation Section (large) 1 1
Module Responsible Prof. Ulf Kulau
Admission Requirements None
Recommended Previous Knowledge
  • Computer Engineering (mandatory)
  • Programming Skills in C (mandatory)
  • Computer Architecture (recommended)
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Motivation:

In the field of computer science we have only limited possibilities to influence the efficiency of the hardware directly, respectively we are dependent on the manufacturers (e.g. of microcontrollers). However, in order to exploit the full potential of the hardware we are given at the system level, we need a deeper understanding of the background, processes and mechanisms of power dissipation in embedded systems. Where does the power dissipation come from, what happens at the hardware level, what mechanisms can I use directly/indirectly, what is the tradeoff between flexibility and efficiency,.... are only a few questions, which will be elaborated and discussed in this event.

Contents of teaching:
  • Motivation and power dissipation on semiconductor level
  • Power dissipation of digital circuits, inparticular CMOS
  • Power Management in Hard- and Software (Sleep Modes, DVS, FS, Undervolting)
  • Energy efficient system design (applications)
  • Energy Harvesting and Transiently Powered Computing (TPC)
Skills

Upon completion of this module, students will have a deeper understanding of hardware and software mechanisms for evaluating and developing energy-efficient embedded systems

  • They have a deeper understanding of the electrotechnical basics of power dissipation in digital systems
  • They can analyze the power dissipation of systems at any level and apply appropriate methods to increase efficiency
  • They can use a variety of standard techniques to achieve "Energy Efficiency by Design"
  • They can model, evaluate as well as implement energy-autonomous systems
Personal Competence
Social Competence

As part of the module, concepts learned in the lecture will be implemented on a hardware platform within small groups. Students learn to work in a team and to develop solutions together. Specific tasks are worked on within the group, whereby cross-group collaboration (exchange) also takes place. The second part is a challenge-based project in which the groups find the most energy-efficient solutions possible in healthy competition with each other. This strengthens the cohesion in the groups and reinforces mutual motivation, support and creativity.


Autonomy

After completing this module, students will be able to independently develop, optimize and evaluate solutions for embedded systems based on the knowledge they have acquired and further technical literature. 
Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Course achievement None
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Electrical Engineering: Specialisation Nanoelectronics and Microsystems Technology: Elective Compulsory
Electrical Engineering: Specialisation Wireless and Sensor Technologies: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory
Course L2870: Energy Efficiency in Embedded Systems
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Ulf Kulau
Language DE/EN
Cycle WiSe
Content Motivation:

In the field of computer science we have only limited possibilities to influence the efficiency of the hardware directly, respectively we are dependent on the manufacturers (e.g. of microcontrollers). However, in order to exploit the full potential of the hardware we are given at the system level, we need a deeper understanding of the background, processes and mechanisms of power dissipation in embedded systems. Where does the power dissipation come from, what happens at the hardware level, what mechanisms can I use directly/indirectly, what is the tradeoff between flexibility and efficiency,.... are only a few questions, which will be elaborated and discussed in this event.

Contents of teaching:
  • Motivation and power dissipation on semiconductor level
  • Power dissipation of digital circuits, inparticular CMOS
  • Power Management in Hard- and Software (Sleep Modes, DVS, FS, Undervolting)
  • Energy efficient system design (applications)
  • Energy Harvesting and Transiently Powered Computing (TPC)
Literature

DE: Die Vorlesung basiert af einer Vielzahl von Quellen, welche in [1.] angegeben sind.

ENG: The lecture is based on multiple sources which are listed in [1.].

  1. Kulau, Ulf: Course: Energy Efficiency in Embedded Systems-A System-Level Perspective for Computer Scientists, EWME, 2018.
  2. Harris, David, and N. Weste: CMOS VLSI Design ed., Pearson Education, 2010
  3. Rabaey, Jan: Low Power Design Essentials (Integrated Circuits and Systems), Springer, 2009
Course L2872: Energy Efficiency in Embedded Systems
Typ Project-/problem-based Learning
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Ulf Kulau
Language DE/EN
Cycle WiSe
Content

In this project-based exercise, the learned aspects for achieving energy-efficient embedded systems are implemented and consolidated in practical environments in a small project. First, a tool set for the implementation of energy efficiency mechanisms is implemented in common exercises by means of defined tasks. In the second part, a challenge-based exercise is carried out in which a system that is as efficient as possible is to be implemented independently. A system based on an AVR micro-controller is used, which can be operated autonomously by a Solar-Energy Harvester.

  1. Task phase: 6 "hands-on" tasks to gain experience and to create a SW library.
  2. Project phase: Implementation of an energy autonomous system with the goal of highest possible energy efficiency (Challenge)   

Literature
Course L2871: Energy Efficiency in Embedded Systems
Typ Recitation Section (large)
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Ulf Kulau
Language DE/EN
Cycle WiSe
Content

In the lecture hall exercise, the theoertical basics taught in the lecture are deepened. This is done through in-depth discussion of relevant aspects, but also through calculation examples, in which a deeper understanding of the topic of energy efficiency in embedded systems is gained. Exercises will be distributed in advance and solutions will be presented in the lecture hall exercise. Contents of the exercise are as follows:

  • Basics and calculation of power dissipation on semiconductor
  • Power dissipation of CMOS using the example of an inverter
  • Influence of the activity factor and external components
  • DVS and scheduling
  • Evaluation to show the benefit of undervolting
  • Aspects of energy harvesting (MPPT)
Literature

Module M1596: Engineering Haptic Systems
Courses
Title Typ Hrs/wk CP
Haptic Technology for Human-Machine-Interfaces (HMI) (L2439) Lecture 4 3
Haptic Technology for Human-Machine-Interfaces (HMI) (L2859) Project-/problem-based Learning 2 3
Module Responsible Prof. Thorsten Kern
Admission Requirements None
Recommended Previous Knowledge We recommend knowledge in the areas of general engineering sciences, mechatronics and/or control-engineering. However also neighbouring technical areas like mechanical-engineering or even process-engineers can join the course and will be introduced into the content properly.
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

This course is an introduction to the design methods and design-requirements to consider when creating haptic systems from scratch. It covers a physiological part, an actuator development part, and goes up to fundamentals of higher system integration with consideration on control theory for more complex projects. Beside design-related topics, it gives a valuable overview on existing haptic applications and research in that field with many examples. This is supported by on-site experiments in the laboratories of M-4.

  • Motivation and application of haptic systems
  • Haptic perception
  • The role of the user in direct system interaction
  • Development of haptic systems
  • Identification of requirements
  • System-structure and control
  • Kinematic fundamentals
  • Actuation & Sensors technology for haptic applications
  • Control and system-design aspects
  • Fundamental considerations in simulating haptics
Skills Executing the course the competency will be developed to apply the general engineering capabilities of the individual course towards the design and application of active haptic systems. The resulting competencies will open an entry into specialized position in avionic-industries, automotive-industry and consumer-device-development.
Personal Competence
Social Competence As a side-effect this module teaches basics of a general design for human-machine-interfaces, independent from the specific application of "haptics". It teaches methods to execute user-studies, judge on user-feedback and how to deal with soft design-requirements which are common when dealing with subjective perception.
Autonomy Independent design-capability of haptic systems, general competency in engineering from a design-perspective
Workload in Hours Independent Study Time 96, Study Time in Lecture 84
Credit points 6
Course achievement
Compulsory Bonus Form Description
Yes 20 % Subject theoretical and practical work Durchführung von Laborversuchen
Examination Subject theoretical and practical work
Examination duration and scale 30 min
Assignment for the Following Curricula Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Product Development and Production: Elective Compulsory
Course L2439: Haptic Technology for Human-Machine-Interfaces (HMI)
Typ Lecture
Hrs/wk 4
CP 3
Workload in Hours Independent Study Time 34, Study Time in Lecture 56
Lecturer Prof. Thorsten Kern
Language EN
Cycle WiSe
Content

This course is an introduction to the design methods and design-requirements to consider when creating haptic systems from scratch. It covers a physiological part, an actuator development part, and goes up to fundamentals of higher system integration with consideration on control theory for more complex projects. Beside design-related topics, it gives a valuable overview on existing haptic applications and research in that field with many examples.

  • Motivation and application of haptic systems
  • Haptic perception
  • The role of the user in direct system interaction
  • Development of haptic systems
  • Identification of requirements
  • System-structure and control
  • Kinematic fundamentals
  • Actuation & Sensors technology for haptic applications
  • Control and system-design aspects
  • Fundamental considerations in simulating haptics
Literature
Course L2859: Haptic Technology for Human-Machine-Interfaces (HMI)
Typ Project-/problem-based Learning
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Thorsten Kern
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

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
Course achievement None
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 III. Mathematics: Elective Compulsory
Computer Science in Engineering: Specialisation III. Mathematics: Elective Compulsory
Interdisciplinary Mathematics: Specialisation Computational Methods in Biomedical Imaging: Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: 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
  • de-convolution
  • inpainting
  • 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 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
Course achievement None
Examination Written exam
Examination duration and scale 90 minutes
Assignment for the Following Curricula Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Core Qualification: 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 Robotics and Computer Science: 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 M1024: Methods of Product Development
Courses
Title Typ Hrs/wk CP
Methods of Product Development (L1254) Lecture 3 3
Methods of Product Development (L1255) Project-/problem-based Learning 2 3
Module Responsible Prof. Dieter Krause
Admission Requirements None
Recommended Previous Knowledge

Basic knowledge of Integrated product development and applying CAE systems
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

After passing the module students are able to:

  • explain technical terms of design methodology,
  • describe essential elements of construction management,
  • describe current problems and the current state of research of integrated product development.
Skills

After passing the module students are able to:

  • select and apply proper construction methods for non-standardized solutions of problems as well as adapt new boundary conditions,
  • solve product development problems with the assistance of a workshop based approach,
  • choose and execute appropriate moderation techniques. 
Personal Competence
Social Competence

After passing the module students are able to:

  • prepare and lead team meetings and moderation processes,
  • work in teams on complex tasks,
  • represent problems and solutions and advance ideas.
Autonomy

After passing the module students are able to:

  • give a structured feedback and accept a critical feedback,
  • implement the accepted feedback autonomous.
Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Course achievement None
Examination Oral exam
Examination duration and scale 30 Minuten
Assignment for the Following Curricula Aircraft Systems Engineering: Core Qualification: Elective Compulsory
International Management and Engineering: Specialisation II. Product Development and Production: Elective Compulsory
Aeronautics: Core Qualification: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Product Development, Materials and Production: Specialisation Product Development: 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
Course L1254: Methods of Product Development
Typ Lecture
Hrs/wk 3
CP 3
Workload in Hours Independent Study Time 48, Study Time in Lecture 42
Lecturer Prof. Dieter Krause
Language DE
Cycle WiSe
Content

Lecture

The lecture extends and enhances the learned content of the module “Integrated Product Development and lightweight design” and is based on the knowledge and skills acquired there.

Topics of the course include in particular:

  • Methods of product development,
  • Presentation techniques,
  • Industrial Design,
  • Design for variety
  • Modularization methods,
  • Design catalogs,
  • Adapted QFD matrix,
  • Systematic material selection,
  • Assembly oriented design,

Construction management

  • CE mark, declaration of conformity including risk assessment,
  • Patents, patent rights, patent monitoring
  • Project management (cost, time, quality) and escalation principles,
  • Development management for mechatronics,
  • Technical Supply Chain Management.

Exercise (PBL)

In the exercise the content presented in the lecture “Integrated Product Development II” and methods of product development and design management will be enhanced.

Students learn an independently moderated and workshop based approach through industry related practice examples to solve complex and currently existing issues in product development. They will learn the ability to apply important methods of product development and design management autonomous and acquire further expertise in the field of integrated product development. Besides personal skills, such as teamwork, guiding discussions and representing work results will be acquired through the workshop based structure of the event under its own planning and management.
Literature
  • Andreasen, M.M., Design for Assembly, Berlin, Springer 1985.
  • Ashby, M. F.: Materials Selection in Mechanical Design, München, Spektrum 2007.
  • Beckmann, H.: Supply Chain Management, Berlin, Springer 2004.
  • Hartmann, M., Rieger, M., Funk, R., Rath, U.: Zielgerichtet moderieren. Ein Handbuch für Führungskräfte, Berater und Trainer, Weinheim, Beltz 2007.
  • Pahl, G., Beitz, W.: Konstruktionslehre, Berlin, Springer 2006.
  • Roth, K.H.: Konstruieren mit Konstruktionskatalogen, Band 1-3, Berlin, Springer 2000.
  • Simpson, T.W., Siddique, Z., Jiao, R.J.: Product Platform and Product Family Design. Methods and Applications, New York, Springer 2013.
Course L1255: Methods of Product Development
Typ Project-/problem-based Learning
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Dieter Krause
Language DE
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) Project-/problem-based Learning 2 2
Module Responsible Dr. Timo Lipka
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
Course achievement
Compulsory Bonus Form Description
No 10 % Presentation
Examination Written exam
Examination duration and scale 2h
Assignment for the Following Curricula Electrical Engineering: Core Qualification: 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
Mechatronics: Core Qualification: Elective Compulsory
Microelectronics and Microsystems: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Bio- and Medical Technology: 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 Dr. Timo Lipka
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 Project-/problem-based Learning
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Dr. Timo Lipka
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

Module M1173: Applied Statistics
Courses
Title Typ Hrs/wk CP
Applied Statistics (L1584) Lecture 2 3
Applied Statistics (L1586) Project-/problem-based Learning 2 2
Applied Statistics (L1585) Recitation Section (small) 1 1
Module Responsible Prof. Michael Morlock
Admission Requirements None
Recommended Previous Knowledge

Basic knowledge of statistical methods
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Students can explain the statistical methods and the conditions of their use.
Skills Students are able to use the statistics program to solve statistics problems and to interpret and depict the results
Personal Competence
Social Competence

Team Work, joined presentation of results
Autonomy

To understand and interpret the question and solve

Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Course achievement None
Examination Written exam
Examination duration and scale 90 minutes, 28 questions
Assignment for the Following Curricula Mechanical Engineering and Management: Specialisation Management: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Biomedical Engineering: Core Qualification: Compulsory
Product Development, Materials and Production: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Bio- and Medical Technology: Elective Compulsory
Course L1584: Applied Statistics
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Michael Morlock
Language DE/EN
Cycle WiSe
Content

The goal is to introduce students to the basic statistical methods and their application to simple problems. The topics include:

•          Chi square test

•          Simple regression and correlation

•          Multiple regression and correlation

•          One way analysis of variance

•          Two way analysis of variance

•          Discriminant analysis

•          Analysis of categorial data

•          Chossing the appropriate statistical method

•          Determining critical sample sizes
Literature

Applied Regression Analysis and Multivariable Methods, 3rd Edition, David G. Kleinbaum Emory University, Lawrence L. Kupper University of North Carolina at Chapel Hill, Keith E. Muller University of North Carolina at Chapel Hill, Azhar Nizam Emory University, Published by Duxbury Press, CB © 1998, ISBN/ISSN: 0-534-20910-6
Course L1586: Applied Statistics
Typ Project-/problem-based Learning
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Michael Morlock
Language DE/EN
Cycle WiSe
Content

The students receive a problem task, which they have to solve in small groups (n=5). They do have to collect their own data and work with them. The results have to be presented in an executive summary at the end of the course.
Literature

Selbst zu finden
Course L1585: Applied Statistics
Typ Recitation Section (small)
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Michael Morlock
Language DE/EN
Cycle WiSe
Content

The different statistical tests are applied for the solution of realistic problems using actual data sets and the most common used commercial statistical software package (SPSS).
Literature

Student Solutions Manual for Kleinbaum/Kupper/Muller/Nizam's Applied Regression Analysis and Multivariable Methods, 3rd Edition, David G. Kleinbaum Emory University Lawrence L. Kupper University of North Carolina at Chapel Hill, Keith E. Muller University of North Carolina at Chapel Hill, Azhar Nizam Emory University, Published by Duxbury Press, Paperbound © 1998, ISBN/ISSN: 0-534-20913-0

Module M1204: Modelling and Optimization in Dynamics
Courses
Title Typ Hrs/wk CP
Flexible Multibody Systems (L1632) Lecture 2 3
Optimization of dynamical systems (L1633) Lecture 2 3
Module Responsible Prof. Robert Seifried
Admission Requirements None
Recommended Previous Knowledge
  • Mathematics I, II, III
  • Mechanics I, II, III, IV
  • Simulation of dynamical Systems
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students demonstrate basic knowledge and understanding of modeling, simulation and analysis of complex rigid and flexible multibody systems and methods for optimizing dynamic systems after successful completion of the module.
Skills

Students are able

+ to think holistically

+ to independently, securly and critically analyze and optimize basic problems of the dynamics of rigid and flexible multibody systems

+ to describe dynamics problems mathematically

+ to optimize dynamics problems
Personal Competence
Social Competence

Students are able to

+ solve problems in heterogeneous groups and to document the corresponding results.
Autonomy

Students are able to

+ assess their knowledge by means of exercises.

+ acquaint themselves with the necessary knowledge to solve research oriented tasks.


Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Aircraft Systems Engineering: Core Qualification: Elective Compulsory
Aeronautics: Core Qualification: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Product Development, Materials and Production: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Core Qualification: Elective Compulsory
Course L1632: Flexible Multibody Systems
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Robert Seifried, Dr. Alexander Held
Language DE
Cycle WiSe
Content
  1. Basics of Multibody Systems
  2. Basics of Continuum Mechanics
  3. Linear finite element modelles and modell reduction
  4. Nonlinear finite element Modelles: absolute nodal coordinate formulation
  5. Kinematics of an elastic body 
  6. Kinetics of an elastic body
  7. System assembly
Literature

Schwertassek, R. und Wallrapp, O.: Dynamik flexibler Mehrkörpersysteme. Braunschweig, Vieweg, 1999.

Seifried, R.: Dynamics of Underactuated Multibody Systems, Springer, 2014.

Shabana, A.A.: Dynamics of Multibody Systems. Cambridge Univ. Press, Cambridge, 2004, 3. Auflage.


Course L1633: Optimization of dynamical systems
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Robert Seifried, Dr. Svenja Drücker
Language DE
Cycle WiSe
Content
  1. Formulation and classification of optimization problems 
  2. Scalar Optimization
  3. Sensitivity Analysis
  4. Unconstrained Parameter Optimization
  5. Constrained Parameter Optimization
  6. Stochastic optimization
  7. Multicriteria Optimization
  8. Topology Optimization
Literature

Bestle, D.: Analyse und Optimierung von Mehrkörpersystemen. Springer, Berlin, 1994.

Nocedal, J. , Wright , S.J. : Numerical Optimization. New York: Springer, 2006.

Module M1305: Seminar Advanced Topics in Control
Courses
Title Typ Hrs/wk CP
Advanced Topics in Control (L1803) Seminar 2 2
Module Responsible NN
Admission Requirements None
Recommended Previous Knowledge
  • Introduction to control systems
  • Control theory and design
  • optimal and robust control
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can explain modern control.
  • Students learn to apply basic control concepts for different tasks
Skills
  • Students acquire knowledge about selected aspects of modern control, based on specified literature
  • Students generalize developed results and present them to the participants
  • Students practice to prepare and give a presentation
Personal Competence
Social Competence
  • Students are capable of developing solutions and present them
  • They are able to provide appropriate feedback and handle constructive criticism of their own results
Autonomy
  • Students evaluate advantages and drawbacks of different forms of presentation for specific tasks and select the best solution
  • Students familiarize themselves with a scientific field, are able of introduce it and follow presentations of other students, such that a scientific discussion develops
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Credit points 2
Course achievement None
Examination Presentation
Examination duration and scale 90 min
Assignment for the Following Curricula Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Course L1803: Advanced Topics in Control
Typ Seminar
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer NN
Language EN
Cycle WiSe/SoSe
Content
  • Seminar on selected topics in modern control
Literature
  • To be specified

Module M1268: Linear and Nonlinear Waves
Courses
Title Typ Hrs/wk CP
Linear and Nonlinear Waves (L1737) Project-/problem-based Learning 4 6
Module Responsible Prof. Norbert Hoffmann
Admission Requirements None
Recommended Previous Knowledge

Calculus, Algebra, Engineering Mechanics, Vibrations. 
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 
  • Students are able to identify and express the need to develop and research new terms and concepts.
Skills
  • Students are able to apply existing research methods and procedures of wave mechanics.
  • Students are able to develop novel research methods and procedures in wave mechanics.
Personal Competence
Social Competence
  • Students can reach working results also in groups.
  • Students can present and communicate working results also in groups.
Autonomy
  • Students are able to approach given research tasks individually.
  • Studetns are able 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
Course achievement None
Examination Written exam
Examination duration and scale 2 Hours
Assignment for the Following Curricula Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Naval Architecture and Ocean Engineering: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Maritime Technology: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory
Course L1737: Linear and Nonlinear Waves
Typ Project-/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

  • Linear Waves
    • Dispersion
    • Phase and Group Velocity
    • Envelopes
    • Discrete Systems
  • Nonlinear Waves
    • Model Equations
    • Solitons, Breathers, Extreme Waves
  • Water Waves, Ocean Waves
    • Airy and Stokes
    • Natural Sea State
    • Kinetic Modelling
  • Other topics
Literature

F.K. Kneubühl: Oscillations and Waves. Springer.

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

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

L.H. Holthuijsen, Waves in Oceanic and Coastal Waters. Cambridge.

And others.

Module M1398: Selected Topics in Multibody Dynamics and Robotics
Courses
Title Typ Hrs/wk CP
Formulas and Vehicles - Dynamics and Control of Autonomous Vehicles (L2869) Integrated Lecture 1 1
Formulas and Vehicles - Introduction into Mobile Underwater Robotics (L1981) Project-/problem-based Learning 4 5
Module Responsible Prof. Robert Seifried
Admission Requirements None
Recommended Previous Knowledge

Mechanics IV, Applied Dynamics or Robotics

Numerical Treatment of Ordinary Differential Equations

Control Systems Theory and Design
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

After successful completion of the module students demonstrate deeper knowledge and understanding in selected application areas of multibody dynamics and robotics
Skills

Students are able

+ to think holistically

+ to independently, securly and critically analyze and optimize basic problems of the dynamics of rigid and flexible multibody systems

+ to describe dynamics problems mathematically

+ to implement dynamical problems on hardware
Personal Competence
Social Competence

Students are able to

+ solve problems in heterogeneous groups and to document the corresponding results and present them
Autonomy

Students are able to

+ assess their knowledge by means of exercises and projects.

+ acquaint themselves with the necessary knowledge to solve research oriented tasks.
Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Course achievement None
Examination Presentation
Examination duration and scale TBA
Assignment for the Following Curricula Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Core Qualification: Elective Compulsory
Course L2869: Formulas and Vehicles - Dynamics and Control of Autonomous Vehicles
Typ Integrated Lecture
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Robert Seifried, Daniel-André Dücker
Language DE
Cycle WiSe
Content
Literature
Course L1981: Formulas and Vehicles - Introduction into Mobile Underwater Robotics
Typ Project-/problem-based Learning
Hrs/wk 4
CP 5
Workload in Hours Independent Study Time 94, Study Time in Lecture 56
Lecturer Prof. Robert Seifried, Daniel-André Dücker
Language DE
Cycle WiSe
Content
Literature

Seifried, R.: Dynamics of underactuated multibody systems, Springer, 2014

Popp, K.; Schiehlen, W.: Ground vehicle dynamics, Springer, 2010

Module M1614: Optics for Engineers
Courses
Title Typ Hrs/wk CP
Optics for Engineers (L2437) Lecture 3 3
Optics for Engineers (L2438) Project-/problem-based Learning 3 3
Module Responsible Prof. Thorsten Kern
Admission Requirements None
Recommended Previous Knowledge - Basics of physics
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Teaching subject ist the design of simple optical systems for illumination and imaging optics

  • Basic values for optical systems and lighting technology
  • Spectrum, black-bodies, color-perception
  • Light-Sources und their characterization
  • Photometrics
  • Ray-Optics
  • Matrix-Optics
  • Stops, Pupils and Windows
  • Light-field Technology
  • Introduction to Wave-Optics
  • Introduction to Holography
Skills

Understandings of optics as part of light and electromagnetic spectrum. Design rules, approach to designing optics
Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 96, Study Time in Lecture 84
Credit points 6
Course achievement
Compulsory Bonus Form Description
Yes None Subject theoretical and practical work Teilnahme an Laborübungen und Simulation
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Electrical Engineering: Specialisation Microwave Engineering, Optics, and Electromagnetic Compatibility: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Core Qualification: Elective Compulsory
Course L2437: Optics for Engineers
Typ Lecture
Hrs/wk 3
CP 3
Workload in Hours Independent Study Time 48, Study Time in Lecture 42
Lecturer Prof. Thorsten Kern
Language EN
Cycle WiSe
Content
  • Basic values for optical systems and lighting technology
  • Spectrum, black-bodies, color-perception
  • Light-Sources und their characterization
  • Photometrics
  • Ray-Optics
  • Matrix-Optics
  • Stops, Pupils and Windows
  • Light-field Technology
  • Introduction to Wave-Optics
  • Introduction to Holography
Literature  
Course L2438: Optics for Engineers
Typ Project-/problem-based Learning
Hrs/wk 3
CP 3
Workload in Hours Independent Study Time 48, Study Time in Lecture 42
Lecturer Prof. Thorsten Kern
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1748: Construction Robotics
Courses
Title Typ Hrs/wk CP
Construction Robotics (L2867) Project-/problem-based Learning 6 6
Module Responsible Prof. Kay Smarsly
Admission Requirements None
Recommended Previous Knowledge

Basics of project-oriented programming
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Basics of robotics

Applications in civil engineering

Kinematics
Skills

Use of specific hardware

Development of software routines

Python programming language

Image processing

Basics of localization (LIDAR, SLAM)
Personal Competence
Social Competence

Teamwork

Communication skills
Autonomy

Independent work

Independent decisions
Workload in Hours Independent Study Time 96, Study Time in Lecture 84
Credit points 6
Course achievement None
Examination Written elaboration
Examination duration and scale ca. 10 Seiten
Assignment for the Following Curricula Civil Engineering: Specialisation Structural Engineering: Elective Compulsory
Civil Engineering: Specialisation Water and Traffic: Elective Compulsory
Civil Engineering: Specialisation Coastal Engineering: Elective Compulsory
Civil Engineering: Specialisation Geotechnical Engineering: Elective Compulsory
Civil Engineering: Specialisation Computational Engineering: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Course L2867: Construction Robotics
Typ Project-/problem-based Learning
Hrs/wk 6
CP 6
Workload in Hours Independent Study Time 96, Study Time in Lecture 84
Lecturer Prof. Kay Smarsly, Jan Stührenberg, Mathias Worm
Language DE/EN
Cycle WiSe
Content
  1. Introduction: Robotics in civil engineering
  2. Presentation of potential topics
  3. Programming of algorithms in Python
  4. Application of software systems: LINUX distribution, ROS, CloudCompare, ...
  5. Application of hardware systems: Petoi Bittle Dog, Raspberry Pi, Arduino, sensing ...
  6. Topics considered for robotics using the Petoi Bittle Dog:
    1. Movement
    2. Use of sensors (camera, infrared, ...)
    3. Data structures/data acquisition
    4. Programming
  7. Topics technically relevant to building inspection:
    1. Geodetic evaluations
    2. Image processing
    3. Localization
Literature

Bock/Linner: Construction Robotics
Verl et al.: Soft Robotics
Pasquale: New Laws of robotics

Module M0806: Technical Acoustics II (Room Acoustics, Computational Methods)
Courses
Title Typ Hrs/wk CP
Technical Acoustics II (Room Acoustics, Computational Methods) (L0519) Lecture 2 3
Technical Acoustics II (Room Acoustics, Computational Methods) (L0521) Recitation Section (large) 2 3
Module Responsible Prof. Benedikt Kriegesmann
Admission Requirements None
Recommended Previous Knowledge

Technical Acoustics I (Acoustic Waves, Noise Protection, Psycho Acoustics)

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 in acoustics regarding room acoustics and computational methods and are able to give an overview of the corresponding theoretical and methodical basis.
Skills

The students are capable to handle engineering problems in acoustics by theory-based application of the demanding computational methods and procedures treated within the module.
Personal Competence
Social Competence

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

The students are able to independently solve challenging acoustical problems in the areas treated within the module. Possible conflicting issues and limitations can be identified and the results are critically scrutinized.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Oral exam
Examination duration and scale 20 min
Assignment for the Following Curricula Aircraft Systems Engineering: Core Qualification: Elective Compulsory
Aeronautics: Core Qualification: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Product Development, Materials and Production: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Product Development and Production: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory
Course L0519: Technical Acoustics II (Room Acoustics, Computational Methods)
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Dr.-Ing. Sören Keuchel
Language EN
Cycle WiSe
Content

- Room acoustics
- Sound absorber

- Standard computations
- Statistical Energy Approaches
- Finite Element Methods
- Boundary Element Methods
- Geometrical acoustics
- Special formulations

- Practical applications
- Hands-on Sessions: Programming of elements (Matlab)
Literature

Cremer, L.; Heckl, M. (1996): Körperschall. Springer Verlag, Berlin
Veit, I. (1988): Technische Akustik. Vogel-Buchverlag, Würzburg
Veit, I. (1988): Flüssigkeitsschall. Vogel-Buchverlag, Würzburg
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 L0521: Technical Acoustics II (Room Acoustics, Computational Methods)
Typ Recitation Section (large)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Dr.-Ing. Sören Keuchel
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0603: Nonlinear Structural Analysis
Courses
Title Typ Hrs/wk CP
Nonlinear Structural Analysis (L0277) Lecture 3 4
Nonlinear Structural Analysis (L0279) Recitation Section (small) 1 2
Module Responsible Prof. Alexander Düster
Admission Requirements None
Recommended Previous Knowledge

Knowledge of partial differential equations is recommended.
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to
+ give an overview of the different nonlinear phenomena in structural mechanics.
+ explain the mechanical background of nonlinear phenomena in structural mechanics.
+ to specify problems of nonlinear structural analysis, to identify them in a given situation and to explain their mathematical and mechanical background.
Skills

Students are able to
+ model nonlinear structural problems.
+ select for a given nonlinear structural problem a suitable computational procedure.
+ apply finite element procedures for nonlinear structural analysis.
+ critically verify and judge results of nonlinear finite elements.
+ to transfer their knowledge of nonlinear solution procedures to new problems.
Personal Competence
Social Competence Students are able to
+ solve problems in heterogeneous groups.
+ present and discuss their results in front of others.
+ give and accept professional constructive criticism.


Autonomy

Students are able to
+ assess their knowledge by means of exercises and E-Learning.
+ acquaint themselves with the necessary knowledge to solve research oriented tasks.
+ to transform the acquired knowledge to similar problems.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Written exam
Examination duration and scale 120 min
Assignment for the Following Curricula Civil Engineering: Specialisation Structural Engineering: Elective Compulsory
Civil Engineering: Specialisation Computational Engineering: Compulsory
International Management and Engineering: Specialisation II. Civil Engineering: Elective Compulsory
Materials Science: Specialisation Modeling: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Product Development, Materials and Production: Core Qualification: Elective Compulsory
Naval Architecture and Ocean Engineering: Core Qualification: Elective Compulsory
Ship and Offshore Technology: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory
Course L0277: Nonlinear Structural Analysis
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Alexander Düster
Language DE/EN
Cycle WiSe
Content

1. Introduction
2. Nonlinear phenomena
3. Mathematical preliminaries
4. Basic equations of continuum mechanics
5. Spatial discretization with finite elements
6. Solution of nonlinear systems of equations
7. Solution of elastoplastic problems
8. Stability problems
9. Contact problems
Literature

[1] Alexander Düster, Nonlinear Structrual Analysis, Lecture Notes, Technische Universität Hamburg-Harburg, 2014.
[2] Peter Wriggers, Nonlinear Finite Element Methods, Springer 2008.
[3] Peter Wriggers, Nichtlineare Finite-Elemente-Methoden, Springer 2001.
[4] Javier Bonet and Richard D. Wood, Nonlinear Continuum Mechanics for Finite Element Analysis, Cambridge University Press, 2008.
Course L0279: Nonlinear Structural Analysis
Typ Recitation Section (small)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Alexander Düster
Language DE/EN
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 NN
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 concepts behind linear and qLPV Model Predictive Control (MPC)
Skills
  • Students can construct 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 can use standard software tools (Matlab robust control toolbox) for these tasks
  • Students can design distributed formation controllers for groups of agents with either LTI or LPV dynamics, using Matlab tools provided
  • Students can design MPC controllers for linear and non-linear systems using Matlab tools
Personal Competence
Social Competence Students can work in small groups and arrive at joint results.
Autonomy

Students can 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
Course achievement None
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory
Aircraft Systems Engineering: Core Qualification: Elective Compulsory
Aeronautics: Core Qualification: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Core Qualification: 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
Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: 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 NN
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

  • Linear and Nonlinear Model Predictive Control based on LMIs
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 NN
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 are able to grasp practical tasks in groups, develop solution strategies independently, define work processes and work on them collaboratively.
The students can critically reflect on the results of other groups, make constructive suggestions for improvement and also incorporate them into their own work.
Autonomy

The students can assess their level of knowledge and document their work results. They can critically evaluate the results achieved and present them in an appropriate argumentative manner to the other groups.
Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Course achievement
Compulsory Bonus Form Description
Yes 10 % Written elaboration
Yes 10 % Presentation
Examination Written exam
Examination duration and scale 90 minutes
Assignment for the Following Curricula Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory
Data Science: Specialisation III. Applications: Elective Compulsory
Data Science: Specialisation IV. Special Focus Area: Elective Compulsory
Electrical Engineering: Specialisation Medical Technology: Elective Compulsory
Interdisciplinary Mathematics: Specialisation Computational Methods in Biomedical Imaging: Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory
Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory
Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory
Biomedical Engineering: Specialisation Medical Technology and Control Theory: Compulsory
Theoretical Mechanical Engineering: Specialisation Bio- and Medical Technology: 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

Module M1919: Sustainable operation of technical assets
Courses
Title Typ Hrs/wk CP
Fundamentals of Maintenance, Repair and Overhaul (MRO) (L3160) Lecture 3 4
Fundamentals of Maintenance, Repair and Overhaul (MRO) (L3161) Recitation Section (large) 1 2
Module Responsible Prof. Gerko Wende
Admission Requirements None
Recommended Previous Knowledge

We recommend knowledge in the areas of general engineering sciences, aeronautics and aircraft systems engineering. Technical fields like mechanical engineering, mechatronics and production engineering will be introduced into the relevant aeronautical content.
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students are able to describe fundamental correlations for the sustainable operation of technical assets and to identify solution approaches for complex optimization problems.
Skills

The students are enabled to apply the general engineering capabilities of the individual course towards the optimization of the sustainability in operation of technical assets. The resulting competencies will open an entry into positions in the development, production and technical operation of sustainable products in the mobility and engineering industries.
Personal Competence
Social Competence

The students are able to work in mixed groups with a clear focus on the approached solutions by respecting the complex environment of multiple stakeholders.
Autonomy

The students are enabled to find solutions for optimization problems and to take required decision for the assessment of determining factors independently.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Aircraft Systems Engineering: Core Qualification: Elective Compulsory
Aeronautics: Core Qualification: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Core Qualification: 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: Specialisation Product Development and Production: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Aircraft Systems Engineering: Elective Compulsory
Course L3160: Fundamentals of Maintenance, Repair and Overhaul (MRO)
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Gerko Wende
Language DE
Cycle WiSe
Content

Fundamentals for the sustainable operation of technical assets by means of maintenance, repair and overhaul (MRO):

  •     Life cycle analytics
  •     Material circularity and service products
  •     Rules and regulations
  •     Processes and production methods
  •     Tools and technologies
  •     Data handling and usage
  •     Design for maintenance
  •     Self-healing technical systems
Literature -
Course L3161: Fundamentals of Maintenance, Repair and Overhaul (MRO)
Typ Recitation Section (large)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Gerko Wende
Language DE
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 discrete 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. The students can relate process automation to methods from robotics and sensor systems as well as to recent topics like 'cyberphysical systems' and 'industry 4.0'.
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 can independently define work processes within their groups, distribute tasks within the group and develop solutions collaboratively.
Autonomy

The students are able to assess their level of knowledge and to document their work results adequately.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement
Compulsory Bonus Form Description
No 10 % Excercises
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 II: Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory
Aircraft Systems Engineering: Core Qualification: Elective Compulsory
International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory
International Management and Engineering: Specialisation II. Product Development and Production: Elective Compulsory
Aeronautics: Core Qualification: Elective Compulsory
Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: 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 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 1/ 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
Course achievement None
Examination Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Computer Science: Specialisation III. Mathematics: Elective Compulsory
Data Science: Specialisation IV. Special Focus Area: Elective Compulsory
Data Science: Specialisation I. Mathematics: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Simulation Technology: 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/EN
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 (224 Seiten)

Ergänzend können die folgenden Lehrbücher herangezogen werden:

  1. Saad, Yousef. Numerical methods for large eigenvalue problems: revised edition. Society for Industrial and Applied Mathematics, 2011.
  2. Saad, Yousef. Iterative methods for sparse linear systems. Society for Industrial and Applied Mathematics, 2003.

  3. Van der Vorst, Henk A. Iterative Krylov methods for large linear systems. No. 13. Cambridge University Press, 2003.

  4. Liesen, Jörg, and Zdenek Strakos. Krylov subspace methods: principles and analysis. Oxford University Press, 2013.
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/EN
Cycle WiSe
Content
Literature Siehe korrespondierende Vorlesung

Supplement Modules

Module M0604: High-Order FEM
Courses
Title Typ Hrs/wk CP
High-Order FEM (L0280) Lecture 3 4
High-Order FEM (L0281) Recitation Section (large) 1 2
Module Responsible Prof. Alexander Düster
Admission Requirements None
Recommended Previous Knowledge

Knowledge of partial differential equations is recommended.
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to
+ give an overview of the different (h, p, hp) finite element procedures.
+ explain high-order finite element procedures.
+ specify problems of finite element procedures, to identify them in a given situation and to explain their mathematical and mechanical background.
Skills

Students are able to
+ apply high-order finite elements to problems of structural mechanics.
+ select for a given problem of structural mechanics a suitable finite element procedure.
+ critically judge results of high-order finite elements.
+ transfer their knowledge of high-order finite elements to new problems.
Personal Competence
Social Competence Students are able to
+ solve problems in heterogeneous groups.
+ present and discuss their results in front of others.
+ give and accept professional constructive criticism.


Autonomy Students are able to
+ assess their knowledge by means of exercises and E-Learning.
+ acquaint themselves with the necessary knowledge to solve research oriented tasks.
+ to transform the acquired knowledge to similar problems.


Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement
Compulsory Bonus Form Description
No 10 % Presentation Forschendes Lernen
Examination Written exam
Examination duration and scale 120 min
Assignment for the Following Curricula Civil Engineering: Specialisation Computational Engineering: Elective Compulsory
International Management and Engineering: Specialisation II. Product Development and Production: Elective Compulsory
Materials Science: Specialisation Modeling: Elective Compulsory
Mechanical Engineering and Management: Specialisation Product Development and Production: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Product Development, Materials and Production: Core Qualification: Elective Compulsory
Naval Architecture and Ocean Engineering: Core Qualification: Elective Compulsory
Technomathematics: Specialisation III. Engineering Science: Elective Compulsory
Theoretical Mechanical Engineering: Core Qualification: Elective Compulsory
Course L0280: High-Order FEM
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Alexander Düster
Language EN
Cycle SoSe
Content

1. Introduction
2. Motivation
3. Hierarchic shape functions
4. Mapping functions
5. Computation of element matrices, assembly, constraint enforcement and solution
6. Convergence characteristics
7. Mechanical models and finite elements for thin-walled structures
8. Computation of thin-walled structures
9. Error estimation and hp-adaptivity
10. High-order fictitious domain methods
Literature

[1] Alexander Düster, High-Order FEM, Lecture Notes, Technische Universität Hamburg-Harburg, 164 pages, 2014
[2] Barna Szabo, Ivo Babuska, Introduction to Finite Element Analysis – Formulation, Verification and Validation, John Wiley & Sons, 2011
Course L0281: High-Order FEM
Typ Recitation Section (large)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Alexander Düster
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0605: Computational Structural Dynamics
Courses
Title Typ Hrs/wk CP
Computational Structural Dynamics (L0282) Lecture 3 4
Computational Structural Dynamics (L0283) Recitation Section (small) 1 2
Module Responsible Prof. Alexander Düster
Admission Requirements None
Recommended Previous Knowledge

Knowledge of partial differential equations is recommended.
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to
+ give an overview of the computational procedures for problems of structural dynamics.
+ explain the application of finite element programs to solve problems of structural dynamics.
+ specify problems of computational structural dynamics, to identify them in a given situation and to explain their mathematical and mechanical background.
Skills

Students are able to
+ model problems of structural dynamics.
+ select a suitable solution procedure for a given problem of structural dynamics.
+ apply computational procedures to solve problems of structural dynamics.
+ verify and critically judge results of computational structural dynamics.
Personal Competence
Social Competence Students are able to
+ solve problems in heterogeneous groups.
+ present and discuss their results in front of others.
+ give and accept professional constructive criticism.


Autonomy

Students are able to
+ assess their knowledge by means of exercises and E-Learning.
+ acquaint themselves with the necessary knowledge to solve research oriented tasks.
+ to transform the acquired knowledge to similar problems.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Written exam
Examination duration and scale 2h
Assignment for the Following Curricula Civil Engineering: Specialisation Computational Engineering: Elective Compulsory
International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory
Materials Science: Specialisation Modeling: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Naval Architecture and Ocean Engineering: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory
Course L0282: Computational Structural Dynamics
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Alexander Düster
Language DE
Cycle SoSe
Content

1. Motivation
2. Basics of dynamics
3. Time integration methods
4. Modal analysis
5. Fourier transform
6. Applications
Literature

[1] K.-J. Bathe, Finite-Elemente-Methoden, Springer, 2002.
[2] J.L. Humar, Dynamics of Structures, Taylor & Francis, 2012.
Course L0283: Computational Structural Dynamics
Typ Recitation Section (small)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Alexander Düster
Language DE
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) 2 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. 

The students are familiar with the contents of lecture and tutorials. They can explain and apply them to new problems.
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 110, Study Time in Lecture 70
Credit points 6
Course achievement None
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Data Science: Specialisation I. Mathematics: Elective Compulsory
Data Science: Specialisation IV. Special Focus Area: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Electrical Engineering: Specialisation Wireless and Sensor Technologies: Elective Compulsory
Computer Science in Engineering: Specialisation II. Engineering Science: 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 EN
Cycle SoSe
Content
  • Introduction to information theory and coding
  • Definitions of information: Self information, entropy
  • Binary entropy function
  • Source coding theorem
  • Entropy of continuous random variables: Differential entropy, differential entropy of uniformly and Gaussian distributed random variables
  • Source coding
    • Principles of lossless source coding
    • Optimal source codes
    • Prefix codes, prefix-free codes, instantaneous codes
    • Morse code
    • Huffman code
    • Shannon code
    • Bounds on the average codeword length
    • Relative entropy, Kullback-Leibler distance, Kullback-Leibler divergence
    • Cross entropy
    • Lempel-Ziv algorithm
    • Lempel-Ziv-Welch (LZW) algorithm
    • Text compression and image compression using variants of the Lempel-Ziv algorithm
  • Channel models
    • AWGN channel
    • Binary-input AWGN channel
    • Binary symmetric channel (BSC)
    • Relationship between AWGN channel and BSC
    • Binary error and erasure channel (BEEC)
    • Binary erasure channel (BEC)
    • Discrete memoryless channels (DMC)
  • Definitions of information for multiple random variables
    • Mutual information and channel capacity
    • Entropy, conditional entropy
    • Chain rules for entropy and mutual information
  • Channel coding theorem
  • Channel capacity of fundamental channels: BSC, BEC, AWGN channel, binary-input AWGN channel etc.
  • Power-limited vs. bandlimited transmission
  • Capacity of parallel AWGN channels
    • Waterfilling
    • Examples: Multiple input multiple output (MIMO) channels, complex equivalent baseband channels, orthogonal frequency division multiplex (OFDM)
  • Source-channel coding theorem, separation theorem
  • Multiuser information theory
    • Multiple access channel (MAC)
    • Broadcast channel
    • Principles of multiple access, time division multiple access (TDMA), frequency division multiple access (FDMA), non-orthogonal multiple access (NOMA), hybrid multiple access
    • Achievable rate regions of TDMA and FDMA with power constraint, energy constraint, power spectral density constraint, respectively
    • Achievable rate region of the two-user and K-user multiple access channels
    • Achievable rate region of the two-user and K user broadcast channels
    • Multiuser diversity
  • Channel coding
    • Principles and types of channel coding
    • Code rate, data rate, Hamming distance, minimum Hamming distance, Hamming weight, minimum Hamming weight
    • Error detecting and error correcting codes
    • Simple block codes: Repetition codes, single parity check codes, Hamming code, etc.
    • Syndrome decoding
    • Representations of binary data
    • Non-binary symbol alphabets and non-binary codes
    • Code and encoder, systematic and non-systematic encoders
    • Properties of Hamming distance and Hamming weight
    • Decoding spheres
    • Perfect codes
    • Linear codes
    • Decoding principles
      • Syndrome decoding
      • Maximum a posteriori probability (MAP) decoding and maximum likelihood (ML) decoding
      • Hard decision and soft decision decoding
      • Log-likelihood ratios (LLRs), boxplus operation
      • MAP and ML decoding using log-likelihood ratios
      • Soft-in soft-out decoders
    • Error rate performance comparison of codes in terms of SNR per info bit vs. SNR per code bit
    • Linear block codes
      • Generator matrix and parity check matrix, properties of generator matrix and parity check matrix
      • Dual codes
    • Low density parity check (LDPC) codes
      • Sparse parity check matrix
      • Tanner graphs, cycles and girth
      • Degree distributions
      • Code rate and degree distribution
      • Regular and irregular LDPC codes
      • Message passing decoding
        • Message passing decoding in binary erasure channels (BEC)
        • Systematic encoding using erasure message passing decoding
        • Message passing decoding in binary symmetric channels (BSC)
          • Extrinsic information
          • Bit-flipping decoding
          • Effects of short cycles in the Tanner graph
          • Alternative bit-flipping decoding
          • Soft decision message passing decoding: Sum product decoding
        • Bit error rate performance of LDPC codes
      • Repeat accumulate codes and variants of repeat accumulate codes
      • Message passing decoding and turbo decoding of repeat accumulate codes
    • Convolutional codes
      • Encoding using shift registers
      • Trellis representation
      • Hard decision and soft decision Viterbi decoding
      • Bit error rate performance of convolutional codes
      • Asymptotic coding gain
      • Viterbi decoding complexity
      • Free distance and optimum convolutional codes
      • Generator polynomial description and octal description
      • Catastrophic convolutional codes
      • Non-systematic and recursive systematic convolutional (RSC) encoders
      • Rate compatible punctured convolutional (RCPC) codes
      • Hybrid automatic repeat request (HARQ) with incremental redundancy
      • Unequal error protection with punctured convolutional codes
      • Error patterns of convolutional codes
    • Concatenated codes
      • Serial concatenated codes
      • Parallel concatenated codes, Turbo codes
      • Iterative decoding, turbo decoding
      • Bit error rate performance of turbo codes
      • Interleaver design for turbo codes
    • Coded modulation
      • Principle of coded modulation
      • Achievable rates with PSK/QAM modulation
      • Trellis coded modulation (TCM)
      • Set partitioning
      • Ungerböck codes
      • Multilevel coding
      • Bit-interleaved 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 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Gerhard Bauch
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1724: Smart Monitoring
Courses
Title Typ Hrs/wk CP
Smart Monitoring (L2762) Integrated Lecture 2 2
Smart Monitoring (L2763) Recitation Section (small) 2 4
Module Responsible Prof. Kay Smarsly
Admission Requirements None
Recommended Previous Knowledge

Basic knowledge or interest in object-oriented modeling, programming, and sensor technologies are helpful. Interest in modern research and teaching areas, such as Internet of Things, Industry 4.0 and cyber-physical systems, as well as the will to deepen skills of scientific working, are required. Basic knowledge in scientific writing and good English skills.
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students will become familiar with the principles and practices of smart monitoring. The students will be able to design decentralized smart systems to be applied for continuous (remote) monitoring of systems in the built and in the natural environment. In addition, the students will learn to design and to implement intelligent sensor systems using state-of-the-art data analysis techniques, modern software design concepts, and embedded computing methodologies. Besides lectures, project work is also part of this module, which will be conducted throughout the semester and will contribute to the grade. In small groups, the students will design smart monitoring systems that integrate a number of “intelligent” sensors to be implemented by the students. Specific focus will be put on the application of machine learning techniques. The smart monitoring systems will be mounted on real-world (built or natural) systems, such as bridges or slopes, or on scaled lab structures for validation purposes. The outcome of every group will be documented in a paper. All students of this module will “automatically” participate with their smart monitoring system in the annual "Smart Monitoring" competition. The written papers and oral examinations form the final grades. The module will be taught in English. Limited enrollment.
Skills
Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Written elaboration
Examination duration and scale 10 pages of work with 15-minute oral presentation
Assignment for the Following Curricula Civil Engineering: Specialisation Water and Traffic: Elective Compulsory
Civil Engineering: Specialisation Geotechnical Engineering: Elective Compulsory
Civil Engineering: Specialisation Coastal Engineering: Elective Compulsory
Civil Engineering: Specialisation Structural Engineering: Elective Compulsory
Environmental Engineering: Specialisation Water Quality and Water Engineering: Elective Compulsory
Environmental Engineering: Specialisation Energy and Resources: Elective Compulsory
Environmental Engineering: Specialisation Environment and Climate: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Water and Environmental Engineering: Specialisation Cities: Elective Compulsory
Water and Environmental Engineering: Specialisation Environment: Elective Compulsory
Water and Environmental Engineering: Specialisation Water: Elective Compulsory
Course L2762: Smart Monitoring
Typ Integrated Lecture
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Kay Smarsly
Language EN
Cycle SoSe
Content

In this course, principles of smart monitoring will be taught, focusing on modern concepts of data acquisition, data storage, and data analysis. Also, fundamentals of intelligent sensors and embedded computing will be illuminated. Autonomous software and decentralized data processing are further crucial parts of the course, including concepts of the Internet of Things, Industry 4.0 and cyber-physical systems. Furthermore, measuring principles, data acquisition systems, data management and data analysis algorithms will be discussed. Besides the theoretical background, numerous practical examples will be shown to demonstrate how smart monitoring may advantageously be used for assessing the condition of systems in the built or natural environment.
Literature
Course L2763: Smart Monitoring
Typ Recitation Section (small)
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Lecturer Prof. Kay Smarsly
Language EN
Cycle SoSe
Content The contents of the exercises are based on the lecture contents. In addition to the exercises, project work will be conducted throughout the semester, which will consume the majority of the workload. As part of the project work, students will design smart monitoring systems that will be tested in the laboratory or in the field. As mentioned in the module description, the students will participate in the “Smart Monitoring” competition, hosted annually by the Institute of Digital and Autonomous Construction. Students are encouraged to contribute their own ideas. The tools required to implement the smart monitoring systems will be taught in the group exercises as well as through external sources, such as video tutorials and literature.
Literature

Module M0769: EMC I: Coupling Mechanisms, Countermeasures and Test Procedures
Courses
Title Typ Hrs/wk CP
EMC I: Coupling Mechanisms, Countermeasures, and Test Procedures (L0743) Lecture 3 4
EMC I: Coupling Mechanisms, Countermeasures, and Test Procedures (L0744) Recitation Section (small) 1 1
EMC I: Coupling Mechanisms, Countermeasures, and Test Procedures (L0745) Practical Course 1 1
Module Responsible Prof. Christian Schuster
Admission Requirements None
Recommended Previous Knowledge

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

Students are able to explain the fundamental principles, inter-dependencies, and methods of Electromagnetic Compatibility of electric and electronic systems and to ensure Electromagnetic Compatibility of such systems. They are able to classify and explain the common interference sources and coupling mechanisms. They are capable of explaining the basic principles of shielding and filtering.  They are able of giving an overview over measurement and simulation methods for the characterization of Electromagnetic Compatibility in electrical engineering practice.

Skills

Students are able to apply a series of modeling methods for the Electromagnetic Compatibility of typical electric and electronic systems. They are able to determine the most important effects that these models are predicting in terms of Electromagnetic Compatibility. They can classify these effects and they can quantitatively analyze them. They are capable of deriving problem solving strategies from these predictions and they can adapt them to applications in electrical engineering practice. They can evaluate their problem solving strategies against each other.
Personal Competence
Social Competence

Students are able to work together on subject related tasks in small groups. They are able to present their results effectively in English, during laboratory work and exercises, e.g..
Autonomy

Students are capable to gather necessary information from the references provided and relate that information to the context of the lecture. They are able to make a connection between their knowledge obtained in this lecture with the content of other lectures (e.g. Theoretical Electrical Engineering and Communication Theory). They can communicate problems and solutions in the field of Electromagnetic Compatibility in english language.

Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Course achievement
Compulsory Bonus Form Description
Yes None Presentation
Examination Oral exam
Examination duration and scale 45 min
Assignment for the Following Curricula Electrical Engineering: Specialisation Microwave Engineering, Optics, and Electromagnetic Compatibility: Elective Compulsory
Electrical Engineering: Specialisation Wireless and Sensor Technologies: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Microelectronics and Microsystems: Specialisation Microelectronics Complements: Elective Compulsory
Course L0743: EMC I: Coupling Mechanisms, Countermeasures, and Test Procedures
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Christian Schuster
Language DE/EN
Cycle SoSe
Content
  • Introduction to Electromagnetic Compatibility (EMC)
  • Interference sources in time an frequency domain
  • Coupling mechanisms
  • Transmission lines and coupling to electromagnetic fields
  • Shielding
  • Filters
  • EMC test procedures
Literature
  • C.R. Paul: "Introduction to Electromagnetic Compatibility", 2nd ed., (Wiley, New Jersey, 2006).
  • A.J. Schwab und W. Kürner: "Elektromagnetische Verträglichkeit", 6. Auflage, (Springer, Berlin 2010).
  • F.M. Tesche, M.V. Ianoz, and T. Karlsson: "EMC Analysis Methods and Computational Models", (Wiley, New York, 1997).
Course L0744: EMC I: Coupling Mechanisms, Countermeasures, and Test Procedures
Typ Recitation Section (small)
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Christian Schuster
Language DE/EN
Cycle SoSe
Content

The exercise sessions serve to deepen the understanding of the concepts of the lecture.
Literature
  • C.R. Paul: "Introduction to Electromagnetic Compatibility", 2nd ed., (Wiley, New Jersey, 2006).
  • A.J. Schwab und W. Kürner: "Elektromagnetische Verträglichkeit", 6. Auflage, (Springer, Berlin 2010).
  • F.M. Tesche, M.V. Ianoz, and T. Karlsson: "EMC Analysis Methods and Computational Models", (Wiley, New York, 1997).
  • Scientific articles and papers
Course L0745: EMC I: Coupling Mechanisms, Countermeasures, and Test Procedures
Typ Practical Course
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Christian Schuster
Language DE/EN
Cycle SoSe
Content

Laboratory experiments serve to practically investigate the following EMC topics:

  • Shielding
  • Conducted EMC test procedures
  • The GTEM-cell as an environment for radiated EMC test
Literature Versuchsbeschreibungen und zugehörige Literatur werden innerhalb der Veranstaltung bereit gestellt.

Module M0603: Nonlinear Structural Analysis
Courses
Title Typ Hrs/wk CP
Nonlinear Structural Analysis (L0277) Lecture 3 4
Nonlinear Structural Analysis (L0279) Recitation Section (small) 1 2
Module Responsible Prof. Alexander Düster
Admission Requirements None
Recommended Previous Knowledge

Knowledge of partial differential equations is recommended.
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to
+ give an overview of the different nonlinear phenomena in structural mechanics.
+ explain the mechanical background of nonlinear phenomena in structural mechanics.
+ to specify problems of nonlinear structural analysis, to identify them in a given situation and to explain their mathematical and mechanical background.
Skills

Students are able to
+ model nonlinear structural problems.
+ select for a given nonlinear structural problem a suitable computational procedure.
+ apply finite element procedures for nonlinear structural analysis.
+ critically verify and judge results of nonlinear finite elements.
+ to transfer their knowledge of nonlinear solution procedures to new problems.
Personal Competence
Social Competence Students are able to
+ solve problems in heterogeneous groups.
+ present and discuss their results in front of others.
+ give and accept professional constructive criticism.


Autonomy

Students are able to
+ assess their knowledge by means of exercises and E-Learning.
+ acquaint themselves with the necessary knowledge to solve research oriented tasks.
+ to transform the acquired knowledge to similar problems.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Written exam
Examination duration and scale 120 min
Assignment for the Following Curricula Civil Engineering: Specialisation Structural Engineering: Elective Compulsory
Civil Engineering: Specialisation Computational Engineering: Compulsory
International Management and Engineering: Specialisation II. Civil Engineering: Elective Compulsory
Materials Science: Specialisation Modeling: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Product Development, Materials and Production: Core Qualification: Elective Compulsory
Naval Architecture and Ocean Engineering: Core Qualification: Elective Compulsory
Ship and Offshore Technology: Core Qualification: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory
Course L0277: Nonlinear Structural Analysis
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Alexander Düster
Language DE/EN
Cycle WiSe
Content

1. Introduction
2. Nonlinear phenomena
3. Mathematical preliminaries
4. Basic equations of continuum mechanics
5. Spatial discretization with finite elements
6. Solution of nonlinear systems of equations
7. Solution of elastoplastic problems
8. Stability problems
9. Contact problems
Literature

[1] Alexander Düster, Nonlinear Structrual Analysis, Lecture Notes, Technische Universität Hamburg-Harburg, 2014.
[2] Peter Wriggers, Nonlinear Finite Element Methods, Springer 2008.
[3] Peter Wriggers, Nichtlineare Finite-Elemente-Methoden, Springer 2001.
[4] Javier Bonet and Richard D. Wood, Nonlinear Continuum Mechanics for Finite Element Analysis, Cambridge University Press, 2008.
Course L0279: Nonlinear Structural Analysis
Typ Recitation Section (small)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Alexander Düster
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0781: EMC II: Signal Integrity and Power Supply of Electronic Systems
Courses
Title Typ Hrs/wk CP
EMC II: Signal Integrity and Power Supply of Electronic Systems (L0770) Lecture 3 4
EMC II: Signal Integrity and Power Supply of Electronic Systems (L0771) Recitation Section (small) 1 1
EMC II: Signal Integrity and Power Supply of Electronic Systems (L0774) Practical Course 1 1
Module Responsible Prof. Christian Schuster
Admission Requirements None
Recommended Previous Knowledge

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

Students are able to explain the fundamental principles, inter-dependencies, and methods of signal and power integrity of electronic systems. They are able to relate signal and power integrity to the context of interference-free design of such systems, i.e. their electromagnetic compatibility. They are capable of explaining the basic behavior of signals and power supply in typical packages and interconnects. They are able to propose and describe problem solving strategies for signal and power integrity issues. They are capable of giving an overview over measurement and simulation methods for characterization of signal and power integrity in electrical engineering practice.


Skills

Students are able to apply a series of modeling methods for characterization of electromagnetic field behavior in packages and interconnect structure of electronic systems. They are able to determine the most important effects that these models are predicting in terms of signal and power integrity. They can classify these effects and they can quantitatively analyze them. They are capable of deriving problem solving strategies from these predictions and they can adapt them to applications in electrical engineering practice. The can evaluate their problem solving strategies against each other.
Personal Competence
Social Competence

Students are able to work together on subject related tasks in small groups. They are able to present their results effectively in English (e.g. during CAD exercises).
Autonomy

Students are capable to gather necessary information from the references provided and relate that information to the context of the lecture. They are able to make a connection between their knowledge obtained in this lecture with the content of other lectures (e.g. theory of electromagnetic fields, communications, and semiconductor circuit design). They can communicate problems and solutions in the field of signal integrity and power supply of interconnect and packages in English.


Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Course achievement
Compulsory Bonus Form Description
Yes None Presentation
Examination Oral exam
Examination duration and scale 45 min
Assignment for the Following Curricula Electrical Engineering: Specialisation Microwave Engineering, Optics, and Electromagnetic Compatibility: Elective Compulsory
Electrical Engineering: Specialisation Nanoelectronics and Microsystems Technology: Elective Compulsory
Electrical Engineering: Specialisation Wireless and Sensor Technologies: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Microelectronics and Microsystems: Specialisation Microelectronics Complements: Elective Compulsory
Course L0770: EMC II: Signal Integrity and Power Supply of Electronic Systems
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Christian Schuster
Language DE/EN
Cycle WiSe
Content

- The role of packages and interconnects in electronic systems

- Components of packages and interconnects in electronic systems

- Main goals and concepts of signal and power integrity of electronic systems

- Repeat of relevant concepts from the theory electromagnetic fields

- Properties of digital signals and systems

- Design and characterization of signal integrity

- Design and characterization of power supply

- Techniques and devices for measurements in time- and frequency-domain

- CAD tools for electrical analysis and design of packages and interconnects

- Connection to overall electromagnetic compatibility of electronic systems
Literature

- J. Franz, "EMV: Störungssicherer Aufbau elektronischer Schaltungen", Springer (2012)

- R. Tummala, "Fundamentals of Microsystems Packaging", McGraw-Hill (2001)

- S. Ramo, J. Whinnery, T. Van Duzer, "Fields and Waves in Communication Electronics", Wiley (1994)

- S. Thierauf, "Understanding Signal Integrity", Artech House (2010)

- M. Swaminathan, A. Engin, "Power Integrity Modeling and Design for Semiconductors and Systems", Prentice-Hall (2007)
Course L0771: EMC II: Signal Integrity and Power Supply of Electronic Systems
Typ Recitation Section (small)
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Christian Schuster
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course
Course L0774: EMC II: Signal Integrity and Power Supply of Electronic Systems
Typ Practical Course
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Christian Schuster
Language DE/EN
Cycle WiSe
Content

- The role of packages and interconnects in electronic systems

- Components of packages and interconnects in electronic systems

- Main goals and concepts of signal and power integrity of electronic systems

- Repeat of relevant concepts from the theory electromagnetic fields

- Properties of digital signals and systems

- Design and characterization of signal integrity

- Design and characterization of power supply

- Techniques and devices for measurements in time- and frequency-domain

- CAD tools for electrical analysis and design of packages and interconnects

- Connection to overall electromagnetic compatibility of electronic systems
Literature

- J. Franz, "EMV: Störungssicherer Aufbau elektronischer Schaltungen", Springer (2012)

- R. Tummala, "Fundamentals of Microsystems Packaging", McGraw-Hill (2001)

- S. Ramo, J. Whinnery, T. Van Duzer, "Fields and Waves in Communication Electronics", Wiley (1994)

- S. Thierauf, "Understanding Signal Integrity", Artech House (2010)

- M. Swaminathan, A. Engin, "Power Integrity Modeling and Design for Semiconductors and Systems", Prentice-Hall (2007)

Thesis

Module M-002: Master Thesis
Courses
Title Typ Hrs/wk CP
Module Responsible Professoren der TUHH
Admission Requirements

  • According to General Regulations §21 (1):

    At least 60 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
Course achievement None
Examination Thesis
Examination duration and scale According to General Regulations
Assignment for the Following Curricula Civil Engineering: Thesis: Compulsory
Bioprocess Engineering: Thesis: Compulsory
Chemical and Bioprocess Engineering: Thesis: Compulsory
Computer Science: Thesis: Compulsory
Data Science: Thesis: Compulsory
Electrical Engineering: Thesis: Compulsory
Energy Systems: Thesis: Compulsory
Environmental Engineering: Thesis: Compulsory
Aircraft Systems Engineering: Thesis: Compulsory
Global Innovation Management: Thesis: Compulsory
Computer Science in Engineering: Thesis: Compulsory
Information and Communication Systems: Thesis: Compulsory
Interdisciplinary Mathematics: 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
Aeronautics: Thesis: Compulsory
Materials Science and Engineering: 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
Teilstudiengang Lehramt Metalltechnik: Thesis: Compulsory
Theoretical Mechanical Engineering: Thesis: Compulsory
Process Engineering: Thesis: Compulsory
Water and Environmental Engineering: Thesis: Compulsory
Certification in Engineering & Advisory in Aviation: Thesis: Compulsory