Program description
Content
Core Qualification
Module M0523: Business & Management 
Module Responsible  Prof. Matthias Meyer 
Admission Requirements  None 
Recommended Previous Knowledge  None 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 

Skills 

Personal Competence  
Social Competence 

Autonomy 

Workload in Hours  Depends on choice of courses 
Credit points  6 
Courses 
Information regarding lectures and courses can be found in the corresponding module handbook published separately. 
Module M0524: Nontechnical 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. Selfreliance, selfmanagement, 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 crossdisciplinarily 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 startups in a goaloriented way. The fields of teaching are augmented by soft skills offers and a foreign language offer. Here, the focus is on encouraging goaloriented 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

Skills 
Professional Competence (Skills) In selected subareas students can

Personal Competence  
Social Competence 
Personal Competences (Social Skills) Students will be able

Autonomy 
Personal Competences (Selfreliance) Students are able in selected areas

Workload in Hours  Depends on choice of courses 
Credit points  6 
Courses 
Information regarding lectures and courses can be found in the corresponding module handbook published separately. 
Module M0804: Research Project and Seminar 

Courses  

Module Responsible  Prof. KarlHeinz Zimmermann 
Admission Requirements  None 
Recommended Previous Knowledge  Basic knowledge and techniques in the chosen field of specialization. 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge  Students are able to acquire advanced knowledge in a specific field of Computer Science or a closely related subject. 
Skills  Students are able to work selfdependent in a field of Computer Science or a closely related field. 
Personal Competence  
Social Competence  
Autonomy  
Workload in Hours  Independent Study Time 372, Study Time in Lecture 168 
Credit points  18 
Course achievement  None 
Examination  Study work 
Examination duration and scale  Presentation of a current research topic (2530 min and 5 min discussion). 
Assignment for the Following Curricula 
Computer Science: Core Qualification: Compulsory Information and Communication Systems: Core Qualification: Compulsory 
Course L1761: Project Work 
Typ  Projection Course 
Hrs/wk  10 
CP  15 
Workload in Hours  Independent Study Time 310, Study Time in Lecture 140 
Lecturer  Dozenten des SD E 
Language  DE/EN 
Cycle  WiSe 
Content 
Current research topics of the chosen specialization. 
Literature 
Aktuelle Literatur zu Forschungsthemen aus der gewählten Vertiefungsrichtung. 
Course L0817: Seminar 
Typ  Seminar 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Dozenten des SD E 
Language  DE/EN 
Cycle  WiSe 
Content 

Literature  Wird vom Veranstalter bekanntgegeben. 
Specialization Computer and Software Engineering
Module M0753: Software Verification 

Courses  

Module Responsible  Prof. Sibylle Schupp  
Admission Requirements  None  
Recommended Previous Knowledge 


Educational Objectives  After taking part successfully, students have reached the following learning results  
Professional Competence  
Knowledge 
Students apply the major verification techniques in model checking and deductive verification. They explain in formal terms syntax and semantics of the underlying logics, and assess the expressivity of different logics as well as their limitations. They classify formal properties of software systems. They find flaws in formal arguments, arising from modeling artifacts or underspecification. 

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

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

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

Workload in Hours  Independent Study Time 124, Study Time in Lecture 56  
Credit points  6  
Course achievement 


Examination  Written exam  
Examination duration and scale  90 min  
Assignment for the Following Curricula 
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation I. Computer Science: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory 
Course L0629: Software Verification 
Typ  Lecture 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Sibylle Schupp 
Language  EN 
Cycle  WiSe 
Content 

Literature 

Course L0630: Software Verification 
Typ  Recitation Section (small) 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Sibylle Schupp 
Language  EN 
Cycle  WiSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M1270: Technical Complementary Course I for CSMS (according to Subject Specific Regulations) 

Courses  

Module Responsible  Prof. KarlHeinz Zimmermann 
Admission Requirements  None 
Recommended Previous Knowledge  None 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
The students acquire advanced knowledge in a technical subject available at TUHH. 
Skills 
The students acquire professional competence in a technical subject available at TUHH. 
Personal Competence  
Social Competence  
Autonomy  
Workload in Hours  Depends on choice of courses 
Credit points  6 
Assignment for the Following Curricula 
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory 
Module M0667: Algorithmic Algebra 

Courses  

Module Responsible  Dr. Prashant Batra 
Admission Requirements  None 
Recommended Previous Knowledge 
Mathe IIII (Real analysis,computing in Vector spaces , principle of complete induction) Diskrete Mathematik I (gropus, rings, ideals, fields; euclidean algorithm) 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
Students can discuss logical connections between the following concepts and explain them by means of examples: Smith normal form, Chinese remainder theorem, grid point sets, integer solution of inequality systems. 
Skills 
Students are able to access independently further logical connections between the concepts with which they have become familiar and are able to verify them. Students are able to develop a suitable solution approach to given problems, to pursue it and to evaluate the results critically, such as in solving multivariate equation systems and in grid point theory. 
Personal Competence  
Social Competence  
Autonomy  
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Credit points  6 
Course achievement  None 
Examination  Oral exam 
Examination duration and scale  30 min 
Assignment for the Following Curricula 
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory 
Course L0422: Algorithmic Algebra 
Typ  Lecture  
Hrs/wk  3  
CP  5  
Workload in Hours  Independent Study Time 108, Study Time in Lecture 42  
Lecturer  Dr. Prashant Batra  
Language  DE  
Cycle  WiSe  
Content 
Extended euclidean algorithm, solution of the Bezoutequation Division with remainder (over rings) fast arithmetic algorithms (conversion, fast multiplications) discrete Fouriertransformation over rings Computation with modular remainders, solving of remainder systems (chinese remainder theorem), solvability of integer linear systems over the integers linearization of polynomial equations matrix approach Sylvestermatrix, elimination elimination in rings, elimination of many variables Buchberger algorithm, Gröbner basis Minkowskis Lattice Point theorem and integervalued optimization LLLalgorithm for construction of 'short' lattice vectors in polynomial time 

Literature 
von zur Gathen, Joachim; Gerhard, Jürgen Modern computer algebra. 3rd ed. (English) Zbl 1277.68002 Yap, Chee Keng Free download for students from author's website: http://cs.nyu.edu/yap/book/berlin/ Cox, David; Little, John; O’Shea, Donal eBook: http://dx.doi.org/10.1007/9780387356518
Koepf, Wolfram springer eBook: http://dx.doi.org/10.1007/3540298959 Kaplan, Michael springer eBook: http://dx.doi.org/10.1007/b137968 
Course L0423: Algorithmic Algebra 
Typ  Recitation Section (small) 
Hrs/wk  1 
CP  1 
Workload in Hours  Independent Study Time 16, Study Time in Lecture 14 
Lecturer  Dr. Prashant Batra 
Language  DE 
Cycle  WiSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M0836: Communication Networks 

Courses  

Module Responsible  Prof. Andreas TimmGiel 
Admission Requirements  None 
Recommended Previous Knowledge 

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 
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory Aircraft Systems Engineering: Specialisation Avionic and Embedded Systems: Elective Compulsory Computational Science and Engineering: Specialisation I. Computer Science: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory 
Course L0897: Analysis and Structure of Communication Networks 
Typ  Lecture 
Hrs/wk  2 
CP  2 
Workload in Hours  Independent Study Time 32, Study Time in Lecture 28 
Lecturer  Prof. Andreas TimmGiel 
Language  EN 
Cycle  WiSe 
Content  
Literature 
Further literature is announced at the beginning of the lecture. 
Course L0899: Selected Topics of Communication Networks 
Typ  Project/problembased Learning 
Hrs/wk  2 
CP  2 
Workload in Hours  Independent Study Time 32, Study Time in Lecture 28 
Lecturer  Prof. Andreas TimmGiel 
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 

Course L0898: Communication Networks Excercise 
Typ  Project/problembased Learning 
Hrs/wk  1 
CP  2 
Workload in Hours  Independent Study Time 46, Study Time in Lecture 14 
Lecturer  Prof. Andreas TimmGiel 
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 

Module M0926: Distributed Algorithms 

Courses  

Module Responsible  Prof. Volker Turau 
Admission Requirements  None 
Recommended Previous Knowledge 

Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge  Students know the main abstractions of distributed algorithms (synchronous/asynchronous model, message passing and shared memory model). They are able to describe complexity measures for distributed algorithms (round , message and memory complexity). They explain well known distributed algorithms for important problems such as leader election, mutual exclusion, graph coloring, spanning trees. They know the fundamental techniques used for randomized algorithms. 
Skills  Students design their own distributed algorithms and analyze their complexity. They make use of known standard algorithms. They compute the complexity of randomized algorithms. 
Personal Competence  
Social Competence  
Autonomy  
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Credit points  6 
Course achievement  None 
Examination  Oral exam 
Examination duration and scale  45 min 
Assignment for the Following Curricula 
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation I. Computer Science: Elective Compulsory 
Course L1071: Distributed Algorithms 
Typ  Lecture 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Volker Turau 
Language  DE/EN 
Cycle  WiSe 
Content 

Literature 

Course L1072: Distributed Algorithms 
Typ  Recitation Section (large) 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Volker Turau 
Language  DE/EN 
Cycle  WiSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M0586: Efficient Algorithms 

Courses  

Module Responsible  Prof. Siegfried Rump 
Admission Requirements  None 
Recommended Previous Knowledge 
Programming in Matlab and/or C Basic knowledge in discrete mathematics 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
The students are able to explain the basic theory and methods of network algorithms and in particular their data structures. They are able to analyze the computational behavior and computing time of linear programming algorithms as well network algorithms. Moreover the students can distinguish between efficiently solvable and NPhard problems. 
Skills 
The students are able to analyze complex tasks and can determine possibilities to transform them into networking algorithms. In particular they can efficiently implement basic algorithms and data structures of LP and network algorithms and identify possible weaknesses. They are able to distinguish between different efficient data structures and are able to use them appropriately. 
Personal Competence  
Social Competence 
The students have the skills to solve problems together in small groups and to present the achieved results in an appropriate manner. 
Autonomy 
The students are able to retrieve necessary informations from the given literature and to combine them with the topics of the lecture. Throughout the lecture they can check their abilities and knowledge on the basis of given exercises and test questions providing an aid to optimize their learning process. 
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Credit points  6 
Course achievement  None 
Examination  Written exam 
Examination duration and scale  90 min 
Assignment for the Following Curricula 
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Modeling and Simulation: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory 
Course L0120: Efficient Algorithms 
Typ  Lecture 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Siegfried Rump 
Language  DE 
Cycle  WiSe 
Content 
 Linear Programming  Data structures  Leftist heaps  Minimum spanning tree  Shortest path  Maximum flow  NPhard problems via maxcut 
Literature 
R. E. Tarjan: Data Structures and Network Algorithms. CBMS 44, Society for Industrial and Applied Mathematics, Philadelphia, PA, 1983. Wesley, 2011 http://algs4.cs.princeton.edu/home/ V. Chvátal, ``Linear Programming'', Freeman, New York, 1983. 
Course L1207: Efficient Algorithms 
Typ  Recitation Section (small) 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Siegfried Rump 
Language  DE 
Cycle  WiSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M1271: Technical Complementary Course II for CSMS (according to Subject Specific Regulations) 

Courses  

Module Responsible  Prof. KarlHeinz Zimmermann 
Admission Requirements  None 
Recommended Previous Knowledge  None 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
Die Studierenden können die wesentlichen Inhalte des technischen Faches im Rahmen eines Vortrages oder einer Diskussion wiedergeben. 
Skills 
The students acquire professional competence in a technical subject available at TUHH. 
Personal Competence  
Social Competence  
Autonomy  
Workload in Hours  Depends on choice of courses 
Credit points  6 
Assignment for the Following Curricula 
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computer Science: Specialisation Intelligence Engineering: Elective Compulsory 
Module M1318: Wireless Sensor Networks 

Courses  

Module Responsible  Prof. BerndChristian Renner 
Admission Requirements  None 
Recommended Previous Knowledge  
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge  
Skills  
Personal Competence  
Social Competence  
Autonomy  
Workload in Hours  Independent Study Time 110, Study Time in Lecture 70 
Credit points  6 
Course achievement  None 
Examination  Oral exam 
Examination duration and scale  30 min 
Assignment for the Following Curricula 
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory 
Course L1815: Wireless Sensor Networks 
Typ  Lecture 
Hrs/wk  2 
CP  2 
Workload in Hours  Independent Study Time 32, Study Time in Lecture 28 
Lecturer  Prof. BerndChristian Renner 
Language  EN 
Cycle  SoSe 
Content  
Literature 
Course L1816: Wireless Sensor Networks 
Typ  Recitation Section (small) 
Hrs/wk  1 
CP  1 
Workload in Hours  Independent Study Time 16, Study Time in Lecture 14 
Lecturer  Prof. BerndChristian Renner 
Language  EN 
Cycle  SoSe 
Content  See interlocking course 
Literature  See interlocking course 
Course L1819: Wireless Sensor Networks: Project 
Typ  Project/problembased Learning 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. BerndChristian Renner 
Language  EN 
Cycle  SoSe 
Content 
The PrBL course part will be performed in small groups of students. Topics are from the field of wireless sensor networks and are loosely related to the lecture contents. Project descriptions and goals are provided but have to be solved by the students as follow:
Throughout the semester, there will be meetings with the supervisor on a regular basis (weekly or biweekly). Details about the topics and course organization will be provided in the first lecture. Please note that the number of participants is limited due to the available capacity (rooms, equipment, supervisors). 
Literature 
Will be provided individually 
Module M0556: Computer Graphics 

Courses  

Module Responsible  Prof. Tobias Knopp 
Admission Requirements  None 
Recommended Previous Knowledge 
Students are expected to have a solid knowledge of objectoriented programming as well as of linear algebra and geometry. 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
Students have acquired a theoretical basis in computer graphics and have a clear understanding of the process of computer animation. 
Skills 
Students have acquired

Personal Competence  
Social Competence 
Students are trained in communicating abstract ideas and are familiar with planning and conducting projects within a small team. 
Autonomy 
Students are able to direct complex computer animation projects. 
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 
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory 
Course L0145: Computer Graphics 
Typ  Lecture 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Tobias Knopp 
Language  EN 
Cycle  SoSe 
Content 
Computer graphics and animation are leading to an unprecedented visual revolution. The course deals with its technological foundations:
Students will be be working on a series of miniprojects which will eventually evolve into a final project. Learning computer graphics and animation resembles learning a musical instrument. Therefore, doing your projects well and in time is essential for performing well on this course. 
Literature 
Alan H. Watt: 3D Computer Graphics. Harlow: Pearson (3rd ed., repr., 2009). Dariush Derakhshani: Introducing Autodesk Maya 2014. New York, NY : Wiley (2013). 
Course L0768: Computer Graphics 
Typ  Recitation Section (small) 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Tobias Knopp 
Language  EN 
Cycle  SoSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M1248: Compilers for Embedded Systems 

Courses  

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 applicationspecific 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
The high demands on compilers for embedded systems make effective code optimizations mandatory. The students learn in particular,
Since compilers for embedded systems often have to optimize for multiple objectives (e.g., average or worstcase 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 highlevel 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 Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Mechatronics: Specialisation System Design: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory 
Course L1692: Compilers for Embedded Systems 
Typ  Lecture 
Hrs/wk  3 
CP  4 
Workload in Hours  Independent Study Time 78, Study Time in Lecture 42 
Lecturer  Prof. Heiko Falk 
Language  DE/EN 
Cycle  SoSe 
Content 

Literature 

Course L1693: Compilers for Embedded Systems 
Typ  Project/problembased 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 M0837: Simulation of Communication Networks 

Courses  

Module Responsible  Prof. Andreas TimmGiel 
Admission Requirements  None 
Recommended Previous Knowledge 

Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
Students are able to explain the necessary stochastics, the discrete event simulation technology and modelling of networks for performance evaluation. 
Skills 
Students are able to apply the method of simulation for performance evaluation to different, also not practiced, problems of communication networks. The students can analyse the obtained results and explain the effects observed in the network. They are able to question their own results. 
Personal Competence  
Social Competence 
Students are able to acquire expert knowledge in groups, present the results, and discuss solution approaches and results. They are able to work out solutions for new problems in small teams. 
Autonomy 
Students are able to transfer independently and in discussion with others the acquired method and expert knowledge to new problems. They can identify missing knowledge and acquire this knowledge independently. 
Workload in Hours  Independent Study Time 110, Study Time in Lecture 70 
Credit points  6 
Course achievement  None 
Examination  Oral exam 
Examination duration and scale  30 min 
Assignment for the Following Curricula 
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Aircraft Systems Engineering: Specialisation Avionic and Embedded Systems: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory 
Course L0887: Simulation of Communication Networks 
Typ  Project/problembased Learning 
Hrs/wk  5 
CP  6 
Workload in Hours  Independent Study Time 110, Study Time in Lecture 70 
Lecturer  Prof. Andreas TimmGiel 
Language  EN 
Cycle  SoSe 
Content 
In the course necessary basic stochastics and the discrete event simulation are introduced. Also simulation models for communication networks, for example, traffic models, mobility models and radio channel models are presented in the lecture. Students work with a simulation tool, where they can directly try out the acquired skills, algorithms and models. At the end of the course increasingly complex networks and protocols are considered and their performance is determined by simulation. 
Literature 
Further literature is announced at the beginning of the lecture. 
Module M0924: Software for Embedded Systems 

Courses  

Module Responsible  Prof. Volker Turau 
Admission Requirements  None 
Recommended Previous Knowledge 

Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge  Students know the basic principles and procedures of software engineering for embedded systems. They are able to describe the usage and pros of event based programming using interrupts. They know the components and functions of a concrete microcontroller. The participants explain requirements of real time systems. They know at least three scheduling algorithms for real time operating systems including their pros and cons. 
Skills  Students build interruptbased programs for a concrete microcontroller. They build and use a preemptive scheduler. They use peripheral components (timer, ADC, EEPROM) to realize complex tasks for embedded systems. To interface with external components they utilize serial protocols. 
Personal Competence  
Social Competence  
Autonomy  
Workload in Hours  Independent Study Time 110, Study Time in Lecture 70 
Credit points  6 
Course achievement  None 
Examination  Written exam 
Examination duration and scale  90 min 
Assignment for the Following Curricula 
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Mechatronics: Specialisation System Design: Elective Compulsory 
Course L1069: Software for Embdedded Systems 
Typ  Lecture 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Volker Turau 
Language  DE/EN 
Cycle  SoSe 
Content 

Literature 

Course L1070: Software for Embdedded Systems 
Typ  Recitation Section (small) 
Hrs/wk  3 
CP  3 
Workload in Hours  Independent Study Time 48, Study Time in Lecture 42 
Lecturer  Prof. Volker Turau 
Language  DE/EN 
Cycle  SoSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M1301: Software Testing 

Courses  

Module Responsible  Prof. Sibylle Schupp 
Admission Requirements  None 
Recommended Previous Knowledge 

Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
Students explain the different phases of testing, describe fundamental techniques of different types of testing, and paraphrase the basic principles of the corresponding test process. They give examples of software development scenarios and the corresponding test type and technique. They explain algorithms used for particular testing techniques and describe possible advantages and limitations. 
Skills 
Students identify the appropriate testing type and technique for a given problem. They adapt and execute respective algorithms to execute a concrete test technique properly. They interpret testing results and execute corresponding steps for proper retest scenarios. They write and analyze test specifications. They apply bug finding techniques for nontrivial problems. 
Personal Competence  
Social Competence 
Students discuss relevant topics in class. They defend their solutions orally. 
Autonomy 
Students can assess their level of knowledge continuously and adjust it appropriately, based on feedback and on selfguided studies. Within limits, they can set their own learning goals. Upon successful completion, students can identify and precisely formulate new problems in academic or applied research in the field of software testing. Within this field, they can conduct independent studies to acquire the necessary competencies and compile their findings in academic reports. They can devise plans to arrive at new solutions or assess existing ones 
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Credit points  6 
Course achievement  None 
Examination  Subject theoretical and practical work 
Examination duration and scale  Software 
Assignment for the Following Curricula 
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory 
Course L1791: Software Testing 
Typ  Lecture 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Sibylle Schupp 
Language  EN 
Cycle  SoSe 
Content 

Literature 

Course L1792: Software Testing 
Typ  Project/problembased Learning 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Sibylle Schupp 
Language  EN 
Cycle  SoSe 
Content 

Literature 

Module M0711: Numerical Mathematics II 

Courses  

Module Responsible  Prof. Sabine Le Borne 
Admission Requirements  None 
Recommended Previous Knowledge 

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

Skills 
Students are able to

Personal Competence  
Social Competence 
Students are able to

Autonomy 
Students are capable

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 Intelligence Engineering: Elective Compulsory Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation III. Mathematics: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory 
Course L0568: Numerical Mathematics II 
Typ  Lecture 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Sabine Le Borne, Dr. JensPeter Zemke 
Language  DE/EN 
Cycle  SoSe 
Content 

Literature 

Course L0569: Numerical Mathematics II 
Typ  Recitation Section (small) 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Sabine Le Borne, Dr. JensPeter Zemke 
Language  DE/EN 
Cycle  SoSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M1397: Model Checking  Proof Engines and Algorithms 

Courses  

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 
Students know

Skills 
Students can

Personal Competence  
Social Competence 
Students

Autonomy  Using accompanying material students independently learn indepth 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  None 
Examination  Oral exam 
Examination duration and scale  30 min 
Assignment for the Following Curricula 
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory 
Course L1979: Model Checking  Proof Engines and Algorithms 
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 
Correctness is a major concern in embedded systems. Model checking can fully automatically proof formal properties about digital hardware or software. Such properties are given in temporal logic, e.g., to prove "No two orthogonal traffic lights will ever be green." And how do the underlying reasoning algorithms work so effectively in practice despite a computational complexity of NP hardness and beyond?
But what are the limitations of model checking? Among other topics, the lecture will consider the following topics:

Literature 
Edmund M. Clarke, Jr., Orna Grumberg, and Doron A. Peled. 1999. Model Checking. MIT Press, Cambridge, MA, USA. A. Biere, A. Biere, M. Heule, H. van Maaren, and T. Walsh. 2009. Handbook of Satisfiability: Volume 185 Frontiers in Artificial Intelligence and Applications. IOS Press, Amsterdam, The Netherlands, The Netherlands. Selected research papers 
Course L1980: Model Checking  Proof Engines and Algorithms 
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 M1405: Randomised Algorithms and Random Graphs 

Courses  

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

Skills 

Personal Competence  
Social Competence 

Autonomy 

Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Credit points  6 
Course achievement  None 
Examination  Oral exam 
Examination duration and scale  30 min 
Assignment for the Following Curricula 
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation III. Mathematics: Elective Compulsory Mathematical Modelling in Engineering: Theory, Numerics, Applications: Specialisation l. Numerics (TUHH): Elective Compulsory 
Course L2010: Randomised Algorithms and Random Graphs 
Typ  Lecture 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Anusch Taraz, Prof. Volker Turau 
Language  DE/EN 
Cycle  SoSe 
Content 
Randomized Algorithms:

Literature 

Course L2011: Randomised Algorithms and Random Graphs 
Typ  Recitation Section (large) 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Anusch Taraz, Prof. Volker Turau 
Language  DE/EN 
Cycle  SoSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M0758: Application Security 

Courses  

Module Responsible  Prof. Dieter Gollmann 
Admission Requirements  None 
Recommended Previous Knowledge  Familiarity with Information security, fundamentals of cryptography, Web protocols and the architecture of the Web 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
Students can name current approaches for securing selected applications, in particular of web applications 
Skills 
Students are capable of

Personal Competence  
Social Competence  Students are capable of appreciating the impact of security problems on those affected and of the potential responsibilities for their resolution. 
Autonomy  Students are capable of acquiring knowledge independently from professional publications, technical standards, and other sources, and are capable of applying newly acquired knowledge to new problems. 
Workload in Hours  Independent Study Time 110, Study Time in Lecture 70 
Credit points  6 
Course achievement  None 
Examination  Written exam 
Examination duration and scale  120 minutes 
Assignment for the Following Curricula 
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory 
Course L0726: Application Security 
Typ  Lecture 
Hrs/wk  3 
CP  3 
Workload in Hours  Independent Study Time 48, Study Time in Lecture 42 
Lecturer  Prof. Dieter Gollmann 
Language  EN 
Cycle  SoSe 
Content 

Literature 
Webseiten der OMG, W3C, OASIS, WSSecurity, OECD, TCG D. Gollmann: Computer Security, 3rd edition, Wiley (2011) R. Anderson: Security Engineering, 2nd edition, Wiley (2008) U. Lang: CORBA Security, Artech House, 2002 
Course L0729: Application Security 
Typ  Recitation Section (small) 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Dieter Gollmann 
Language  EN 
Cycle  SoSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M1400: Design of Dependable Systems 

Courses  

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.,
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


Autonomy  Using accompanying material students independently learn indepth 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 


Examination  Oral exam  
Examination duration and scale  30 min  
Assignment for the Following Curricula 
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation I. Computer Science: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory Mechatronics: Specialisation System Design: Elective Compulsory Microelectronics and Microsystems: Specialisation Embedded Systems: 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:
Contents The module introduces the basic concepts for the design and the analysis of dependable systems. Design examples for getting practical handsonexperience in dependable design techniques. The module focuses towards embedded systems. The following topics are covered:

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 M1337: Curves, Cryptosystems and Quantum Computing 

Courses  

Module Responsible  Prof. KarlHeinz Zimmermann 
Admission Requirements  None 
Recommended Previous Knowledge 
Higher algebra, linear algebra, and mathematical analysis. 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge  The students understand the basic theory of elliptic curves, classical cryptosysteme, basic methods of cryptanalysis, cryptography of elliptic curves, quantum computing and the postquantum computing scenario, algebraic codes over curves, and the famous theorem of RiemannRoch. 
Skills  The students are in the position to apply the group law of elliptic curves, to find out if a curve is nonsingular, to sketch cryptographic algorithms that make use of elliptic curves, to specify quantum algorithms, and to determine the parameters of algebraic codes defined over curves. 
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 new knowledge from specific standard books and to associate the acquired knowledge to 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  25 min 
Assignment for the Following Curricula 
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory 
Course L1870: Curves, Cryptosystems and Quantum Computing 
Typ  Lecture 
Hrs/wk  4 
CP  6 
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Lecturer  Prof. KarlHeinz Zimmermann 
Language  DE/EN 
Cycle  SoSe 
Content  
Literature 
Module M0839: Traffic Engineering 

Courses  

Module Responsible  Prof. Andreas TimmGiel 
Admission Requirements  None 
Recommended Previous Knowledge 

Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
Students are able to describe methods for planning, optimisation and performance evaluation of communication networks. 
Skills 
Students are able to solve typical planning and optimisation tasks for communication networks. Furthermore they are able to evaluate the network performance using queuing theory. Students are able to apply independently what they have learned to other and new problems. They can present their results in front of experts and discuss them. 
Personal Competence  
Social Competence  
Autonomy 
Students are able to acquire the necessary expert knowledge to understand the functionality and performance of new communication networks independently. 
Workload in Hours  Independent Study Time 110, Study Time in Lecture 70 
Credit points  6 
Course achievement  None 
Examination  Oral exam 
Examination duration and scale  30 min 
Assignment for the Following Curricula 
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory 
Course L0902: Seminar Traffic Engineering 
Typ  Seminar 
Hrs/wk  2 
CP  2 
Workload in Hours  Independent Study Time 32, Study Time in Lecture 28 
Lecturer  Prof. Andreas TimmGiel, Dr. Phuong Nga Tran 
Language  EN 
Cycle  WiSe 
Content  Selected applications of methods for planning, optimization, and performance evaluation of communication networks, which have been introduced in the traffic engineering lecture are prepared by the students and presented in a seminar. 
Literature 

Course L0900: Traffic Engineering 
Typ  Lecture 
Hrs/wk  2 
CP  2 
Workload in Hours  Independent Study Time 32, Study Time in Lecture 28 
Lecturer  Prof. Andreas TimmGiel, Dr. Phuong Nga Tran 
Language  EN 
Cycle  WiSe 
Content 
Network Planning and Optimization 
Literature 
Literatur: 
Course L0901: Traffic Engineering Exercises 
Typ  Recitation Section (small) 
Hrs/wk  1 
CP  2 
Workload in Hours  Independent Study Time 46, Study Time in Lecture 14 
Lecturer  Prof. Andreas TimmGiel 
Language  EN 
Cycle  WiSe 
Content 
Accompanying exercise for the traffic engineering course 
Literature 
Literatur: 
Module M0910: Advanced SystemonChip Design (Lab) 

Courses  

Module Responsible  Prof. Heiko Falk 
Admission Requirements  None 
Recommended Previous Knowledge 
Successful completion of the practical FPGA lab of module "Computer Architecture" is a mandatory prerequisite. 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
This module provides indepth, handson experience on advanced concepts of computer architecture. Using the Hardware Description Language VHDL and using reconfigurable FPGA hardware boards, students learn how to design complex computer systems (socalled systemsonchip, SoCs), that are commonly found in the domain of embedded systems, in actual hardware. Starting with a simple processor architecture, the students learn to how realize instructionprocessing of a computer processor according to the principle of pipelining. They implement different styles of cachebased memory hierarchies, examine strategies for dynamic scheduling of machine instructions and for branch prediction, and finally construct a complex MPSoC system (multiprocessor systemonchip) that consists of multiple processor cores that are connected via a shared bus. 
Skills 
Students will be able to analyze, how highly specific and individual computer systems can be constructed using a library of given standard components. They evaluate the interferences between the physical structure of a computer system and the software executed thereon. This way, they will be enabled to estimate the effects of design decision at the hardware level on the performance of the entire system, to evaluate the whole and complex system and to propose design options to improve a system. 
Personal Competence  
Social Competence 
Students are able to solve similar problems alone or in a group and to present the results accordingly. 
Autonomy 
Students are able to acquire new knowledge from specific literature, to transform this knowledge into actual implementations of complex hardware structures, and to associate this knowledge with contents of other classes. 
Workload in Hours  Independent Study Time 138, Study Time in Lecture 42 
Credit points  6 
Course achievement  None 
Examination  Subject theoretical and practical work 
Examination duration and scale  VHDL Codes and FPGAbased implementations 
Assignment for the Following Curricula 
Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory 
Course L1061: Advanced SystemonChip Design 
Typ  Project/problembased Learning 
Hrs/wk  3 
CP  6 
Workload in Hours  Independent Study Time 138, Study Time in Lecture 42 
Lecturer  Prof. Heiko Falk 
Language  DE/EN 
Cycle  WiSe 
Content 

Literature 

Specialization Intelligence Engineering
Module M1270: Technical Complementary Course I for CSMS (according to Subject Specific Regulations) 

Courses  

Module Responsible  Prof. KarlHeinz Zimmermann 
Admission Requirements  None 
Recommended Previous Knowledge  None 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
The students acquire advanced knowledge in a technical subject available at TUHH. 
Skills 
The students acquire professional competence in a technical subject available at TUHH. 
Personal Competence  
Social Competence  
Autonomy  
Workload in Hours  Depends on choice of courses 
Credit points  6 
Assignment for the Following Curricula 
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory 
Module M0550: Digital Image Analysis 

Courses  

Module Responsible  Prof. RolfRainer Grigat 
Admission Requirements  None 
Recommended Previous Knowledge 
System theory of onedimensional signals (convolution and correlation, sampling theory, interpolation and decimation, Fourier transform, linear timeinvariant systems), linear algebra (Eigenvalue decomposition, SVD), basic stochastics and statistics (expectation values, influence of sample size, correlation and covariance, normal distribution and its parameters), basics of Matlab, basics in optics 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
Students can

Skills 
Students are able to
Students can solve simple arithmetical problems relating to the specification and design of image processing and image analysis systems. Students are able to assess different solution approaches in multidimensional decisionmaking areas. Students can undertake a prototypical analysis of processes in Matlab. 
Personal Competence  
Social Competence 
k.A. 
Autonomy 
Students can solve image analysis tasks independently using the relevant literature. 
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Credit points  6 
Course achievement  None 
Examination  Written exam 
Examination duration and scale  60 Minutes, Content of Lecture and materials in StudIP 
Assignment for the Following Curricula 
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory 
Course L0126: Digital Image Analysis 
Typ  Lecture 
Hrs/wk  4 
CP  6 
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Lecturer  Prof. RolfRainer Grigat 
Language  EN 
Cycle  WiSe 
Content 

Literature 
Bredies/Lorenz, Mathematische Bildverarbeitung, Vieweg, 2011 
Module M0677: Digital Signal Processing and Digital Filters 

Courses  

Module Responsible  Prof. Gerhard Bauch 
Admission Requirements  None 
Recommended Previous Knowledge 

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 discretetime signals and are able to describe and analyse signals and systems in time and image domain. They know basic structures of digital filters and can identify and assess important properties including stability. They are aware of the effects caused by quantization of filter coefficients and signals. They are familiar with the basics of adaptive filters. They can perform traditional and parametric methods of spectrum estimation, also taking a limited observation window into account. 
Skills  The students are able to apply methods of digital signal processing to new problems. They can choose and parameterize suitable filter striuctures. In particular, the can design adaptive filters according to the minimum mean squared error (MMSE) criterion and develop an efficient implementation, e.g. based on the LMS or RLS algorithm. Furthermore, the students are able to apply methods of spectrum estimation and to take the effects of a limited observation window into account. 
Personal Competence  
Social Competence 
The students can jointly solve specific problems. 
Autonomy 
The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system. 
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Credit points  6 
Course achievement  None 
Examination  Written exam 
Examination duration and scale  90 min 
Assignment for the Following Curricula 
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and 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 Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: 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 

Literature 
K.D. Kammeyer, K. Kroschel: Digitale Signalverarbeitung. Vieweg Teubner. V. Oppenheim, R. W. Schafer, J. R. Buck: Zeitdiskrete Signalverarbeitung. Pearson StudiumA. V. W. Hess: Digitale Filter. Teubner. Oppenheim, R. W. Schafer: Digital signal processing. Prentice Hall. S. Haykin: Adaptive flter theory. L. B. Jackson: Digital filters and signal processing. Kluwer. T.W. Parks, C.S. Burrus: Digital filter design. Wiley. 
Course L0447: Digital Signal Processing and Digital Filters 
Typ  Recitation Section (large) 
Hrs/wk  1 
CP  2 
Workload in Hours  Independent Study Time 46, Study Time in Lecture 14 
Lecturer  Prof. Gerhard Bauch 
Language  EN 
Cycle  WiSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M0563: Robotics 

Courses  

Module Responsible  Prof. Uwe Weltin 
Admission Requirements  None 
Recommended Previous Knowledge 
Fundamentals of electrical engineering Broad knowledge of mechanics Fundamentals of control theory 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge  Students are able to describe fundamental properties of robots and solution approaches for multiple problems in robotics. 
Skills 
Students are able to derive and solve equations of motion for various manipulators. Students can generate trajectories in various coordinate systems. Students can design linear and partially nonlinear controllers for robotic manipulators. 
Personal Competence  
Social Competence  Students are able to work goaloriented in small mixed groups. 
Autonomy 
Students are able to recognize and improve knowledge deficits independently. With instructor assistance, students are able to evaluate their own knowledge level and define a further course of study. 
Workload in Hours  Independent Study Time 110, Study Time in Lecture 70 
Credit points  6 
Course achievement  None 
Examination  Written exam 
Examination duration and scale  120 min 
Assignment for the Following Curricula 
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Aircraft Systems Engineering: Specialisation Aircraft Systems: Elective Compulsory International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory International Management and Engineering: Specialisation II. Product Development and Production: Elective Compulsory Mechanical Engineering and Management: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Product Development, Materials and Production: Specialisation Product Development: Elective Compulsory Product Development, Materials and Production: Specialisation Production: Elective Compulsory Product Development, Materials and Production: Specialisation Materials: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Product Development and Production: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory 
Course L0168: Robotics: Modelling and Control 
Typ  Lecture 
Hrs/wk  3 
CP  3 
Workload in Hours  Independent Study Time 48, Study Time in Lecture 42 
Lecturer  Prof. Uwe Weltin 
Language  EN 
Cycle  WiSe 
Content 
Fundamental kinematics of rigid body systems NewtonEuler 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 0201543613 
Course L1305: Robotics: Modelling and Control 
Typ  Recitation Section (small) 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Uwe Weltin 
Language  EN 
Cycle  WiSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M0633: Industrial Process Automation 

Courses  

Module Responsible  Prof. Alexander Schlaefer  
Admission Requirements  None  
Recommended Previous Knowledge 
mathematics and optimization methods 

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 work in teams to solve problems. 

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

Workload in Hours  Independent Study Time 124, Study Time in Lecture 56  
Credit points  6  
Course achievement 


Examination  Written exam  
Examination duration and scale  90 minutes  
Assignment for the Following Curricula 
Bioprocess Engineering: Specialisation A  General Bioprocess Engineering: Elective Compulsory 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 Chemical and Bioprocess Engineering: Specialisation Chemical Process Engineering: Elective Compulsory Chemical and Bioprocess Engineering: Specialisation General Process Engineering: Elective Compulsory Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory Aircraft Systems Engineering: Specialisation Cabin Systems: Elective Compulsory Aircraft Systems Engineering: Specialisation Cabin Systems: Elective Compulsory International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Process Engineering: Specialisation Chemical Process Engineering: Elective Compulsory Process Engineering: Specialisation Process Engineering: Elective Compulsory 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 
Literature 
J. Lunze: „Automatisierungstechnik“, Oldenbourg Verlag, 2012 
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 M0549: Scientific Computing and Accuracy 

Courses  

Module Responsible  Prof. Siegfried Rump 
Admission Requirements  None 
Recommended Previous Knowledge 
Basic knowledge in numerics 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
The students have deeper knowledge of numerical and seminumerical methods with the goal to compute principally exact and accurate error bounds. For several fundamental problems they know algorithms with the verification of the correctness of the computed result. 
Skills 
The students can devise algorithms for several basic problems which compute rigorous error bounds for the solution and analyze the sensitivity with respect to variation of the input data as well. 
Personal Competence  
Social Competence 
The students have the skills to solve problems together in small groups and to present the achieved results in an appropriate manner. 
Autonomy 
The students are able to retrieve necessary informations from the given literature and to combine them with the topics of the lecture. Throughout the lecture they can check their abilities and knowledge on the basis of given exercises and test questions providing an aid to optimize their learning process. 
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Credit points  6 
Course achievement  None 
Examination  Oral exam 
Examination duration and scale  30 min 
Assignment for the Following Curricula 
Bioprocess Engineering: Specialisation A  General Bioprocess Engineering: Elective Compulsory Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Process Engineering: Specialisation Process Engineering: Elective Compulsory Process Engineering: Specialisation Chemical Process Engineering: Elective Compulsory 
Course L0122: Verification Methods 
Typ  Lecture 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Siegfried Rump 
Language  DE 
Cycle  WiSe 
Content 

Literature 
Neumaier: Interval Methods for Systems of Equations. In: Encyclopedia of Mathematics and its Applications. Cambridge University Press, 1990 S.M. Rump. Verification methods: Rigorous results using floatingpoint arithmetic. Acta Numerica, 19:287449, 2010. 
Course L1208: Verification Methods 
Typ  Recitation Section (small) 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Siegfried Rump 
Language  DE 
Cycle  WiSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M0623: Intelligent Systems in Medicine 

Courses  

Module Responsible  Prof. Alexander Schlaefer  
Admission Requirements  None  
Recommended Previous Knowledge 


Educational Objectives  After taking part successfully, students have reached the following learning results  
Professional Competence  
Knowledge 
The students are able to analyze and solve clinical treatment planning and decision support problems using methods for search, optimization, and planning. They are able to explain methods for classification and their respective advantages and disadvantages in clinical contexts. The students can compare different methods for representing medical knowledge. They can evaluate methods in the context of clinical data and explain challenges due to the clinical nature of the data and its acquisition and due to privacy and safety requirements. 

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

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

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

Workload in Hours  Independent Study Time 110, Study Time in Lecture 70  
Credit points  6  
Course achievement 


Examination  Written exam  
Examination duration and scale  90 minutes  
Assignment for the Following Curricula 
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective 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 
Literature 
Russel & Norvig: Artificial Intelligence: a Modern Approach, 2012 
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 M0676: Digital Communications 

Courses  

Module Responsible  Prof. Gerhard Bauch  
Admission Requirements  None  
Recommended Previous Knowledge 


Educational Objectives  After taking part successfully, students have reached the following learning results  
Professional Competence  
Knowledge  The students are able to understand, compare and design modern digital information transmission schemes. They are familiar with the properties of linear and nonlinear digital modulation methods. They can describe distortions caused by transmission channels and design and evaluate detectors including channel estimation and equalization. They know the principles of single carrier transmission and multicarrier transmission as well as the fundamentals of basic multiple access schemes.  
Skills  The students are able to design and analyse a digital information transmission scheme including multiple access. They are able to choose a digital modulation scheme taking into account transmission rate, required bandwidth, error probability, and further signal properties. They can design an appropriate detector including channel estimation and equalization taking into account performance and complexity properties of suboptimum solutions. They are able to set parameters of a single carrier or multi carrier transmission scheme and trade the properties of both approaches against each other.  
Personal Competence  
Social Competence 
The students can jointly solve specific problems. 

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

Workload in Hours  Independent Study Time 124, Study Time in Lecture 56  
Credit points  6  
Course achievement 


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

Literature 
K. Kammeyer: Nachrichtenübertragung, Teubner P.A. Höher: Grundlagen der digitalen Informationsübertragung, Teubner. J.G. Proakis, M. Salehi: Digital Communications. McGrawHill. S. Haykin: Communication Systems. Wiley R.G. Gallager: Principles of Digital Communication. Cambridge A. Goldsmith: Wireless Communication. Cambridge. D. Tse, P. Viswanath: Fundamentals of Wireless Communication. Cambridge. 
Course L0445: Digital Communications 
Typ  Recitation Section (large) 
Hrs/wk  1 
CP  2 
Workload in Hours  Independent Study Time 46, Study Time in Lecture 14 
Lecturer  Prof. Gerhard Bauch 
Language  DE/EN 
Cycle  WiSe 
Content  See interlocking course 
Literature  See interlocking course 
Course L0646: Laboratory Digital Communications 
Typ  Practical Course 
Hrs/wk  1 
CP  1 
Workload in Hours  Independent Study Time 16, Study Time in Lecture 14 
Lecturer  Prof. Gerhard Bauch 
Language  DE/EN 
Cycle  WiSe 
Content 
 DSL transmission  Random processes  Digital data transmission 
Literature 
K. Kammeyer: Nachrichtenübertragung, Teubner P.A. Höher: Grundlagen der digitalen Informationsübertragung, Teubner. J.G. Proakis, M. Salehi: Digital Communications. McGrawHill. S. Haykin: Communication Systems. Wiley R.G. Gallager: Principles of Digital Communication. Cambridge A. Goldsmith: Wireless Communication. Cambridge. D. Tse, P. Viswanath: Fundamentals of Wireless Communication. Cambridge. 
Module M0846: Control Systems Theory and Design 

Courses  

Module Responsible  Prof. Herbert Werner 
Admission Requirements  None 
Recommended Previous Knowledge  Introduction to Control Systems 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 

Skills 

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 online 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 
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Core Qualification: Compulsory Energy Systems: Core Qualification: Elective Compulsory Aircraft Systems Engineering: Specialisation Aircraft Systems: Compulsory Aircraft Systems Engineering: Specialisation Avionic and Embedded Systems: Elective Compulsory Computational Science and Engineering: Specialisation II. Engineering Science: Elective Compulsory International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory Mechatronics: Core Qualification: Compulsory Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory Biomedical Engineering: Specialisation Medical Technology and Control Theory: Compulsory Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory Product Development, Materials and Production: Core Qualification: Elective Compulsory Theoretical Mechanical Engineering: Core Qualification: Compulsory 
Course L0656: Control Systems Theory and Design 
Typ  Lecture 
Hrs/wk  2 
CP  4 
Workload in Hours  Independent Study Time 92, Study Time in Lecture 28 
Lecturer  Prof. Herbert Werner 
Language  EN 
Cycle  WiSe 
Content 
State space methods (singleinput singleoutput) • State space models and transfer functions, state feedback Digital Control System identification and model order reduction Case study 
Literature 

Course L0657: Control Systems Theory and Design 
Typ  Recitation Section (small) 
Hrs/wk  2 
CP  2 
Workload in Hours  Independent Study Time 32, Study Time in Lecture 28 
Lecturer  Prof. Herbert Werner 
Language  EN 
Cycle  WiSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M0881: Mathematical Image Processing 

Courses  

Module Responsible  Prof. Marko Lindner 
Admission Requirements  None 
Recommended Previous Knowledge 

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

Skills 
Students are able to

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 

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 Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Modeling and Simulation: Elective Compulsory Computational Science and Engineering: Specialisation III. Mathematics: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Process Engineering: Specialisation Process Engineering: Elective Compulsory 
Course L0991: Mathematical Image Processing 
Typ  Lecture 
Hrs/wk  3 
CP  4 
Workload in Hours  Independent Study Time 78, Study Time in Lecture 42 
Lecturer  Prof. Marko Lindner, Dr. Christian Seifert 
Language  DE/EN 
Cycle  WiSe 
Content 

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  

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 realworld 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 multiagent 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 multiagent situations students will apply techniques for finding different equilibria states,e.g., Nash equilibria. For multiagent 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 Intelligence Engineering: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics 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 

Literature 

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 M1336: Soft Computing  Introduction to Machine Learning 

Courses  

Module Responsible  Prof. KarlHeinz Zimmermann 
Admission Requirements  None 
Recommended Previous Knowledge 
Bachelor in Computer Science. Basics in higher mathematics are inevitable, like calculus, linear algebra, graph theory, and optimization. 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
Students are able to formalize, compute, and analyze belief networks, alignments of sequences, hidden Markov models, phylogenetic tree models, neural networks, and fuzzy controllers. In particular, inference and learning in belief networks are important topics that the students should be able to master. 
Skills  Students can apply the relevant algorithms and determine their complexity, and they can make use of the statistics language R. 
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 new knowledge from newer literature and to associate the acquired knowledge to other fields. 
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 Intelligence Engineering: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory 
Course L1869: Soft Computing 
Typ  Lecture 
Hrs/wk  4 
CP  6 
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Lecturer  Prof. KarlHeinz Zimmermann, Dr. Mehwish Saleemi 
Language  DE/EN 
Cycle  WiSe 
Content 
Students are able to formalize, compute, and analyze belief
networks, alignments of sequences, hidden Markov models, phylogenetic
tree models, neural networks, and fuzzy controllers. In particular,
inference and learning in belief networks are important
topics that the students should be able to master. Students can apply the relevant algorithms and determine their complexity, and they can make use of the statistics language R. 
Literature 
1. David Barber, Bayes Reasoning and Machine Learning, Cambridge Univ. Press, Cambridge, 2012. 
Module M1271: Technical Complementary Course II for CSMS (according to Subject Specific Regulations) 

Courses  

Module Responsible  Prof. KarlHeinz Zimmermann 
Admission Requirements  None 
Recommended Previous Knowledge  None 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
Die Studierenden können die wesentlichen Inhalte des technischen Faches im Rahmen eines Vortrages oder einer Diskussion wiedergeben. 
Skills 
The students acquire professional competence in a technical subject available at TUHH. 
Personal Competence  
Social Competence  
Autonomy  
Workload in Hours  Depends on choice of courses 
Credit points  6 
Assignment for the Following Curricula 
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computer Science: Specialisation Intelligence Engineering: Elective Compulsory 
Module M1302: Applied Humanoid Robotics 

Courses  

Module Responsible  Patrick Göttsch 
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 96, Study Time in Lecture 84 
Credit points  6 
Course achievement  None 
Examination  Written elaboration 
Examination duration and scale  510 pages 
Assignment for the Following Curricula 
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Bio and Medical Technology: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory 
Course L1794: Applied Humanoid Robotics 
Typ  Project/problembased 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/ 
Content 

Literature 

Module M0551: Pattern Recognition and Data Compression 

Courses  

Module Responsible  Prof. RolfRainer Grigat 
Admission Requirements  None 
Recommended Previous Knowledge 
Linear algebra (including PCA, unitary transforms), stochastics and statistics, binary arithmetics 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
Students can name the basic concepts of pattern recognition and data compression. Students are able to discuss logical connections between the concepts covered in the course and to explain them by means of examples. 
Skills 
Students can apply statistical methods to classification problems in pattern recognition and to prediction in data compression. On a sound theoretical and methodical basis they can analyze characteristic value assignments and classifications and describe data compression and video signal coding. They are able to use highly sophisticated methods and processes of the subject area. Students are capable of assessing different solution approaches in multidimensional decisionmaking areas. 
Personal Competence  
Social Competence 
k.A. 
Autonomy 
Students are capable of identifying problems independently and of solving them scientifically, using the methods they have learnt. 
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Credit points  6 
Course achievement  None 
Examination  Written exam 
Examination duration and scale  60 Minutes, Content of Lecture and materials in StudIP 
Assignment for the Following Curricula 
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory 
Course L0128: Pattern Recognition and Data Compression 
Typ  Lecture 
Hrs/wk  4 
CP  6 
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Lecturer  Prof. RolfRainer Grigat 
Language  EN 
Cycle  SoSe 
Content 
Structure of a pattern recognition system, statistical decision theory, classification based on statistical models, polynomial regression, dimension reduction, multilayer perceptron regression, radial basis functions, support vector machines, unsupervised learning and clustering, algorithmindependent machine learning, mixture models and EM, adaptive basis function models and boosting, Markov random fields Information, entropy, redundancy, mutual information, Markov processes, basic coding schemes (code length, run length coding, prefixfree codes), entropy coding (Huffman, arithmetic coding), dictionary coding (LZ77/Deflate/LZMA2, LZ78/LZW), prediction, DPCM, CALIC, quantization (scalar and vector quantization), transform coding, prediction, decorrelation (DPCM, DCT, hybrid DCT, JPEG, JPEGLS), motion estimation, subband coding, wavelets, HEVC (H.265,MPEGH) 
Literature 
Schürmann: Pattern Classification, Wiley 1996 Salomon, Data Compression, the Complete Reference, Springer, 2000 
Module M0630: Robotics and Navigation in Medicine 

Courses  

Module Responsible  Prof. Alexander Schlaefer  
Admission Requirements  None  
Recommended Previous Knowledge 


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

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

Workload in Hours  Independent Study Time 110, Study Time in Lecture 70  
Credit points  6  
Course achievement 


Examination  Written exam  
Examination duration and scale  90 minutes  
Assignment for the Following Curricula 
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory Product Development, Materials and Production: Specialisation Product Development: Elective Compulsory Product Development, Materials and Production: Specialisation Production: Elective Compulsory Product Development, Materials and Production: Specialisation Materials: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Bio and Medical Technology: Elective Compulsory 
Course L0335: Robotics and Navigation in Medicine 
Typ  Lecture 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Alexander Schlaefer 
Language  EN 
Cycle  SoSe 
Content 
 kinematics 
Literature 
Spong et al.: Robot Modeling and Control, 2005 
Course L0338: Robotics and Navigation in Medicine 
Typ  Project Seminar 
Hrs/wk  2 
CP  2 
Workload in Hours  Independent Study Time 32, Study Time in Lecture 28 
Lecturer  Prof. Alexander Schlaefer 
Language  EN 
Cycle  SoSe 
Content  See interlocking course 
Literature  See interlocking course 
Course L0336: Robotics and Navigation in Medicine 
Typ  Recitation Section (small) 
Hrs/wk  1 
CP  1 
Workload in Hours  Independent Study Time 16, Study Time in Lecture 14 
Lecturer  Prof. Alexander Schlaefer 
Language  EN 
Cycle  SoSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M0673: Information Theory and Coding 

Courses  

Module Responsible  Prof. Gerhard Bauch 
Admission Requirements  None 
Recommended Previous Knowledge 

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 errorfree data transmission over noisy channels. They understand the principles of source coding as well as errordetecting and errorcorrecting channel coding. They are familiar with the principles of decoding, in particular with modern methods of iterative decoding. They know fundamental coding schemes, their properties and decoding algorithms. 
Skills  The students are able to determine the limits of data compression as well as of data transmission through noisy channels and based on those limits to design basic parameters of a transmission scheme. They can estimate the parameters of an errordetecting or errorcorrecting channel coding scheme for achieving certain performance targets. They are able to compare the properties of basic channel coding and decoding schemes regarding error correction capabilities, decoding delay, decoding complexity and to decide for a suitable method. They are capable of implementing basic coding and decoding schemes in software. 
Personal Competence  
Social Competence 
The students can jointly solve specific problems. 
Autonomy 
The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system. 
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Credit points  6 
Course achievement  None 
Examination  Written exam 
Examination duration and scale  90 min 
Assignment for the Following Curricula 
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and Engineering: Specialisation 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  DE/EN 
Cycle  SoSe 
Content 

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. WhileyVCH Cover, T., Thomas, J.: Elements of information theory. Wiley. 
Course L0438: Information Theory and Coding 
Typ  Recitation Section (large) 
Hrs/wk  1 
CP  2 
Workload in Hours  Independent Study Time 46, Study Time in Lecture 14 
Lecturer  Prof. Gerhard Bauch 
Language  DE/EN 
Cycle  SoSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M1310: Discrete Differential Geometry 

Courses  

Module Responsible  Prof. KarlHeinz Zimmermann 
Admission Requirements  None 
Recommended Previous Knowledge 
Linear Algebra, Multivariate Calculus 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
These lectures are on geometrical aspects of the solutions of differential equations and their treatment on the computer. The required basics from linear algebra and analysis are reviewed at the beginning. Applications are to curved surfaces in space, to mechanics and mechatronics, to different types of field equations, and to the tranfer of mathematical constructions to data types, compiler functions, programming languages, and special compute circuits.  basic prerequisites from linear algebra, tensors, exterior algebra, Clifford algebras  basic prerequisites from coordinatefree analysis, vector fields and differential forms, integration, discretization  local differential geometry: connections, symplectic geometry and Hamiltonian systems, Riemannian geometry, discretization  global differential geometry: manifolds, Lie groups, fiber bundles, random processes, space and time 
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  Oral exam 
Examination duration and scale  25 min 
Assignment for the Following Curricula 
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory 
Course L1808: Discrete Differential Geometry 
Typ  Lecture 
Hrs/wk  4 
CP  6 
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Lecturer  Prof. Georg Friedrich MayerLindenberg 
Language  DE/EN 
Cycle  SoSe 
Content 
These lectures deal with geometric aspects of differential equations and with their treatment on the computer. The prerequisites from linear algebra and analysis are reviewed at the beginning. Applications are to curved surfaces, to classical mechanics and mechatronics, to various field equations, to computer graphics and to transferring mathematical constructions to data types, compiler functions, programming languages, and special hardware. Keywords: Basics from linear algebra, tensors, exterior algebra, Clifford algebras, tuple types Basics of coordinatefree analysis, vector fields and differential forms, integration, discrete exterior calculus Local differential geometry: connections, symplectic geometry, Riemannian geometry, discrete mechanics and connections Global differential geometry: manifolds, Lie groups, fibre bundles, Fourier decompositions, random processes, space and time 
Literature 
Agricola, Friedrich, Vektoranalysis, Vieweg/Teubner 2010 A.C. Da Silva, Lectures on Symplectic Geometry, Springer L.N. Math. 1764 J. Snygg, Differential Geometry using Clifford's Algebra, Birkhäuser 2010 T. Frankel, The Geometry of Physics, Cambridge U. P. 2012 M.Desbrun et al., Discrete exterior calculus, arXiv:math/0508341v2 J.Marsden et al., Discrete Mechanics and Variational Integrators, Acta numerica. 2001 
Module M0711: Numerical Mathematics II 

Courses  

Module Responsible  Prof. Sabine Le Borne 
Admission Requirements  None 
Recommended Previous Knowledge 

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

Skills 
Students are able to

Personal Competence  
Social Competence 
Students are able to

Autonomy 
Students are capable

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 Intelligence Engineering: Elective Compulsory Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation III. Mathematics: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory 
Course L0568: Numerical Mathematics II 
Typ  Lecture 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Sabine Le Borne, Dr. JensPeter Zemke 
Language  DE/EN 
Cycle  SoSe 
Content 

Literature 

Course L0569: Numerical Mathematics II 
Typ  Recitation Section (small) 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Sabine Le Borne, Dr. JensPeter Zemke 
Language  DE/EN 
Cycle  SoSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M0840: Optimal and Robust Control 

Courses  

Module Responsible  Prof. Herbert Werner 
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  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 
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory Energy Systems: Core Qualification: Elective Compulsory Aircraft Systems Engineering: Specialisation Aircraft Systems: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Mechatronics: Specialisation System Design: Elective Compulsory Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory Product Development, Materials and Production: Specialisation Product Development: Elective Compulsory Product Development, Materials and Production: Specialisation Production: Elective Compulsory Product Development, Materials and Production: Specialisation Materials: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Core Qualification: Elective Compulsory 
Course L0658: Optimal and Robust Control 
Typ  Lecture 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Herbert Werner 
Language  EN 
Cycle  SoSe 
Content 

Literature 

Course L0659: Optimal and Robust Control 
Typ  Recitation Section (small) 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Herbert Werner 
Language  EN 
Cycle  SoSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M0627: Machine Learning and Data Mining 

Courses  

Module Responsible  NN 
Admission Requirements  None 
Recommended Previous Knowledge 

Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
Students can explain the difference between instancebased and modelbased 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 firstorder 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., kmeans 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 Intelligence Engineering: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory 
Course L0340: Machine Learning and Data Mining 
Typ  Lecture 
Hrs/wk  2 
CP  4 
Workload in Hours  Independent Study Time 92, Study Time in Lecture 28 
Lecturer  Rainer Marrone 
Language  EN 
Cycle  SoSe 
Content 

Literature 

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 M0832: Advanced Topics in Control 

Courses  

Module Responsible  Prof. Herbert Werner 
Admission Requirements  None 
Recommended Previous Knowledge  Hinfinity optimal control, mixedsensitivity design, linear matrix inequalities 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 

Skills 

Personal Competence  
Social Competence  Students can work in small groups and arrive at joint results. 
Autonomy 
Students are able to find required information in sources provided (lecture notes, literature, software documentation) and use it to solve given problems. 
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Credit points  6 
Course achievement  None 
Examination  Oral exam 
Examination duration and scale  30 min 
Assignment for the Following Curricula 
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory Aircraft Systems Engineering: Specialisation Aircraft Systems: Elective Compulsory Aircraft Systems Engineering: Specialisation Avionic Systems: Elective Compulsory International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory Mechatronics: Specialisation System Design: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory Biomedical Engineering: Specialisation 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: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Core Qualification: 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  Prof. Herbert Werner 
Language  EN 
Cycle  WiSe 
Content 

Literature 

Course L0662: Advanced Topics in Control 
Typ  Recitation Section (small) 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Herbert Werner 
Language  EN 
Cycle  WiSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M0552: 3D Computer Vision 

Courses  

Module Responsible  Prof. RolfRainer Grigat 
Admission Requirements  None 
Recommended Previous Knowledge 

Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
Students can explain and describe the field of projective geometry. 
Skills 
Students are capable of
With assistance from the teacher students are able to link the contents of the three subject areas (modules)
in practical assignments. 
Personal Competence  
Social Competence 
Students can collaborate in a small team on the practical realization and testing of a system to reconstruct a threedimensional scene or to evaluate volume data sets. 
Autonomy 
Students are able to solve simple tasks independently with reference to the contents of the lectures and the exercise sets. Students are able to solve detailed problems independently with the aid of the tutorial’s programming task. 
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Credit points  6 
Course achievement  None 
Examination  Written exam 
Examination duration and scale  60 Minutes, Content of Lecture and materials in StudIP 
Assignment for the Following Curricula 
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory 
Course L0129: 3D Computer Vision 
Typ  Lecture 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. RolfRainer Grigat 
Language  EN 
Cycle  WiSe 
Content 

Literature 

Course L0130: 3D Computer Vision 
Typ  Recitation Section (small) 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. RolfRainer Grigat 
Language  EN 
Cycle  WiSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M1552: Mathematics of Neural Networks 

Courses  

Module Responsible  Dr. JensPeter Zemke 
Admission Requirements  None 
Recommended Previous Knowledge 

Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge  Students are able to name, state and classify stateoftheart 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

Autonomy 
Students are able to

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 Intelligence Engineering: Elective Compulsory Computer Science: Specialisation III. Mathematics: Elective Compulsory Computational Science and Engineering: Specialisation III. Mathematics: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory 
Course L2322: Mathematics of Neural Networks 
Typ  Lecture 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Dr. JensPeter Zemke 
Language  DE/EN 
Cycle  WiSe 
Content 

Literature 

Course L2323: Mathematics of Neural Networks 
Typ  Recitation Section (small) 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Dr. JensPeter Zemke 
Language  DE/EN 
Cycle  WiSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M0738: Digital Audio Signal Processing 

Courses  

Module Responsible  Prof. Udo Zölzer 
Admission Requirements  None 
Recommended Previous Knowledge 
Signals and Systems 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
Die Studierenden können die grundlegenden Verfahren und Methoden der digitalen Audiosignalverarbeitung erklären. Sie können die wesentlichen physikalischen Effekte bei der Sprach und Audiosignalverarbeitung erläutern und in Kategorien einordnen. Sie können einen Überblick der numerischen Methoden und messtechnischen Charakterisierung von Algorithmen zur Audiosignalverarbeitung geben. Sie können die erarbeiteten Algorithmen auf weitere Anwendungen im Bereich der Informationstechnik und Informatik abstrahieren. 
Skills 
The students will be able to apply methods and techniques from audio signal processing in the fields of mobile and internet communication. They can rely on elementary algorithms of audio signal processing in form of Matlab code and interactive JAVA applets. They can study parameter modifications and evaluate the influence on human perception and technical applications in a variety of applications beyond audio signal processing. Students can perform measurements in time and frequency domain in order to give objective and subjective quality measures with respect to the methods and applications. 
Personal Competence  
Social Competence 
The students can work in small groups to study special tasks and problems and will be enforced to present their results with adequate methods during the exercise. 
Autonomy 
The students will be able to retrieve information out of the relevant literature in the field and putt hem into the context of the lecture. They can relate their gathered knowledge and relate them to other lectures (signals and systems, digital communication systems, image and video processing, and pattern recognition). They will be prepared to understand and communicate problems and effects in the field audio signal processing. 
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Credit points  6 
Course achievement  None 
Examination  Written exam 
Examination duration and scale  45 min 
Assignment for the Following Curricula 
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory 
Course L0650: Digital Audio Signal Processing 
Typ  Lecture 
Hrs/wk  3 
CP  4 
Workload in Hours  Independent Study Time 78, Study Time in Lecture 42 
Lecturer  Prof. Udo Zölzer 
Language  EN 
Cycle  WiSe 
Content 

Literature 
 U. Zölzer, Digitale Audiosignalverarbeitung, 3. Aufl., B.G. Teubner, 2005.  U. Zölzer, Digitale Audio Signal Processing, 2nd Edition, J. Wiley & Sons, 2005.  U. Zölzer (Ed), Digital Audio Effects, 2nd Edition, J. Wiley & Sons, 2011.

Course L0651: Digital Audio Signal Processing 
Typ  Recitation Section (large) 
Hrs/wk  1 
CP  2 
Workload in Hours  Independent Study Time 46, Study Time in Lecture 14 
Lecturer  Prof. Udo Zölzer 
Language  EN 
Cycle  WiSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M1249: Medical Imaging 

Courses  

Module Responsible  Prof. Tobias Knopp 
Admission Requirements  None 
Recommended Previous Knowledge  
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge  
Skills  
Personal Competence  
Social Competence  
Autonomy  
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Credit points  6 
Course achievement  None 
Examination  Written exam 
Examination duration and scale  90 min 
Assignment for the Following Curricula 
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Bio and Medical Technology: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory 
Course L1694: Medical Imaging 
Typ  Lecture 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Tobias Knopp 
Language  DE 
Cycle  WiSe 
Content  
Literature 
Bildgebende Verfahren in der Medizin; O. Dössel; Springer, Berlin, 2000 Bildgebende Systeme für die medizinische Diagnostik; H. Morneburg (Hrsg.); Publicis MCD, München, 1995 Introduction to the Mathematics of Medical Imaging; C. L.Epstein; Siam, Philadelphia, 2008 Medical Image Processing, Reconstruction and Restoration; J. Jan; Taylor and Francis, Boca Raton, 2006 Principles of Magnetic Resonance Imaging; Z.P. Liang and P. C. Lauterbur; IEEE Press, New York, 1999 
Course L1695: Medical Imaging 
Typ  Recitation Section (small) 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Tobias Knopp 
Language  DE 
Cycle  WiSe 
Content  See interlocking course 
Literature  See interlocking course 
Thesis
Module M002: Master Thesis 

Courses  

Module Responsible  Professoren der TUHH 
Admission Requirements 

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

Skills 
The students are able:

Personal Competence  
Social Competence 
Students can

Autonomy 
Students are able:

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 Electrical Engineering: Thesis: Compulsory Energy and Environmental Engineering: Thesis: Compulsory Energy Systems: Thesis: Compulsory Environmental Engineering: Thesis: Compulsory Aircraft Systems Engineering: Thesis: Compulsory Global Innovation Management: Thesis: Compulsory Computational Science and Engineering: Thesis: Compulsory Information and Communication Systems: Thesis: Compulsory International Management and Engineering: Thesis: Compulsory Joint European Master in Environmental Studies  Cities and Sustainability: Thesis: Compulsory Logistics, Infrastructure and Mobility: Thesis: Compulsory Materials Science: Thesis: Compulsory Mathematical Modelling in Engineering: Theory, Numerics, Applications: 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 